Constructed Ponds for the Treatment of Urban Stormwater – Biotic Processes Influencing the Removal of Nitrogen, Phosphorus, and Carbon
Constructed Ponds for the Treatment of Urban Stormwater - Biotic Processes Influencing the Removal of Nitrogen, Phosphorus and Carbon
Author Bayley, Mark
Published 2007
Thesis Type Thesis (PhD Doctorate)
School Griffith School of Engineering
DOI https://doi.org/10.25904/1912/2832
Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from http://hdl.handle.net/10072/367630
Griffith Research Online https://research-repository.griffith.edu.au Constructed ponds for the treatment of urban stormwater – biotic processes influencing the removal of Nitrogen, Phosphorus, and Carbon.
Mark L. Bayley (BAppSc, Hons)
November 2007
Thesis submitted in fulfilment of the requirements of the degree of Doctor of Philosophy
School of Engineering Griffith University Nathan, 4111 Queensland Australia Forward This thesis presents and discusses the research undertaken by the author between 2003 and 2006, with the majority of field and laboratory work undertaken in 2004 and 2005. Each chapter within this thesis is written as a series of complimentary, but stand alone chapters. The thesis can be structurally divided into three sections; 1. “Why and How” – three chapters that provide a general introduction into the subject topic, outlining the main aims of the investigation. The study site is introduced, along with standard methodologies to proceeding chapters. a. Chapter 1: Introduction b. Chapter 2: The study site c. Chapter 3: Standard methods to research chapters 2. “What” – eight research based chapters that address the aims presented in the introduction. a. Chapter 4: Hydraulic characteristics b. Chapter 5: Nutrient reduction during storm events c. Chapter 6: Factors governing water quality d. Chapter 7: Phytoplankton biomass and community composition e. Chapter 8: Pelagic and benthic carbon dynamics f. Chapter 9: Inorganic nitrogen and phosphorus cycling 3. “Conclusions” – two chapters that provide a general discussion and conclusion on the research undertaken, management guidelines and conceptual diagrams on the flux of nitrogen, carbon and phosphorus within the studied system. a. Chapter 10: Building a conceptual model b. Chapter 11: Design and management
Each chapter within Section 2 is written following a standard scientific paper format, using Abstract, Introduction, Methods, Results, Discussion and Conclusion sub headings. All references are listed at the end of the thesis to minimise repetition and ease the reader’s task of reference searching. Chapters in Sections 1 and 3 follow a more ‘text book’ type format, presenting and discussing subjects and ideas in a sequential and logical manner.
ii Table of Contents
FORWARD...... II
ABSTRACT ...... VII
ACKNOWLEDGEMENTS...... X
LIST OF FIGURES...... XI
LIST OF TABLES...... XIV
LIST OF SYMBOLS AND ACRONYMS...... XVI
GLOSSARY...... XVII
1 CHAPTER 1: INTRODUCTION ...... 26
1.1 STORMWATER – THE NEED FOR TREATMENT ...... 27 1.2 TREATING STORMWATER...... 29 1.3 KNOWLEDGE GAPS ...... 31 1.3.1 AN LIMNOLOGICAL APPROACH TO STORMWATER TREATMENT POND AND WETLAND RESEARCH…...... 33 1.4 PROJECT AIMS AND OBJECTIVES ...... 34
2 CHAPTER 2: THE STUDY SITE ...... 36
2.1 LOCATION OF STUDY SITE...... 37 2.1.1 URBAN STORMWATER TREATMENT – A CASE STUDY WITHIN THE MORETON BAY CATCHMENT...... 38 2.2 A SUBTROPICAL CLIMATE...... 45 2.3 THE CATCHMENT ...... 46
3 CHAPTER 3: STANDARD METHODS TO RESEARCH CHAPTERS...... 48
3.1 INTRODUCTION ...... 49 3.2 WATER SAMPLE COLLECTION AND STORAGE...... 49 3.3 WATER SAMPLE ANALYSIS...... 50 3.3.1 NITROGEN AND PHOSPHORUS ANALYSIS...... 50 3.3.2 TOC AND DOC...... 52 3.4 PHYSICOCHEMICAL WATER QUALITY PARAMETERS ...... 53 3.5 CHLOROPHYLL A ...... 54
4 CHAPTER 4: HYDRAULIC CHARACTERISTICS OF A SUBTROPICAL URBAN CATCHMENT AND STORMWATER TREATMENT POND ...... 56
4.1 ABSTRACT ...... 57
iii 4.2 INTRODUCTION ...... 58 4.2.1 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 61 4.3 METHODS ...... 62 4.3.1 THE BWC CATCHMENT ...... 62 4.3.2 THE BWC SYSTEM...... 62 4.3.3 DATA ANALYSIS ...... 65 4.4 RESULTS ...... 68 4.4.1 THE BWC CATCHMENT ...... 68 4.4.2 THE BWC SYSTEM...... 70 4.4.3 STORMWATER FLOW INTO THE BWC SYSTEM...... 71 4.5 DISCUSSION ...... 76 4.5.1 THE GENERATION OF STORMWATER ...... 76 4.5.2 STORMWATER STORAGE WITHIN AND FLOW OUT OF THE BWC SYSTEM ...... 79 4.6 CONCLUSION ...... 81
5 CHAPTER 5: NUTRIENT REDUCTION DURING STORM EVENTS ...... 82
5.1 ABSTRACT ...... 83 5.2 INTRODUCTION ...... 84 5.2.1 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 86 5.3 METHODS ...... 87 5.3.1 FIELD COLLECTION...... 87 5.3.2 HYDROLOGY AND RAINFALL DATA...... 88 5.3.3 EVENT MEAN CONCENTRATION, LOADS AND TREATMENT PERFORMANCE CALCULATIONS ...... 89 5.3.4 MONITORED STORM EVENTS ...... 91 5.4 RESULTS ...... 93 5.5 DISCUSSION ...... 101 5.6 CONCLUSIONS ...... 104
6 CHAPTER 6: ABIOTIC FACTORS INFLUENCING N, P & C DYNAMICS...... 105
6.1 ABSTRACT ...... 106 6.2 INTRODUCTION ...... 107 6.2.1 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 113 6.3 METHODS ...... 114 6.3.1 FIELD SAMPLING ...... 114 6.3.2 LABORATORY ANALYSIS ...... 118 6.3.3 STATISTICAL ANALYSIS...... 121 6.4 RESULTS ...... 122 6.4.1 PHYSICOCHEMICAL WATER PARAMETERS ...... 122 6.4.2 NUTRIENT SPECIATION AND CONCENTRATIONS...... 126 6.4.3 EPIPHYTON ...... 136 6.4.4 DATA REDUCTION USING MULTIVARIATE ANALYSIS ...... 140 PELAGIC VS. LITTORAL ZONE NUTRIENT CONCENTRATIONS...... 147 6.5 DISCUSSION ...... 149 6.5.1 NUTRIENT SPECIATION WITHIN THE BWC SYSTEM ...... 149 6.5.2 DRIVING FACTORS DETERMINING WATER QUALITY WITHIN THE BWC SYSTEM...... 155 6.5.3 EPIPHYTON BIOMASS...... 156 6.6 CONCLUSIONS ...... 159
iv 7 CHAPTER 7: PHYTOPLANKTON: MOTIVATING FACTORS DETERMINING BIOMASS AND COMMUNITY COMPOSITION ...... 161
7.1 ABSTRACT ...... 162 7.2 INTRODUCTION ...... 163 7.2.1 PHYTOPLANKTON COMMUNITIES AND THE IMPORTANCE OF SPECIES SUCCESSION.. 163 7.2.2 FRESHWATER PHYTOPLANKTON TAXONOMY ...... 168 7.2.3 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 171 7.3 METHODOLOGY...... 172 7.3.1 FIELD TECHNIQUES...... 172 7.3.2 LABORATORY TECHNIQUES...... 175 7.3.3 STATISTICAL ANALYSIS...... 176 7.4 RESULTS ...... 177 7.4.1 FORTNIGHTLY CHLOROPHYLL A AND PAR PROFILES ...... 177 7.4.2 TOTAL CELL COUNTS...... 178 7.4.3 PHYTOPLANKTON FAMILY GROUPS...... 179 7.5 DISCUSSION ...... 186 7.5.1 SPATIAL VARIATION IN PHYTOPLANKTON BIOMASS ...... 186 7.5.2 TEMPORAL VARIATION IN PHYTOPLANKTON BIOMASS...... 189 7.5.3 PHYTOPLANKTON COMMUNITY STRUCTURE AND COMPOSITION...... 192 7.5.4 CYANOBACTERIA ...... 192 7.5.5 TOTAL CELL COUNTS (EXCLUDING CYANOBACTERIA) ...... 193 7.6 CONCLUSION ...... 196
8 CHAPTER 8: THE TROPHIC STATUS OF URBAN STORMWATER PONDS: PELAGIC AND BENTHIC CARBON DYNAMICS...... 197
8.1 ABSTRACT ...... 198 8.2 INTRODUCTION ...... 199 8.2.1 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 202 8.3 METHODOLOGY...... 203 8.3.1 PHYTOPLANKTON PRODUCTION ...... 203 8.3.2 BACTERIOPLANKTON PRODUCTION ...... 207 8.3.3 BENTHIC PRODUCTION...... 216 8.4 RESULTS ...... 220 8.4.1 PHYTOPLANKTON PRODUCTION ...... 220 8.4.2 BACTERIOPLANKTON PRODUCTION ...... 223 8.4.3 BENTHIC PRODUCTION...... 227 8.5 DISCUSSION ...... 230 8.5.1 PHYTOPLANKTON PRODUCTION ...... 230 8.5.2 BACTERIOPLANKTON PRODUCTION ...... 235 8.5.3 BENTHIC PRODUCTION - HETEROTROPHY VS. AUTOTROPHY...... 242 8.6 CONCLUSION ...... 249
9 CHAPTER 9: CYCLING OF INORGANIC NITROGEN AND PHOSPHORUS IN THE PELAGIC AND BENTHIC ZONE ...... 250
9.1 ABSTRACT ...... 251 9.2 INTRODUCTION ...... 252 9.2.2 RESEARCH AIMS, OBJECTIVES AND RESEARCH QUESTIONS ...... 254 9.3 METHODOLOGY...... 256 9.3.1 THE PELAGIC ZONE...... 256
v 9.3.2 THE BENTHIC ZONE ...... 263 9.4 RESULTS ...... 267 9.4.1 THE PELAGIC ZONE...... 267 9.4.2 THE BENTHIC ZONE ...... 276 9.5 DISCUSSION ...... 281 9.5.1 THE PELAGIC ZONE...... 281 9.5.2 THE BENTHIC ZONE ...... 288 9.6 CONCLUSION ...... 291
10 CHAPTER 10: A SERIES OF CONCEPTUAL MODELS...... 293
BUILDING A CONCEPTUAL MODEL ...... 294 THE CONCEPTUAL MODELS ...... 299 THE FATE OF N, P AND DOC ON AN ANNUAL TIME SCALE…...... 302
11 CHAPTER 11: CONCLUSION AND DESIGN IMPLICATIONS FOR STORMWATER TREATMENT PONDS ...... 305
11.1 THE PROBLEM ...... 306 11.2 BROAD RESEARCH CONCLUSIONS...... 309 11.3 THE SOLUTION - DESIGN OF PONDS FOR THE TREATMENT OF URBAN RUNOFF .... 310 11.4 FUTURE RESEARCH QUESTIONS ...... 312
12 CHAPTER 12: APPENDIXES...... 314
REFERENCES ...... 339
vi Abstract Stormwater ponds for the treatment of urban runoff are being increasingly used as ‘treatment’ technologies in the management of urban stormwater world wide. Although stormwater ponds have been proven to be effective at reducing the concentration of nutrients from urban stormwater, the exact processes leading to this reduction remains largely unknown and unquantified. With stormwater management fast becoming a managed and mitigated water quality issue, the importance of our understanding on how these ecosystem function increases. A stormwater pond system designed and built for the treatment of urban runoff in 2001 was used as the study site for the PhD investigation. The primary goal of the PhD study was to investigate and quantify biological processes leading to N, P, and C load reduction within the inlet and outlet ponds at the Bridgewater Creek Stormwater Treatment System (BWC System) in Brisbane, Southeast Queensland Australia. The BWC System consists of 6 interconnected ponds spanning a total surface area of 0.8 ha, and draining 198 ha catchment consisting almost entirely of residential, single lot development. The initial sedimentation basin (Pond 1) and the final polishing pond (Pond 6) of the BWC System were the focus of the investigation.
Based on rainfall runoff rates, 29.5% of the catchment feeding into the stormwater treatment pond system was classed as ‘directly connected impervious area’, with each storm event delivering a mean 18.3ML of urban runoff. Data showed that storm events generating greater than 9.89 mm of rainfall produced runoff volumes greater than the storage capacity of the BWC System, thus triggering the high flow bypass channel. During the study period (2003- 2005), 56.9% of all generated stormwater bypassed the BWC System, attributed to the small size of the pond system compared to its catchment drainage area. Within Ponds 2-6 of the treatment system, stormwater was retained for up to 9 days. Nutrient load reduction within the pond system was calculated at 50%, and 16% for TN and DOC respectively, but TP loads increased by 30% within the pond system. The export of TP loads greater than input for the given events within the BWC System was attributed, in part, to sediment resuspension and transportation due to the lack of significant macrophyte stands to stabilize sediment within the system. Possibly a result of this, the BWC System rarely achieved TP water quality objectives, set by the Brisbane City Council, but TN, Org-N, NH4, NOx and PO4 objectives where achieved more than 40% of the time.m Using Principal Component Analysis, three factors where found to significantly influence the variability of water quality within the pond system on a temporal scale. That being, ‘storm events’, ‘phosphorus dynamics’, and/or ‘DIN dynamics’. These factors effectively describe the abiotic or biotic variables driving water quality within the ponds at any given point in time. Key physicochemical parameters driving
vii water quality in the ponds included pH, redox potential and DO. Water quality was similar between pelagic and littoral zones of both ponds within the BWC System.
The phytoplankton community within the BWC System was characteristic of polluted freshwaters, dominated by mixotrophic green algae. Cyanobacteria blooms within the ponds was minimal, with one bloom of Anabaena spp. occurring in Apr-04. Phytoplankton production rates were indicative of eutrophic freshwater environments (0.6-20 gC m-2 day-1), with production limited by light. Bacterioplankton production far exceeded that of phytoplankton production (8-210 gC m-2 day-1) during all incubations, defining the pond system as net heterotrophic. These high rates of bacterioplankton production coupled with the phytoplankton production suggested that the mineralsation of gross particulate organic matter entering the system during stormevents may be providing the nutrients nessasary to fuel such high production rates. Within Pond 1, nutrient movement across the benthic/water column interface were calculated at 0.73, 0.37 and 0.47 mg m-2 day-1 for TN, TP and DOC. In Pond 6, there was a gross loss of nutrients from the water column to the sediment zone for TN and TP (0.24 and 0.03 mg m-2 day-1) but not for DOC (sediment to water movement, 0.17 mg m-2 day-1). As in Pond 6, if sunlight can penetrate the benthos and gross particulate matter is low, Benthic Macro Algae can inhabit the benthos and enhace nutrient loss from the water column. During the mesocosom experiments the Ceratophyllum/epiphyton complex exhausted ammonia concentrations within the incubation jars and reduced PO4-P to background concentrations. Attached epiphyton and phytoplankton community displayed similar characteristics in PO4-P concentration reduction from the water column, with biotic uptake within the incubation jars limited to 0.03 and 0.02 mg L-1 day-1 respectively (0.14 mg L-1 day-1 for Ceratophyllum/epiphyton complex). From these incubation experiments, the important role of submerged macrophyte communities in nutrient reduction from the water coloumn was proven.
The research presented in this thesis justifies the use of stormwater ponds for urban stormwater management. The ability of submerged macrophytes at enhancing nutrient removal from stormwater was demonstrated. Phytoplankton and Bacterioplankton production and associated inorganic N and P uptake within the pond system did not correspond to measured and calculated storm flow inputs, indicating a large ‘unmeasured source’ – assumed to be the input of large particulate organic matter into the system during storm events.
viii I declare that the work presented in this thesis, to the best of my knowledge and belief, is original and my own except as acknowledged in the text, and that the material has not been submitted, in whole or part, for a degree at this or any other university.
Mark Bayley
ix Acknowledgements The author of this thesis greatly acknowledges the financial support of the Australian Federal Government (APA Scholarship), Griffith University (Top Up Scholarship and part project running costs) and the Co-operative Research Centre for Catchment Hydrology (project running costs).
This PhD thesis has been the result of many hours in the field, more hours in the laboratory and even more hours in front of the computer. Throughout all stages of this thesis my supervisors Margaret Greenway, Peter Pollard and Graham Jenkins have provided invaluable advice, direction and time to ‘the cause’. Specifically, Margaret Greenway waded through my many drafts of chapters (always in such a timely manner), and continually pointed me in the right direction. Thanks Peter, for introducing me to the amazing world of microbes, and the integral role the play in our very existence. During the field work component of my PhD, Carolyn Polson gave very freely of her time. Many a methodology problem (not to mention life problem) was solved while floating with Carolyn on a half deflated dingy in the rain…In the early stages of my canditure, Michele Burford provided priceless methodology advice, also giving freely of her time to read drafts of chapters and answer many emails throughout my entire candidature.
Leigh Davison and Tom Headley. You are the ones that sent me down this road. Thanks for the friendship, belief, encouragement, and support. To my girls; Sara, Lily and Poppy. Sara: your belief in me has never waived. Thank you for standing strong by my side for the last 4 years. Lily: I know, I know. If there is one person that understands I am sure it is you. Thank you for your patience and continual understanding of my mood swings. Poppy: no matter how late at night I worked, your smiley face at 6:30am was a blessing to even the foggiest of mornings. Its been as much a rollercoaster ride for me as it has you. We can get off now. All my love.\
And to Dad – who painstakingly drafted my final version of this thesis. He now knows more about stormwater ponds and, more importantly, more about my grammatical shortcomings. Thanks Dad. I owe you one.
x List of Figures Figure 1-1: Runoff generated from a storm event...... 28 Figure 1-2: Non point source loads into Moreton Bay ...... 29 Figure 1-3: Key micro-trophic groups in stormwater ponds and wetlands...... 34 Figure 2-1: N and P loads into Moreton Bay...... 37 Figure 2-2: Location of the BWC System ...... 40 Figure 2-3: Bridgewater Creek Stormwater Treatment System (BWC System)...... 41 Figure 2-4: Bathymetry map of the BWC System...... 42 Figure 2-5: Mean month climatic variables for Brisbane...... 45 Figure 2-6: Location of the BWC System ...... 47 Figure 4-1: Hydraulic division of BWC System...... 63 Figure 4-2: Outlet structure theoretical of the BWC System...... 64 Figure 4-3: Location of spot heights taken for Bathmetry of BWC System...... 65 Figure 4-4: Daily precipitation within the BWC catchment...... 68 Figure 4-5: Directly connected impervious area of the BWC catchment...... 70 Figure 4-6: Bathymetry of BWC stormwater wetland...... 70 Figure 4-7: Correlation analysis: total storm rainfall vs bypassed stormwater...... 72 Figure 4-8: Modelled storm rainfall to trigger high flow bypass channel...... 73 Figure 4-9: Theoretical length of time for stormwater to drain ...... 73 Figure 4-10: Correlation analysis; Pond 6 outlet flow rate vs water height ...... 74 Figure 4-11: Length of time water spent at differing water levels within Ponds 2-6. .75 Figure 4-12: Precipitation within the BWC Catchment...... 76 Figure 4-13: Proportion of runoff detained within Ponds 2-6...... 79 Figure 4-14: Major hydraulic factors within the BWC Catchment and System...... 81 Figure 5-1: Water sample sites for stormwater nutrient reduction ...... 87 Figure 5-2: Mean event EMC to and from Ponds 1 and 6...... 95 Figure 5-3: Mean event TN and TP loads...... 97 Figure 5-4: Mean event nutrient loads to and from Ponds 1 and 6...... 99 Figure 5-6: Yearly load reduction of TN, TP and DOC within the BWC System. ...104 Figure 6-1: Littoral and pelagic zonation within freshwater environments...... 107 Figure 6-2: Nitrogen cycle in freshwater ecosystems...... 108 Figure 6-3: Phosphorus cycle in the freshwater ecosystems ...... 109 Figure 6-4: The carbon cycle in the pelagic zone freshwater ecosystems...... 110 Figure 6-6: Water quality sampling locations within Ponds 1, 5 and 6...... 114 Figure 6-8: Isopleth diagram; water temperature in Pond 1 and Pond 6 ...... 124 Figure 6-9: Isopleth diagram; DO concentration in Pond 1 and Pond 6...... 125 Figure 6-10: Isopleth diagram; redox potential within Pond 1 and Pond 6...... 127 Figure 6-11: Isopleth diagram; pH within Pond 1 and Pond 6...... 128 Figure 6-12: N speciation within the pelagic zones of Ponds 1 and 6...... 132 Figure 6-13: N and P speciation within the littoral zone of Ponds 1 and 5 ...... 134 Figure 6-14: P speciation within the pelagic zones of Ponds 1 and 6135 Figure 6-15: DOC concentration within the pelagic zones of Ponds 1 andPond 6....136 Figure 6-16: Changing epiphyton biomass within Pond 1 and 5...... 139 Figure 6-17: Biomass measurements of epiphyton colonising glass slides...... 140 Figure 6-18: PCA in Pond 1 of the BWS System...... 142 Figure 6-19: Dendrogram displaying Pond 1 hierarchal cluster analysis ...... 143 Figure 6-20:PCA in Pond 6 of the BWS System...... 144 Figure 6-21: Dendrogram displaying Pond 6 hierarchal cluster analysis ...... 145 Figure 6-22: Regression analysis; pelagic vs littoral nitrogen concentrations...... 147
xi Figure 6-23: Regression analysis; pelagic vs littoral phosphorus concentrations .....148 Figure 6-24: Regression analysis; pelagic vs littoral DOC concentration...... 148 Figure 6-25: Correlation analysis; phytoplankton biomass vs NH4-N...... 151 Figure 7-1: Location of monitoring sites for phytoplankton biomass and species identification...... 172 Figure 7-2: YSI SNODE 6600 fluorescence probe calibration chart ...... 173 Figure 7-3: Chlorophyll a concentrations within Pond 1 and 6...... 178 Figure 7-4: Isopleth diagrams; Chlorophyll a and % incident PAR...... 180 Figure 7-5: Pond 1 and 6 total phytoplankton cell counts ...... 181 Figure 7-6: Correlation analysis; phytoplankton cell counts vs Chlorophyll a...... 181 Figure 7-7 Cyanobacteria colonies within Ponds 1 and 6 of the BWC System...... 182 Figure 7-8 Frequency of phytoplankton family occurrence...... 182 Figure 7-9: Presence and abundance of phytoplankton in Pond 1...... 184 Figure 7-10: Presence and abundance of phytoplankton in Pond 6...... 185 Figure 7-11: Correlation analysis; phytoplankton biomass vs N:P ratio ...... 192 Figure 8-1: The Microbial Loop ...... 201 Figure 8-2: Location of sample sites in Ponds 1 and 6...... 203 Figure 8-3: Schematic of 14C incubation undertaken...... 205 Figure 8-4: PI curve. Phytoplankton primary production plotted against PAR irradiance...... 206 Figure 8-5: Location of sample sites within Ponds 1 and 6 of the BWC System...... 207 Figure 8-6: Bacterioplankton growth...... 208 Figure 8-7a-b: Correlation analysis; radioactivity vs incubation time...... 209 Figure 8-8: Correlation analysis; volume [methyl-3H] thymidine vs radioactivity. ..210 Figure 8-9: Summary of bacterioplankton incubation ...... 211 Figure 8-10: Epifloresnce photograph ...... 215 Figure 8-11: Location of incubation chambers...... 216 Figure 8-12: Schematic of the incubation chambers...... 217 Figure 8-13: Daily rainfall within the BWC System catchment...... 218 Figure 8-14: Total phytoplankton community primary production...... 222 Figure 8-15: Production of differing size fractions of phytoplankton...... 223 Figure 8-16: Bacterioplankton cell counts...... 225 Figure 8-17: Bacterioplankton specific growth rate ...... 225 Figure 8-18: Bacterioplankton population doubling time...... 226 Figure 8-19: Bacterioplankton productivity...... 227 Figure 8-20: DO concentration within Benthic Metabolism Chambers...... 229 Figure 8-21: Increase in bacterioplankton concentration with increases in the trophic status of the ecosystem...... 236 Figure 8-22: Correlation analysis; bacterioplankton production vs temperature...... 238 Figure 8-23: Correlation analysis; PO4-P vs bacterioplankton productivity ...... 239 Figure 8-24: Correlation analysis; Bacterioplankton production vs PO4-P...... 240 Figure 8-25: Correlation analysis; bacterioplankton production vs DOC...... 242 Figure 8-26: GPP24/R24 ratio versus total 2 week rainfall preceding the deployment of the chambers within Ponds 1 and 6...... 244 Figure 9-9: Schematic of 7 day field bioassay’s...... 261 Figure 9-2: Location of incubation chambers...... 263 Figure 9-3: Daily rainfall within the BWC System catchment...... 264 Figure 9-4: Schematic of the incubation chambers...... 265 Figure 9-5: Reduction of inorganic N and P via phytoplankton community...... 270 Figure 9-6: NH4-N reduction verus Chlorophyll a...... 270
xii Figure 9-7: PO4-P reduction verus Chlorophyll a...... 271 Figure 9-8: NOx-N reduction verus Chlorophyll a...... 271 Figure 9-9: Reduction in NOx concentration with increased incubation length ...... 271 Figure 9-10: Reduction of inorganic N and P via epiphytic community...... 272 Figure 9-11: Reduction of inorganic N and P via Ceratophyllum demersum/epiphyton complex...... 273 Figure 9-12: Reduction of inorganic N and P via Potamogeton javanicus/epiphyton complex...... 274 Figure 9-13: Potamogeton javanicus/epiphyton community biomass...... 275 Figure 9-14: Inorganic nutrient reduction rates ...... 276 Figure 9-15: NOx-N and NH4-N concentration versus increased incubation time ...279 Figure 9-16: Mean DOC vs NOx-N concentration within incubation chambers ...... 280 Figure 10-1: Yearly load reduction of TN, TP and DOC within the BWC System. .300 Figure 10-2: Modelled N and P removal via phytoplankton uptake...... 301 Figure 10-3: Modelled N and P removal via bacterioplankton uptake...... 301 Figure 10-4: Modelled yearly flux of nutrients across the benthos/water column interface...... 302 Figure 10-5: Gross conceptual model...... 303 Figure 11-1: Meeting water quality guidelines within the BWC System...... 307 Figure 11-2: Frequency of pond water achieving water quality guidelines...... 308
xiii List of Tables Table 1-1: Technologies used to prevent, transport and treat stormwater within Australia...... 30 Table 3-1: Chemicals used to make stock standard solutions...... 51 Table 4-1: Generated volumetric runoff co-efficients ...... 69 Table 4-2: Water holding capacity of Pond 1 and Ponds 2-671 Table 4-3: Characteristics of storm events investigated in this study...... 72 Table 4-4: A comparison of catchment volumetric runoff coefficients ...... 77 Table 5-1: Characteristics of the 6 monitored storm events...... 92 Table 5-2: Storm event EMC’s within the BWC System...... 94 Table 5-3: Mean nutrient loads to, within and from the BWC system ...... 96 Table 5-4: Mean load reduction of nutrients within Pond 1 and 6 ...... 98 Table 5-5: Mean load removal efficiency’s of nutrients within Pond 1 and 6 ...... 100 Table 5-6: A comparison of nutrient concentration of urban stormwater ...... 101 Table 5-7: Yearly load for nutrients sourced from the BWC Catchment, that entering Pond 1, and that exiting Pond 6 to receiving waters...... 102 Table 6-1: Pond 1 water quality parameter...... 122 Table 6-2: Pond 5 water quality parameters ...... 123 Table 6-3: Mean nutrient concentrations within Pond 1...... 129 Table 6-4: Mean nutrient concentrations within Pond 6...... 130 Table 6-5: Pond 1 water quality parameters ...... 131 Table 6-6: Pond 5 water quality parameters ...... 131 Table 6-7: Correlation analysis; epiphyton biomass vs N and P concentrations...... 137 Table 6-8: Pond 1 epiphyton Chlorophyll a and AFDW biomass quantification. ....137 Table 6-9: Pond 5 epiphyton Chlorophyll a and AFDW biomass quantification. ....138 Table 6-10: Biomass measurements of epiphyton colonising glass slides ...... 138 Table 6-11: Results for the extracted PC for Pond 1...... 141 Table 6-12: Pooled data used for hierarchal cluster dendrogram for Pond 1...... 142 Table 6-13: Results for the extracted PC for Pond 6...... 144 Table 6-14: Pooled data used for hierarchal cluster dendrogram for Pond 6...... 145 Table 6-15: Pearson correlation matrix for Pond 1...... 146 Table 6-16: Pearson correlation matrix for Pond 6...... 146 Table 6-17: Differences in epiphyton biomass between Ponds 1 and 5 ...... 156 Table 7-1: Factors controlling phytoplankton community and species succession...164 Table 7-2: Presence/absence of phytoplankton family groups in Pond 1...... 183 Table 7-3: Presence/absence of phytoplankton family groups in Pond 6...... 183 Table 7-4: Regression analysis; chlorophyll a vs % incident PAR...... 190 Table 8-1: Physicochemical water quality parameters within Ponds 1 and 6...... 212 Table 8-2: Rainfall and hydraulic conditions of Ponds 1 and 6...... 212 Table 8-3: Perspex incubation chambers specifications...... 217 Table 8-4: Water quality results from each pond on the 24-Aug and 14-Dec...... 220 Table 8-5: Photosynthetic characteristics of the phytoplankton community...... 221 Table 8-6: Photosynthetic maxima (Pmax) size fractionation ...... 222 Table 8-7: Nutrient results from incubation water used for each [methyl-3H] thymidine incubation ...... 224 Table 8-8: Bacterioplankton concentration and production...... 225 Table 8-9: Benthic production and respiration rates...... 228 Table 8-10: Comparison of freshwater pelagic phytoplankton production ...... 234 Table 8-11: Review of published bacterioplankton productivity studies...... 237
xiv Table 8-12: Comparison of GPP24/R24...... 248 Table 9-1: Nutrient spiking cocktail...... 258 Table 9-2: Summary of methods used in bioassays...... 260 Table 9-3: Perspex incubation chambers specifications...... 264 Table 9-4: Calculated response of the phytoplankton community ...... 268 Table 9-5: Calculated inorganic nutrient concentrations resulting from the biotic interactions with the added ‘nutrient spike’...... 269 Table 9-6: Potamogeton javanicus/epiphyton community biomass measurements ..274 Table 9-7: Inorganic nutrient reduction rates for the four bioassays preformed ...... 276 Table 9-8: Raw nutrient concentration within incubation chambers...... 278 Table 9-9: Mean and SE (italic numbers) values for measured nutrients within incubation chambers ...... 279 Table 9-10: Nutrient movement across the benthos/water column interface ...... 280 Table 11-1: Freshwater water quality objectives for the assessment of water quality in Brisbane’s waterways (Webb 2000)...... 307
xv List of Symbols and Acronyms
Units g grams mm millimetres ha area in hectares µg L-1 microgram per ML mega litres litre mg L-1 milligrams per µ micrometer litre °C degrees Celsius m3 sec-1 or m3 day-1 cubic metres per KL Kilolitres second or day m3 cubic metres
Symbols
BCC Brisbane City Council P Phosphorus BWC System Bridgewater Creek PAR Photosynthetic Active Stormwater Treatment Radiation Pond System pH index of the C Carbon concentration of Chl. a Chlorophyll a hydrogen in solution: a DOC Dissolved Organic measure of acidity
Carbon PO4-P orthophosphate EMC Event Mean PP Primary Production
Concentration R24 Gross Respiration over
GPP24 Gross Primary 24 hours Production over 24 SA:V Surface area to volume hours ratio N Nitrogen TN Total Nitrogen
NH4-N Ammonia TP Total Phosphorus NOx-N Oxidised Nitrogen w/w wet weight DOC Dissolved Organic carbon
xvi Glossary This glossary has been written to aid the reader/s while examining this thesis. Important terms, phrases and words relating the nutrient fluxes within stormwater ponds have been include. The definitions provided were source from 5 text books; 1. Algae and Elemental Cycling in Wetlands, (Vymazal 1995); 2. Freshwater Microbiology, (Sigee 2004); 3. Limnology: Lake and River Ecosystems, (Wetzel 2001); 4. Environmental Chemistry 6th Ed., (Manahan 1994); and 5. Biology: Concepts and Connections, (Campbell et al. 1997).
Adsorbed – The adhesion of molecules to the surface a particular substrate, i.e. a soil particle or algal cell wall. Aerobe – An organism that grows in the presence of atmospheric oxygen. Aerobic – Oxygenated conditions within a given environment, i.e. soil or water. Algae – A diverse group of simple organisms that have chlorophyll a as there main photosynthetic pigment and lack a sterile covering of cells around reproductive cells. Algal bloom – A dense population of planktonic algae resulting from natural or anthropogenic causes. Algal exudate – Extracellular release of soluble compounds by algae into the water column, sediment or biofilm depending on habitat. Algal indicators – A particular algal species or group of species with distinct ecological preferences. Their presence indicates aspects such as high nutrient levels, high pH, high turbulence etc. Allochthonous material – Material that is sourced external to an aquatic ecosystem, either soluble or particulate. Ammonia – One of the main inorganic forms of nitrogen within aquatic ecosystems. Ammonia is generated by biological dissimilation of nitrate and the decomposition of + organic matter. Molecular notation - NH4 + Ammonification – Conversion of NH4 from organic nitrogen compounds via mineralization by bacteria Anabolic – Biochemical process/s leading to an increase in potential energy, involving synthesis of macromolecules or energy rich bonds.
xvii Anaerobe - An organism that grows in the absence of free oxygen Anaerobic – Deoxygenated, or oxygen free conditions within a given environment, i.e. soil or water Anion – Negatively charged ions Anoxic – Without oxygen Anoxygenic photosynthesis – Photosynthetic process in which electron donors (eg. organic matter, sulphide) are used which do not result in oxygen evolution, i.e. photosynthetic bacteria. Autochthonous material - Material that is sourced from within an aquatic ecosystem, either soluble or particulate. Bacteria – Prokaryote organism containing bacterial rRNA. Bacteriochlorophyll – A modified chlorophyll present in photosynthetic bacteria. Bacterioplankton – Bacteria inhabiting the pelagic zone within aquatic ecosystems. Benthos – The habitat and organisms inhabiting the bottom of water bodies Benthic zone– The bottom surface of an aquatic ecosystem. Benthic algae – Algae that inhabit the benthic zone within aquatic ecosystems. Biodiversity – The range of taxonomic, phenotype or genetic characteristics within a population or community. Biofilm – A community of micro-organisms occurring at a physical water/solid interface, typically present within a layer of polysaccharide that is secreted by the community. Biomass – The total mass of all living organisms or set of organisms. Bottom up control – Restricted population growth of a group of organism due to limitations in nutrient supply. Carbon flux - The movement of carbon through an ecosystem Cation – Positively charged ions Chemosynthesis – The process whereby bacteria oxidise organic compounds for energy. Chemotrophs – Organisms that obtain energy from the oxidation of chemical compounds. Chemoheterotrophs – All fungi, protozoans and most bacteria that use organic sources for both energy and carbon. Chemoautotrophs – Microorganisms that use inorganic CO2 for biomass production and oxidise certain ions for energy (i.e. H2, NH4, S).
xviii Chlorophyll a – Primary photosynthetic pigment of all oxygen-evolving photosynthetic organisms, including algae, cyanobacteria and photosynthetic bacteria (excluding photosynthetic sulphur bacteria). Community – A naturally occurring assemblage of organisms living within a particular habitat. Conductivity or Specific conductance – Measure of the resistance of a solution/water to electrical flow, with increasing resistance corresponding with declining ion concentration within the solution/water. Thus, the purer the water is, that is, the lower its salinity, the greater its resistance to electrical flow. Cyanobacteria – A major prokaryotic organism that contains chlorophyll a and carries out oxygenic photosynthesis. - - Denitrification – Biochemical reduction of oxidised nitrogen anions (NO3 and NO2 ) in the presence or organic matter to nitrogen gas (N2) by facultative anaerobic bacteria in both aerobic and anaerobic conditions. Detritus – Small pieces of dead or decomposing organic matter. Dissolved inorganic carbon – Inorganic carbon molecules that are dissolved within a solution (<0.45µm in size). 3- Dissolved inorganic phosphorus – PO4 within a given water sample filtered through a 0.45µm filter. + - - Dissolved inorganic nitrogen - NH4 , NO3 , and NO2 within a given water sample filtered through a 0.45µm filter. Dissolved organic carbon – Organic carbon compounds that are dissolved within a solution (<0.45µm in size). Dissolved organic phosphorus – Compounds, amino acids, enzymes and/or proteins containing phosphorus that are less than 45µm in size. Dissolved organic nitrogen – Compounds, amino acids, enzymes and/or proteins containing nitrogen that are less than 45µm in size. Dissolved Oxygen – A measure of the amount of oxygen dissolved within a water body. Diurnal – A day/night period Ecosystem – A self-regulating biological community living in a defined habitat. Emergent macrophyte – Water plant with below ground biomass rooted beneath a permanent water level, with the majority of the above ground biomass exposed to the air.
xix Epifluorescence – Microscopy technique used for bacteria and viral cell counts. Epilimnion – The surface layer of a stratified water body Epilithic – Occurring on gravel, pebbles and large rocks Epipelic – Occurring on fine sediments and mud. Epiphytic– Associated with, or attached to plant surfaces – including macrophytes, phytoplankton, and benthic algae Eutrophication – Environmental increase in soluble inorganic plant nutrients such as phosphates and nitrates. Eutrophic – An aquatic ecosystem rich in soluble plant nutrients, and high in productivity. Facultative – Bacteria that can respire both aerobically and anaerobically. Fluorescence – Emit light in response to a particular wavelength of light. Free floating macrophyte – Water plant growing and floating on the surface of a water body with roots suspended within the water column. Grazing – term used in reference to zooplankton consuming phytoplankton as a means of obtaining organic compounds necessary for metabolism. Gross productivity – The underlying rate of biomass synthesis in a population of organisms without any deduction of biomass loss. Heterotroph – An organism that use organic molecules as its principle source of carbon. Heterotrophic – Algae – Algae that use organic molecules as its principle source of carbon. Generally present in response to photosynthetic limitation. Heterotrophic – Bacteria – Bacteria that use organic molecules as its principle source of carbon. The main heterotrophic organism in freshwaters. Holoplanktonic – Aquatic organisms that are present in the water column over most of the annual cycle. Hypolimnion – The lower region of the water column. Inorganic carbon – Carbon existing in a purely molecular form. Most common - 2- inorganic carbon molecules are CO2, HCO3 , CO3 , and H2CO3. + - - Inorganic nitrogen – NH4 , NO3 , and NO2 . 3- Inorganic phosphorus – orthophosphate, (PO4 ) Lake – A large freshwater body of water, slow moving with a large percentage of open water. Lentic – A standing freshwater body.
xx Littoral – The peripheral shoreline at the edge of lakes and rivers Lotic – A flowing freshwater body. Macrophytes – A large aquatic plant. Meroplanktonic – Aquatic organisms that only have limited existence in the water column. Most of the annual cycle is spent either within the sediment or outside the aquatic system. Mesocosom – An environmental enclosure used for experimental purposes – typical range between 10-10,000L in size. Mesotrophic – An aquatic ecosystem with moderate plant nutrient loading and productivity. An intermittent state between oligotrophic and eutrophic. Metabolism – The net amount of energy consumed and produced by a particular cell or community. Metalimnion – The middle layer of a stratified water column. Microcosm - An environmental enclosure used for experimental purposes – typical range between 1-10L in size. Micro-organism – An organism that in not clearly visible to the naked eye, requiring a microscope for detailed observation and identification. Microplankton – Unicellular or multicellular planktonic organisms in the size range 20-200µm. Micro-trophic – small to microscopic groups within the freshwater environment occupying a trophic level within the food web. Including bacteria, phytoplankton. Migration – The vertical movement within the water column Mineralization – The release/conversion of inorganic nutrients from the microbial breakdown of organic matter. Mixotrophy – The ability of an organism to combine autotrophic and heterotrophic nutrition. Mortality – Death Nanoplankton – Unicellular planktonic organisms in the size range 2-20µm. Net productivity – The observed rate of biomass synthesis in a population of organisms equal to he observe gross productivity minus intrinsic losses. Intrinsic losses occur due to internal metabolic process in appose to extrinsic loss like grazing and parasitism. - Nitrate – NO3 , dominant form of oxidised inorganic nitrogen within freshwaters.
xxi Nitrification – The biological conversion of organic and inorganic nitrogenous compounds from a reduced state to a more oxidised state. - Nitrite – NO2 , oxidised inorganic form of nitrogen. Usually present in very small quantities in freshwater environments Nitrogen fixation – The ability of prokaryotes to utilize nitrogen gas as a nitrogen + source in metabolism, converting N2 to NH4 using the enzyme nitrogenase. Nitrogen flux – The movement of nitrogen through an ecosystem Oligotrophic – An aquatic ecosystem poor in inorganic nutrient concentration and low in productivity. Organic carbon – Carbon bound within organic molecules, minerals and biotic life. Organic matter – Matter formed from the breakdown and accumulation of biotic life. Organic nitrogen – Organic forms of nitrogen within water, including polypeptides, complex organic compounds, amino nitrogen compounds and organic matter. Organic phosphorus - Organic forms of phosphorus within water, including mineral phosphorus, complex organic compounds and molecules and organic matter. Organotrophy – Direct uptake of soluble organic compounds 3- Orthophosphate – Major form of inorganic phosphorus within freshwaters (PO4 ) PAR – Photosynthetic Active Radiation. The wavelength/s of light emitted from the sun that is used as the energy source for photosynthesis. Particulate organic carbon – Organic carbon molecules in particulate form or attached to particles greater than 0.45µm in size. Particulate organic nitrogen – Organic nitrogen molecules in particulate form or attached to particles greater than 0.45µm in size. Particulate phosphorus– Organic phosphorus molecules in particulate form or attached to particles greater than 0.45µm in size. Pelagic – The water column of a water body. Epiphyton – A community of mainly plant like organisms (including algae, bacteria and fungi) present on underwater substratum. pH – A measure of the hydrogen ion activity of water. Specifically, pH is defined as the logarithm of the reciprocal of the concentration of free hydrogen ions. Phagotrophy – Ingestion and intracellular digestion of particulate organic matter Phosphorus flux – The movement of phosphorus through an ecosystem Photoinhibition – The reduction in metabolic production caused by high light intensity
xxii Photoautotrophs – An organism that uses solar energy to manufacture organic compounds by photosynthesis. Photoheterotroph – A few specialised bacteria that use photoenergy, but are dependant on organic matter for a carbon source. Photosynthesis – The process of obtaining organic carbon to fuel cellular metabolic processes from the conversion of water and carbon dioxide in the presence of sunlight. Phycosphere – The microenvironment that occurs within and immediately around the surface of algal cells. Phytoplankton – Free floating photosynthetic micro-organisms, including both algae and bacteria. Picoplankton – Unicellular planktonic organisms in the size range 0.2-2µm. Mainly prokaryotes. Plankton – Free floating pelagic organisms including algae, bacteria, and viruses. Pond – A permanent water body dominated by open water areas, with macrophytes restricted to fringing emergents. Primary productivity – Synthesis of biomass by photosynthetic organism. Productivity – The rate of increase in biomass in a population of organisms. Rate of production – A measure of the amount of production over a given period of time. Redox potential – A measure of the oxidising or reducing intensity or condition of a solution. The redox potential is a measure of the free energy change per mole of electrons associated with a given reaction. Reservoir – A large anthropogenic water body created to store water. Respiration – Cellular formation of energy rich molecules (ATP, ADP, acids) from organic matter. Can occur under aerobic or anaerobic conditions with varying rates of efficiency. Secci disk – A field tool used to estimate the turbidity of a given water body. Secondary production – Synthesis of biomass by heterotrophic organisms, involves biomass conversion along the food chain. Sedimentation – The loss of particles suspended within the pelagic zone of water bodies via sinking and associated accumulation within the benthic environment. Senescent – The period of time a plant enters its non-growing stage. Usually over the winter months in a annual cycle.
xxiii Sloughing – The loss of periphyton growing on or attached to surfaces via high velocity water flow. Stationary phase – The end phase of growth in a laboratory batch culture when population growth ceases and the growth curve levels off. Stratified – The vertical structuring of static or very slow moving water bodies into three distinct layers – the epilimniom, metalimnion, and hypolimnion. Submerged macrophyte – Multicellular plant living entirely below the surface of the water. Some species have flowering parts exposed to the atmosphere. Succession – The temporal sequence of organisms that occurs in a developing community. Top down control – Limitation in the population growth of a particular group of organisms by the activities of predators, parasites or antagonists. Total nitrogen – All nitrogen within a water sample in particulate and dissolved forms. Total Phosphorus – All phosphorus within a water sample in particulate and dissolved forms. Trophic – Connected with nutrition and feeding. Term used to describe the nutrient status of a water body and the feeding relationships of freshwater biota. Turbidity – A physical measure of water clarity, either done in the field or laboratory. Virions – The extracellular phase of a virus life cycle, present as free virus particle within the water media. Viroplankton – The aquatic population of free particulate viruses (virions). An important competent of femtoplankton and DOC. Water column – The zone within a water body between the sediment and water surface. Wetland – An area permanently or periodically inundated by water which is dominated by emergent and submerged macrophytes. Zooplankton – Invertebrate planktonic organisms.
xxiv Section 1: Why and How
25 1 Chapter 1: Introduction
26 1.1 Stormwater – the need for treatment
The move from a highly pervious ‘natural’ environment to that of a relatively impervious ‘urban’ environment (one that dominates most populated areas) has, in some part, followed the automotive evolution. The invention of the car in the late 1800’s resulted in paved roads being embraced by local and federal government’s, with certain countries creating legislation that not only supported the construction of paved roads, but encouraged it and subsidised it as well (Stone 2004). Most towns, suburbs and cities throughout the world are now planned and built to accommodate the car, with the U.S. paving about half a million acres of land per year for automotive purposes (Ferguson 1998). Impervious areas within catchments influences the ‘natural’ cycling of water, preventing it from infiltrating through the soil profile where it can be used by plants, transported to underground aquifers, and/or be stored by sediments and organic matter. In the advent of a rainfall event within an impervious catchment, water falls and collects on impervious surfaces, is transported via gutters, culverts and drains (collecting accumulated pollutants along the way), and enters surface waters with intense volume not naturally experienced by natural water courses (Herricks 1995). This in turn results in high short term hydraulic loads on urban creeks and streams (often causing stream bank erosion) and, depending on the characteristics of the catchment, pollution of receiving waters.
Figure 1-1 illustrates the effect of storm generated runoff within rural, semi-urban, and city landscapes. It shows that peak runoff rate (represented by flow rate on the Y axis) increases with increased urbanisation. This is primarily caused by an increase in impervious areas within the catchments generating more runoff at a quicker rate. It has been determined that a direct correlation exists between the amount or percentage of impervious area within a given catchment and the volume of runoff generated from a storm event (Arnold and Gibbons 1996).
27 Flow rate Flow
Time Rural Semi-urban City
Figure 1-1: Runoff generated from a storm event within a rural, semi-urban, and city catchment. Adapted from Butler and Davis (2000).
Many researchers have stated that stormwater can or has the the potential to have similar polluting impacts on a receiving environment as municipal wastewater. It often containings high concentrations of organic matter, nutrients, heavy metals, and pathogens (Johengen and LaRock 1993; Herricks 1995; Ferguson 1998; Butler and Davies 2000; Braskerud 2002; Walker and Hurl 2002). Inorganic forms of nitrogen and phosphorus are of particular importance in stormwater management due to the known role these nutrients play (in excessive quantities) in the degradation of urban waterways throughout Australia and the world (Pointon et al. 2003; Taebi and Droste 2004).
Relative to the broad study site investigated in this thesis, N and P loads from non point sources (of which stormwater is one) into Moreton Bay, S.E. Queensland Australia, have been estimated by Eyre and McKee (2002) and modelled using AQUALM-XP (McAlister and Walden 1999). Given these two sources of non-point N and P load quantities, N and P loads into Moreton Bay are in excess of 2000 and 300 t yr-1 respectively (Figure 1-2), and account for 78% and 33% of the total load of TN and TP into Moreton Bay. The story is much the same in US cities, with non-point sources of pollution (like stormwater) contributing 45%, 75%, and 65% of the pollutant loads within estuaries, lakes, and rivers respectively (Johengen and LaRock
28 1993). Pollutants within stormwater which threaten aquatic ecosystems include nutrients, heavy metals, organic matter, pathogens, and suspended particulate matter.
Figure 1-2: Non point source loads into Moreton Bay From the Brisbane River, Logan River and Caboolture River catchments.. 1 McAlister and Walden (1999) 2 Eyre and McKee (2002)
1.2 Treating stormwater Improvement of stormwater quality within urban catchments can potentially occur at three distinct levels – (1) through source control, (2) during transport within the catchment, and (3) through treatment technologies/systems. These three improvement ‘levels’ do not necessarily operate separately from each another and can work in unison. For example, a stormwater swale can act as a transport drain during storm events while also offering some degree of treatment through filtration and source control through infiltration. Table 1-1 outlines current trends and technologies used to control stormwater quantity and quality within urban areas in Australia (Wong et al. 1999; Wong et al. 2000; Greenway 2005). As shown in Table 1-1, and reported in many reports, papers and books, ponds and wetlands are commonly used within urban catchments to treat stormwater prior to entering receiving waters (Duncan 1995; Herricks 1995; Ferguson 1998; Lawrence and Breen 1998; Wong et al. 2000).
When investigating wetlands and ponds for the treatment of stormwater, it is beneficial to understand how they differ. Ponds tend to be deep water bodies (average depth of 2m) with macrophyte growth restricted to the fringe, while wetlands are
29 generally shallow waterbodies (average depth of 30cm) dominated by macrophytes over their entire surface area (Wetzel 2001).
The use of ponds and wetlands for the treatment of urban stormwater most likelyevolved from the successful and wide spread use of ponds and wetlands for the treatment of municipal wastewater. Constructing ponds specifically for polluted water treatment (and habitat restoration) first emerged in the early 1970’s throughout North America (Kadlec and Tilton 1979). Upon monitoring of these early pond and wetland ‘treatment systems’, their purification ability was noticed and by 1973 the first intentionally engineered free water surface flow wetland for the specific purpose of wastewater treatment was constructed at Brookhaven National Laboratory, New York (Kadlec and Tilton 1979). By the mid to late 1980’s the US began treating urban and agricultural run off with constructed ponds and wetlands (Hammer 1988; Kadlec et al. 2000). What followed was the worldwide use (predominantly in the US and Australia) of constructed ponds and wetlands for the treatment of urban and agricultural run off (Hammer 1988; Reed et al. 1995; Kadlec and Knight 1996; Wong and Geiger 1997; Wong et al. 1999).
Table 1-1: Technologies used to prevent, transport and treat stormwater within Australia. Technology Source Trans- Treat- Comments control port ment Rainwater tanks * Reduction in generated stormwater through storage. Porus pavement * * Reduction in stormwater volume and treatment through infiltration. Swales (vegetated * * * Potentially reduces stormwater volume through strips) infiltration (and hence treatment), and transports excessive volumes of stormwater. Buffer Strips * * Treatment prior to entering waterways and reduction in stormwater volume via infiltration Infiltration trenches * * Treatment of stormwater via infiltration through an engineered filtrate and reduction in stormwater volume. Infiltration trench * Treatment of stormwater via infiltration through an engineered filtrate. Wetlands * * Treatment and ability to store stormwater, decreasing volume of water entering a stream from a storm event. Ponds and/or * * Treatment and ability to store stormwater, detention basins decreasing volume of water entering a stream from a storm event. Litter traps * Removal of gross litter from a water stream. Many different designs available. Oil and grease traps * Treatment, new technology not widely adopted.
30 1.3 Knowledge gaps To date, research on constructed ponds and wetlands for the treatment of urban stormwater has been based around basic in/out water quality data, sedimentation rates and nutrient and sediment removal modelling (Lloyd et al. 1998; Larm 2000; Birch et al. 2004; Fink and Mitsch 2004; Greenway and Jenkins 2004; Hares and Ward 2004; Holland et al. 2005; Kohler et al. 2005; Zhang and Mitsch 2005). Few published studies have focused on the long term behaviour or ability of stormwater ponds and wetlands to treat urban stormwater, and/or what parts of a pond/wetland ecosystem are most effective at removing stormwater pollutants (Wong et al. 2000, Carleton et al. 2001). Published studies investigating the adaptation stage, or early development stage, of stormwater ponds and wetlands on water quality/treatment efficiency are also not widespread.
In recent years, research on constructed ponds and wetlands (including that undertaken on municipal effluent wastewater treatment ponds and wetlands) has started to move from looking at basic pollutant reduction (performance data) (Kadlec and Tilton 1979; Gersberg et al. 1983) to the investigation of pollutant dynamics how pollutants act or react within the pond and wetland environment (Kadlec 1999; Lee et al. 1999; Platzer 1999; Burgoon 2000). At a similar time to this movement, scientific research papers began reporting on the effects of abiotic factors influencing pollutant removal - in particular nutrient removal (DeLaune et al. 1998; Ennabili et al. 1998; Griffin JR. et al. 1999; Khoshmanesh et al. 1999; Burgoon 2000).
In one of the few papers discussing stormwater wetlands, Carleton et al. (2001) highlighted the lack of design guidance available, probably resulting from the lack of comprehensive long term studies in their operation. Overseas (and in Australia), the general perception is that stormwater treatment ponds perform as a function of hydraulic loading rate and detention time - which is, of course, related to storm intensity, runoff volume and pond size (Johengen and LaRock 1993; Kadlec et al. 2000).
31 One of the major findings of stormwater wetland research in the US was the importance of proper sizing of ponds and the effect of re-suspension of sediment particles on pollutant retention within the pond during a storm event (Johengen and LaRock 1993; Carleton et al. 2001).
Australian stormwater pond and wetland research commenced approximately 10 years following research undertaken in the US and Europe, in the early 1990’s. The University of Melbourne, Monash University and the University of Adelaide were some of the first institutions to monitor, report and present scientific data on stormwater ponds and wetlands at conferences, and in scientific journals (Duncan 1998; Lloyd et al. 1998; Somes and Wong 1998). The initial development of Australian stormwater constructed ponds and wetlands was led, predominantly, by engineers and hydrologists, looking at the application of municipal wastewater constructed ponds and wetlands to stormwater constructed ponds and wetlands. Research focused on thermal stratification, particle size distribution (related to attached pollutants), bacteria removal, and modelling various pollutants through the system (Wong and Geiger 1997; Duncan 1998; Lloyd et al. 1998; Somes and Wong 1998; Davies and Bavor 2000; Walker 2001).
More recently, research on the use of ponds and wetlands, for both stormwater treatment and municipal wastewater treatment has been driven in a more limnological and multidisciplinary direction (Davies and Bavor 2000; Davies et al. 2003; Characklis et al. 2005; Dierberg et al. 2005; Jenkins and Greenway 2005; Mermillod- Blondin et al. 2005). This has led to a research approach which extends far beyond that of the previous ‘black box’ approach, and one that will ultimately result in an in- depth knowledge of how and why ponds and wetlands remove pollutants. Which can then be used to produce more effective design guidelines.
32 1.3.1 Limnological approach to stormwater treatment pond and wetland research… Within freshwater environments, there exist a number of key micro-trophic groups that influence ecosystem function, nutrient cycling and overall waterway health. These, as reported in many limnological and freshwater biology texts (Horne and Goldman 1994; Wetzel 2001; Sigee 2004) include; • Planktonic algal and bacterial communities; • Zooplankton; • Attached micro-organisms on submerged surfaces; and • Sediment microflora and fauna.
Being freshwater environments, constructed ponds and wetlands for stormwater treatment would be expected to function as a result of the above communities interactions with nutrients (both autochthonous and allochthonous sourced) along with the varied hydraulic regime determined by rainfall and storm events within the catchment. To date, there has been little research on the above mentioned micro- trophic communities in relation to the treatment of stormwater and their influence/role in nutrient reduction (one of the primary aims of stormwater treatment ponds and wetlands) in constructed ponds and wetlands for. Figure 1-3 provides a diagrammatic representation of the key micro-trophic groups that occur within stormwater ponds and wetlands, highlighting areas of little understanding/research. It also highlights a lack of real time hydraulic analysis on flow rates through of stormwater ponds and wetlands and their influence on system performance. Although hydraulic investigations on stormwater ponds and wetlands have been published recently (Werner and Kadlec 1996; Wong et al. 1999; Jenkins and Greenway 2005; Zhang and Mitsch 2005), the vast majority of these are based on hydraulic models lacking input from the investigation of the actual hydraulic behaviour of stormwater ponds and wetlands.
33 Urban stormwater runoff
? Flow to receiving waters
P P N, P & C ? N, P & C ?
N, P & C ?
N, P & C
? Key
Stormwater flow rates and CP & W hydraulic Pelagic phytoplankton community efficiency.
N, P & C movement between sediment/water Periphyton/biofilm attached to P interface macrophyte hosts
Benthic metabolism and nutrient movement Areas of limited knowledge across the benthos. water column interface ?
Bacterioplankton community N, P & C N, P & C fluxes within CP & W's
Figure 1-3: Diagrammatic representation of the key micro-trophic groups in stormwater treatment ponds and wetlands.
1.4 Project aims and objectives
When investigating the areas of research presented in this thesis, it was decided to conduct as much research as possible in the field, and avoid small scale laboratory based experiments whereby data collected is based on highly controlled laboratory conditions – not actual field conditions. Thus, an underlining aim throughout this entire thesis was to conduct all research within the field.
34 The primary aim of the research presented in this thesis was to investigate and, where possible, quantify • nitrogen (N), phosphorus (P) and carbon (C) dynamics within stormwater treatment ponds; and • the role of key micro-trophic groups (as identified in Figure 1-3) in N, P, and C removal within stormwater treatment ponds.
Six research based chapters (Chapters 4-9) are presented to address the following main objectives within a constructed stormwater treatment pond system; a) To assess the hydraulic behaviour of the pond system under base and storm flow conditions. b) To assess the background water chemistry and determine factors driving water quality variability within the pond system. c) To assess the pond systems stormwater ‘treatment’ ability. d) To assess the phytoplankton species composition within the pond system and identify factors controlling their presence and abundance. e) To determine the productivity of the pelagic phytoplankton community within the pond system, and assess their role in inorganic nutrient cycling f) To measure the productivity of bacterioplankton within the pond system and assess their role in inorganic nutrient cycling within the BWC System. g) To assess the rate of benthic metabolism, and provide preliminary investigations into inorganic nutrient fluxes across the sediment/water interface in the pond system. h) To assess the influence of submerged macrophytes, epiphyton and phytoplankton communities on inorganic nitrogen and phosphorus uptake and cycling within the pelagic and littoral zone of the pond system.
35 2 Chapter2: The Study Site
36 2.1 Location of study site
This PhD investigation was undertaken using one study site – the Bridgewater Creek Stormwater Treatment System (BWC System). In a broad context, the BWC System is located within the Moreton Bay catchment. Moreton Bay is a subtropical, semi- enclosed embayment located adjacent to the city of Brisbane, South East Queensland Australia (Figure 2-2). Moreton Bay is a relatively shallow bay, with a mean depth of 6.25 m and a maximum depth of 29 m. It covers a total area of 1523 km2 and accepts runoff from a catchment of 21220 km2. Over 1.5 million reside within the catchment (Dennison and Abal 1999). According to Skinner et al. (1998), the catchment draining into Moreton Bay has one of the fastest growing populations in Australia. The Moreton Bay coastline produces about 15% of the states’ commercial fisheries catch, while the area represents only 3% of the coastline (Chilvers et al. 2005). In 1998 it was estimated that approximately 5783 ton of N and 1522 ton of P entered Moreton Bay per yr. – both values derived from a combination of modelled and measured data from Eyre and McKee (2002) and McAlister and Walden (McAlister and Walden 1999). Point source inputs of N and P into Moreton Bay were 3383 and 1182 tons per yr respectively, accounting for 58% of the total N and 77% of the total P entering Moreton Bay (Figure 2-1) (Economics 1995). Interestingly, almost half of the N entering Moreton Bay is sourced from non-point sources, while 23% of the P is derived from non-point sources. Thus, diffuse (or non-point) loads of N and P into Moreton Bay are of obvious importance to protecting the long term social, environmental and economic values of Moreton Bay.
Point sources Atmos. deposition 3383 tons N/y 68 tons N/y Non-point sources 1182 tons P/y 2.5-250 tons P/y 2400 tons N/y 340 tons P/y Moreton Bay Groundwater sources; insignificant
Figure 2-1: N and P loads into Moreton Bay per year in 1998 (from Eyre and McKee 2002)
37 2.1.1 Urban stormwater treatment – a case study within the Moreton Bay catchment The BWC System was designed and constructed under a joint initiative involving private industry, City Design (of the Brisbane City Council), and the Co-operative Research Centre for Catchment Hydrology. The BWC System was constructed for the treatment of storm generated urban runoff (a major diffuse pollution source). The BWC System is located approximately 5km south of the Brisbane CBD at Bowies Flat, Cooparoo in South East Queensland, Australia (Figure 2-2). It was built on the Bridgewater Creek floodplain in the lower to mid reaches of the creek, at Bowies Flat parklands on the southern side of Old Cleveland Road. Stormwater enters the BWC System from Bridgewater Creek, discharges to Norman Creek, the Brisbane River and ultimately into Moreton Bay.
The BWC System was commissioned in September 2001 as part of Brisbane City Council’s (BCC) Bridgewater Creek Water Quality Improvement Project (BCWQIP), commencing in May 1999. The BCWQIP aimed at identifying the most efficient and effective method of improving the water quality of Bridgewater Creek, recommending the construction of an artificial wetland within Bowies Flat, Cooparoo. BCC, in collaboration with local residents, developed a number of project aims associated with the BCWQIP. These included: • Reducing stormwater pollution, specifically fine sediments and nutrients, entering the Brisbane River and Moreton Bay. • Enhancing the ecological values of Bridgewater Creek and downstream receiving waters. • Enhancing the visual amenity and recreational values of Bowies Flat and its surrounds. • Improving community awareness, while providing educational and research opportunities for local, regional and national stakeholders, including local schools, research institutions and stormwater industries. • Ensuring risks associated within the project, such as flooding, public safety and mosquitos are examined and minimised.
38 The BWC System consists of six clay lined interconnected ponds (Figures 2-3 and 2- 4). Pond 1 acts primarily as a sedimentation basin (1000m2 in size), with stormwater entering from two concrete lined canals on the south-eastern and western corners of the pond (Figure 2-3). Stormwater is screened via two trash racks in each of the channels prior to entering the sedimentation basin. Pond 1 has an operating level of 4.5m AHD, and is designed to operate at a constant depth of 2 metres. The primary aim of Pond 1 is to remove sediment and associated pollutants before they enter the proceeding ponds. Stormwater exiting Pond 1 subsequently moves through Ponds 2 to 6 via surface flow (total size of approximately 7000m2). The outlet structure of the BWC System is located on the northern tip of Pond 6 with water draining from the system at 4.25m AHD. The outlet structure has been designed to allow stormwater to ‘back up’ within Ponds 6 through to 2, increasing the time stormwater resides within the system and thus maximising its treatment. The primary aim of Ponds 2-6 within the BWC System is to remove nutrients from stormwater, provide aquatic habitat and enhance the aesthetics of the Bowies Flat area.
In periods of high hydraulic flow into the BWC System (resulting from heavy rainfalls within the catchment), stormwater bypasses Ponds 2-6 via an overflow spillway and grassed channel north-west of the outlet structure of Pond 1 (Figure 2-3), Stormwater bypasses Ponds 2-6 when the water level within Pond 1 exceeds 5.01m AHD. The main function of the bypass weir and channel is to direct high volumes of stormwater away for ponds 2-6 to alleviate the damaging effects of water velocity on wetland vegetation and to avoid the resuspension of particles from the benthic environment.
39 Moreton Bay Australia Queensland Brisbane
Brisbane River
Norman Creek
BWC System
Figure 2-2: Location of the BWC System in a global and local context. Red dotted line shows stormwater flow folowwing the BWC System into Norman Creek and the Brisbane River. Images sourced from GoogleEarthTM on the 16-Apr-06.
40 Pond 6
Pond 4
Pond 2 Pond 5
Pond 3
Pond 1
Figure 2-3: Bridgewater Creek Stormwater Treatment System (BWC System). Yellow arrows display inlet channels. Red arrows display outlet from treatment ponds. White arrows display water flow through ponds in low flow conditions. Broken white arrow displays water flow in high flow conditions. Notice the foot bridge between Ponds 4 & 5 and Ponds 2 & 3. (Source: BCC, 2002)
41 Figure 2-4: Bathymetry map of the BWC System, showing 50cm contour intervals. (Source: Data collected onsite – refer to Chapter 4)
42 The original plantings within the BWC System were designe to create a complex arrangement of pond and wetland ecotone's, including both fringing and banded emergent macrophyte zones, ephemeral marshes and submerged macrophyte zones. Under this planting scheme, the BWC System was to develop into a densely vegetated wetland ecosystem. Unfortunately, macrophyte establishment and ongoing growth and recruitment was poor leading to the system developing into a pond based system with macrophytes generally restricted to the fringe of ponds. Given the extensive planting of the BWC System, the expectation of the designers, BCC and a number of local researchers was that the system develop into a densely vegetated wetland environment. Unfortunately, many of the macrophytes planted have been progressively dying, with area once covered by emergent macrophyte now being open water sections (Greenway et al. 2006a,b).
At the time of this PhD investigation all six ponds of the BWC System supported some degree of emergent macrophytes at the pond banks. Emergent macrophyte species present during investigation included Baumea articulata, Bolboschoenus fluviatilis, Juncus usitatus, Lepironia articulata, Persicaria decipiens, Schoenoplectus mucronatus, Schoenoplectus validus, and Typha latifolia (Greenway et al. 2006). No submerged macrophyte species were planted within the BWC System, however in September 2004 Potamogeton javanicus was found in high densities in Pond 6, and by early 2005 was present at varying densities in Ponds 5 and 4.
To address the aims of this thesis, it was decided to base all research within Ponds 1 and 6 of the BWC System, with the exception of data presented in Chapter 4 on the hydraulic behaviour of the BWC System. Ponds 1 and 6 where chosen because they were the respective inlet and outlet ponds. At the time this study commenced, Pond 1 was predominantly a deep pond system with 2-4m fringing emergent macrophytes (Schoenoplectus validus) (total macrophyte coverage of ~15%), while Pond 6 was predominantly a shallow pond system with bands of emergent macrophyte Schoenoplectus mucronatus (total macrophyte coverage of ~10%). However, by early 2004, the bands of Schoenoplectus mucronatus within Pond 6 were no longer present, and by September 2004 submerged macrophyte Potamogeton javanicus had colonised the pond.
43 Plates 2-1 and 2-2 show Ponds 1 and 6 at rthe BWC System respectively. During high flow events, Ponds 2, 3, 4, 5 and 6 overflow into each other, effectively creating 1 large pond (Plate 2-3) separated from Pond 1 by a substantial land bridge.
a b
Plate 2-1: Pond 1 during low flow (a – 31-Mar-04) and post high flow (b – 15-Dec-03) conditions.
a b Plate 2-2: Pond 6 in low flow conditions, open water (a) and macrophyte section (b). Photographs taken on 31-Mar-04.
a b Plate 2-3: Connectivity of water flow between Ponds 2, 3, 4, 5, and 6, looking north (a) and south (b) from foot bridge (see Figure 2-2). Photographs taken on 15-Dec-03.
44 2.2 A subtropical climate
Brisbane experiences a subtropical climate (Lat. -27.4778° south, Long. 153.0306° east), with a mean 122 rainy days per year totalling a mean annual precipitation of 1185mm. Figures 2-4a-d display historic climate characteristics of the Brisbane City area from 1840 through to 1994.
Figure 2-5: Mean monthly climatic variables for Brisbane from the Bureau of Meteorology data set between 1840 and 1994. (a) Mean monthly air temperature at 9am with ƔLQGLFDWLQJ mean monthly maximum and żLQGLFDWLQJPHDQPRQWKO\PLQLPXPWHPSHUDWXUHV E 0HDQ monthly rainfall. (c) Mean monthly relative humidity at 3am and 3pm. (d) Mean daily evaporation as measured by E PAN.
45 2.3 The Catchment
The 0.8 ha BWC System is located within the lower reaches of the Bridgewater Creek Catchment, on the flood plain of Bridgewater Creek – formally a 2 ha park area known as Bowies Flat. The catchment area draining into the BWC System is approximately 200ha in size, being fully developed land of predominantly residential land use (Figure 2-6). Stand alone houses account for approximately 86% of all dwellings within the BWC catchment, with townhouses and units accounting for approximately 13% (BRISbites 2000). The majority of houses within the catchment are renovated ‘Queenslanders’, owing to most of the residential development within the Camp Hill area (suburb in which the BWC System is located) taking place following the Second World War.
46 Figure 2-6: Location of the BWC System in relation to catchment, land use within the catchment and stormwater drainage lines. ( ) indicates stormwater flow gauges. Adapted from BCC, 2003.
47 3 Chapter 3: Standard methods to research chapters
48 3.1 Introduction Chapters 5-9 focus on the behaviour of N, P and C within stormwater treatment ponds. Some of the methods used are consistent across these chapters, thus to reduce repetition within the thesis, this chapter presents standard methodologies relevant to more than one chapter.
3.2 Water sample collection and storage A one litre water sample bottler was used to collect water samples from locations (specified in individual research chapters) within the BWC System. Sampling bottles were submerged at the desired water sampling depth within the water column (upside down), and filled with water by slowly turning the water bottle right side up. When water samples were required from a depth in the water column greater than 1m, 1 L water sample bottles were fixed to a pole and plugged with a rubber stopper attached to a string line. The pole was then lowered to the desired depth, and the rubber stopper pulled out, allowing filling to occur.
From each 1 L water sample, a number of sub samples were commonly taken for the analysis of; • Dissolved inorganic nitrogen and phosphorus (30mL), • Total nitrogen and phosphorus (30mL), • Dissolved organic carbon (30mL), • Total carbon (30mL),
For the analysis of TN, TP and TOC, water was decanted from 1 L water samples into sterile 30mL polycarbonate vials and placed directly on ice in the field. For the analysis of NO3-N and NO2-N (collectively NOx-N), NH4-N, PO4-P and DOC, 30 mL of water was decanted and filtered through a 0.45µm Millipore syringe filter into sterile 30mL polycarbonate vials and placed directly on ice in the field. Upon the completion of a days field work, all water samples collected were transported the laboratory and frozen in as -4°C freezer to await analysis.
49 3.3 Water sample analysis
3.3.1 Nitrogen and phosphorus analysis
Water samples for the determination of TN, NH4-N, NOx-N, TP, and PO4-P were analysed colour-metrically, using a Lachat 8000 FIA automated analyser located at Griffith University. Defrosted samples were decanted in sterile 10mL FIA polycarbonate vials, and placed within FIA sampling racks.
Dissolved nutrient samples were analysed concurrently, using QuikChem® Methods
31-115-01-3-A, 31-107-04-1-D, and 31-107-06-1-B for PO4-P, NOx-N and NH4-N respectively. All methods used were based on those published in the Standard Methods in the Examination of Water and Wastewater (Franson 1998).
Total Nitrogen and Phosphorus concentration within collected water samples were analysed using PO4-P and NOx-N methods following a digestion procedure. The digestion procedure undertaken is outlined in Standard Methods in the Examination of Water and Wastewater (Franson 1998).
Standard range A total of 10 standards were run for each analysis, covering the concentration range likely to be measured within the water samples. Typically, the standard range used was between 0 and 2ppm for PO4-P, and 0 and 10ppm for NH4-N and NOx-N. Working standards were made from the dilution of stock standard solutions on the day of or day before FIA analysis. Stock standard solutions were made by dissolving specific chemicals within 1L of 18.1XOWUDSXUHZDWHU 7DEOH3-1). Standard stock solutions were refrigerated in sterile plastic containers until required, with new stock standards made periodically in accordance to their shelf life,
Quality control Before each analysis run of the FIA, the machine was thoroughly cleaned using bleach, sodium hydroxide/EDTA and/or 18.1 XOWUD SXUH ZDWHU 3XPS tubes and dialysis membranes were replaced and the cadmium column renewed (or replaced if
50 needed). Before the commencement of each FIA run, a minimum r value of 0.9997 for each standard calibrated curve was obtained. The cadmium column efficiency was also tested to ensure the efficient reduction of NO3 to NO2. The sampling probe attached to the FIA was rinsed with 18.1 XOWUD SXUH water following each water sample analysis.
Table 3-1: Chemicals used to make stock standard solutions. Measuring Chemical Molecular Molecular Amount to add Shelf life weight of weight of what in 1000ppm chemical measuring solution P as PO4 KH2PO4 136.0854 39.9736 4.394 1 month N as NO3 KNO3 101.1032 14.0067 7.217 1 week N as NO2 NaNO2 68.9953 14.0067 4.926 6 months N as NH4 NH4Cl 53.4912 14.0067 3.819 1 month
Each analysis run consisted of a given number of water samples, plus quality control standards. A standard analysis run was done following each 11 water samples analysed by the FIA to ensure effective machine performance throughout the duration of the run. Each standard sample run consisted of 6 samples, comprising (in order of placement); 1. Blank. 2. Certified Reference Material (from Queensland Health Scientific Laboratories). 3. Blank 4. Low/High standard (alternating every 11 samples)
5. NO3-N only low/high standard (alternating every 11 samples). 6. Blank
By running the standard run after each 11 samples it was possible to gauge the accuracy of the machine during the run, and between numerous runs conducted throughout the sampling and analysis period.
The Certified Reference Material was purchased from the Queensland Health Scientific Laboratory, and is an actual water sample from polluted and unpolluted sites within Brisbane, with a certified concentration for a range of compounds. This certification is based on the average concentration of specific compounds using
51 standard FIA methods of that water sample as measured by 33 laboratories around Australia.
3.3.2 TOC and DOC
Total and dissolved organic and inorganic carbon was analysed using a Shizmatzo 800 Total Carbon Analyser, attached to a 93 sample automatic sampler located at Griffith University. Water samples for DOC and TOC were defrosted at room temperature and decanted in 25mL acid washed glass vials. To obtain the organic carbon fraction of both total or dissolved carbon, the total carbon concentration and total inorganic carbon concentration was obtained, with the difference being the organic fraction (TOC or DOC = TC – IC).
Standard range A 5 point standard range for both total carbon (TC) and inorganic carbon (IC) standards was used in TOC analysis, spanning the likely concentration of both TC and IC within the water samples. Stock standard solutions were made using 18.2XOWUD pure water that had been UV treated and filtered through a 0.02µm filter. For the preparation of the TC stock standard, 2.125g of reagent grade potassium hydrogen phthalate previously dried at 105-120°C for 1 hour and cooled in a desiccator, was added to a 1L volumetric flask and dissolved in 18.2 XOWUD SXUH ZDWHU )RU WKH preparation of the IC stock standard, 3.50g of reagent grade sodium hydrogen carbonate previously dried for 2 hours in a silica gel desiccator, along with 4.41g of sodium carbonate previously dried for 1 hour at 280-290°C and cooled in a desiccator was added to a 1L volumetric flask and dissolved in 18.2XOWUDSXUHZDWHU7KHVH stock standards were used for the preparation of the working standards, and kept within the refrigerator in plastic sterile PET 1L bottles. All working standards were made fresh daily using 18.2XOWUDSXUHZDWHU
Quality control To ensure the quality of TOC and DOC analysis, water samples were not analysed until the standard curve for both TC and IC was greater than 0.9997. A standard
52 sample run was also carried out following every 12 water samples within each run to ensure machine accuracy and to compare data between sampling runs. Each standard sample run consisted of 4 standards; 1. TC standard, 2. IC standard, 3. Certified Reference Material standard, and 4. Blank.
Each water sample result from the Shizmatzo 800 Total Carbon Analyser represents an average of between 2-5 actual readings taken from the one sample. If the standard variance between the initial two readings was greater than 2%, thebn further readings were taken until the variance was less than or equal to 2%. If high variances occured, the sample was highlighted and a re run of that particular sample was undertaken. Sampling tubes within the Shizmatzo 800 Total Carbon Analyser where rinsed with 18.2XOWUDSXUHZDWHUEHIRUHDQGDIWHUHDFKZDWHUVDPSOH
Upon completion of each sampling run, the 30mL glass vials used were rinsed with deionised water 5 times and placed in an acid bath overnight. Vials were then rinsed with deionised water and specific water samples decanted into vials.
3.4 Physicochemical water quality parameters
A YSI SNODE™ 6600 multimeter was used for the collection of basic physicochemical water quality data. Dissolved oxygen, conductivity, turbidity, pH, temperature, salinity, redox potential and % fluorescence values where obtained using the YSI SNODE™ 6600 multimeter. All probes within the SNODE™ 6600 were calibrated in accordance to manfuctures instrucions using labarotory grade buffer and standard solutions the day before use following the instruction manual provided by YSI.
53 3.5 Chlorophyll a
The process for obtaining Chlorophyll a concentration of water samples was conducted according to the Standard Methods in the Examination of Water and Wastewater (Franson 1998). Briefly, 1 litre of water was saturated with Magnesium Chloride (MgCl) power, wrapped in foil to exclude light and placed on ice in the field. Sample water was filterd immediately upon arrival to the laboratory on the day of water sample collection using 45mm polycarbonate filter papers. At least 500mL of water sample was filtered, using a low vacuum suction so as not to lyase chlorophyll a from the phytoplankton cells. Filter papers were then placed in dark 10mL centrifuge tubes and frozen until analysis. Analysis was undertaken using a UV/VIS spectrophotometer, Schimadzu UV160A, according to the Standard Methods in the Examination of Water and Wastewater (Franson 1998).
54 Section 2: What
55 4 Chapter 4: Hydraulic characteristics of a subtropical urban catchment and stormwater treatment pond
56 4.1 Abstract
The successful application of constructed ponds for the treatment of stormwater relies heavily on the movement of water into, within and out of the pond system. The aim of this chapter is to present hydraulic information describing the BWC Catchment and BWC System under storm flow conditions and investigate the detention of stormwater within Ponds 2-6 of the BWC System. Catchment precipitation, urban runoff quantity and storm flow through the BWC System was measured continuously between Jan- 2004 and Jan-2005, using insitu instrumentation. Annual rainfall patterns over the study period were characteristic of that of a subtropical climate, with the BWC Catchment exhibiting a volumetric rainfall runoff coefficient of 0.28. The calculated Directly Connected Impervious Area of the catchment was29.5%, which is well within the range that leads to highly degraded urban waterways.
Storm events within the BWC Catchment producing rainfall in excess of 9.9mm triggered the high flow bypass channel in Pond 1 of the BWC System. This channel operated frequently over the course of the study period, channelling 56.9% of storm derived catchment runoff away from the main treatment section of the BWC System. In Ponds 2-6, any given plug of stormwater was detained for a maximum of 9 days, with water storage in Ponds 2-6 exhibiting prolonged ponding time.
Key words: Rainfall, Storm Event, Urban runoff, Runoff Coefficient, Detention Time
57 4.2 Introduction
The volume, intensity and duration of stormwater runoff from catchments is the result of the interaction between land use, individual storm intensity and the duration and frequency of storm events (Marsh 1993; Werner and Kadlec 2000; Goonetilleke et al. 2005). The Directly Connected Impervious Area (DCIA) within urban catchments is the relationship between the total event rainfall and the amount of runoff generated within the catchment from that particular rainfall event (Butler and Davies 2000; Walsh 2004). The DCIA within urban catchments is significantly linked to the water quality and ecological health of urban waterways (Walsh 2004). In many urban areas worldwide, constructed ponds are utilized to improve the water quality and ecological health of urban waterways and ameliorate the polluting nature of stormwater.
Functionally speaking, the hydraulic behaviour of pond treatment systems is one of the most important abiotic factors governing their treatment efficiency, controlling the time polluted waters spend within the system. This in turn effects the biotic community inhabiting the system (i.e. fish, macrophytes, macro invertebrates, phytoplankton, epiphyton, bacterioplankton, viroplankton), the abiotic water parameters of the system (i.e. water D.O., Redox Potential, inorganic and organic nutrients), and the opportunity for the interaction between these biotic and abiotic features (Blenkinsopp and Lock 1994; Burns and Walker 2000; Aldous et al. 2005; Brock et al. 2005; Buyukates and Roelke 2005).
Unlike municipal wastewater treatment ponds, stormwater treatment ponds receive a stochastic water supply driven by rainfall within a defined catchment boundary. Thus, the water supply to stormwater treatment ponds will vary with the intensity and duration of rainfall events as well as the time between rainfall events (Werner and Kadlec 2000). The hydraulic behaviour of stormwater ponds can be simply described as the result of urban runoff entering the system, and the behaviour of the water within the system. Both of which effect the treatment efficiency and impact strongly on the biotic and abiotic factors that govern pollutant removal pathways (Reed et al. 1995; Kadlec and Knight 1996; Kadlec et al. 2000).
58 From an engineering point-of-view, constructed ponds for the treatment of polluted waters can be viewed as fixed film bioreactors, exhibiting pollutant removal characteristics dependant on hydraulic retention time (Crites 1998). Hydraulic retention time (HRT) refers to the time a given volume of water spends within a treatment system (Reed et al. 1995; Jenkins and Greenway 2005). In the case of the BWC System, a pond system. The HRT of a pond system is the result of the combined influence of water flow into and out of the system, and the storage capacity of water within the system itself. Traditionally, treatment ponds were designed to treat polluted waters for a given period of time to achieve the desired effluent water quality. For example, on-site wastewater treatment wetlands are often designed with a HRT of 7 days to achieve a 50% reduction in TN concentration. Owing to the relatively steady state hydraulic conditions which apply to onsite wastewater production, a ‘plug’ of water would be expected to remain within the wetland for a minimum of 7 days and have a TN concentration of approximately half that of when it entered the system (Brix 1987; Headley et al. 2000; Werner and Kadlec 2000; Davison 2002). This typical for most hydraulically steady state pond and wetlands systems, including sewage treatment lagoons, ponds and tertiary treatment wetlands.
Water movement through constructed water treatment ponds defines the hydraulic extent (the area of water inundation) and is a major determinate of biotic species compositions within ponds and wetlands (Mitch and Gosselink 2000). The biotic composition of constructed ponds for the treatment of polluted waters governs, in part, the pathway for nutrient transformation and removal from the water column. The hydraulic nature of ponds also influences the soil and nutrient pools within the pond, which in turn influences the biotic components of the pond. The relationship between the hydraulic nature of ponds, the biotic community inhabiting them and their nutrient removal capabilities is very much bi-directional (Figure 4-1). For example, the flow of water through a pond system will determine the length of time water spends within the pond, and thus the opportunity for interactions between the nutrients within the water column and the biotic community inhabiting the pond. Additionally, the flow of water into a pond will determine the type and abundance of biota present and thus the type of ‘interactions’ likely to occur. Also, allochanthous material entering the pond via hydraulic flow will alter the abiotic water quality parameters of the pond (DO, Redox Potential, Conductivity) which will in turn
59 influence the type of biotic communities present and the behaviour of nutrients within the pond. Ultimately, the hydrology of ponds has a major impact on their ability to remove pollutants and act as water quality improvement technologies.
Hydraulic flow
Abiotic water Nutrirent quality loss/removal
Biotic communitiy
Figure 4-1: The effect of hydraulic flow on biotic and abiotic components of constructed treatment ponds.
The dynamic hydraulic behaviour of stormwater treatment ponds and wetlands impatcs on many biotic and abiotic factors important to their ‘treatment efficiency’. Fluctuating water levels within the studied system have been shown by (Greenway et al. 2006) to have negative impacts on macrophyte establishment, recruitment and overall survival. An ecosystem response reported to occur in a number of other hydraulically fluctuating wetland systems (Ewing 1996; Budelsky and Galatowitsch 2000; Casanova and Brock 2000).
In addition to macrophyte impacts, water level fluctuations and the velocity of stormwater flow through pond systems can have a negative effect on epiphytic algae attached to submerged macrophyte surfaces – an important ecosystem component within the littoral environment and one potentially capable of considerable nutrient reduction (Blenkinsopp and Lock 1994; Kahn and Wetzel 1999; Burns and Walker 2000; Lewis et al. 2002; Dodds 2003). The stochastic delivery of stormwater also has an indirect influence on the ponds biotic and abiotic factors through the supply/regulation of inorganic nutrients, supply and deposition of suspended particulate matter, and transport of pollutants potentially harmful to biota (Reinelt and Horner 1995; Taylor et al. 2004; Walsh 2004; Buyukates and Roelke 2005; Holland et al. 2005; Taylor et al. 2005).
60 Studies into the hydraulic behaviour of stormwater treatment ponds have generally been limited to model based studies that investigate the application of various treatment models on pollutant reduction – predominantly suspended solids (Wong and Somes 1995; Werner and Kadlec 1996; Wong and Geiger 1997; Werner and Kadlec 2000, 2000). To date, there is an overall lack of published research papers that report and discuss field investigations on the actual hydraulic conditions of stormwater treatment ponds and wetlands under varying hydraulic conditions. Additionally, there appears to be a gap in the published literature on quantifying stormwater flow generated during rainfall events from subtropical urban catchments in respect to its delivery to stormwater treatment ponds.
4.2.1 Research aims, objectives and research questions The aim of this chapter is to determine and investigate the hydraulic characteristics of the BWC catchment and the BWC System under storm event/flow conditions. The use of the term ‘hydraulic characteristics’ refers to the nature of stormwater generation within the catchment and its movement within and through the BWC System.
Specific objectives were to; • Determine the relationship between rainfall and runoff within the BWC catchment. • Monitor stormwater flow rates into, and out of the BWC System to determine pond HRT and frequency and extent of stormwater inundation. • Determine the amount of stormwater recieving ‘treatment’ by the BWC System based on the volume entering the system vs. the volume of stormwater delivered by the BWC catchment.
Key research questions were; • How much stormwater is treated by the BWC System based on the amount of urban runoff produced from the BWC Catchment? • How long is water retained within Ponds 2-6 of the BWC System?
61 4.3 Methods
4.3.1 The BWC Catchment
The BWC catchment was described in detail in Chapter 2. Precipitation within the catchment was measured using a BCC rain gauge located in Camp Hill (BCC station code NMR596), just outside the BWC Catchment. Precipitation was recorded ‘as fallen’ and stored on an onsite data logger. Stormwater flow generated from rainfall events within the catchment was measured using two Doppler Flow Gauges located at the south eastern and western inlet channels to Pond 1 of the BWC System (Figure 4- 1). Flow rates were calculated every 5 minutes from water height and velocity measurements taken using the Doppler Flow gauges and stored usinf onsite data loggers. All stormwater generated as a result of rainfall within the BWC Catchment passed through these flow gauges, thus the total stormwater flow from the entire catchment was recorded (refer to Figure 2-6, Chapter 2 displaying drainage lines and locations of flow gauges). Stormwater runoff from the BWC catchment (also equals Pond 1 influent) was measured continuously between January 2004 and February 2005. All flow and precipitation data was retrieved from onsite data loggers on a monthly basis using a lap-top computer by BCC staff.
4.3.2 The BWC System
From a hydraulic perspective, the BWC System can be divided into two sections (Figure 4-1). A primary sedimentation basin (termed Pond 1) and a 5 pond treatment zone (termed Ponds 2-6). Stormwater enters Pond 1 of BWC System via the two inlet channels in the south eastern and south western corners. Stormwater entering Pond 1 is detained until the water height of the pond reaches > 4.5m AHD, when it then begins to flow into Pond 2 via an underground drainage pipe.
62 Water level indicator
Ponds 2-6
Pond 1
Figure 4-1: Hydraulic division of BWC System. Small white arrows indicate the surface flow of water between the ponds. Yellow arrows show underground outlet flows from Ponds 1 and 2-6, with broken yellow arrow showing water flow via the high flow bypass channel. Red crosses show the location of flow meters, with the red dot showing the location of the bypass channel level sensor. Red arrows display pathway of water flow during storm flow (short circuiting).
When large volumes of stormwater enter Pond 1 of the BWC System, an overflow weir connected to a bypass channel transports stormwater east of Ponds 2-6 (Figure 4- 1). The bypass channel becomes active when the water level within Pond 1 rises above 5.01m AHD. Upon entry to Pond 2, stormwater flows via surface flow through Ponds 3, 4, 5 and 6 to the outlet structure located at the northern end of Pond 6 (refer to Figure 4-1). The outlet structure in Pond 6 consists of a plate with seven holes set at varying heights to provide approximate linear outflow at different water levels (Figure 4-2). When the water level within Ponds 2-6 exceeds 5.01m AHD, stormwater overflows via a weir set behind the outlet structure in Pond 6.
To measure the level of water within Ponds 2-6, a water level indicator was installed within Pond 6 in February 2004 (refer to Figure 4-1). The water level (along with date and time) within Ponds 2-6 was recorded 25 times during periods of storm flow through the BWC System (ad hoc approach). Specific water level measurements (at 1cm increments) were taken directly following storm events to assess time taken for water within Ponds 2-6 to drain.
63 Figure 4-2: Theoretical flow rate of water exiting Pond 6 via the outlet structure. Flow rate of water is based on water level within Ponds 2-6. Note weir level.
Field gauges –stormwater flow within the BWC System Stormwater flow into the BWC System was measured at the inlet channels to Pond 1. These measurements were also used to determine total BWC catchment runoff. As stated earlier, these flow gauges measured stormwater flow every 5 minutes between January 2004 and February 2005. In addition to the measurement of flow entering the BWC System, water flow was measured within the outlet pipe of Pond 6 in the same fashion and under the same sampling regime to that of the Pond 1 inlet pipes described earlier. Pond 6 flow measurments represent flow of ‘treated stormwater’ out of the BWC System. To determine whether the high flow bypass was triggered during large storm events within the catchment, a level sensor at the inlet of the bypass channel recorded the water level every 30 minutes (Figure 4-1) (if the water level recorded for a particular time step was below 0.0m, no bypass was recorded, where the water level recorded was above 0.0m, a bypass event was recorded).
All data obtained using the Doppler flow gauges in Ponds 1 and 6 and the bypass level sensor were recorded and stored to on-site data loggers until retrieval via a lap- top computer on a monthly basis by BCC staff.
64 An additional Doppler flow meter was located within the outlet pipe between Pond 1 and Pond 2. Unfortunately, this meter was extremely temperamental over the course of the study period. As such, the data recorded by the meter was deemed of poor quality and not used in the analysis of water movement within the system.
Bathymetry of the BWC System The volume of the entire BWC System was determined in December of 2004. This involved the 3 dimensional mapping of the wetland surface using a GT 701 Total Station. Spot heights of the wetland’s bottom surface were taken every 20-50cm depending on the incline of the surface and imported into Surfer 7.0 – a geographical mapping and data interpretation program. Figure 4-3 displays the location of spot heights taken within the BWC System.
Figure 4-3: Location of spot heights taken for the determination of BWC System bathymetry.
4.3.3 Data analysis
Microsoft Excel®, SigmaPlot® 6.0, SPSS® 14.0.1 and Surfer® 7.0 computer programs were used exclusively for data analysis described in this chapter.
The BWC Catchment The volumetric runoff coefficient was calculated (using Equation 4-1) to quantify the percentage of stormwater generated(Wong et al. 2000). This is a s useful measure when comparing catchments from a particular rainfall event within a catchment of known size.
65 Rp= Q/() PA w (4-1) where;
Rp = volumetric runoff coefficient Q = Total catchment stormwater flow (m3) P = Precipitation in catchment (m) 2 Aw = area of catchment draining into wetland (m )
A total of 36 rainfall events spanning the entire year of 2004 were investigated to determine a mean volumetric runoff coefficient for the BWC catchment. Rainfall events were chosen on the basis of a complete data set for individual rainfall events and total catchment storm flow data. Of these 36 storm events, 15 events were selected for further hydraulic analysis of the BWC System, based on the completeness of the dat sets for individual rainfall events and total catchment storm flows. All equations following relate to the investigation of these 15 storm events.
As a result of the high flow bypass channel in the studied stormwater treatment wetland, a percentage of stormwater from certain storm events bypassed Ponds 2-6. The amount of stormwater that bypassed in any given storm event was determined using Equation 4-2, and the percentage of stormwater bypassed from that particular storm event was calculated using Equation 4-3.
BP=() QP1 eastin + Q P 1 westin − Q P 1 out (4-2) Where; BP = amount of water bypassed (m3) 3 QP1eastin = Pond 1 east inflow (m ) 3 QP1westin = Pond 1 west inflow (m ) 3 QP1out = Pond 1 out flow (m )
BP%= ( BP /( QP1 eastin + Q P 1 westin )).100 (4-3) where; BP% = percentage of Pond 1 inflow bypassed, (%)
66 Due to the failure of the Pond 1 outlet flow meter1, a standard equation based on water inflow, outflow and wetland volume could not be used to calculate the Hydraulic Retention Time (HRT) of stormwater within Pond 1 and Ponds 2-6 of the BWC stormwater wetland. Accordinlgy, Equation 4-4 was used to estimate the hydraulic retention time of Ponds 2-6.
HRTn=(( V y − V x .1000000.2) /( Q P2− 6 out − y + Q P 2 − 6 out − x )) / 3600 (4-4) where;
HRTn = nominal hydraulic retention time at time y, (hrs)
Vy = volume of Ponds 2-6 at time y, (ML)
Vx = volume of Ponds 2-6 at time x, (ML) -1 QP2-6out-y = Pond 2-6 outflow at time y, (L. sec ) -1 QP2-6out-x = Pond 2-6 outflow at time x, (L. sec )
Due to the lack of Pond 1 outflow data and the fact that Pond 1 inflow regularly bypassed Ponds 2-6, the HRT for Pond 1 could not be calculated accurately. As such, little of the results and discussion is focussed on the hydraulic behaviour of Pond 1.
1 Data generated from gauge was unrepresentative of the actual flow occurring between Ponds 1 and 2. i.e. No flow was recorded during known flow times on numerous occasions, and total flow recorded from flow gauge during many storm events was substantially less than that of flow recorded leaving Pond 6. 67 4.4 Results
4.4.1 The BWC Catchment
Throughout the study period, January 2004 – February 2005, a total of 1122mm of rain fell within the catchment. Rainfall was recoreded on 89 days (Figure 4-4), the majority of which occurred during the months of February, March, November, and December of 2004 and January and February of 2005, exhibiting a typical summer wet, winter dry climate pattern for the region.
Figure 4-4: Daily precipitation within the BWC catchment over the study period.
Table 4-1 displays a list of run-off coefficients calculated from the 36 storm events investigated for the determination of the BWC Catchment runoff coefficient. The volumetric runoff coefficient calculated (using Equation 4-1) from 36 storm events during the study period varied somewhat, with the overall runoff coefficient being 0.282.
By calculating the direct runoff from each storm event monitored and relating it to the total rainfall for each storm event, the Directly Connected Impervious Area (DCIA) within the BWC catchment can be calculated. Figure 4-5 displays the correlation
68 between total storm event precipitation and the total stormwater runoff derived from that particular storm event.
Table 4-1: Generated volumetric runoff co-efficients for 36 hydraulically monitored storm events
Day of storm Precipitation Stormwater runoff Run off co- event (mm) (m3) efficient 03-Jan-04 4 767 0.1 04-Jan-04 11 3,116 0.15 10-Jan-04 5 480 0.05 11-Jan-04 1 32 0.01 14-Jan-04 168 110,351 0.34 17-Jan-04 15 6,352 0.22 19-Jan-04 4 879 0.11 20-Jan-04 14 6,344 0.23 22-Jan-04 1 11 0.01 24-Jan-04 50 36,065 0.37 25-Jan-04 30 25,786 0.44 28-Jan-04 5 1,408 0.14 29-Jan-04 3 1,307 0.22 02-Feb-04 68 58,662 0.44 09-Feb-04 1 26 0.01 23-Feb-04 18 10,892 0.31 25-Feb-04 5 2,414 0.25 05-Mar-04 99 84,237 0.44 18-Mar-04 35 13,560 0.20 23-Mar-04 5 2,477 0.25 05-Apr-04 10 2,214 0.11 16-Apr-04 15 3,939 0.14 28-Apr-04 16 6,229 0.20 08-May-04 19 9,776 0.26 08-Jun-04 6 2,487 0.21 10-Jul-04 1 478 0.25 02-Aug-04 3 341 0.06 18-Aug-04 14 8,473 0.31 31-Aug-04 12 2,984 0.13 05-Sep-04 5 904 0.09 09-Sep-04 5 1,911 0.20 20-Sep-04 2 58 0.02 17-Oct-04 62 31,026 0.26 05-Nov-04 152 44,830 0.15 21-Nov-04 8 1,730 0.11 17-Jan-05 28 11,557 0.21 Mean 25 13725 0.28 SE 6.61 4146 0.02
69 Figure 4-5: Directly connected impervious area of the BWC catchment (n=36). The slope of the linear regression line equals the DCIA. Note: 95th confidence intervals
4.4.2 The BWC System
Volume of BWC treatment wetland The volume of water contained within the BWC System changes dramatically based on the amount of rain falling within the catchment. Figure 4-6 displays a contour map of the BWC System as of December 2004. From the bathymetry shown in Figure 4-6, the water holding capacity of both Pond 1 and Ponds 2-6 of BWC System was calculated at differing water heights by Surfer® 7.0 using a Kriging griding method (Table 4-2).
Figure 4-6: Bathymetry of BWC stormwater wetland. Contour interval = 0.5m. All units displayed in metres above AHD.
70 Table 4-2: Water holding capacity of Pond 1 and Ponds 2-6 at differing water heights.
Water height Pond 1 volume Ponds 2-6 volume Total volume (m AHD) (ML) (ML) (ML) 3.75 0.87 2.68 3.55 4.00 1.11 3.80 4.91 4.25 1.36 5.16 6.52 4.50 1.66 6.65 8.31 4.75 1.98 8.22 10.2 5.01 2.37 9.95 12.3
4.4.3 Stormwater flow into the BWC System.
The hydraulic characteristics (HRT, flow, water level, flooding time) of the BWC System during, and immediately following 15 storm events was investigated.
The volume of stormwater generated within the BWC catchment from the 15 individual storm events monitored varied dramatically, depending on the amount of rainf falling within the catchment. Table 4-3 displays the amount of stormwater runoff entering the BWC System from each of the storm events monitored, the amount of stormwater that bypassed the system, and the percentage of flow from each storm event that bypassed. As can be seen from Table 4-3, the majority of the monitored storm events bypassed Ponds 2-6 with a mean 56.9% of total inflow bypassing the treatment wetland. Thus, only 43% of the volume of all stormwater generated within the BWC Catchment from over 80% of all storm events obtained some level of treatment via Ponds 2-6 of the BWC System.
Due to the frequency with which stormwater bypassed Ponds 2-6, the relationship between storm event rainfall and the volume of runoff bypassing Ponds 2-6 was investigated (Figure 4-7). Using the regression equation displayed on Figure 4-7, the amount of rainfall falling within the BWC catchment needed to trigger the high flow bypass channel can be modeled (Figure 4-8). This modeling indicated that the high flow bypass channel was triggered when the total rainfall from a storm event within the BWC catchment was greater than 9.89mm, which correlates with the observed results displayed in Table 4-3.
71 Table 4-3: Characteristics of storm events investigated in this study.
Storm Date of Total Duration Volumetric P1 Amount % of inflow event storm event precipitation of storm runoff inflow bypassed bypassed number (mm) event coefficient (ML) (hrs) (ML) 1 02-Feb-04 68 32.23 0.442 58.75 46.01 78.3 2 22-Feb-04 18 30.4 0.310 10.89 4.32 39.9 3 05-Mar-04 99 37.85 0.436 84.36 74.50 88.3 4 18-Mar-04 35 2.75 0.199 13.60 7.62 56.0 5 16-Apr-04 15 55.46 0.135 3.96 1.60 40.4 6 28-Apr-05 16 31.03 0.200 6.25 2.14 34.3 7 08-May-04 19 11.083 0.264 9.80 4.76 48.5 8 02-Aug-04 4 8.43 0.044 0.35 No bypass No bypass 9 18-Aug-04 14 5.83 0.310 8.49 3.57 42.0 10 31-Aug-04 12 33.43 0.128 3.00 1.26 42.0 11 04-Sep-04 5 37.76 0.093 0.91 No bypass No bypass 12 09-Sep-04 5 11.33 0.327 1.91 1.04 57.5 13 17-Oct-04 62 66.3 0.257 31.10 21.83 70.2 14 05-Nov-04 152 101.56 0.151 45.02 38.49 85.5 15 20-Nov-04 8 50.18 0.111 1.74 No bypass No bypass Mean 18.7 17.3 56.9 SE 6.5 6.83 5.5
Figure 4-7: Correlation analysis between total storm rainfall and the amount of stormwater that bypassed BWC stormwater wetland.
Water retention and depth changes within Ponds 2-6 Using Equation 4-4, the theoretical hydraulic residence time, or the time taken for Ponds 2-6 to drain (below 4.065m AHD) can be calculated as a function of the water level within Ponds 2-6 (Figure 4-9a). As shown in Figure 4-9, the theoretical time for water to drain from Ponds 2-6 of the BWC System ranges between 0.5 and 8.4 days. These figures make no allowance for the time it takes the system to fill and stabilise at a constant level. Figure 4-10 displays the comparison between the theoretical time to drain and the observed time (incorporating allowances for the time taken to fill the 72 BWC System) to drain Ponds 2-6. It can be noted that the observed time drain is generally greater than the theoretical time to drain.
Figure 4-8: Modelled total storm rainfall need to trigger high flow bypass channel.
Figure 4-9: (a) Theoretical time for stormwater to drain to <4.065 m AHD, (b) Theoretical vs. observed drainage time for Ponds 2-6.
73 There was a string correlation between Pond 6 outflow and water height in Ponds 2-6 (Figure 10), indicating a highly significant (p = 0.0005) relationship based on a 3rd Order Polynomial regression. Based on this correlation, it was possible to use the equation displayed in Figure 10 to predict the water level in Ponds 2-6 on any day there was outflow from Pond 6. This model is, however, limited. If there was no flow out of Ponds 2-6, the water level could only be estimated to be <4.065m AHD. Further, if the water height within the ponds was above 5.01m AHD, water would commence flowinf over the weir at the exit of Pond 6. Thus, the flow rate measured if the water level of the ponds was above 5.01m AHD would not be representative of the volume of water leaving the BWC System. To quickly summarize, this analysis was only useful to predict water heights within Ponds 2-6 between 4.065m and 5.01m AHD.
Figure 4-10: Correlation analysis between Pond 6 outlet flow rate and water height within Ponds 2-6. Red line on graph represents the modelled theoretical relationship between flow rate and water height based on the outlet structure design.
Using the equation of the 3rd order polynomial trend line displayed in Figure 4-10 on a 365 day data covering the period 5th January 2004 to 16th February 2005 it was possible to predict the level of standing water within Ponds 2-6. Figure 4-11 shows the amount of time water spent above particular water levels, and the percentage of total time spent at that particular water level over the monitoring period.
74 Figure 4-11: Length of time water spent at differing water levels within Ponds 2-6 over the 365 day continuos data set.
75 4.5 Discussion
4.5.1 The generation of stormwater The study period for the hydraulic investigation into the BWC catchment and BWC System spanned 13 months, from Jan-2004 to Feb-2005. Over this time, the precipitation pattern was a typical summer wet, winter dry for subtropical climatic regions (Figure 4-12). Between Oct-2004 and Dec-2004, precipitation within the BWC Catchment was greater than, or very close to, the 90th percentile. Meaning that during these months, precipitation within the catchment was greater than 90% of the recorded monthly values for the historical 154 year data set. On the other hand, during the winter months of the study period precipitation within the catchment was lower than that of the historical mean, hovering close to the 10th percentile value. Over the study period the rainfall pattern within the BWC Catchment was higly varible when compared to historical values. This was, conveniently, a good situation to study urban runoff generated within the BWC Catchment. This variability in the precipitation (in comparison to the reported means) provided a range of extreme conditions to assess the behaviour of urban runoff generated within the BWC Catchment and its movement through the BWC System.
Figure 4-12: Precipitation within the BWC Catchment in comparison to mean, 10th, 50th, and 90th percentile values taken from historical Brisbane CBD precipitation data. Historical data was sourced from the Bureau of Meteorology data set spanning 154 years.
The volumetric runoff coefficient is a hydrological measure of the amount of runoff generated from a given catchment during a storm event. The coefficient (refer to
76 Equation 4-1) is a gross measure that enables one to compare the runoff from various catchments, taking into account the land use, soil characteristic and drainage system within the catchment. Table 4-4 compares the calculated mean runoff coefficient for the BWC Catchment derived from this study with that used by the local government authority (BCC) and values reported in a eminent text book exploring wetlands and the urban environment (Mitsch and Gooselink 2000).
Table 4-4: A comparison of catchment volumetric runoff coefficients calculated in this study, used by the local government authority and that reported in the literature.
Volumetric Runoff coefficient Reference Land use Min Max Mean BWC Catchment 0.09 0.44 0.20 This study BCC 0.37 Parks and cemeteries 0.10 0.25 0.18 Playgrounds 0.20 0.35 0.28 Suburban 0.25 0.40 0.33 Mitch and Single urban dwellings 0.30 0.50 0.40 Gooselink, 2000 Multiple fam. dwellings 0.40 0.75 0.58 industrial-light 0.50 0.80 0.65 industrial-heavy 0.60 0.90 0.75
The variance in the volumetric runoff coefficients calculated for each of the 36 storm events analysed (Table 4-1), and that displayed in Table 4-4, can be attributed to the length, duration and intensity of each individual storm event along, the length of time between storm events, and the soil moisture characteristic within the catchment prior to and during each storm event. The mean volumetric runoff coefficient calculated for the BWC Catchment from this study was less than that reported by BCC, and within the range of that reported in literature on catchments with similar land uses.
The Directly Connected Impervious Area (DCIA) is a hydrological concept based on the relationship between rainfall within a given catchment and the runoff generated from that particular rainfall event (Exum et al. 2005). Figure 4-7 shows this relationship for the BWC Catchment, with the gradient of the linear regression line being the measure of the DCIA within the BWC Catchment - 0.295 or 29.5% of the total catchment area. The DCIA, or the connected impervious area (US terminology) within a catchment can be determined in many ways. These include by direct measurement, as was that used in this investigation, via 2-D models (Mignot et al. 2006), via population density / % impervious cover relationships (Strankowski 1972;
77 Graham et al. 1974); GVS & DD 1999), or via satellite imagery (Sleavin et al. 2000) or from a combination of some or all of the various methods.
In a massive national study into the impervious area of suburban and urban environments within south-eastern USA, Exum et al. (2005) reported similar results to the DCIA calculated in this study for similar land uses. There is an obvious relationship between the DCIA and the catchment volumetric runoff coefficient - that being, with increased impervious cover within a catchment, the rainfall runoff coefficient increases (Schueler 1987). The range in runoff coefficients calculated and displayed in Table 4-4 compare favourably with those of Schueler (1987), making theb relationship he determined between catchment impervious area and associated volumetric runoff coefficients.
Stormwater flow into the BWC System The overflow by-pass channel of the BWC System was ‘activated’, during most rainfall events, with only 3 storm events out of the 15 monitored not activating the channel and a total of 74% of all stormwater generated within the BWC Catchment bypassing the BWC System altogether. From study modelling, it was estimated that 9.9mm of rainfall within the BWC Catchment was needed to activate the bypass channel – directing storm water around the BWC System and effectively transporting that proportion of stormwater ‘untreated’ to downstream receiving waters.
The use of high flow bypass channels in constructed stormwater treatment ponds allows the diversion of waters away from the main treatment area during periods of heavy and prolonged rainfall. This protects the main treatment wetland against scouring and macrophyte damage associated with high velocity flows (Wong et al. 1999). There is, however, appear to be a trade off between providing a bypassing channel for ‘excess’ stormwater and treating the stormwater produced by a given catchment– an issue that will be discussed in detail in Chapter 12.
78 4.5.2 Stormwater storage and outflow from the BWC System Upon exiting Pond 1, stormwater within the BWC System enters a series of interconnected ponds, with the discharge pipe controlling flow located at the northern section of Pond 6. The time stormwater resided within Ponds 2-6 of the BWC System was analysed theoretically and through direct onservations which determined pond water taking up to 8.4 days to drain from Ponds 2-6. Observed results were somewhat similar to theoretical results which were based on engineering calculations on the outlet pipe. At the maximum water level within Ponds 2-6, water was detained within the system for just under 9 days.
The term ‘Hydraulic Effectiveness’, introduced into scientific literature by Wong and Somes (Wong and Somes 1995), can be used to describe the overall percentage of runoff which can be expected to be treated by a given pond or wetland system. Adapting this approach to actual volume and flow rate data obtained for Ponds 2-6 of the BWC System, the Hydraulic Effectiveness of these ponds can be assessed as a function of the ‘probabilistic residence time distribution’ (PRTD) (Figure 4-13). In comparing the outcome from this study to modelled simulations reported by Wong et al. (Wong et al. 1999) Figure 4-13 shows a somewhat atypical PRTD curve, lacking a ‘tail’ – most likely due to the influence of the overflow weir on pond detention times. Although somewhat speculative, Figure 4-13 effectively highlights that the detention of stormwater run off within Ponds 2-6 of the BWC System is the result of the changing volume of the ponds and changing stormwater inflow/outflow flow rates.
Figure 4-13: Proportion of runoff detained within Ponds 2-6.
79 Extending the relationship between Ponds 2-6 flow rates, volumes and PRTD further, Figure 4-11 displays modelled water height within Ponds 2-6 from a continuous data set spanning an entire year (based on Pond 6 outlet flow rates). Water height within Ponds 2-6 was modelled at levels above 4.065m AHD for the entire year, above 4.55m AHD for 50 days of the year and at it maximum capacity for 14 days of the year. This information is of particular importance when looking at the impact of increased water ponding and detention time on the survival and recruitment of macrophytes – both submerged and emergent. The adverse effects of an erratic hydraulic regime on wetland plant communities has been reported in the literature (Ewing 1996; Budelsky and Galatowitsch 2000; Schutten et al. 2005), and been investigated extensively in the studied wetland by other authors (Jenkins and Greenway 2005; Greenway et al. 2006; Greenway et al. 2006). All these investigations indicate the common hydraulic factor that impacts negatively on macrophytes communities within ponds and wetlands is the frequency and duration of ponding waters. Extended and frequent periods of water inundation can potentially reduce oxygen availability to macrophytes, as well as promoting sediment deposition from the water column that can smother new or existing macrophyte growth.
80 4.6 Conclusion
Summarising the main results from the research presented in this chapter, Figure 4-14 provides a summary diagram of the hydraulic characteristics of the BWC Catchment and the BWC System. Shown in this Figure are the main hydraulic characteristics of the BWC Catchment and the BWC System which include: • the Catchment volumetric runoff co-efficient of 0.26; • the catchment area classed as being ‘directly connected impervious area’ (29.5%); • the BWC Catchment mean urban runoff per storm event (18.3 ML), with 56.9% of this bypassing the BWC System; • the extended detention time of up to 9 days for stormwater entering Ponds 2-6, following a PRTD trend, similar to that reported in published literature;
Given that 43% of all urban runoff produced within the BWC Catchment receives treatment from the entire BWC System, the detention time (up to 9 days) of stormwater within Ponds 2-6 would be a major determinate of the quality of water exiting the system. This detention time would (1), allow the removal of inorganic nutrients from the water column by phytoplankton, bacterioplankton and epiphyton communities and (2), promote the opportunity for the sedimentation of particulate matter within the water column. Conversly, the extended detention time of stormwater within Ponds 2-6 may be detrimental to the macrophyte communities within the Ponds.
Catchment runoff Runoff coefficient = 0.28 DCIA = 29.5% High flow bypass channel Mean storm runoff volume = 18.3ML 9.9mm catchment rainfall needed to trigger bypass Pond 1 56.9% of all generated stormwater Storage volume range 11.1 to 23.7 ML Flow into Pond 2 Receiving 52% of all catchment runoff
Ponds 2-6 Storage volume range Out flow from Pond 6 26.8 – 99.5 ML Extended detention of stormwater – up to 9 days Figure 4-14: Diagrammatic representation of major hydraulic factors within the BWC Catchment and BWC System.
81 5 Chapter 5: Nutrient reduction during storm events
82 5.1 Abstract
Stormwater treatment devices are commonly installed in urban catchments to meet local and state water quality objectives, and reduce the concentration of nutrients delivered to receiving waters. The aim of this chapter is to quantify the N, P, and C load sourced from the BWC Catchment during storm events, and assess the effectiveness of the BWC System in reducing these catchment loads. Using hydrological data from the previous chapter, along with Event Mean Concentration’s (EMC) taken at inlet and outlet points from the BWC System, the amount of N, P, and C generated within the BWC Catchment and that removed by the BWC System was assessed. The pollutant concentration and loads sourced within the BWC Catchment were typical of subtropical urban catchments. With the catchment producing an annual load of 6, 0.8 and 27 kg of TN, TP and DOC per ha per year respectively. Using EMC’s from 5 storm events occurring within the BWC Catchment, stormwater was measured to contain a mean 2.39 mg. L-1 of TN, 0.33 mg. L-1 of TP and 11 mg. L- 1 of DOC. Oxidised nitrogen dominated the TN pool, with the TP pool containing roughly equal quantities of both Other-P and PO4-P compounds. Pond 1 and Pond 6 behaved somewhat differently in regards to stormwater treatment, with Pond 1 reducing all measured nutrients except TP and Other-P and Pond 6 reducing only TP and Other-P. Based on concentration loss, the BWC System only provided TN and NOx-N removal. However, when assessing nutrient load reduction, the BWC System showed TN, Org-N, NOx-N, TP, Other-P and DOC reduction, with increases in NH4-
N and PO4-P loads across the entire system. This highliggts the importance of investigating nutrient loads (as opposed to nutrient concentrations) when evaluating constructed ponds for urban stormwater treatment.
Keywords: EMC, Catchment Runoff Loads, Treatment Performance, Stormwater, Treatment Efficiency, Nitrogen, Phosphorus, Dissolved Organic Carbon
83 5.2 Introduction
Arnold and Gibbons (1996) reported that significant overall reductions in stream and waterway health occurr when the total catchment has greater than 10% impervious coverage. For impervious areas greater than 30%, Arnold and Gibbons reported that streams and waterways within catchments tend to become severely degraded and polluted. In most urban catchments around the world, impervious areas range between 10% and 95%, depending of the type of land use. As such, urban waterways are known to be some of the most degraded streams in our landscape, and are characterised by high bacterial concentrations, high oxygen demand, and high concentrations of solid particulate matter, nutrients, heavy metals and organic compounds (Walsh 2004; Kohler et al. 2005; Taylor et al. 2005). It has been estimated in the U.S. that non-point sources of pollution (like stormwater) contribute 45%, 75%, and 65% of the pollutant loads within estuaries, lakes, and rivers respectively (Johengen and LaRock 1993). The main stormwater pollutants threatening aquatic ecosystems include nutrients, heavy metals, organic matter, pathogens, and suspended particulate matter (Brinkmann 1985; Wong et al. 2000; Dechesne et al. 2004). Many authors claim that stormwater can, and often has the potential, to be as polluting the a receiving environment as municipal wastewater (Johengen and LaRock 1993; Herricks 1995; Ferguson 1998; Butler and Davies 2000; Braskerud 2002; Walker and Hurl 2002).
Constant urban growth within Australia, coupled with increasing loss of open space and the reduction of waterway health, has resulted in stormwater and its associated environmental impacts becoming a major environmental concern (Lawrence and Breen 1998). In response to this concern, and under relevant federal laws, (the Environmental Protection Act [1994] and the Environmental Protection – Water – Policy [1977]) the local government authority, BCC, have developed a set of water quality objectives for stormwater entering receiving waters to help water managers reduce the impact of stormwater on receiving environments (Table 5-1).
Local government authorities across Australia are installing pond and wetland systems to meet water quality objectives and help ameliorate the polluting impacts of
84 urban storm water. As discussed earlier in this thesis, the ability of constructed ponds to successfully reduce urban catchment nutrient loads to receiving waters (and meet increasingly stringent environmental guidelines) in subtropical Australia is not well documented.
Table 5-1: Freshwater water quality objectives for the assessment of water quality in Brisbane’s waterways (Webb 2000). Indicator Units Upper limit Org-N mg.L-1 0.5 + -1 NH4 mg.L 0.035 NOx mg.L-1 0.13 TN mg.L-1 0.65 -1 PO4 mg.L 0.035 TP mg.L-1 0.07 TSS mg.L-1 15 Chl. a µg. L-1 8
In order to assess the ability of constructed ponds to reduce urban nutrient loads, one must either manually sample during rainfall events or have expensive automated systems that sample from a designated location at a predetermined rate/time. Unfortunately there are inherent problems with both, (i.e. getting researchers out in the rain for extended periods of time and storing water samples collected automatically to ensure collected water represents that of the actual stormwater sampled). These factors issues have most likely contributed to the shortage of studies investigating pollutant removal characteristic of constructed ponds under rainfall event flow conditions. Of the limited published literature on investigation onto stormwater pollutant reduction during storm events, most have been conducted using automatic sampling techniques, often involving taking 24hr composite water samples without adequate preservative to ensure nutrient concentrations don’t change following collection {Kohler, 2005 #508; Fink, 2004 #509; Kayhanian, 2007 #937}. Thus, there appears to be a lack of knowledge within the literature regarding the actual treatment ability of constructed stormwater ponds during storm events. Additionally, the outflow concentration of pollutants from constructed stormwater ponds during and following storm events has not been widely reported.
85 5.2.1 Research aims, objectives and research questions The aim of this chapter is to investigate nutrient reduction of urban runoff entering the BWC System during storm events.
Specific objectives of this investigation are to; • determine the dominant form of N and P entering the ponds from the BWC Catchment; • quantify yearly loads of N, P, and C generated within the BWC Catchment; and • assess the degree of N, P, and C reduction from stormwater entering the BWC System.
Key research questions were; • Does the BWC System reduce N, P, and C loads delivered to it from the catchment? And if so, at what rate? • How does the BWC System compare with other pond/wetland systems for the removal of nutrients from urban stormwater water?
86 5.3 Methods
5.3.1 Field collection
The BWC System was described in detail in Chapter 2, with the hydrology of the system investigated and discussed in Chapter 4. To assess the treatment of stormwater by the BWC System, a number of water quality sampling sites within the system were established (Figure 5-1) concentrating on the location of inflow and outflow channels/pipes and the movement of water between ponds. Water quality sampling was restricted to Ponds 1 and 6 for reasons outlined in Chapter 2.
Water samples taken for the analysis of nutrients during and after storm events were taken from sites displayed in Figure 2. Pond 1 inflow was sampled until flow rates returned to base flow conditions. Pond 1 outflow was sampled until now flow between Ponds 1 and 2 was visually observed, with Pond 6 in and Pond 6 out sampled until no flow was observed in the outlet pipe of Pond 6. The length of time water flowed into Pond 6 was assumed to be equal to the length of time water flowed out of Pond 6. Water samples were taken from the above locations manually using a 1 litre pre washed and sterile sampling bottle. Each 1 litre water sample bottle was sub sampled for TN, TP, NH4-N, NOx-N, PO4-P and DOC according to the methods outlined in Chapter 3. Samples where analysed according to that outlined in Sections 3-1 and 3-2, Chapter 3.
Pond 6 out
Pond 6 in
Pond 5 Pond 1 west
Pond 1 out
Pond 1 east
Figure 5-1: Water sample sites for the assessment of stormwater nutrient reduction within the BWC System. Red dots indicate water sampling sites.
87 5.3.2 Hydrology and rainfall data
Water flow through the BWC System was measured using a range of Doppler flow gauges, as described in the preceding chapter. Total stormwater flow for each storm event at the specified monitoring sites (displayed in Figure 2) was obtained directly from this data base – with the exception of the ‘Pond 1 out and Pond 6 in’ monitoring sites. Pond 1 out and Pond 6 in flow volumes where calculated using Equations 5-1 and 5-2 respectively.
=Q +(.)(.) ET SA + P SA (5-1) Q P1 out P6 out P 2− 6 P 2 − 6 where; 3 QP1out = Flow of stormwater out of Pond 1, m 3 QP6out = Flow of stormwater out of Pond 6, m ET= evaporation, m 2 SAP2-6 = Surface area of Ponds 2-6, m P = precipitation, m
=Q +(..) ET SA + P SA (5-2) Q P6 in P6 out P 6 P 6 where; 3 QP6in = Flow of stormwater into Pond 6, m 2 SAP6 = Surface area of Pond 6, m
The total yearly (January 2004 – January 2005) hydraulic load of stormwater sourced from the BWC Catchment, that entered Pond 1 and that exited the BWC System via Pond 6 was determined using Equations 5-3, 5-4 and 5-5 respectively.
(∑QP1 in ) Q = (5-3) catch_ year 1000 where;
88 Qcatch_yeart = yearly storm flow from BWC catchment, ML
(∑QP6 out ) −( P. SA) + ( ET .365. SA) Q = (5-4) P1 in _ year 1000 where;
QP1in_year = yearly storm flow into BWC system, ML
(∑QP6 out ) Q = (5-5) P6 out _ year 1000 where;
QP6out_year = yearly storm flow into BWC system, ML
Rainfall was measured ‘as fallen’ using an automated rain gauge located just outside the BWC Catchment (refer to Chapter 4). Evaporation data, used in Equations 5-1 and 5-2, was sourced from the Brisbane Airport Bureau of Meteorology weather station and is reported as a monthly mean (mm. d-1).
5.3.3 Event mean concentration, loads and treatment performance calculations
The nutrient concentration within stormwater from a particular storm event often declines over time, and it is generally believed that the initial flow of urban runoff contains the highest concentration of nutrients (Lee et al. 2004; Soller et al. 2005). The nutrient load into, between and out of Ponds 1 and 6 of the BWC System was assessed per storm event using Event Mean Concentration’s (EMC) and hydraulic flow data from each sampling point displayed in Figure 5-1. The EMC of storm flow is a measure of the total concentration of nutrients from a storm event, and if executed properly, is an accepted means of describing and quantifying nutrient concentrations within stormwater derived from storm events (Smullen et al. 1999; Larm 2000; Kim et al. 2004). The use of EMC’s as a measure of the pollutants entering receiving waters during storm events has been done using both manual and automatic water
89 sampling techniques over a range of catchment types and diffuse pollution sources. The Queensland Department of Natural Resources, Mines and Waters has used EMC’s based on 5 manual ‘grab’ samples for the estimation of nutrient loads entering the Balonne River during rainfall events (Waters 2006), while Lee et al. (2002) also used grab samples (on average greater than 5 samples) to calculate EMC’s for the first flush of urban stormwater in California, USA. Among the published literature two approaches have been reported for the development of EMC’s. The first uses water samples and hydraulic flow measurements from one individual storm event, while the second approach groups water samples and hydraulic flow measurements from numerous storm events and analyses the results collectively (Burton Jr. and Pitt 2002; Lee et al. 2002; Kim et al. 2004; Kim et al. 2005; Waters 2006). It is important to note that at this stage there is some degree of error associated with the use of EMC’s and the final calculated value is anestimation only (Burton Jr. and Pitt 2002).
As stated earlier, water samples were collected from sites as close to the beginning of the storm event as possible, and samplying continued until stormwater flow ceased. Thus, over the course of each storm event numerous water samples where taken to measure the concentration of nutrients from the initial ‘first flush’ to the final flow of stormwater derived from that particular storm event. For each storm event, the EMC was calculated according to Equation 5-6.
(∑QCi. i ) EMCx = (5-6) ∑Qi where; -1 EMCx = Event mean concentration, mg. L 3 Qi = flow rate of stormwater at time i, m /s -1 Ci = concentration of nutrient at time i, mg. L
Using the total hydraulic load calculated for each storm event at each of the various sampling points, the total load of nutrients entering the BWC System (Pond 1 in) was calculated (Equation 5-7).
90 To measure the level of stormwater treatment by Ponds 1 and 6, pollutant reduction and removal efficiency calculations were used (Equations 5-8 and 5-9 respectively).
q. EMC LR = x x (5-7) x 1000 where; -2 LRx = nutrient loading rate per storm x, g. m 3 qx = flow rate per unit surface area per storm x, m per storm
LRRx=LR in − LR out (5-8) where; LRRx = Load reduction rate per storm x, g. m-2 -2 LRin = Loading rate in, g. m -2 LRout = Loading rate out, g. m
LR− LR RE = 100. in out (5-9) LRin where; RE = removal efficiency, %
5.3.4 Monitored storm events
A total of 5 storm events were monitored between January 2004 and December 2004. Storm events chosen for analysis were done so using the following criteria; 1. Dry flow conditions prior to storm event. Dry conditions were determined by the lack of outflow from Pond 6 and the inflow of water into Pond 1 being at a ‘base flow’ rate. 2. Adequate availability of time. Determined by the author having an extended time window to allow for the sampling effort. 3. Adequate representation of the hydraulic data from previous chapter.
91 Table 5-2 displays the characteristics of each storm event monitored, indicating total storm precipitation, length of storm and total pond flow (m3) into and out of Ponds 1 and 6.
Table 5-2: Characteristics of the 6 monitored storm events.
Storm Storm event date Storm Total Monthly Total flow (m3) event event rainfall mean ET P1 in P1 out P6 in P6 out no. duration (mm) (mm d-1) (hrs) 1 16th Apr, 04 55.5 15 4.5 3594 2209 2073 2053 2 8th May, 04 11.1 19 3.2 9776 5103 4947 4925 3 18th Aug, 04 5.8 14 4.1 8473 4979 4852 4834 4 17th Oct, 04 66.3 62 6.3 31026 9437 8959 8891 5 7th Nov, 04 101.6 152 7.2 44831 6860 5745 5586
92 5.4 Results
Given the stochastic nature of stormwater generation within the BWC Catchment, and the variation in sampling effort required for each storm event, data from the 5 events has been grouped together, and analysed with respect to the EMC of nutrients. Appendix A displays raw nutrient concentrations for each of the 5 monitored storm events (Tables A.A 1-5). Table 5-2 displays EMC’s of dissolved and total N, P, and C at the four monitoring locations located within the BWC System (as per Figure 5- 1). The concentration of nutrients moving through Ponds 1 and 6 varied between storm events, with box and whisker plots displaying the variability of N, P, and C concentrations at the monitoring sites within the BWC System (Figure 5-2). TN and NOx-N showed a general decrease in concentration from Pond 1 inlet to Pond 6 outlet, will all other sampled nutrients did not exhibit any consistent reduction in concentration throughout the monitored storm events.
Using hydraulic flow data from the previous chapter, the hydraulic load of stormwater moving through Ponds 1 and 6 was obtained. It is important to note here that all flow entering Pond 1 of the BWC System did not move through the whole treatment system – much of it ‘bypassed’ (as discussed in the previous chapter). Therefore, the hydraulic load calculations are based on flow through Ponds 1 and 6, not total flow into Pond 1. Table 5-2, provides the characteristics of each storm event monitored, indicating total storm precipitation, length of storm and total flow (m3) into and out of Ponds 1 and 6. There were significant variations in the hydraulic characteristics of each storm event, with flow rates entering Pond 1 ranging from 3,594 to 44,830 m3. Using this data, along with that contained in Table 5-3, the nutrient load moving to, within and from Ponds 1 and 6 was calculated (Table 5-4). Nitrogen loads entering the BWC system from stormwater (stormwater by-passing Pond 1 via high flow bypass was classed as not entering) ranged from 5.32 to 12.24 g. m-2, with a mean load of 11.94 g. m-2 (Table 5-4). Of the TN load, NOx- N was the largest contributor, -2 averaging 8.67g. m , with NH4-N contributing only a small amount to the TN load (mean 0.78 g. m-2) (Figure 5-4). TP loads into and through the BWC System where, by and large, dominated by Other-P (Figure 5-4).
93 Table 5-3: Storm event EMC’s within the BWC System. Note: All samples from each sampling location have been grouped to calculate the event mean concentration. Units in mg. L-1.
Nutrient /sample location Statistical variable n min max mean SE Pond 1 inlet TN 48 0.31 7.76 2.39 0.30 Org-N 47 0.05 4.69 0.78 0.13 NH4-N 47 0.01 1.69 0.14 0.04 NOx-N 47 0.07 5.16 1.50 0.22 TP 48 0.07 1.03 0.33 0.03 Other-P 46 0.00 0.58 0.19 0.02 PO4-P 47 0.02 0.69 0.14 0.02 DOC 44 3.81 20.45 11.02 0.69 Pond 1 outlet TN 33 0.18 5.89 1.43 0.27 Org-N 28 0.02 1.70 0.49 0.11 NH4-N 34 0.00 0.31 0.10 0.01 NOx-N 34 0.02 4.13 0.84 0.20 TP 34 0.08 0.93 0.40 0.03 Other-P 34 0.01 0.88 0.28 0.03 PO4-P 34 0.01 0.46 0.12 0.02 DOC 32 2.69 20.32 8.75 1.04 Pond 6 inlet TN 36 0.14 4.30 1.09 0.20 Org-N 31 0.04 1.62 0.52 0.10 NH4-N 36 0.00 0.72 0.12 0.02 NOx-N 36 0.00 2.52 0.47 0.13 TP 36 0.17 0.69 0.36 0.02 Other-P 36 0.01 0.69 0.23 0.03 PO4-P 36 0.00 0.61 0.14 0.03 DOC 35 2.94 22.64 9.67 1.07 Pond 6 outlet TN 25 0.17 3.95 1.49 0.24 Org-N 20 0.07 1.81 0.77 0.14 NH4-N 26 0.01 0.58 0.18 0.03 NOx-N 26 0.00 2.15 0.59 0.16 TP 26 0.10 0.48 0.31 0.02 Other-P 26 0.00 0.34 0.14 0.02 PO4-P 26 0.00 0.39 0.18 0.03 DOC 24 2.53 22.03 11.44 1.17
94 Figure 5-2: Storm event EMC’s within the BWC System. Note: All samples from each sampling location have been grouped to calculate the event mean concentration. Red line represents mean, with black dots showing outliers.
95 Table 5-4: Mean nutrient loads to, within and from the BWC system during the 5 monitored storm events. n = 5; Units in g m-2 of pond surface area. Calculated using Eq 5.7.
Nutrient /sample location Statistical variable min max mean SE Pond 1 inlet TN 5.32 22.70 13.79 2.88 Org-N 1.74 7.43 4.52 0.94 NH4-N 0.30 1.28 0.78 0.16 NOx-N 3.35 14.27 8.67 1.81 TP 0.73 3.12 1.90 0.40 Other-P 0.42 1.81 1.10 0.23 PO4-P 0.32 1.37 0.83 0.17 DOC 24.57 104.79 63.66 13.29 Pond 1 outlet TN 3.17 13.54 8.20 1.71 Org-N 1.09 4.65 2.82 0.59 NH4-N 0.21 0.91 0.55 0.11 NOx-N 1.86 7.94 4.81 1.00 TP 0.89 3.78 2.29 0.48 Other-P 0.61 2.62 1.59 0.33 PO4-P 0.27 1.17 0.71 0.15 DOC 19.33 82.57 50.02 10.42 Pond 6 inlet TN 2.27 9.80 5.82 1.21 Org-N 1.08 4.69 2.78 0.58 NH4-N 0.25 1.09 0.64 0.13 NOx-N 0.98 4.23 2.51 0.52 TP 0.76 3.26 1.94 0.40 Other-P 0.47 2.05 1.22 0.25 PO4-P 0.28 1.23 0.73 0.15 DOC 20.03 86.60 51.38 10.66 Pond 6 outlet TN 3.05 13.21 7.81 1.62 Org-N 1.58 6.84 4.04 0.84 NH4-N 0.37 1.62 0.96 0.20 NOx-N 1.22 5.27 3.11 0.65 TP 0.64 2.76 1.63 0.34 Other-P 0.29 1.27 0.75 0.16 PO4-P 0.38 1.63 0.96 0.20 DOC 23.49 101.72 60.15 12.50
96 Figure 5-3: Mean TN and TP loads to, within and from the BWC system during the 5 monitored storm events. n = 5; Units in g m-2.
97 Based on the mean of the calculated nutrient loads moving to, within and from Ponds 1 and 6, there was an overall gross reduction in TN, NOx-N, TP, and Other-P, with increases in NH4-N, PO4-P. DOC nutrient loads changed little from that entering Pond 1 and that leaving Pond 6 (Figure 5-4). Table 5-5 provides a summary of the nutrient load reductions within Ponds 1 and 6, and from the BWC System as a whole, with Table 5-6 displaying calculated removal efficiencies.
It is important to note, however, that the SE of the reported means for the various water sample locations was, at times, large (see Table 5.4). Thus the accuracy of this data is somewhat speculative. Nevertheless, the reported values a worthy of further reporting and discussion due to the little real-time information published in the literature in regards to constructed pond and wetland treatment of stormwater runoff.
Table 5-5: Mean load reduction of nutrients within Pond 1 and 6 over the 5 monitored storm events (n = 5). Bold numbers indicate to an overall load reduction, with negative numbers indicating an overall load increase.
Nutrient /sample location Statistical variable min max mean SE Pond 1 TN 2.15 9.17 5.59 1.17 Org-N 0.65 2.78 1.70 0.36 NH4-N 0.09 0.38 0.23 0.05 NOx-N 1.49 6.33 3.86 0.81 TP -0.66 -0.15 -0.40 0.08 Other-P -0.81 -0.19 -0.49 0.10 PO4-P 0.05 0.20 0.13 0.03 DOC 5.24 22.22 13.64 2.88 Pond 6 TN -3.40 -0.78 -1.99 0.42 Org-N -2.15 -0.49 -1.26 0.26 NH4-N -0.54 -0.12 -0.32 0.07 NOx-N -1.04 -0.24 -0.61 0.13 TP 0.12 0.51 0.31 0.06 Other-P 0.18 0.78 0.47 0.10 PO4-P -0.39 -0.09 -0.23 0.05 DOC -15.12 -3.45 -8.77 1.86 BWC system (Ponds 1 and 6) TN 2.27 9.50 5.98 1.32 Org-N 0.16 1.19 0.47 0.20 NH4-N -0.34 -0.07 -0.18 0.05 NOx-N 2.13 9.01 5.56 1.18 TP 0.09 0.57 0.26 0.09 Other-P 0.13 0.54 0.35 0.08 PO4-P -0.26 -0.01 -0.13 0.04 DOC -0.22 13.47 3.51 2.55
98 Figure 5-4: Mean event nutrient load to, within and from the BWC system. Red line displaying mean (n = 5).
99 Table 5-6: Mean load removal efficiency of nutrients within Pond 1 and 6 over the 5 monitored storm events (n = 5). Bold numbers refer to an overall reduction, with negative numbers referring to an overall increase.
Nutrient /sample location Statistical variable min max mean SE Pond 1 TN 40.20 41.30 40.52 0.20 Org-N 37.20 38.40 37.56 0.22 NH4-N 29.20 30.50 29.56 0.24 NOx-N 44.20 45.20 44.48 0.19 TP -21.70 -19.40 -21.02 0.42 Other-P -45.20 -42.40 -44.38 0.51 PO4-P 14.50 16.10 14.98 0.29 DOC 20.90 22.40 21.36 0.27 Pond 6 TN -35.30 -32.00 -34.32 0.60 Org-N -46.40 -42.80 -45.36 0.66 NH4-N -50.10 -46.50 -49.08 0.66 NOx-N -25.10 -22.10 -24.24 0.55 TP 15.10 17.20 15.72 0.38 Other-P 38.00 39.50 38.46 0.27 PO4-P -32.50 -29.30 -31.60 0.59 DOC -17.90 -15.10 -17.10 0.51 BWC system (Ponds 1 and 6) TN 39.80 50.50 43.00 1.95 Org-N 4.90 21.80 9.92 3.08 NH4-N -30.70 -7.50 -23.80 4.23 NOx-N 61.80 68.60 63.86 1.23 TP 8.50 24.70 13.36 2.95 Other-P 27.70 40.50 31.50 2.34 PO4-P -22.70 -1.00 -16.24 3.95 DOC -0.40 17.40 4.92 3.24
100 5.5 Discussion The quantification of nutrient concentrations in urban stormwater has received moderate attention in the scientific literature over the past 10-15 years. Table 5-7 displays a review of published results for nutrient concentrations within stormwater draining urban catchments during local rainfall events. By and large, the concentration of nutrients within urban stormwater entering the BWC System was within the range reported in the literature, and similar to previous research findings from the studied catchment and that of another catchment 20km from the studied catchment (Greenway 2005).
Table 5-7: A comparison of nutrient concentrations in urban stormwater during rainfall events. ** Same system as system studied in this investigation.
Catchment location, Nutrient Reference size (ha) TN NH4-N NOx-N TP PO4-P DOC Korea, 74.4 16.65 - 2.85 8.3 3.97 - (Lee and Bang 2000) Korea, 230 12.1 - 0.5 7.8 6.44 - (Lee and Bang 2000) Korea, 557.9 12.86 - 0.56 10.2 5.86 - (Lee and Bang 2000) Korea, 86.5 9.7 - 0.9 7.7 2.05 - (Lee and Bang 2000) Melbourne, varied 2.13 0.29 0.74 - - - (Taylor et al. 2005) catchment size Iran, 360 6.65 - - 0.274 - - (Taebi and Droste 2004) Israel, varied - 1.8 3.7 0.592 - - (Asaf et al. 2004) catchment size USA, varied - - 0.8- - 0.5- - (Marsh 1993) catchment size 3.48 2.45 USA, 10.1 - 2.70 1.38 0.31 - - (Kohler et al. 2005) Sweden, 960 1.98 - 2.61 0.334 - - (Larm 2000) USA, 6.2 1.81 1.15 0.22 0.216 0.089 - (Kadlec et al. 2000) 2.39 0.35 (Smullen et al. 1999) 2.51 0.337 (USEPA 1983) USA, review 2.0 0.44 0.6 0.27 0.13 (Pitt et al. 2004) documents from 3.16 0.86 0.5 0.15 (USEPA 1996) numerous 2 0.73 0.38 (USEPA 2005) catchments 2.98 0.522 (Droste and Hartt 1975) 3.2 1.25 0.34 (Butler and Davies 2000) Sydney, 48 4.38 - - 0.14 - - (Birch et al. 2004) Brisbane, 160 1.27 0.10 0.44 0.11 0.06 - (Greenway 2005) Brisbane, 197** 2.71 0.68 0.26 0.4 0.15 - (Greenway 2005) Brisbane, 160 2.25 0.12 1.76 0.23 0.22 - (Greenway 2002) Brisbane, 197 2.39 0.14 1.5 0.33 0.14 11 This study-
TN and TP loads from the BWC catchment into Pond 1 of the BWC System averaged 5.32 and 0.73 g m-2 respectively based on EMC and the total hydraulic load per storm event. For all 5 sampled storm events much of the TN load consisted of NOx-N with
NH4-N contributing little to the overall TN load (Figure 5-3). TP loads from the BWC Catchment were dominated by Other-P compounds, most likely particulate inorganic and organic phosphorus (Henderson 2006). Drawing on EMC data
101 presented in Table 5-3, along with hydraulic data from the previous chapter, the yearly nutrient load from the BWC Catchment entering and exiting the BWC System was calculated (Table 5-8). The BWC Catchment exported, annually, 1170kg, 160.8 kg and 5400 kg of TN, TP and DOC respectively. The load of TN, TP and DOC entering the BWC System for ‘treatment’ was considerably less than that generated from the catchment (Table 5-8), owing to the high percentage of flow bypassing the BWC System (as discussed in the previous Chapter)
Table 5-8: Yearly load for nutrients sourced from the BWC Catchment, entering Pond 1, and exiting Pond 6 to receiving waters. All units in kg. yr-1 unless stated otherwise..
Yearly load BWC System Removal Nutrient retention efficiency BWC -2 -1 P1 inlet P6 out (g.m .yr ) (%) Catchment TN 1170.0 342.0 165.7 22.0 51.5 Org-N 383.0 112.0 74.6 4.7 33.4 NH4-N 66.2 19.4 14.6 0.6 24.7 NOx-N 736.0 215.0 127.0 11.0 40.9 TP 161.0 47.0 60.7 -1.7 -29.1 Other-P 93.2 27.3 42.0 -1.8 -53.8 PO4-P 70.6 20.6 18.7 0.2 9.2 DOC 5400.0 1579.0 1325.0 31.8 16.1
Standardising the load calculations shown in Table 5-7 using the total catchment area, the BWC catchment exported 6, 0.8 and 27 kg of TN, TP and DOC per ha per year respectively (Figure 5-5). The reported nutrient loads from the BWC Catchment are within that of the range reported in the relevant literature, however no reported loading rates for DOC were found within the literature (Whipple and Hunter 1977; Dames and Moore 1990; Butler and Davies 2000; Taebi and Droste 2004).
The reported values were also Figure 5-5: Yearly export of total nutrients within the BWC Catchment during 2004. consistent with those obtained from a number of ‘modelled’ investigations within urban catchments in the same city as the studied catchment (Australia 1991; McAlister and Walden 1999). Thus, nutrient
102 loading rates from the BWC Catchment appear to be representative and indicative of those from a subtropical urban residential catchment.
But how well does the BWC System treat the generated pollutants? On a yearly basis, the BWC System provides some degree of nutrient reduction for TN, Org-N, NOx, NH4-N, PO4-P and DOC but acted as a source for TP and Other-P nutrients. The overall nutrient retention for the BWC System nutrient is displayed in Table 5-8. TN is within the range reported for created and restored ponds and wetlands receiving urban stormwater runoff (Fink and Mitsch 2004). TP retention was not measured within the BWC System, although PO4-P retention was evident. This can be attributed to the production of Other-P within the BWC System (-1.84g.m-2.yr-1 retention rate). As stated by Carleton et al. (2001), TP removal in stormwater wetlands and ponds is more a function of hydraulic retention time (as opposed to hydraulic loading rate), with the removal efficiency increasing with the surface area of the treatment pond (pond surface area/total catchment area). The BWC System area ratio equals 0.04, which when applied to Carleton et al.’s (2001) data set, fits well in respect to TP removal efficiency vs. area ratio. Thus, poor TP removal within the BWC System is most likely due to the cause of an undersized treatment system.
The areal loading rate per studied pond of the BWC System during the 5 monitored storm events presents a somewhat different story. Pond 1 showed marked reductions -2 in TN, Org-N, NOx-N, NH4-N, PO4-P and DOC (g. m ) and increases in TP and Other-P loads to receiving ponds. Pond 6, on the other hand, showed load reductions in only TP and Other-P and load increases in TN, Org-N, NOx-N, NH4-N, PO4-P and DOC. Looking at the ponds as separate treatment elements within a treatment system, Pond 1 was more effective at nutrient reduction than that of Pond 6. The exact reasoning’s behind this are most likely complex and multidimensional. It was beyond the scope of this chapter to investigate limitations to nutrient reduction within Ponds 1 and 6 and the BWC System as a whole, however this issue in further discussed in a number in following chapters more specifically related to biotic nutrient dynamics and in Chapter 12 in light of the research presented in the following chapters
103 5.6 Conclusions
Figure 5-6 summarises the yearly reductions in N, P, and C loads from urban stormwater produced within the BWC Catchment, and treated by the BWC System. Pollutant concentrations and loads produced within the BWC Catchment were indicative of subtropical urban catchments, with the catchment producing an annual load of 6, 0.8 and 27 kg of TN, TP and DOC per ha per year. Using EMC’s from 5 storm events occurring within the BWC Catchment, stormwater was measured to contain a mean 2.39 mg. L-1 of TN, 0.33 mg. L-1 of TP and 11 mg. L-1 of DOC. Oxidised nitrogen dominated the TN pool, with the TP pool containing roughly equal quantities of both Other-P and PO4-P compounds. Pond 1 and Pond 6 behaved somewhat differently with respects to stormwater treatment, with Pond 1 reducing all measured nutrients except TP and Other-P and Pond 6 reducing only TP and Other-P. Based on concentration loss, the BWC System as a whole only provided TN and NOx-N removal. However, when assessing nutrient load reduction, the BWC System provided TN, Org-N, NOx-N, TP, Other-P and DOC reductions, and increases in
NH4-N and PO4-P loads across the entire system. Thus highlighting the importance of investigating nutrient loads (as opposed to nutrient concentrations) when evaluating the performace of constructed ponds for urban stormwater treatment.
Catchment runoff High flow bypass channel 1.2 tonne N 0.9 tonne N 0.3 tonne P 0.26 tonne P 5.4 tonne DOC 3.9 tonne DOC
Pond 1 Flow into BWC System Nutrient reduction, with 0.3 tonne N the exception of TP and 0.04 tonne P Other-P 1.5 tonne DOC
Nutrient removal Ponds 2-6 Catchment runoff within the BWC Reduction of TP and Out flow from nutrient reduction System Other-P only Pond 6 515% N 51.5% N -8.5% P -29% P 0.17tonne N 4.7% DOC 16% DOC 0.06 tonne P 1.3 tonne DOC
Figure 5-6: Diagrammatic representation of 2004-2005 yearly load reduction of TN, TP and DOC within the BWC System and from catchment runoff loads.
104 6 Chapter 6: Abiotic factors influencing N, P & C dynamics
Page 105 of 376 6.1 Abstract The use of constructed ponds for the reduction of nutrients in urban stormwater relies on the interaction of N, P and C compounds within the pond environment. This interaction will govern the background concentration of these nutrients, and ultimately determines whether the system meets set water quality objectives. The aim of this chapter is to investigate abiotic factors influencing N, P and C speciation within the pelagic and littoral zones of Ponds 1 and 5/6 of the BWC System.
Water samples from the pelagic and littoral zones within the BWC System were taken at fortnightly intervals for 1 year (Jan-04 to Jan-05) and analysed for N, P and C concentration and a range of physicochemical parameters. TN concentration was + dominated by Org-N within both ponds except during the winter months, where NH4 dominated. Within Pond 1, nitrogen species were similar in concentration between the littoral and pelagic zone, with neither zone exhibiting consistently reduced concentrations. The pelagic zone within Pond 5/6 showed reduced concentrations of
Nitrogen. TP within both studied ponds was dominated by Other-P, with PO4-P closely linked to stormwater inflow, Redox Potential, pH and DO concentration of the water column. PO4-P concentration was generally less in the littoral zones of Pond 1 (in comparison to the pelagic zone), but similar in both zones in Pond 5/6. Stormwater delivery into the BWC System significantly increased the concentration of DOC within Pond 1, and to a lesser extent, Ponds 5/6. Water quality within Ponds 1 and 5/6 was extremely variable on a temporal scale. Through multivariate analysis (PCA), it was determined that the driving factors governing water quality within Ponds 1 and 5/6 of the BWC System included hydraulic flow sourced from storm events within the catchments, phosphorus and its associated relationships with the abiotic water quality parameters Redox Potential, pH and DO concentration, and DIN. Given these ‘factors’ governing water quality variability, both ponds were statistically grouped (through hierarchal cluster analysis) in accordance with the volume of urban stormwater flow to and within the system.
Keywords: Nitrogen, Phosphorus, Dissolved Organic Carbon, Principal Component Analysis, Cluster Analysis, Epiphyton
Page 106 of 376 6.2 Introduction Freshwater ecosystems can be functionally divided into two sections: the pelagic ecosystem and the littoral ecosystem (Wetzel 2001). The pelagic ecosystem is defined as the zone of a water body devoid of macrophyte vegetation. The littoral ecosystem, on the other hand, is defined as the zone where macrophyte vegetation is present. Figure 6-1 is a schematic diagram showing the potential boundaries of pelagic and littoral zones within a pond ecosystem. On a global scale, the pelagic ecosystems within freshwater bodies occupy the largest volume of all freshwater environments (Sigee 2004). The littoral environment within freshwater bodies generally occurs around the fringe of the water body, but can extend to surrounding small islands within water bodies, or for some distance into the water body depending on the vertical fall along the horizontal plane of the surface of the benthic zone. In contrast to the littoral zone, the pelagic zone is generally restricted to the middle of a water body, away from the banks. The pelagic zone can often dominate a freshwater ecosystem when water depths are high, bank slopes are steep, water flow velocities are great or the benthic zone is unable to support macrophyte growth (Kadlec and Knight 1996). The interactions between micro-organisms and higher trophic organisms within the pelagic and littoral zones of ponds and wetlands will influence the trophic status of the pond, and hence influence the concentrations of nutrients within the pond and that exiting from ponds to receiving waters (Vymazal 1995; Scinto and Reddy 2003; Kisand and Noges 2004; Sigee 2004).
Eulittoral Littoral Upper Littoral zone Middle Littoral Lower Littoral Pelagic zone
Figure 6-1: Littoral and pelagic zones within freshwater environments. Adapted from Hutchinson 1967 and Wetzel 2001.
Page 107 of 376 The concentration of N, P and C within the pelagic and littoral zones of stormwater ponds, and in that exiting to receiving waters, is a result of the interaction between physical, biological and chemical processes (many described in detail within Chapter 1) within the pelagic and littoral zones. This interaction highlights nutrient speciation and transformations within a given ecosystem as important factors in governing the final concentration of N, P and C within stormwater ponds.
It would now be beneficial to introduce the theoretical movement of N, P, and C within freshwater ecosystems. Figures 6-2, 6-3 and 6-5 display the movement of N, P and C within the pelagic zone of freshwater ecosystems respectively. The concentration of N, P, and C within the pelagic and littoral zones can be essentially viewed as the result from the interactions of the processes described in Figures 6-2, 6- 3 and 6-4 which are influenced further by hydraulic and abiotic water parameters.
guano, fishing Land
NH3 Atmospheric N2 Freshwater flow, stormwater fixation , sewage Dissolved N Consumers; effluent, 2 rain zooplankton & Primary & Secondary feeding larger fauna Volatilization denitrification producers uptake excretion DIN death
decay Non living DON particulate N denitrification decay sedimentation
ammonification uptake - + NO3 NH4 Particulate N nitrification decay ion exchange DON decay
+ Fixed NH4
Sediments
Figure 6-2: Nitrogen cycle in the pelagic zone of freshwater ecosystems. Boxes indicate pools of nitrogen with black arrows representing movement between the pools. This cycle assumes steady state conditions. DON = Dissolved organic nitrogen; DIN = Dissolved inorganic nitrogen. Adapted from (Valiela 1995; Herbert 1999).
Page 108 of 376 guano, fishing Land
Freshwater flow, stormwater, grazing Consumers; sewage effluent, Primary & Secondary rain zooplankton & producers death larger fauna exudation uptake decay Part DOP P excretion, death absorption & hydrolysis Insoluble P DIP precipitation & dissolution compounds
regeneration sedimentation
Sediments
Figure 6-3: Phosphorus cycle in a freshwater ecosystem. Boxes indicate pools of phosphorus with black arrows representing movement between the pools. This cycle assumes steady state conditions. DOP = dissolved organic phosphorus; DIP = dissolved inorganic phosphorus; Part P = particulate phosphorus. Adapted from (Horne and Goldman 1994; Valiela 1995; Herbert 1999).
Page 109 of 376 Freshwater flow, stormwater, rain CO2 (gas)
CO2 respiration (dissolved)
photosynthesis exudation heterotrophy death Primary viral lysis producers
POC consumption grazing grazing DOC death excretion Animal consumers heterotrophy
grazing
Microbial consumers
aggregation, adsorption
sinking decay by enzymes
resuspension adsorption resuspension
Sediments
Figure 6-4: The carbon cycle in the pelagic zone of a freshwater ecosystem. Boxes indicate pools of phosphorus with black arrows representing movement between the pools. POC = Particulate organic carbon; DOC = dissolved organic carbon. Adapted from (Valiela 1995; Vymazal 1995; Wetzel 2001).
In a stormwater systems, the hydraulic characteristics of ponds will govern the; allochthonous input of N, P, and C; control the time water spends within the system (defined as the hydraulic residence time); influence fluid mixing within the water column that in turn enhance atmospheric inputs of oxygen, nitrogen, and carbon dioxide into the water column, and provide a means of distributing phytoplankton, bacterioplankton and zooplankton within the water column (Duncan 1995; Werner and Kadlec 1996; Wong et al. 1999; Jenkins and Greenway 2005). Abiotic water characteristics regulate many microbial mediated processes that govern N, P, and C dynamics, as well as the biochemical uptake and release of P across the sediment/water interface. Main physicochemical water quality parameters likely to
Page 110 of 376 influence N, P, and C dynamics within the littoral and pelagic zone of stormwater treatment ponds include pH, Redox Potential, Dissolved Oxygen and Temperature.
The pH of freshwater bodies is best and most simply defined as the concentration of free hydrogen (H+) ions within the water body, and is linked strongly to the - dissociation of HCO3 and the subsequent production of OH ions (Manahan 1994). Autotrophic production increases OH- concentration through the dissociation of
HCO3 to meet photosynthetic CO2 requirements, which inturn increases the pH.
Conversely, respiration produces free CO2, thereby lowering pH levels (Kadlec and Knight 1996; Kadlec et al. 2000). The dynamic behaviour of pH has a major influence on N, P, and C cycling within the ecosystem. For example, direct ammonia volatilization within wetland and pond soils can provide a significant pathway for N removal when pH levels exceed 8.0. It has also been hypothesized that nitrification and denitrification rates are reduced in acidic water (<5.0) (Reddy et al. 1989; Azov and Tregubova 1995). In the case of phosphorus, pH affects adsorption/desorption chemical reactions influencing the movement of P across the sediment water interface.
The redox potential is a measure of the potential for a chemical species to acquire electrons. The measure is based on the activity of electrons between two compounds (ZoBell 1946; Wetzel 2001). The compound that losses an electron is said to be oxidised, while the compound that gains an electron is said to be reduced. The redox potential is measured on a scale from -400mV to +600mV (Figure 5) (Wetzel 2001). The nitrogen cycle is intimately linked to the redox potential, with nitrification reactions occurring exclusively in an environment that has a redox potential above 300mV, and denitrification occurring within an environment that has a redox potential range between 100-300 mV (Kadlec and
Figure 6-5: Redox potential scale. Adapted from Vymazal (1995) and Kadlec (1999a). Page 111 of 376 Knight 1996). The influence of redox potential on phosphorus is more indirect, with - significant release of PO4 from precipitates and mineral bound pools occurring when redox potential fall below 0mV (DeLaune et al. 1981; Wieβner et al. 2005). The redox potential also governs the pathway of inorganic carbon through the decomposition processes (Vymazal 1995).
The concentration of DO within freshwater bodies is subject to diurnal, vertical and seasonal variation. DO is essential for aerobic respiratory metabolism. The concentration of DO is a balance between atmospheric and photosynthetic inputs and losses occur via chemical and biological oxidation (Horne and Goldman 1994; Tadesse et al. 2004). Thus the concentration of DO within freshwaters can indicate the trophic state of an ecosystem, which would in turn provide likely pathways for N, P, and C movement within the ecosystem. In eutrophic waters, DO concentrations tend to be highly variable over a 24 h period, while oligotrophic water bodies exhibit less varible DO range (Tadesse et al. 2004).
Water temperature influences a number of factors that contribute to the dynamics of N, P and C with freshwater bodies. Firstly, temperature within a water body can stratify the water column, limiting mixing between a nutrient rich ‘bottom’ strata and a nutrient poor ‘top’ strata (Lomas et al. 2002). Seasonal impacts can be important, partically when a water column shifts from stratified to unstratified (usually in late summer and autumn). This process provides an influx of bio available nutrients to primary consumers inhabiting the upper water stratum(Ydstebo et al. 2000; Tadesse et al. 2004). Secondly, temperature also affects the cycling, and rate of, N, P, and C biogeochemical reactions by altering the rate of microbial metabolism. In specific - reference to the denitrification of NO3 , higher temperatures greatly increase the efficiency of the bacterium responsible for denitrification, with N2 being the dominate product at high temperature, and N2O being the dominant product at low temperatures (Shammas 1986; Bachand and Horne 2000).
Page 112 of 376 6.2.1 Research aims, objectives and research questions In stormwater treatment ponds the pelagic ecosystem often dominates (with respect to surface area), with littoral zones restricted to small fringing bands. This is often due to specific design objectives of ponds employed for stormwater management and treatment. That is, the hydraulic retention of stormwater and suspended particulate removal (Lawrence and Breen 1998; Lloyd et al. 1998; Macky 2003; Greenway and Jenkins 2004). The aim of this chapter is to report on investigations undertaken into the temporal and spatial changes in N, P and C concentrations and speciation within the pelagic and littoral zones of the BWC System.
Specific objectives of the investigation were to; • Determine the influence of a range of physicochemical parameters on the pelagic and littoral concentrations of N, P, and C. • Investigate the influence of water depth on N, P, and C concentrations within the pelagic zone. • Investigate the influence of storm induced hydraulic loading on N, P, and C concentrations within pelagic and littoral zones. • Quantify epiphyton biomass within littoral zones.
Specific research questions for this investigation were; • Which zone, pelagic or littoral, within the BWC System has consistently lower N, P, and C concentrations? • Does epiphyton biomass within the littoral zone change spatially and temporally, and is it influenced by storm flows? • Does epiphyton biomass influence the concentration of N, P, and C within the littoral zones the BWC System?
Page 113 of 376 6.3 Methods
6.3.1 Field sampling
Between January 2004 and February 2005, fortnightly water quality monitoring was undertaken within the BWC System. Twelve sampling locations were chosen within the BWC System (Figure 6-6), with six sites located within the pelagic zones of Ponds 1 and 6, and six sites located within the littoral zones of Ponds 1 and 5. Sample sites where chosen in Ponds 1 and 6 to measure a representative area of the pelagic and littoral zones. The water quality monitoring period was less for the littoral sites than for the pelagic sites, lasting only a six-month period between July 2004 and January 2005. It was the original plan to use littoral zones within Pond 6 for sampling, but due to massive macrophyte die back no such zones existed (Greenway et al. 2006).
Figure 6-6: Water quality sampling locations within Ponds 1, 5 and 6 of the BWC System. Red crosses indicate pelagic water sampling sites, with yellow crosses indicating littoral sampling sites.
Physicochemical parameters DO, Temperature, Redox Potential, and pH were measured using a field multimeter water quality probe. The probe used, a YSI SNODE 6600 multimeter, was calibrated in accordance with the procedured outlined in Chapter 3 and lowered into the water from the shaded side of an inflatable dingy. Within the pelagic zones of Ponds 1 and
Page 114 of 376 6, three readings for DO, Temperature, Redox Potential, and pH were taken at 20cm depth intervals through the water column at each sample site. Readings were stored automatically to the memory of the data logger attached to the probe. Within the littoral zones of Ponds 1 and 5, the YSI SNODE 6600 was submerged at a depth 20cm, and three measurements were taken at each site.
Nutrient sampling Following measurements taken with the YSI SNODE 6600 multimeter, water from the upper 20cm of the water column and lower 20cm of the water column within the pelagic sites was taken for determination of N, P, and C concentrations. Consistent with the methods presented in Chapter 3, 1 L composite water samples were taken from each sample location which were sub-sampled for the later laboratory determination of NH4-N, NOx-N, PO4-P, DOC, TN and TP in the field.
Epiphyton biomass sampling Three methods where used to quantify epiphyton biomass within Ponds 1 and 5/6. To assess the initial growth rate of epiphyton in each of the pond ecosystems, a glass slide experiment was conducted in accordance with methods outlined in the Standard Methods for the Examination of Waters and Wastewaters (Franson 1998). On the 15- Sept-04 and 20-Jan-05, 45 glass slides (75mm x 25mmx 1mm) were fixed to a free- floating structure in both Ponds 1 and 6. care was taken to ensure that all glass slides where submerged. Over 14 days beginning on the 15-Sept-04 and 20-Jan-05, 3 slides where removed daily, placed in 50mL centrifuge tubes, wrapped in foil and placed on ice in the field. Centrifuge tubes containing glass slides where then placed in a freezer prior to laboratory analysis.
To assess the ‘standing crop’ biomass of epiphyton within Ponds 1 and 5, epiphyton biomass samples within the littoral zones of Ponds 1 and 5 were collected. These were taken following the collection of abiotic water parameters (pH, Redox Potential, DO, Temperature, N, P and C samples) to ensure representative readings/measurements were not influenced by actions associated with epiphyton biomass sampling. From each of the three locations within Ponds 1 and 5, 3
Page 115 of 376 Schoenoplectus validus stems were cut at the base and removed for the analysis of the attached epiphytic community (Figure 6-7). The actual length of stem cut varied according to the height of water within each pond and depth of sediment. To standardise the age of stems sampled (to ensure sampled epiphytic communities were comparable) all Schoenoplectus validus stems collected protruded greater than 50cm from the water surface. This ensured an adequate length of time for the epiphyton community to develop to a ‘standing crop’ level – approximately 14-20 days according to Franson et al. (1998).
3 complete cut stems from each site - submersed length Epiphyton removed - upper and lower and grouped from 3 submerged circumference stems at each sample site.
Submersed section scaped on-site for epiphyton biomass calculations
Figure 6-7: Quantification of epiphyton on submerged sections of Schoenoplectus validus stems.
Each cut stem was measured with respect to total stem length, length of submerged stem (within the water column) and the circumference of the stem at the upper and lower points of the submerged section. Epiphyton was then scraped from the submerged section of stem with the back of a sterile surgical scalpel and placed in a 60mL specimen jar, wrapped in foil and placed on ice. Scraped epiphyton from each of the three stems was pooled to provide sufficient biomass for laboratory analysis. Upon completion of field work, epiphyton samples were transported back to the laboratory freezer to await analysis.
To assess the biomass of epiphyton down the depth profile of the water column within Ponds 1 and 6, artificial permanent racks where installed in Ponds 1 and 6 (Plate 6-1). Following a pilot study to test a suitable artificial material for epiphyton growth
Page 116 of 376 (TableS 6-1 and 6-2) 150 removable epiphyton biomass growth rods (polyethylene 4mm irrigation tubbing) extending from the benthos to 10cm above the surface of the pond. The original plan with this approach was to remove 3-9 rods from each pond monthly for 12 months and assess the biomass of epiphyton at 5cm depth intervals within both ponds. However, within the first month of installation this method proved ineffective in monitoring epiphyton communities within Ponds 1 and 6 and was therefore abandoned. The racks holding the artificial colonisation rods within the water column became a frequent perch for water fowl, and at times provided an ideal nesting site for residence ducks. Faecal matter resulting from continued water fowl activity on top of and immediately around the racks within Ponds 1 and 6 became obvious. The racks also acted much like a gross pollutant trap (although installed in areas away from the direct path of stormwater flow). This resulted in large amounts a litter accumulating within and around the racks often making it near impossible to remove the rods within epiphyton scraped off within the water column.
Table 6-1: Periphyton biomass measurements of artificial and natural substrates exposed to a 2 week colonization period. Substrate Shape area of Ash free weight Chlorophyll a substrate (m2) (mg / m2) (mg / m2) 75mm Glass slide Rectangle 0.06 0.99 2.5 4mm Poly pipe Cylinder 0.09 0.99 2.2 Schoenoplectus Cylinder 0.09 0.99 2.5 validus Bolboschoenus Cylinder 0.05 0.1 2.7 fluviatilis
Table 6-2: Significant differences between periphyton colonization on substrates. Bold figures refer to significant difference between substrates. Substrates ANOVA p-value (significance = <0.05) Schoenoplectus validus - Bolboschoenus 0.014 fluviatilis 4mm Poly Pipe - Schoenoplectus validus 0.278 4mm Poly Pipe - Bolboschoenus fluviatilis 0.055 75mm Glass Slide - 4mm Poly Pipe 0.168 75mm Glass Slide - Bolboschoenus fluviatilis 0.008 75mm Glass Slide - Schoenoplectus validus 0.760
Page 117 of 376 6.3.2 Laboratory analysis
Water quality
The laboratory analysis of NH4-N, NOx-N, PO4-P, DOC, TN and TP within each water sample was conducted in accordance with Standard Methods for the Examination of Waters and Wastewaters (Franson 1998). Specific methods for the laboratory analysis of NH4-N, NOx-N, PO4-P, DOC, TOC, TN and TP have been outlined in detail previously in Chapter 3. Briefly, NH4-N, NOx-N and PO4-P were analysed as pure undiluted samples concurrently using FIA colour metric methods. TN and TP were also analysed concurrently using FIA colour metric methods, but where subject to a digestion procedure that oxidised all available N or P forms within the water sample to either NOx-N or PO4-P. Analysis was then conducted using
NOx-N and PO4-P colour metric methods. Organic N and Other-P compounds where determined via calculation using Equations 6-1 and 6-2 respectively.
OrgNconc= TN conc −() NOx − N conc + NH4 − N conc (6-1) where; -1 OrgNconc = Organic nitrogen concentration, mgL -1 TNconc = Total nitrogen concentration, mgL -1 NOx-Nconc = Oxidised nitrogen concentration, mgL + -1 NH4 conc = Ammonia concentration, mgL
Other− Pconc = TP conc − PO4 − P conc (6-2) where; -1 Other-Pconc = Organic phosphorus concentration, mgL -1 TPconc = Total phosphorus concentration, mgL -1 PO4-Pconc = Phosphate concentration, mgL
Epiphyton Epiphyton removed from Schoenoplectus validus stems was quantified as biomass per cm2 of colonised surface substrate based on Chlorophyll a concentration and Ash Free Dry Weight (AFDW). The surface area colonised by the epiphyton was calculated from cylindrical area calculations using the dimensions of the Schoenoplectus validus
Page 118 of 376 stems taken in the field. The total amount of epiphyton collected at each sample site within Ponds 1 and 5 (taken from the sum of the 3 Schoenoplectus validus stems) was weighted (wet weight [w/w]) upon defrosting, with a known w/w assigned for Chlorophyll a analysis and a known w/w assigned for AFDW analysis. To ensure that the w/w of epiphyton from individual samples were comparable upon defrosting and indicative of the community colonising the Schoenoplectus validus stems, epiphyton samples where drained (gravity) to remove excess water not directly associated within the epiphyton community. The exact w/w of each sample analysed varied with respect to the total w/w of each sample – meaning that the w/w allocated for either Chlorophyll a or AFDW analyses differed from sample to sample. All results presented were standardised to percentage w/w of epiphyton to total colonised surface area on the Schoenoplectus validus stem based on the amount of epiphyton allocated to each analysis and the total surface area of the Schoenoplectus validus stems in which the epiphyton colonised.
Chlorophyll a analysis was undertaken by dissolving a known w/w of epiphyton in
90% acetone saturated with MaCO3 in a 10mL conical test tube. Each sample was sonicated for 1hr in a sonication bath, and placed in a centrifuge for 10 minutes at 3000 rpm. The concentration of Chlorophyll a was then measured using a Schimadzu UV160A UV/VIS spectrophotometer according to the Standard Methods in the Examination of Water and Wastewater (Franson 1998). Biomass of epiphyton colonising Schoenoplectus validus stems based on Chlorophyll a was then calculated using Equation 6-3 (Franson 1998). AFDW of epiphyton was determined according to the Standard Methods in the Examination of Water and Wastewater (Franson 1998). Briefly, a known w/w of epiphyton was dried for 1hr at 105°C, placed in a desiccator to cool, weighed, then ignited in a furnace for 1hr at 500°C, cooled in a desiccator, rewet with reagent grade pure water, redried at 105°C and weighed. The AFDW biomass of the epiphyton was then determined using Equation 6-4 (Franson 1998).
The Autotrophic Index (AI) is an approximate means of determining the trophic nature of epiphyton, and is an arbitrary value commonly used as a gross and rapid measure of the trophic status of epiphyton communities. The AI of sampled epiphyton was calculated using Equation 6-5 (Franson 1998).
Page 119 of 376 26.7⋅ (664 − 665 ) ⋅V chl. a = b a 1 peri AL⋅ (6-3) where; chl.aperi = concentration of Chlorophyll a within epiphyton extract,
V1 = volume of extract, L A = area of sampled substrate, cm2 L = light path length or width of cuvette, cm
664a, 665b =optical densities of 90% acetone extract before and after acidification respectively
AFDW= DW − AF (6-4) where; AF = Weight of epiphyton ash minus crucible, mg. cm-2 DW = Weight of dry epiphyton minus crucible, mg. cm-2
AFDW AI = (6-5) Chl. a where; AI = Autotrophic index
Each glass slide collected from Ponds 1 and 6 was analysed for Chlorophyll a concentration. To each 50mL centrifuge tube containing a glass slide, 50mL of 90% acetone (saturated with MgCO3) was added and let sit for 1 hour in the dark. Each centrifuge tube was then sonicated using a sonication probe for 30 seconds. Tubes where then centrifuged for 10 minutes at 3000 rpm, after which 5mL was decanted for the analysis of Chlorophyll a inaccordance with methods outlined in Chapter 3. Biomass of epiphyton colonising each slide was then determined using Equation 3.
Page 120 of 376 6.3.3 Statistical analysis
Multivariate statistical analysis was performed on the data set generated from the analysis describes in this chapter. Principal Component Analysis and Hierarchal Cluster Analysis was undertaken using statistical packages SPSS® 14.01 and SYSSTAT® 11. Total fortnightly stormwater flow entering Pond 1 and exiting Pond 6 from Chapter 4 are included in this analysis.
Page 121 of 376 6.4 Results
The field sampling’s and analyses outlined above generated an enormous quantity of data. As a result, a data reduction method (PCA and Cluster analysis) was used to highlight significant factors governing water quality variance within Ponds 1 and 6. Individual water quality parameters for the various pelagic and littoral sites are outlined initially. PCA and Cluster analysis is then used to explain and interpret variance/relationships within the data set.
6.4.1 Physicochemical water parameters
Using the three sampling locations within the pelagic zones of Ponds 1 and 6 (see Figure 6-6) as replicates, Tables AB 1 to 4 (located in Appendix B due to size) provide the mean value of each respective abiotic water parameter (temperature, DO, redox potential and pH), with the SE for each data set displayed in parentheses. Each of the three littoral sites within Ponds 1 and 5 were also used as replicates, with the mean and SE values for the range of abiotic water quality parameters measured within each pond presented in Tables 6-3 and 6-4 respectively. These tables show the change in abiotic water parameter/s from July 2004 through to January 2005 in (most cases) at fortnightly increments.
Table 6-3: Pond 1 water quality parameters for the duration of the sampling regime. Results displayed are means of three replicates within pond, with SE’s shown in parentheses.
23 Jul 11 Aug 24 Aug 15 Oct 3 Nov 17 Nov 1 Dec 16 Dec 7 Jan 04 04 04 04 04 04 04 04 05 Temp. 17.8 18.4 20.1 26.9 25.5 26.4 26.8 25.8 29 (°C) 0.25 0.17 0.57 0.19 0.32 0.01 0.05 0.02 0.24 DO 15.6 10.3 5.11 6.29 14.3 18.9 6.62 1.12 4.32 (mg L-1) 0.91 0.61 0.35 0.14 1.68 0.11 0.24 0.17 0.32 pH 7.81 7.28 6.67 7.22 7.40 7.61 7.29 7.09 7.08 0.03 0.02 0.01 0.05 0.07 0.02 0.05 0.00 0.09 Redox Potential 128 110 -27.8 51.0 135 169 94.4 56.3 152 (mV) 27.3 9.50 11.3 10.3 13.0 1.44 24.6 0.38 11.7
Page 122 of 376 Table 6-4: Pond 5 water quality parameters for the duration of the sampling regime. Results displayed are means of three replicates within pond, with SE’s shown in parentheses.
23 Jul 11 Aug 24 Aug 15 Oct 3 Nov 17 Nov 1 Dec 16 Dec 7 Jan 04 04 04 04 04 04 04 04 05 Temp. 17.4 19.6 19.5 24.8 26.1 26.2 26.6 26.4 31.0 (°C) 0.11 0.12 0.05 0.7 0.03 0.05 0.18 0.02 0.12 DO 9.09 10.02 3.8 3.69 10.36 7.97 4.52 2.82 4.64 (mg L-1) 0.49 0.27 0.14 0.25 0.48 1.14 0.38 0.27 0.17 pH 6.95 7.39 6.8 6.97 7.48 7.45 7.64 7.38 7.08 0.01 0.08 0.02 0.04 0.02 0.04 0.11 0.16 0.03 Redox Potential 141 191 105 77.4 107 83.4 56.8 64.4 153 (mV) 3.80 3.40 4.60 11.5 16.1 19.4 1.90 4.10 17.8
Temperature Figure 6-8 displays the change in water temperature with increasing depth within the pelagic zones of Ponds 1 and 6 over the study period. Water temperature in Ponds 1 and 6 varied, as expected, with the time of year and water depth. Surface water temperatures in Ponds 1 and 6 reached a maximum of 29.4°C and 28.4°C respectively on the 18th February, 2004. Surface water temperatures fell to a minimum of 17.3°C for Pond 1 and 13.5°C for Pond 6 on the 11th August, 2004. No significant difference in temperature was found between each of the three sample sites within Pond 1 or Pond 6, with graphed figures being the mean of the three sample locations within each pond. As can be seen in Figure 8, water temperature decreases in Ponds 1 and 6 around the beginning of March and begins to increase towards to end of August reaching a maximum around January and February. Water temperature also decreased with depth, with a surface mix layer present throughout most of the sampling regime. As one would expect, water temperature within the littoral zones of Ponds 1 and 5 exhibited seasonal variation, reaching a minimum of 17.4 °C in July and a maximum of 31.0 °C in January. There was little variation in temperature within and between the littoral zones of both ponds.
Page 123 of 376 Surface mixing layer present
Surface mixing layer present
Figure 6-8: Isopleth diagram representing changing water temperature in (a) Pond 1 and (b) Pond 6. Thick black line showing the surface mix layer (greater than 0.25oC change between sample depths).
Dissolved Oxygen Figure 6-9 displays the change in water DO concentration with increasing depth within the pelagic zones of Ponds 1 and 6 throughout the study period. The DO concentration within Ponds 1 and 6 of the BWC System changed dramatically both on a temporal and spatial time scale. DO decreased with increased depth in both Ponds 1 and 6, and fluctuated between fortnightly sampling considerably. The highest and lowest recorded concentrations of DO occurred in Pond 1 and peaked at an extreme high of 27.7 mg L-1, falling to a low of 0.06 mg L-1. In Pond 6, DO peaked at a high of 15.5 mg L-1 on the 3rd of November, with the lowest recording occurring on the 10 March at 0.17 mg L-1. DO concentration within Pond 6 followed a similar fortnightly
Page 124 of 376 trend to that of Pond 1. However, the concentration of DO within Pond 6 fluctuated within a much smaller range than in Pond 1 and rarely fell below 1 mg L-1. Within the littoral zones, DO concentration within Pond 1 was always greater than that of Pond 5, at times reaching concentrations above 15mg L-1. Variance on a spatial scale within Ponds 1 and 5 was minor (see Tables AB 1-4).
Figure 6-9: Isopleth diagram representing spatial and temporal change in DO concentration within (a) Pond 1 and (b) Pond 6.
Redox Potential The redox potential within the BWC System differed markably between ponds, between fortnightly sampling, and with increasing water column depth. Figure 6-10 provides an isopleth diagram to display changes in redox Potential. Figure 6-10 also highlights the redox potential zones where nitrification and denitrification occur. Interestingly, redox potential within the Pond 1 water column was within or below the ‘denitrification zone’ 100% of the time, and within the ‘sulphate reduction zone’ for a
Page 125 of 376 considerable period of time. The Pond 6 water column was also ‘reducing’ in nature, with almost all profiles being within the ‘denitrification zone’. Surprisingly, water within both Ponds 1 and 6 never entered the ‘nitrification zone’ (redox potential greater than 300mV). The Redox Potential within the littoral zones of Pond 1 and 5 showed a similar temporal pattern to that of DO. Over the course of the sampling regime, Pond 1 exhibited a larger range (-27.8 to 168.5) than Pond 5 (56.8 to 191.1). Pond water within the littoral zones of both ponds did not enter the ‘nitrification’ zone (>300mV), with Pond 1 water being within the denitrification zone (0-300mV) during 8 of the 9 sampling fortnights. The littoral zone in Pond 5 was within the denitrification zone 100% of the time.
pH The pH within the pelagic zones of Ponds 1 and 6 varied between 6.03 and 8.73 – within the range of most open freshwater lakes (Wetzel 2001). pH decreased with increased depth, and did not show any obvious seasonal variation. Figure 6-11 displays isopleth diagrams of pH change.
The pH within the littoral zones of Ponds 1 and 5 did not substantially vary between ponds and between sampling sites within ponds, ranging from 7.81 to 6.67, occupying a narrower range to that of the pelagic sites within Ponds 1 and 6.
6.4.2 Nutrient speciation and concentrations
Nutrient concentrations within the pelagic zones of Pond 1 and 6 varied greatly between fortnightly sampling and ponds but varied little between the upper and lower sampling depths of the water column. TN, NH4-N, NOx-N, TP, PO4-P, DOC and TOC concentrations from the upper and lower water stratums are presented in Tables 6-5 and 6-6. Using the three sites within the littoral zones of Pond 1 and 5 as replicates, mean and SE values for the N, P, and C concentrations are presented in Tables 6-7 and 6-8 respectively. Water column nutrient (N, P, and C) concentrations within the littoral zones of Ponds 1 and 5 varied temporarily, not showing significant spatial variation within respective ponds– as shown by the SE’s displayed in Tables 6- 7 and 6-8.
Page 126 of 376 Denitrification zone zonezone
Sulphate reduction zone
Denitrification zone
Sulphate reduction zone
Figure 6-10: Isopleth diagram representing spatial and temporal change in redox potential within (a) Pond 1 and (b) Pond 6. Thick black line 0mV contour, and the boundary between the denitrification zone and sulphate reduction zone.
Page 127 of 376 Figure 6-11: Isopleth diagram representing spatial and temporal change in pH within (a) Pond 1 and (b) Pond 6.
Page 128 of 376 Table 6-5: Mean nutrient concentrations within Pond 1 over the entire sampling regime. All units in mgL-1. SE in parentheses.
Sampling TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC date Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower 21-Jan-04 2.54 3.11 2.35 2.74 0.05 0.30 0.13 0.07 0.27 0.29 0.13 0.15 0.14 0.14 17.41 19.17 0.22 0.22 0.20 0.20 0.01 0.06 0.04 0.05 0.00 0.01 0.01 0.00 0.01 0.01 1.10 1.64 04-Feb-04 2.61 2.80 0.99 0.94 0.10 0.14 1.52 1.72 0.28 0.30 0.13 0.10 0.15 0.20 20.30 17.99 0.23 0.06 0.04 0.05 0.00 0.05 0.19 0.03 0.02 0.04 0.01 0.04 0.02 0.02 1.47 3.06 18-Feb-04 0.82 0.80 0.38 0.06 0.44 0.74 0.00 0.00 0.22 0.44 0.13 0.36 0.09 0.08 15.45 15.88 0.05 0.05 0.07 0.01 0.02 0.05 0.00 0.00 0.03 0.03 0.02 0.02 0.01 0.01 1.35 2.15 10-Mar-04 0.53 0.65 0.28 0.34 0.12 0.26 0.13 0.04 0.19 0.18 0.09 0.05 0.10 0.13 12.55 14.60 0.09 0.05 0.10 0.16 0.10 0.16 0.07 0.04 0.06 0.02 0.03 0.04 0.04 0.03 0.78 1.11 24-Mar-04 0.29 0.37 0.18 0.20 0.04 0.04 0.07 0.13 0.31 0.29 0.19 0.15 0.12 0.14 8.07 9.13 0.07 0.04 0.06 0.03 0.02 0.00 0.01 0.01 0.06 0.06 0.06 0.04 0.02 0.03 0.41 0.60 08-Apr-04 0.28 0.32 0.13 0.19 0.03 0.03 0.11 0.10 0.62 0.53 0.55 0.45 0.06 0.08 3.58 3.65 0.01 0.03 0.01 0.05 0.01 0.02 0.00 0.01 0.03 0.06 0.02 0.06 0.00 0.01 0.08 0.10 22-Apr-04 0.43 0.33 0.30 0.22 0.03 0.03 0.10 0.08 0.69 0.52 0.67 0.49 0.02 0.03 7.12 6.37 0.07 0.03 0.08 0.04 0.01 0.01 0.00 0.02 0.11 0.06 0.11 0.06 0.00 0.00 0.27 0.01 10-May-04 0.26 0.23 0.09 0.02 0.06 0.06 0.12 0.15 0.41 0.35 0.35 0.28 0.06 0.07 4.81 4.21 0.01 0.01 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.00 0.10 0.58 19-May-04 0.23 0.28 0.09 0.15 0.04 0.03 0.09 0.10 0.46 0.50 0.43 0.47 0.03 0.03 6.37 5.89 0.03 0.03 0.03 0.03 0.01 0.01 0.01 0.00 0.06 0.05 0.06 0.04 0.01 0.01 0.59 0.52 03-Jun-04 0.54 0.53 0.36 0.33 0.04 0.07 0.13 0.13 0.53 0.65 0.53 0.63 0.00 0.02 8.68 9.17 0.06 0.05 0.05 0.03 0.02 0.04 0.01 0.01 0.05 0.05 0.05 0.05 0.00 0.01 0.30 0.16 18-Jun-04 0.40 0.34 0.27 0.19 0.03 0.05 0.10 0.10 0.66 0.57 0.62 0.53 0.04 0.04 7.82 9.11 0.05 0.01 0.05 0.00 0.00 0.01 0.00 0.00 0.07 0.05 0.06 0.05 0.01 0.00 2.85 1.43 01-Jul-04 0.42 0.44 0.17 0.15 0.15 0.17 0.10 0.12 0.22 0.32 0.21 0.30 0.01 0.02 8.00 7.77 0.02 0.01 0.04 0.02 0.03 0.02 0.02 0.00 0.03 0.02 0.03 0.02 0.01 0.01 0.16 0.36 12-Jul-04 0.71 0.62 0.57 0.49 0.02 0.02 0.12 0.10 0.57 0.57 0.57 0.57 0.00 0.00 16.98 12.20 0.14 0.12 0.14 0.13 0.02 0.01 0.02 0.01 0.14 0.15 0.14 0.15 0.00 0.00 0.56 1.53 11-Aug-04 0.55 0.55 0.53 0.53 0.01 0.01 0.01 0.01 0.28 0.25 0.25 0.21 0.03 0.03 7.79 7.88 0.08 0.05 0.08 0.05 0.00 0.00 0.00 0.00 0.02 0.03 0.02 0.03 0.00 0.00 1.27 1.44 24-Aug-04 0.60 0.48 0.56 0.29 0.03 0.17 0.02 0.02 0.85 0.72 0.63 0.51 0.22 0.22 6.91 11.55 0.06 0.02 0.06 0.13 0.02 0.14 0.00 0.00 0.14 0.09 0.08 0.06 0.06 0.03 0.28 0.57 15-Oct-04 0.88 0.70 0.87 0.67 0.00 0.03 0.00 0.00 0.51 0.48 0.50 0.47 0.01 0.01 15.30 14.43 0.10 0.26 0.09 0.26 0.00 0.00 0.00 0.00 0.14 0.21 0.13 0.21 0.01 0.01 0.44 0.98 03-Nov-04 1.60 1.90 1.57 1.88 0.03 0.02 0.00 0.00 0.99 1.21 0.76 0.96 0.23 0.25 17.43 21.11 0.24 0.27 0.22 0.27 0.02 0.01 0.00 0.00 0.37 0.29 0.31 0.26 0.06 0.04 1.00 3.34 17-Nov-04 1.25 1.53 1.16 1.12 0.09 0.33 0.00 0.08 0.37 0.47 0.29 0.36 0.08 0.11 19.02 18.75 0.08 0.15 0.08 0.05 0.07 0.15 0.00 0.04 0.10 0.08 0.05 0.04 0.05 0.04 1.49 0.81 01-Dec-04 1.27 1.78 0.99 1.19 0.13 0.44 0.15 0.14 0.52 0.67 0.35 0.48 0.16 0.19 11.18 10.77 0.30 0.35 0.32 0.23 0.06 0.24 0.07 0.05 0.16 0.13 0.13 0.12 0.03 0.02 0.15 0.70 16-Dec-04 1.29 1.42 0.83 0.65 0.16 0.56 0.29 0.21 0.54 0.66 0.29 0.38 0.25 0.27 19.79 22.26 0.04 0.17 0.07 0.13 0.06 0.40 0.15 0.13 0.02 0.14 0.02 0.15 0.01 0.01 0.28 2.46 07-Jan-05 0.76 0.84 0.28 0.28 0.07 0.11 0.42 0.45 0.34 0.39 0.22 0.21 0.13 0.17 6.99 7.86 0.05 0.03 0.07 0.06 0.03 0.06 0.02 0.01 0.03 0.02 0.02 0.02 0.01 0.01 0.38 0.28 20-Jan-05 0.83 0.81 0.66 0.64 0.08 0.04 0.09 0.13 0.56 0.58 0.19 0.28 0.37 0.30 5.29 6.11 0.04 0.04 0.08 0.05 0.05 0.02 0.07 0.05 0.04 0.06 0.09 0.06 0.07 0.02 0.48 0.41
Page 129 of 376 Table 6-6: Mean nutrient concentrations within Pond 6 over the entire sampling regime. All units in mgL-1. SE in parentheses.
Sampling TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC date Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower 21-Jan-04 4.06 2.85 3.84 2.51 0.00 0.11 0.22 0.23 0.33 0.28 0.16 0.09 0.18 0.20 0.33 0.28 1.19 0.21 1.22 0.22 0.00 0.01 0.05 0.04 0.04 0.01 0.06 0.01 0.03 0.02 0.04 0.01 04-Feb-04 2.32 1.75 0.95 0.66 0.17 0.21 1.21 0.88 0.29 0.28 0.02 0.03 0.27 0.25 0.29 0.28 0.61 0.11 0.51 0.18 0.02 0.02 0.12 0.15 0.04 0.02 0.00 0.03 0.03 0.05 0.04 0.02 18-Feb-04 0.58 0.65 0.54 0.62 0.04 0.03 0.00 0.00 0.21 0.24 0.15 0.17 0.07 0.07 0.21 0.24 0.04 0.02 0.04 0.02 0.01 0.01 0.00 0.00 0.04 0.02 0.04 0.02 0.02 0.01 0.04 0.02 10-Mar-04 0.64 0.57 0.46 0.37 0.02 0.02 0.15 0.18 0.24 0.19 0.13 0.07 0.10 0.11 0.24 0.19 0.02 0.11 0.04 0.03 0.00 0.00 0.02 0.08 0.05 0.01 0.03 0.02 0.02 0.02 0.05 0.01 24-Mar-04 0.28 0.37 0.25 0.34 0.03 0.03 0.00 0.00 0.19 0.20 0.08 0.08 0.11 0.12 0.19 0.20 0.02 0.05 0.02 0.05 0.01 0.00 0.00 0.00 0.01 0.02 0.02 0.02 0.01 0.02 0.01 0.02 08-Apr-04 0.33 0.32 0.21 0.19 0.02 0.03 0.10 0.10 0.61 0.51 0.60 0.51 0.00 0.00 0.61 0.51 0.08 0.06 0.07 0.06 0.01 0.01 0.00 0.00 0.05 0.03 0.05 0.03 0.00 0.00 0.05 0.03 22-Apr-04 2.06 0.37 0.20 0.23 0.02 0.04 0.10 0.10 0.41 0.49 0.41 0.49 0.00 0.00 0.41 0.49 0.04 0.03 0.04 0.04 0.01 0.02 0.00 0.00 0.08 0.04 0.08 0.04 0.00 0.00 0.08 0.04 10-May-04 1.06 0.23 0.13 0.10 0.01 0.03 0.11 0.10 0.36 0.33 0.35 0.33 0.01 0.00 0.36 0.33 0.02 0.01 0.02 0.01 0.00 0.01 0.00 0.01 0.02 0.01 0.03 0.01 0.00 0.00 0.02 0.01 19-May-04 0.07 0.25 0.15 0.09 0.04 0.05 0.10 0.11 0.44 0.32 0.44 0.32 0.00 0.00 0.44 0.32 0.03 0.02 0.03 0.01 0.00 0.01 0.00 0.00 0.05 0.07 0.05 0.07 0.00 0.00 0.05 0.07 03-Jun-04 0.04 0.49 0.28 0.36 0.02 0.03 0.10 0.10 0.48 0.62 0.48 0.62 0.00 0.00 0.48 0.62 0.06 0.01 0.05 0.01 0.01 0.02 0.00 0.00 0.10 0.02 0.10 0.02 0.00 0.00 0.10 0.02 18-Jun-04 0.03 0.48 0.11 0.12 0.26 0.26 0.11 0.09 0.27 0.24 0.27 0.24 0.00 0.00 0.27 0.24 0.04 0.03 0.05 0.04 0.01 0.00 0.00 0.02 0.02 0.01 0.02 0.01 0.00 0.00 0.02 0.01 01-Jul-04 0.13 0.63 0.11 0.06 0.43 0.44 0.13 0.13 0.21 0.18 0.21 0.18 0.00 0.00 0.21 0.18 0.04 0.01 0.04 0.01 0.01 0.01 0.00 0.00 0.02 0.03 0.02 0.03 0.00 0.00 0.02 0.03 12-Jul-04 0.07 0.74 0.17 0.09 0.44 0.52 0.12 0.12 0.16 0.11 0.16 0.11 0.00 0.00 0.16 0.11 0.06 0.03 0.03 0.03 0.04 0.02 0.01 0.00 0.01 0.01 0.01 0.01 0.00 0.00 0.01 0.01 11-Aug-04 0.04 0.50 0.08 0.16 0.23 0.22 0.12 0.12 0.01 0.04 0.00 0.03 0.01 0.01 0.01 0.04 0.08 0.14 0.07 0.14 0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.02 0.00 0.00 0.01 0.02 24-Aug-04 0.05 0.42 0.11 0.11 0.29 0.27 0.03 0.03 0.20 0.21 0.15 0.17 0.05 0.04 0.20 0.21 0.02 0.04 0.04 0.05 0.02 0.01 0.00 0.01 0.02 0.02 0.01 0.02 0.00 0.01 0.02 0.02 15-Oct-04 0.11 0.33 0.36 0.32 -0.01 0.00 0.00 0.00 0.14 0.18 0.10 0.14 0.04 0.04 0.14 0.18 0.01 . 0.02 . 0.01 . 0.00 . 0.01 . 0.01 . 0.01 . 0.01 . 03-Nov-04 0.07 0.64 0.61 0.56 0.02 0.08 0.00 0.00 0.38 0.42 0.26 0.30 0.12 0.12 0.38 0.42 0.03 0.03 0.04 0.03 0.01 0.05 0.00 0.00 0.02 0.00 0.02 0.01 0.01 0.01 0.02 0.00 17-Nov-04 0.06 1.20 1.15 1.17 0.01 0.03 0.00 0.00 0.57 0.77 0.35 0.51 0.21 0.26 0.57 0.77 0.04 0.02 0.04 0.02 0.00 0.00 0.00 0.01 0.02 0.12 0.01 0.06 0.01 0.07 0.02 0.12 01-Dec-04 0.10 0.49 0.35 0.38 0.02 0.09 0.00 0.02 0.67 0.71 0.38 0.31 0.29 0.40 0.67 0.71 0.01 0.10 0.01 0.11 0.00 0.01 0.00 0.01 0.03 0.04 0.04 0.08 0.01 0.12 0.03 0.04 16-Dec-04 0.14 1.18 0.88 0.98 0.04 0.16 0.01 0.04 0.61 0.93 0.24 0.39 0.37 0.55 0.61 0.93 0.04 0.29 0.05 0.31 0.01 0.01 0.01 0.02 0.00 0.32 0.01 0.16 0.02 0.17 0.00 0.32 07-Jan-05 0.04 1.37 0.97 0.68 0.24 0.66 0.10 0.03 0.65 0.87 0.37 0.47 0.28 0.40 0.65 0.87 0.08 0.12 0.12 0.16 0.04 0.29 0.01 0.02 0.03 0.25 0.03 0.15 0.01 0.10 0.03 0.25 20-Jan-05 0.02 0.86 0.54 0.65 0.24 0.16 0.02 0.06 0.79 0.81 0.26 0.32 0.53 0.49 0.79 0.81 0.04 0.04 0.05 0.05 0.03 0.06 0.01 0.04 0.00 0.03 0.02 0.03 0.01 0.01 0.00 0.03
Page 130 of 376 Table 6-7: Pond 1 water quality parameters for the duration of the sampling regime. Results displayed are means of three replicates within the pond, with SE’s shown in parentheses. n = 3
23 Jul 11 Aug 24 Aug 15 Oct 3 Nov 17 Nov 1 Dec 16 Dec 7 Jan 04 04 04 04 04 04 04 04 05 TP 0.773 0.366 0.549 0.215 0.642 0.379 0.682 0.453 0.509 (mg L-1) 0.084 0.139 0.049 0.025 0.079 0.113 0.020 0.013 0.005 Other-P 0.763 0.334 0.385 0.097 0.568 0.357 0.575 0.226 0.383 (mg L-1) 0.087 0.141 0.054 0.024 0.082 0.117 0.026 0.017 0.000 PO4-P 0.010 0.032 0.164 0.119 0.074 0.022 0.107 0.226 0.126 (mg L-1) 0.004 0.002 0.010 0.001 0.004 0.005 0.010 0.004 0.005 TN 0.617 0.594 0.532 0.535 0.714 1.158 0.914 1.344 1.091 (mg L-1) 0.060 0.215 0.037 0.039 0.011 0.052 0.014 0.001 0.039 Org-N 0.499 0.348 0.436 0.508 0.384 1.037 0.573 0.638 0.447 (mg L-1) 0.053 0.384 0.052 0.035 0.016 0.037 0.054 0.049 0.031 NOx-N 0.101 0.017 0.019 0.000 0.061 0.085 0.034 0.502 0.501 (mg L-1) 0.000 0.001 0.001 0.000 0.012 0.029 0.012 0.037 0.012 NH4-N 0.017 0.229 0.077 0.027 0.269 0.036 0.306 0.204 0.142 (mg L-1) 0.009 0.215 0.014 0.009 0.002 0.015 0.040 0.014 0.019 DOC n/d 4.989 9.991 14.067 16.680 17.807 11.471 18.085 11.260 (mg L-1) 0.499 0.333 0.523 0.865 0.323 0.859 0.188 2.067
Table 6-8: Pond 5 water quality parameters for the duration of the sampling regime. Results displayed are means of three replicates within thepond, with SE’s shown in parentheses. n = 3
23 Jul 11 Aug 24 Aug 15 Oct 3 Nov 17 Nov 1 Dec 16 Dec 7 Jan 04 04 04 04 04 04 04 04 05 TP 0.141 0.049 0.273 0.140 0.573 0.551 0.433 0.830 0.326 (mg L-1) 0.013 0.016 0.005 0.008 0.038 0.137 0.035 0.092 0.088 Other-P 0.141 0.036 0.222 0.115 0.411 0.279 0.247 0.458 0.143 (mg L-1) 0.013 0.017 0.006 0.007 0.048 0.162 0.042 0.109 0.088 PO4-P 0.000 0.013 0.051 0.025 0.163 0.272 0.186 0.373 0.184 (mg L-1) 0.000 0.001 0.002 0.004 0.011 0.025 0.018 0.018 0.002 TN 0.824 0.808 0.507 0.339 1.307 1.291 1.246 1.160 0.852 (mg L-1) 0.052 0.024 0.021 0.011 0.045 0.015 0.053 0.016 0.180 Org-N 0.113 0.239 0.098 0.195 1.215 1.224 0.917 1.014 0.366 (mg L-1) 0.053 0.179 0.022 0.033 0.085 0.020 0.059 0.034 0.170 NOx-N 0.145 0.105 0.033 0.008 0.000 0.010 0.001 0.038 0.206 (mg L-1) 0.002 0.011 0.003 0.008 0.000 0.005 0.001 0.008 0.011 NH4-N 0.567 0.464 0.377 0.135 0.092 0.057 0.329 0.108 0.280 (mg L-1) 0.002 0.195 0.007 0.017 0.042 0.015 0.020 0.016 0.003 DOC n/d 5.011 9.069 8.783 10.127 22.283 14.227 15.290 14.477 (mg L-1) 1.007 0.099 0.810 0.043 0.310 0.130 0.481 0.187
Nitrogen Figure 6-12 displays TN concentration within the pelagic zones of Ponds 1 and 6 as stacked bars of Org-N, NH4-N, and NOx-N concentration, effectively showing the contribution of each N species to the TN concentration of the water sample. As shown in Figure 6-12, Org-N dominated TN concentration, with the exception of profiles taken between the months June to August in Pond 6 where NH4-N was dominant, and profiles taken on the 4th of February in both Ponds 1 and 6 where NOx
Page 131 of 376 was dominant. Moving to the littoral zones of Ponds 1 and 6, Figure 6-13 displays stacked bars of Org-N, NH4-N, and NOx-N concentration. TN concentration. Org-N dominated the TN pool in both ponds for the entire study period, with the exception of
Pond 5 between the months of July and August, where NH4-N concentrations dominated.
Figure 6-12: N speciation within the upper 20cm and lower 20cm of pelagic zones within
Ponds 1 and 6 presented as PO4-P and Other-P stacked bars.
Page 132 of 376 Phosphorus Concentrations of phosphorus varied considerably between fortnightly sampling, with TP being dominated by Other-P throughout the year. From around November through till March, PO4-P contributes significantly to the TP concentration of each fortnightly sampling regime. However, between April and October, TP concentrations were predominantly Other-P, with very little to no PO4-P within the water samples (Figure 6-14). As indicated by the extremely low SE values within Tables 6-3 and 6-4, phosphorus concentrations did not, with the exception of one fortnightly sampling, differ significantly between the upper and lower water stratums in Pond 1 or Pond 6. Within the littoral zone, Other-P dominated the TP pool in both ponds throughout the duration of the entire sampling regime (Figure 6-13). Within the littoral zone of Pond 5, TP concentrations were greater during the summer sampling fortnights than that of the winter/spring fortnightly sampling. TP concentrations in the littoral zone of Pond 1 did not exhibit any noticeable temporal variation.
Carbon Dissolved organic carbon (DOC) concentrations within Ponds 1 and 6 is displayed graphically in Figure 6-15, with concentrations ranging between 3.9 and 22.2 mg L-1. DOC concentration was statistically similar between the upper and lower water stratums in both ponds (as indicated by extremely low SE, Table 6-3 and 6-4), and varied little between ponds. DOC concentration did, however follow a similar fortnightly trend to that of TN. DOC within the littoral zones of Ponds 1 and 5 ranged from 4.99 to 22.3 mg. L-1, again with no obvious trends between sampling dates and ponds.
Page 133 of 376 Figure 6-13: N and P speciation within the littoral zone of Ponds 1 and 5. Total height of bars indicated TN and TP concentrations respectively. Plotted values are the mean of three replicates.
Page 134 of 376 Figure 6-14: P speciation within the upper 20cm and lower 20cm of pelagic zones within
Ponds 1 and 6 presented as PO4-P and Other-P stacked bars.
Page 135 of 376 Figure 6-15: DOC concentration within the upper 20cm and lower 20cm of pelagic zones within (a) Ponds 1 and (b) Pond 6 over the course of the sampling regime.
6.4.3 Epiphyton
Standing crop – Schoenoplectus validus stems Tables 6-9 and 6-10 display the results from Chlorophyll a and AFDW biomass calculations on epiphyton scraped from Schoenoplectus validus stems in Ponds 1 and 5 respectively. One point to note from these tables is that the surface area calculations for both Chlorophyll a and AFDW are shown as that allocated from the collected stems of Schoenoplectus validus for the various analysis’. Thus, the total surface area sampled for epiphyton equals that of the surface area displayed in Columns 3 and 6 of each Table. Using each of the sample sites in Ponds 1 and 5 as replicates, the change in epiphyton biomass over time is shown in Figure 6-16. Additional to changes in biomass, Figure 6-16 also displays the changing mean Autotrophic Index. As separate measures of biomass, AFDW was generally higher than Chlorophyll a in both Ponds, with the exception of 3 sampling dates in both ponds 1 and 5. The Autotrophic Index, shown in red on Figure 6-16 shows this temporal variation in epiphyton biomass, reaching peaks when Chlorophyll a concentration was low on the 3rd November in Pond 1 and on the 1st of December within Pond 5. Nutrient concentrations within the water column of the littoral zones of Ponds 1 and 5 showed limited relationship to epiphyton biomass (Table 6-11), restricted to Pond 5/6 epiphyton and NOx-N, TP, PO4-P and Other-P nutrient concentrations.
Page 136 of 376 Growth rate to standing crop biomass – Glass slides From the glass slide biomass measurements of epiphyton within Ponds 1 and 6 (Table 6-12), epiphyton growth in Ponds 1 and 6 indicated a sigmoid relationship (Figure 6- 17). Standing crop biomass of epiphyton, as measured using Chlorophyll a was JUHDWHVW LQ 3RQG GXULQJ VXPPHU DW ȝJ &KO a cm-2, with Pond 1 biomass not H[FHHGLQJȝJ&KOa cm-2 in either the summer or early spring sampling.
Table 6-9: Correlation analysis between epiphyton biomass within Ponds 1 and 5 and nitrogen and phosphorus water column concentrations
TN NOx-N NH4-N TP PO4-P Other-P Pond 1 5 1 5 1 5 1 5 1 5 1 5 AFDW n/r n/r n/r n/r n/r n/r n/r n/r n/r n/r n/r n/r Chlorophyll a n/r n/r 0.26 n/r n/r n/r n/r 0.45 n/r 0.49 n/r 0.33
Table 6-10: Epiphyton biomass as determined using Chlorophyll a and AFDW biomass within Pond 1.
Chlorophyll a AFDW Sample Site Surface Chlorophyll Biomass Surface AFDW Biomass date area a Chlorophyll a area AFDW AFDW (cm2) (mg) (mg cm-2) (cm2) (g) (mg cm-2) 23/07/2004 1 803 8.6 0.044 604.9 0.453 0.749 2 341 8.4 0.105 260.9 0.461 1.767 3 710 18.4 0.149 587.1 0.690 1.175 11/08/2004 1 888 2.3 0.012 698.6 0.605 0.866 2 376 16.9 0.191 287.6 0.426 1.481 3 546 89.1 0.620 402.0 0.179 0.445 24/08/2004 1 326 32.0 0.339 231.8 0.317 1.368 2 890 14.7 0.105 750.2 1.013 1.350 3 519 24.3 0.094 261.2 0.201 0.769 15/10/2004 1 502 162.5 2.007 421.1 0.289 0.686 2 1011 71.5 0.380 822.8 0.460 0.559 3 757 117.0 1.193 621.9 0.375 0.623 3/11/2004 1 564 80.4 0.858 469.9 0.342 0.728 2 642 14.7 0.065 415.3 0.452 1.088 3 527 29.0 0.219 394.3 0.304 0.771 17/11/2004 1 749 0.6 0.004 572.2 0.664 1.160 2 1202 9.6 0.045 991.1 0.961 0.970 3 339 13.6 0.083 174.4 0.299 1.714 1/12/2004 1 502 264.1 5.830 456.5 0.342 0.749 2 724 186.0 2.182 638.5 0.450 0.705 3 613 225.1 4.006 547.5 0.396 0.727 16/12/2004 1 486 79.6 1.142 416.8 0.238 0.571 2 499 149.7 2.709 443.4 0.426 0.961 3 493 114.6 1.926 430.1 0.332 0.766 7/01/2005 1 482 12.0 0.153 403.5 0.325 0.805 2 449 17.5 0.078 223.1 0.326 1.461 3 466 14.8 0.115 313.3 0.326 1.133
Page 137 of 376 Table 6-11: Epiphyton biomass as determined using Chlorophyll a and AFDW biomass within Pond 5.
Chlorophyll a AFDW Sample Site Surface Chlorophyll BIOMASS Surface AFDW of BIOMASS date area a Chlorophyll area epiphyton AFDW of a AFDW epiphyton (cm2) (mg) (mg cm-2) (cm2) (g) (mg cm-2) 23/07/2004 1 271 65.8 0.732 181.2 0.297 1.639 2 290 25.6 0.345 216.1 0.142 0.657 3 335 31.9 0.343 242.5 0.247 1.019 11/08/2004 1 219 15.7 0.305 167.0 0.163 0.976 2 243 87.9 1.535 185.3 0.150 0.810 3 277 47.7 0.587 196.0 0.159 0.811 24/08/2004 1 307 77.7 0.538 163.0 0.142 0.871 2 243 72.3 0.414 68.3 0.093 1.361 3 293 88.2 0.703 167.7 0.277 1.652 15/10/2004 1 278 77.6 0.701 167.9 0.270 1.608 2 307 68.1 0.728 213.5 0.365 1.710 3 329 61.4 0.485 202.2 0.201 0.994 3/11/2004 1 308 68.6 1.339 257.0 0.336 1.307 2 285 65.7 0.743 197.0 0.329 1.670 3 310 51.4 0.663 232.2 0.360 1.551 17/11/2004 1 272 93.6 1.890 222.3 0.161 0.724 2 885 103.2 0.507 681.2 0.176 0.258 3 170 131.9 3.034 126.2 0.134 1.062 1/12/2004 1 302 1.8 0.037 253.5 0.419 1.653 2 304 1.5 0.084 285.9 0.457 1.598 3 330 2.0 0.026 253.7 0.215 0.847 16/12/2004 1 511 96.0 0.990 414.4 0.283 0.683 2 456 198.6 3.847 404.2 0.327 0.809 3 431 208.4 7.297 402.9 0.530 1.315 7/01/2005 1 667 200.4 2.843 596.3 0.520 0.872 2 769 104.5 1.904 713.7 0.617 0.864 3 629 167.3 3.060 574.6 0.616 1.072
Table 6-12: Biomass measurements of epiphyton colonising glass slides in Pond 1 and 6. Values shown are the mean of 3 repliFDWHV 8QLWVLQȝJ&KOa. cm-2. Exposure 15-Sept-04 20-Jan-05 time (d) Pond 1 Pond 6 Pond 1 Pond 6 Mean SE Mean SE Mean SE Mean SE 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 2 0.000 0.000 0.027 0.047 0.080 0.010 0.083 0.030 3 0.018 0.005 0.048 0.014 0.100 0.001 0.086 0.012 4 0.050 0.018 0.049 0.009 0.110 0.024 0.260 0.004 5 0.082 0.020 0.072 0.016 0.150 0.045 0.738 0.021 6 0.095 0.041 0.085 0.071 0.209 0.058 1.100 0.012 7 0.104 0.009 0.220 0.099 0.306 0.099 1.990 0.098 8 0.109 0.005 0.333 0.127 0.432 0.008 3.100 0.120 9 0.201 0.040 1.030 0.134 0.555 0.012 3.440 0.114 10 0.300 0.098 1.954 0.342 0.601 0.090 4.001 0.096 11 0.412 0.111 3.032 0.200 0.647 0.078 4.360 0.156 12 0.530 0.095 3.534 0.020 0.701 0.160 4.589 0.200 13 0.595 0.178 4.054 0.600 0.786 0.134 4.900 0.267 14 0.638 0.064 4.146 0.548 0.796 0.156 4.983 0.600 15 0.675 0.124 4.200 0.450 0.805 0.111 5.023 0.324
Page 138 of 376 Figure 6-16: Changing epiphyton biomass within (a) Pond 1 and (b) Pond 5 over a 6 month period. Values displayed are the means of three replicates (Sites 1, 2, and 3 within each pond), with error bars showing standard error.
Page 139 of 376 Figure 6-17: Biomass measurements of epiphyton colonising glass slides in Pond 1 and 6. Dots displayed are the mean of 3 replicates, with SE bars.
6.4.4 Data reduction using multivariate analysis
Principal Component Analysis (PCA) and cluster analysis PCA was used to tease out the driving factors governing water quality within the pelagic and littoral zones of Ponds 1 and 5/6. To conduct this, Pond 1 and Pond 5/6 data was separated. Each respective data set included physicochemical and nutrient data from the upper and lower water stratums of the pelagic zone as well as data from the littoral zone. Data sets included mean values for each parameter using the three sampling sites within each zone of the ponds (pelagic and littoral), over the entire sampling regime. Additionally, total stormwater flow through Ponds 1 and 6 for each fortnightly sampling was included in each data set (data sourced from Chapter 5). A total of 53 samples for each measured variable were used for Pond 1 and Pond 5/6 PCA.
Pond 1
Using latent root criterion, 4 principal components were found to significantly influence the variability of the data within Pond 1. These principal components, shown in Table 6-13, explain 77.8% of the variability within data, with the first two components explaining 53.8%. When the PC loadings are projected on a space
Page 140 of 376 defined by the first two principal components (Figure 6-18), TN, Urban Runoff and DOC are shown to be the dominant factors controlling variability within the first PC, with Org-N, PO4-P, NOx-N, NH4-N and Temperature all contributing to the variability, but to a lessor extent. Factors shown as significant loading within PC1, all correlate strongly to runoff, as shown in Table 6-17. Thus the first principal component could be described, or termed, as being “event driven”. The second principal component, dominated by TP, Org-P PO4-P and the physicochemical parameters pH, Redox Potential and DO (Figure 6-18) could be described as “phosphorus driven”, given that pH, DO and Redox Potential all significantly influence the phosphorus cycle to some degree. The third PC, dominated by Org-N,
NH4-N, Other-P, TP, DO and pH could be described as “Organic N/Other-P driven”, due to the dominate influence of Org-N and Other-P as well as the high correlation between Org-P and TP, TP and pH variables (Table 6-16). PC 4, dominated by NH4- N and NOx-N, can be described as being DIN driven, for obvious reasons.
Table 6-13: Results for the extracted PC for Pond 1. Bold numbers indicate significant factor loadings.
Component 1 2 3 4 “Organic N / Descriptive name Other P “Event driven” “Phos. Driven” driven” “DIN driven” TN 0.898 0.013 0.236 0.129 ORG-N 0.698 -0.116 0.451 -0.059 NH4-N 0.445 -0.274 -0.317 -0.537 NOx-N 0.505 0.362 -0.128 0.638 TP -0.092 -0.640 0.675 0.173 OTHER-P -0.373 -0.521 0.696 0.069 PO4-P 0.611 -0.317 0.012 0.244 DOC 0.778 -0.001 0.304 -0.241 Temperature 0.607 0.258 -0.014 -0.306 Dissolved Oxygen 0.015 0.606 0.644 -0.244 Redox potential -0.404 0.756 0.209 0.183 pH -0.046 0.887 0.325 -0.117 Storm flow through pond 0.882 0.147 -0.166 0.204 Variance explained (%) 32.2 21.6 15.4 8.5 Cumulative (%) 32.2 53.8 69.2 77.8
Using all significant factor loadings within PC 1 and PC2, (which includes all significant factors for all 4 PC’s) a cluster analysis was preformed to assess temporal variability of data within Pond 1. Cluster analysis is a multivariate technique used to group data sets based on the characteristics they possess. Based on the entire data set pooled into monthly groups (Table 6-12), the temporal variation of water quality
Page 141 of 376 within Pond 1 was assessed using hierarchal cluster dendrograms (Figure 6-19). As can be seen from this figure, two clusters are formed at an Euclidean distance of approximately 160,000 essentially dividing the data into summer/wet and winter/dry month categories. Within the summer/wet cluster, the month of February separates at a Euclidean distance of approximately 5000, with the months of December, January and March being grouped much closer together. The month of November separated at a Euclidean distance of approximately 2500, being quite dissimilar to December, January March and February months. Looking at the data that was used to construct the dendrogram, it can be seen that the clusters are very much grouped according to Runoff volume, which was significantly correlated to all variables, with the exception of pH, Redox Potential, DO, and TP (Table 6-17).
Figure 6-18: Principal Component Analysis of measured water quality parameters in Pond 1 of the BWS System. First two principal components are shown, with principal component loadings of the water quality parameters.
Table 6-14: Pooled monthly data used to construct hierarchal cluster dendrogram for Pond 1.
Org- NH4- NOx- Org- PO4- TN N N N TP P P DOC Temp DO Redox pH Runoff Jan 1.43 1.06 0.11 0.26 0.42 0.22 0.20 10.58 26.44 2.47 7.9 6.76 11018 Feb 1.76 0.59 0.36 0.81 0.31 0.18 0.13 17.40 26.49 4.31 -49.5 7.03 17837 Mar 0.46 0.25 0.11 0.09 0.24 0.12 0.12 11.09 24.89 1.47 -102.6 6.78 10625 Apr 0.34 0.21 0.03 0.10 0.59 0.54 0.05 5.18 23.35 2.96 43.5 6.74 2168 Jun 0.25 0.09 0.05 0.11 0.43 0.38 0.05 5.32 20.20 2.81 102.8 6.63 4531 Jul 0.45 0.29 0.05 0.12 0.60 0.58 0.03 8.69 17.03 3.10 25.4 6.92 38 Aug 0.56 0.38 0.08 0.11 0.49 0.48 0.01 9.99 15.22 28.67 85.7 6.96 459 Sep 0.55 0.45 0.09 0.02 0.50 0.39 0.12 8.19 17.49 6.43 36.1 6.87 2587 Oct 0.71 0.68 0.02 0.00 0.40 0.36 0.05 14.60 24.70 4.50 -3.2 6.81 3387 Nov 1.36 1.19 0.13 0.04 0.68 0.55 0.13 18.47 25.24 13.13 -11.6 7.07 7275 Dec 1.34 0.81 0.30 0.22 0.59 0.38 0.20 15.59 25.40 4.86 -36.3 6.71 11422
Page 142 of 376 Figure 6-19: Dendrogram displaying Pond 1 hierarchal cluster analysis, based on all significant factor loadings within PC1 and PC2 PCA table (Table 6-11).
Pond 6 As was the case with Pond 1, 4 principal components were found to significantly affect the variability of data within Pond 6 on a spatial and temporal scale (using latent root criteria). These principal components, shown in Table 6-15, explain 74.7% of the variability within data, with the first two components explaining 50.6%. When the PC loadings are projected on a space defined by the first two principal components (Figure 6-20), Temperature, DOC, PO4-P, TP and Org-N are displayed as the significant dominate factors for PC1 with NOx-N, Other-P, and TP being the main significant loadings for PC 2. Much like Pond 1, PC 1 can be described, or termed, as “event driven”, with all significant loadings strongly associated with storm flow through Pond 6 (Table 6-18). Strongest factor loadings within PC 2 were TN, TP,
Other-P, NOx-N and Redox Potential, with a strong correlation between TP and PO4- P (see Table 6-18). Thus, PC 2 can be described as being “TN/TP driven”. With loadings dominated by DIN and physicochemical parameters pH and Redox Potential, PC 3 can be described as “DIN driven”. With only two significant factor loading associated with PC 4 (NH4-N and pH), “NH4-N/pH driven” appears to be a suitable descriptive name.
Page 143 of 376 Using all the significant factors within all extracted PC (Table 6-15), cluster analysis was preformed to assess the temporal variability of data within Pond 6. Using the entire data set pooled into monthly groups (Table 6-16), the temporal variation of water quality within Ponds 6 has been assessed using hierarchal cluster dendrograms (Figure 6-21).
Table 6-15: Results for the extracted PC for Pond 6. Bold numbers indicate significant factor loadings.
Component 1 2 3 4 “TN/TP Descriptive name “Event “DIN driven” driven” driven” TN 0.581 0.545 -0.266 -0.135 ORG-N 0.740 0.432 0.037 -0.144 NH4-N -0.268 -0.045 -0.489 0.707 NOx-N 0.128 0.544 -0.396 0.026 TP 0.708 -0.595 0.185 -0.010 OTHER-P 0.255 -0.686 0.344 -0.300 PO4-P 0.823 -0.204 -0.081 0.292 DOC 0.746 0.156 0.149 0.192 Temperature 0.714 0.160 0.446 0.103 Dissolved Oxygen -0.283 0.432 0.691 0.174 Redox potential -0.597 0.518 0.247 -0.306 pH -0.065 0.256 0.804 0.377 Storm flow through Pond 0.702 0.375 -0.201 -0.158 Variance explained (%) 32.5 18.2 15.9 8.1 Cumulative (%) 32.5 50.6 66.6 74.7
Figure 6-20: Principal Component Analysis of measured water quality parameters in Pond 6 of the BWS System. First two principal components are shown, with principal component loadings of the water quality parameters.
Page 144 of 376 As depicted by the Pond 6 dendrogram (Figure 6-21), Pond 6 divides into two clusters quite early. These two clusters separate the summer months of December, January, February and March from the rest of the year, with December becoming isolated from the cluster at a Euclidean distance of 1000 – primarly due to increased volume of stormwater flow through the pond (see Table 6-18). Looking at the remaining months, July and August separate and form an isolated cluster away from October, September, November, April and June at an Euclidean distance of approximately 2000 – again most likely due to the limited storm flow through Pond 6 during these months. Like Pond 1 the clusters formed in the dendrogram are very much grouped according to stormwater flow through Pond 6.
Table 6-16: Pooled monthly data used to construct hierarchal cluster dendrogram for Pond 6.
Org- NH4- NOx- Org- PO4- TN N N N TP P P DOC Temp DO Redox pH Runoff Jan 1.44 1.37 0.24 0.12 0.58 0.26 0.32 13.32 26.84 2.44 58.70 6.83 7225 Feb 1.33 0.69 0.11 0.52 0.26 0.09 0.17 17.64 25.22 3.14 114.71 6.98 7329 Mar 0.47 0.36 0.03 0.08 0.20 0.09 0.11 10.31 24.54 5.39 133.33 6.94 6631 Apr 0.77 0.21 0.03 0.10 0.51 0.50 0.00 7.23 23.52 5.66 149.00 7.06 1754 Jun 0.40 0.12 0.03 0.11 0.36 0.36 0.00 4.70 18.68 4.74 192.75 6.75 2002 Jul 0.26 0.22 0.14 0.10 0.40 0.40 0.00 5.85 16.42 4.02 90.50 7.10 9 Aug 0.48 0.11 0.48 0.13 0.16 0.16 0.00 6.35 15.18 6.17 151.86 6.92 151 Sep 0.39 0.13 0.31 0.07 0.13 0.10 0.03 7.24 17.24 5.53 185.65 7.02 2495 Oct 0.26 0.29 0.04 0.00 0.15 0.12 0.04 10.12 23.47 3.37 137.24 7.25 1129 Nov 0.67 0.80 0.06 0.00 0.46 0.32 0.13 9.70 25.62 11.02 194.93 7.71 2996 Dec 0.76 0.90 0.09 0.01 0.67 0.35 0.32 17.04 25.50 4.77 53.99 7.18 9055
Figure 6-21: Dendrogram displaying Pond 6 hierarchal cluster analysis, based on all significant factor loadings within PC1 PC2, PC3 and PC4 (Table 6-13).
Page 145 of 376 Table 6-17: Pearson correlation matrix for Pond 1. n= 53. Significant correlations indicated in bold (p < 0.05).
Red. TN Org-N NH4-N NOx TP Other-P PO4-P DOC Temp. DO Pot. pH Runoff TN 1 Org-N 0.860 1 NH4-N 0.269 0.059 1 NO-X 0.527 0.078 -0.001 1 TP 0.019 0.162 -0.071 -0.209 1 Other-P -0.181 0.008 -0.171 -0.324 0.901 1 PO4-P 0.441 0.353 0.214 0.234 0.304 -0.139 1 DOC 0.712 0.632 0.384 0.248 0.067 -0.054 0.271 1 Temp. 0.381 0.290 0.279 0.180 -0.163 -0.352 0.400 0.384 1 DO 0.113 0.181 -0.202 0.023 0.017 0.073 -0.121 0.208 0.223 1 Red. Pot. -0.298 -0.317 -0.447 0.125 -0.215 -0.054 -0.373 -0.308 -0.064 0.483 1 pH 0.020 -0.026 -0.246 0.205 -0.332 -0.209 -0.301 0.104 0.207 0.738 0.721 1 Runoff 0.719 0.441 0.316 0.630 -0.215 -0.456 0.512 0.639 0.526 -0.074 -0.192 0.010 1
Table 6-18: Pearson correlation matrix for Pond 6. n= 53. Significant correlations indicated in bold (p < 0.05).
Red. TN Org-N NH4-N NOx TP Other-P PO4-P DOC Temp. DO Pot. pH Runoff TN 1 Org-N 0.771 1 NH4-N -0.014 -0.226 1 NOx 0.439 0.135 0.093 1 TP 0.079 0.272 -0.192 -0.175 1 Other-P -0.113 -0.006 -0.264 -0.315 0.763 1 PO4-P 0.244 0.426 -0.018 0.057 0.743 0.135 1 DOC 0.375 0.578 -0.224 0.029 0.346 -0.014 0.544 1 Temp. 0.303 0.496 -0.397 0.003 0.435 0.094 0.565 0.636 1 DO -0.078 0.004 -0.069 0.002 -0.231 -0.067 -0.290 -0.157 0.110 1 Red. Pot. -0.138 -0.165 -0.133 0.052 -0.622 -0.348 -0.594 -0.368 -0.236 0.497 1 pH -0.114 0.037 -0.134 -0.091 -0.045 0.033 -0.112 0.154 0.311 0.692 0.187 1 Runoff 0.575 0.620 -0.196 0.344 0.268 -0.102 0.520 0.420 0.409 -0.125 -0.213 -0.154 1
Page 146 of 376 Pelagic vs. littoral zone nutrient concentrations.
Figure 6-22 displays a simple regression analysis between nitrogen concentrations within the pelagic and littoral zones of Ponds 1 and 5/6 to assess which zone supported the lowest concentration of nitrogen species. Within Pond 1, concentrations of nitrogen species were generally similar between the littoral and pelagic zones, however, concentrations of nitrogen species were consistently higher in the littoral zone of Pond 5/6 than the pelagic zone. Phosphorus concentrations, on the other hand, were generally less in the littoral zone of Pond 1 than in the pelagic zone (Figure 6-23). Within Pond 5/6 phosphorus concentrations were somewhat scattered, indicating that neither of the pond zones had a strong influence on the concentrations of phosphorus. DOC concentration within Ponds 1 and 5/6 did not appear to be influenced by the ‘zone’ (Figure 6-24).
Figure 6-22: Regression comparison of pelagic and littoral nitrogen concentrations within Ponds 1 and 5/6. Diagonal line represents a 1:1 relationship. Plotted dots represent the mean (n=3) within each pond zone per sampling date.
Page 147 of 376 Figure 6-23: Regression comparison of pelagic and littoral phosphorus concentrations within Ponds 1 and 5/6. Diagonal line represents a 1:1 relationship. Plotted dots represent the mean (n=3) within each pond zone per sampling date.
Figure 6-24: Regression comparison of pelagic and littoral DOC within Ponds 1 and 5/6. Diagonal line represents a 1:1 relationship. Plotted dots represent the mean (n=3) within each pond zone per sampling date.
Page 148 of 376 6.5 Discussion
6.5.1 Nutrient speciation within the BWC System
Nitrogen Nitrogen concentrations within Ponds 1 and 6 were statistically similar between the upper and lower 20cm water sampling depths, most likely attributed to the lack of significant and prolonged water column stratification throughout the duration of the study. As displayed in Figure 6-8, a surface mixing layer was present during most months of the study but did not exhibit any influence on the concentration of measured nutrients. Water column stratification is generally a result of thermal stratification, which is usually restricted to deep water bodies greater than 5-10m (Wetzel 2001; Sigee 2004). Water temperature exhibited seasonal variation, displaying a significant relationship to the concentration of TN, Org-N and NH4-N in both ponds within the pelagic and littoral zones. TN concentration was dominated by Org-N most of the time in both ponds (pelagic and littoral zones). Compounds and substances that contribute to the Org-N pool within wetlands and ponds include both dissolved (urea proteins and nucleic acids) and particulate forms (living and dead biota) (Kadlec et al. 2000; Wiegner and Seitzinger 2004). In eutrophic ponds and wetlands throughout Australia and the world, TN concentration is often dominated by Org-N, with much of the Org-N fraction being that of insoluble particulate living and dead biota (Gordon et al. 1981; Lund and Davis 2000; Coveney et al. 2002; Lau and Lanc 2002; Lau and Lane 2002; Ruley and Rusch 2002). The Org-N contribution to the overall nitrogen pool in freshwaters increases with its trophic status, from oligotrophic through to hyper eutrophic, possibly caused by the enhanced presence of the productive algae cells as a result of the increases in trophic status. Hydraulic loads into the BWC System increased water column TN concentration. Not surprising.
Between the months July – August, TN concentration within the pelagic zone of Pond
6 and the littoral zone of Pond 5 was dominated by NH4-N (refer to Figure 6-12 and 6-13), with little contribution of Org-N and NOx-N to the overall TN pool. To recap,
Page 149 of 376 + the NH4 pool within aquatic environments is controlled by Org-N mineralization
(increasing NH4-N), nitrification (reducing NH4-N), direct biotic uptake by + phytoplankton and bacteria (reducing NH4-N), and direct inputs of NH4 into the system from autochthonous and allochthonous sources. The enhanced NH4-N concentration observed in Ponds 5 and 6 between June and August could be explained by the combination of a number of interrelated factors;
Firstly, NH4-N was found to increase in concentration with decreased Org-N concentration between June and August, (negative linear correlation; Pond 6 2 2 r 0.05 (1), 10= 0.55 and Pond 5 r 0.0005(1), 3 = 0.97). During the other months, no significant relationship was observed. This relationship suggests that the mineralization of Org-N between June and August in Pond 6 has a direct
impact on the NH4-N pool within Pond 6 – a reported relationship in ponds, wetlands and saturated sediments (Burford and Lorenzen 2003; Cook et al. 2004; Fong et al. 2004).
Secondly, low redox potential within Ponds 5 and 6 over the course of the
study would have reduced the loss of NH4-N through nitrification. As shown in Figure 6-10, the redox potential of pond water throughout the sampling regime was consistently below the nitrification range, thus limiting the productivity of the bacterium responsible for nitrification (Zhu and Sikora 1995; Wieβner et al. 2005).
Thirdly, phytoplankton uptake of NH4-N is preferred over that of NOx-N (Vymazal 1995). Thus one may expect a higher concentration of NOx-N than
NH4-N in a system dominated by pelagic zones inhabited by productive phytoplankton communities. Figure 6-25 displays phytoplankton biomass as a
function of NH4-N concentration within the water column of Pond 6 and littoral zone of Pond 5 between June and August. It can be seen that increased
phytoplankton biomass results in decreased NH4-N concentration, possibly
because the enhanced removal of NH4-N results in reduced background
concentrations of NH4-N within the water column. Algal control of water
column nutrients and their preferential uptake of NH4-N have been reported
Page 150 of 376 extensively in the literature (Elser et al. 2002; Kobayashi and Church 2003; Domingues et al. 2005).
Figure 6-25: Correlation analysis between phytoplankton biomass and NH4-N concentration within the littoral and pelagic zones of Pond 5/6 in July and August. Phytoplankton biomass data taken from Chapter 7 Regression line showing exponential decay, single 3 parameter relationship.
When comparing TN, Org-N, NOx-N and NH4-N concentrations between the littoral and pelagic zones of Ponds 1 and 5/6, there was no clear indication of reduced concentrations of any of the nitrogen species within Pond 1. Nitrogen species did, however, show marked reductions (when compared to concentrations within the littoral zone) in the pelagic zone of Pond 5/6. Such a reduction was somewhat unexpected and can possibly be attributed to the sampling procedures. As the location of the littoral zone within Pond 6 proper was not achievable (see methods section), the location of the littoral sampling sites were located within Pond 5 – slightly upstream of Pond 6. It is possible the concentration of nitrogen within Pond 5 (both within the littoral and pelagic zones) was greater than that of Pond 6, thus giving the result shown in Figure 6-23.
Page 151 of 376 Phosphorus The TP pool measured throughout the sampling period within Ponds 1 and 6 was a result of the interrelated biotic and abiotic factors displayed in Figure 6-3. Having - only measured TP and PO4-P at all sampling sites, the difference in concentration - between TP and PO4-P would consist of particulate organic phosphorus (POP), particulate inorganic phosphorus (PIP), and organic phosphorus compounds. In this investigation, all these potential phosphorus compounds have been grouped together and reported as Other-P. Due to the high contribution of Other-P to the total concentration of TP within the pelagic and littoral zones of Ponds 1 and 5/6 (Other-P concentration contributing <95% to the TP concentrations between April and August within the pelagic zones of Ponds 1 and 6-see Figure 6-14), the behaviour of Other-P is of obvious importance. In a study on a eutrophic lake ecosystem, Coveney et al. (2002) reported similar results to this study, with the majority of TP concentration consisting of particulate organic phosphorus (POP), with Lund and Davis (2000) also reporting similar results from a eutrophic wetland in Western Australia.
There was no apparent correlation between hydraulic flow into the BWC System and TP concentration. However, on closer examination, the hydraulic loading into the
BWC System did influence the concentration of PO4-P within the pelagic and littoral zones (Table 6-15 and 6-16). The relationship between the Hydraulic loading and
PO4-P loading displayed an ‘exponential rise to maximum trend’, stating that increased hydraulic load into the BWC System increases the PO4-P concentration within the water column to a maximum point, then plateau’s somewhat. Thinking on a catchment level, the occurrence of rainfall and generation of stormwater will wash
PO4-P into the BWC System – as reported by many published research papers measuring the polluting nature of stormwater (Herricks 1995; Newman and Pietro 2001; Greenway 2002; Fink and Mitsch 2004). Thus one may expect that the ‘first- flush’ of stormwater would contain the highest concentration of pollutants and thus deliver a heaver load of PO4-P to receiving waters than from runoff generated later in the storm event (Kim et al. 2005; Soller et al. 2005). With continued flow of stormwater into a pond/wetland treatment system one could then expect the PO4-P concentration within the receiving water body would reach a maximum or threshold.
This could occur for a number of reasons. Firstly, the PO4-P load into Pond 1 may
Page 152 of 376 stimulate the productivity of the biotic community, and enhance the removal of PO4-P from the water column, with the biotic community reaching an optimum removal efficiency whereby it removes additional inputs of PO4-P into the system but makes - no gross reduction in the concentration of PO4-P within the water column. Many phytoplankton species responsible for PO4-P removal in pelagic ecosystems are capable of luxury P compensation. That is in nutrient enriched conditions, the uptake of PO4-P becomes greater than that required for cell growth, maintenance and reproduction. Phosphorus that has been taken up via luxury compensation is stored within the cells of the microbiota for use in nutrient limited situations (Currie and Kalff 1984; Sigee 2004). Thus hypothetically, the microbiota community within
Pond 1 may remove PO4-P from the water column at rates dependant on loads (i.e. the concentration of PO4-P within the water column equals the balance between PO4-P entering the system and PO4-P removed from the water column by microbiota luxury phosphorus consumption).
In all freshwater systems, the loss of PO4-P via adsorption and precipitation is very important, as is the input of PO4-P via desorption pathways (Newman and Pietro 2001; Aldous et al. 2005). Adsorption/desorption reactions, as well as precipitation reactions, involving PO4-P are governed by pH, redox potential, DO, and the occurrence and concentration of a range of inorganic cations. Within both the littoral and pelagic zones of Ponds 1 and 5/6 there were statistically significant correlations between PO4-P and the individual abiotic parameters Redox Potential, pH, and DO (Tables 6-15 and 6-16). These relationships, although varying in strength between ponds, suggests the importance of adsorption/desorption and precipitation reactions of
PO4-P on the biogeochemical cycling of phosphorus within stormwater ponds (Manahan 1994; Kadlec 1997, 1999).
It was beyond the scope of this investigation to look deeply into the adsorption/ desorption/ precipitation reaction of PO4-P. However from the abiotic data presented, it can be stated that PO4-P desorption in both Ponds 1 and 6 is likely due to anaerobic conditions typified by low DO concentrations and redox potential (see Figures 6-9 and 6-10). Additionally, the abiotic conditions within the ponds are also ideal, at certain times, for adsorption – most likely restricted to the aerobic water column when
Page 153 of 376 stormwater supplies provide a ready source of particulate sediment, and redox potentials are above that of -100mV.
PO4-P concentration within the littoral zone of Pond 1 was generally less than that for the pelagic zone, possibly due to the observed relationships between PO4-P concentrations and littoral water column Chlorophyll a biomass. Phosphorus concentrations within the pelagic and littoral zones of Ponds 5/6 did not show any marked differences. As for nitrogen, this result was somewhat unexpected and can possibly attributed to the sampling procedures adopted of the investigation.
Dissolved Organic Carbon The concentration of DOC within the pelagic and littoral zones of Ponds 1 and 5/6 varied in a similar pattern to that of N and P and did not show any spatial variation in relation to pelagic or littoral zones. Due to the unstratified nature of the ponds, little or no variation between sampling depths in either of the ponds was measured. The DOC concentration range within Ponds 1 and 5/6 was similar (3.6-20.3 mg.L-1 and 3.9-21.5 mg.L-1 in Ponds 1 and 6 respectively). Figure 6-5, shown earlier in the introduction, displays the DOC cycle within freshwaters. DOC concentration within the BWC System the result of DOC inputs (both allochthonous and autochthonous) and outputs (ecosystem metabolism, hydraulic flow, and sedimentation). The external supply (allochthonous) of DOC within the BWC System would be expected to be high, owing to the ready supply of DOC from urban catchments (Herricks 1995; Wiegner and Seitzinger 2004; Kohler et al. 2005). Allochthonous DOC entering the BWC System was found to be greatest during periods of high stormwater flow, showing a strong correlation (Tables 6-15 and 6-16), indicating that the concentration of DOC within the column of Ponds 1 and 6 is influenced strongly by stormwater flow into the system. Given that DOC pool within the water column of freshwaters is strongly influenced, both on an input and output and level, by both phytoplankton and bacterioplankton (and possibly viral plankton), the behaviour of these biotic groups would likely influence the concentration of DOC within the water column. For example, productive phytoplankton communities have been reported to exude appreciable amount of DOC directly from their cells (Coveney 1982; Coveney and Wetzel 1995), with heterotrophic organisms either directly consuming exudate
Page 154 of 376 sourced DOC or utilizing the DOC from the pool present within the water column (del Giorgio et al. 1997; Almeida et al. 2005; Kramer et al. 2005).
Over the course of the study, DOC never reached a concentration less than 3.0 mg.L-1, with concentrations exceeding 10 mg.L-1 over 50% of the time. In freshwater systems where DOC concentrations exceed 10 mg.L-1, bacterioplankton production has been found to dominate water column production (Jansson et al. 2000; Drahare et al. 2002) – with this being the likely scenario. Given the presumed high supply of allochthonous DOC entering the BWC System via stormwater runoff from the BWC catchment, bacterioplankton production can be presumed to be unlimited from an “organic carbon energy supply” perspective during and following times of hydraulic loading to the system. In a study of 11 North American and Scandinavian lakes, Jansson et al. (2000) reported that increased allochthonous input of DOC above 5mg.L-1 increased bacterioplankton production, but decreased phytoplankton production. Given that the average concentration of DOC within both Ponds 1 and 6 was above that of 5 mg.L-1 it is likely that the system is dominated by heterotrophic production most-of-the-time (a subject discussed further in Chapter 9). Drakare et al. (2002) found that the input of allochthonous carbon from high flow storm driven events increased the DOC within the water column and effectively altered the trophic status of the lake. The study reported that production in the subject lakes was bacterioplankton dominated for up to 20 days following a major flood event, when concentrations of DOC within the water column exceeded 15mg L-1. Following 20 days, the studied systems were autotrophic dominated. This phenomena was attributed by the authors to be DOC driven, with bacterioplankton production left ‘un- restricted’ due to the abundance of DOC and thus out competing phytoplankton for inorganic nutrients.
6.5.2 Driving factors determining water quality within the BWC System.
Having grouped all data generated within this chapter, along with select data from Chapter 4, multivariate statistical analysis’ highlighted driving factors governing water quality variation within the BWC System. Through Principal Component Analysis, approximately one third of the water quality variation exhibited within
Page 155 of 376 Ponds 1 and 6 over the duration of the study regime was attributed to stormwater flow and its associated nutrients inputs – as indicated by stormwater flow correlating significantly with that on the TN, Org-N, NH4-N, NOx-N, PO4-P and DOC concentration within Ponds 1 and 6 (Tables 6-17 and 6-18). The strong influence by stormwater flow into the BWC System on water quality variability is not surprising given that large input of nutrients the system receives from stormwater (as discussed in Chapter 5). Hierarchal cluster analysis preformed on Ponds 1 and 6 further displayed this result, showing the grouping of data in close accordance with monthly stormwater flow into and through the system. Within Pond 1, and to a lessor extend
Pond 6, the dynamic behaviour of phosphorus (TP, Other-P and PO4-P) and its strongly linked abiotic physicochemical water quality parameters (pH, Redox Potential and DO) played a significant role in governing water quality variation (explaining 21.6% and 18.2% of the variation within Ponds 1 and 6 respectively). This result highlights the close interrelationship between phosphorus and the abiotic physicochemical water quality parameters pH, Redox Potential and DO, suggesting the importance of adsorption/desorption reactions governing phosphorus dynamics within constructed ponds for the treatment of stormwater.
6.5.3 Epiphyton biomass
Epiphyton biomass colonising the stems of Schoenoplectus validus stems were found to be statistically different between Ponds 1 and 5 66% of the time. Epiphyton biomass was greater in Pond 5 than Pond 1, on a Chlorophyll a basis, three times over the course of the study period and, on a AFDW basis, one time (Table 6-19).
Table 6-19: Differences in epiphyton biomass between Ponds 1 and 5. Shaded column represent no difference, with significant differences determined using one way ANOVA with replication.
23 Jul, 11 Aug, 24 Aug, 15 Oct, 3 Nov, 17 Nov, 1 Dec, 16 Dec, 7 Jan, 04 04 04 04 04 04 04 04 05 Chl a Chl a AFDW AFDW Chl a Chl a P5>P1 P5>P1 P1>P5 P5>P1 P1>P5 P5>P1 p=0.048 p=0.023 p=0.048 p=0.014 p=0.02 p=0.002
Page 156 of 376 Changes in epiphyton biomass within the littoral zone of freshwaters is controlled by numerous factors. That is, light, nutrient supply (N, P, and C), herbivory, habitat space and location, water turbulence and death, decay and recruitment (Mosisch et al. 1999; Wetzel 2001; Flynn et al. 2002). From the range of correlation analysis performed (Table 6-9), Pond 1 nutrient and physicochemical parameters were, by and large, not found to significantly alter the biomass of epiphyton, nor was epiphyton biomass found to be significantly related to reducing the concentration of nutrients within the water column of the littoral zone. Increased water column NOx-N concentration was found to weakly correlate with increased epiphyton biomass (Chlorophyll a). The story is somewhat different in Pond 5. Epiphyton biomass on 3- the Schoenoplectus validus stems were linearly correlated with TP, Org-P and PO4 concentration within the littoral water column. Indicating the increase in epiphytic Chlorophyll a biomass with increased phosphorus concentration. This physiological response of epiphyton to increased phosphorus availability is supported by numerous studies world wide (McCormick and Scinto 1999), with many of the authors stating 3- the importance of epiphyton biomass in PO4 cycling within ponds and wetlands (Hwang et al. 1998; Havens et al. 1999; McCormick et al. 2001; Dodds 2003; Scinto and Reddy 2003; DeBusk et al. 2004; Larned et al. 2004). However, another study has found that epiphyton in eutrophic water bodies, like that of this study, can quickly 3- absorb PO4 from the water column, but then squander it (McCormick et al. 2001).
This can be compounded by the reduced rate of precipitation at high CO2 levels within the water column – often high in eutrophic water bodes as a result of high microbial respiration.
The glass slide experiment showed that epiphyton exhibited ‘sigmoid’ growth characteristics in both ponds, reaching a maximum standing crop of 0.805 and 5.02 mg Chl a cm-2 for Ponds 1 and 6 respectively (Figure 6-17). Compared to epiphyton biomass values measured on stems of submerged Schoenoplectus validus, these values are similar for Pond 1 but substantially higher for Pond 6. The increased epiphyton standing crop values reported in Pond 5/6 could possibly be attributed to the glass slides being subject to greater light intensities and reduced grazing pressure during their exposure time than that of the epiphyton colonising submerged stems of Schoenoplectus validus. This would have been likely due to the location of the glass slides within Pond 6 as location was not subject to the same biotic and abiotic
Page 157 of 376 variables as that of the submerged stems of Schoenoplectus validus stands within Pond 5. Nevertheless, the standing crop of epiphyton within Ponds 1 and 5/6 are within the range reported in the literature for subtropical lakes (Vymazal and Richardson 1995; Havens et al. 1999; Wetzel 2001; Laugaste and Lessok 2004).
The observed variation in epiphyton biomass within Ponds 1 and 5 over the study period is, especially in Pond 1, likely to be influenced by factors other than that measured in this investigation. These include light availability, herbivory, and death, decay and recruitment factors. Within Pond 5, phosphorus concentrations were shown to influence epiphyton biomass, but to what extent and for how long still remain unknown. From published literature, the cycling of phosphorus influenced by epiphyton in a short term compartment with quick uptake and release.
Page 158 of 376 6.6 Conclusions
Water column temperature within Ponds 1 and 6 showed seasonal temporal, rather than spatial variation within both ponds and statistically similar N, P, and C concentrations between the water sampling depths. Water temperature influenced TN and Org-N concentration within all sample sites except the littoral zone of Pond 1. TN concentration was dominated by Org-N within all ponds except during the winter + months, where NH4 dominated. Stormwater flow into Pond 1 increased TN concentration, with Pond 5/6 showing increased Org-N concentration under increased hydraulic loading. Within Pond 1, nitrogen species were similar in concentration between the littoral and pelagic zone, with neither zone exhibiting consistently reduced concentrations. The pelagic zone within Pond 5/6 on the other hand showed reduced concentrations of Nitrogen. However this may have been the result of the location of sampling sites. TP within both studied ponds was dominated by Other-P, with PO4-P within Pond 1 was closely linked to stormwater inflow, and the Redox Potential, pH and DO concentration of the water column – indicating the involvement of adsorption/desorption and precipitation reactions in the dynamics of phosphorus.
PO4-P concentration was generally less in the littoral zones of Pond 1 (in comparison to the pelagic zone), but similar in both zones in Pond 5/6. DOC within Ponds 1 and 5/6 did not show any spatial variation in relation to littoral or pelagic zones. Stormwater delivery into the BWC System did significantly increase the concentration of DOC within Pond 1, and to a lessor extent, Ponds 5/6.
Epiphytic biomass colonising the submerged stems of Schoenoplectus validus were statistically different between sampled ponds, but did not appear to be influenced by any of the physicochemical water quality parameters measured. Epiphytic biomass within Pond 5/6 showed close relationships within water column phosphorus concentrations, however their role in providing an environment for the reduced concentrations of N, P, or C was not shown.
Water quality within Ponds 1 and 5/6 of the BWC System was extremely variable on a temporal scale. Via multivariate analysis (PCA), driving factors governing water quality within Ponds 1 and 6 of the BWC System included hydraulic flow sourced from storm events within the catchments, phosphorus at its associated relationships
Page 159 of 376 with the abiotic water quality parameters Redox Potential, pH and DO concentration, and DIN. Given these ‘factors’ governing water quality variability, both ponds were statistically grouped (through hierarchal cluster analysis) in close accordance to the volume of urban stormwater flow to and within the system.
Page 160 of 376 7 Chapter7: Phytoplankton: Motivating factors determining biomass and community composition
Page 161 of 376 7.1 Abstract Phytoplankton occupy an important component of pond ecosystems. They provide a pathway for inorganic nutrient loss, re-oxygenate the water column and provide a food source for high trophic levels. Unfortunately, phytoplankton can create management issues within ponds – having the potential to grow unrestrained, resulting in blooms and a reduction in pond water quality. The aim of this chapter was two fold. Firstly to quantify spatial and temporal changes in phytoplankton biomass and species composition within Ponds 1 and 6 of the BWC System, and secondly to identify abiotic water quality parameters driving the presence and abundance of phytoplankton. Between April 2004 and January 2005, fortnightly water samples were taken in Ponds 1 and 6 for phytoplankton cell enumeration. Phytoplankton biomass was measured down the depth profile within Ponds 1 and 6 via chlorophyll a concentration using an in situ fluorescence probe calibrated to pond chlorophyll a extractions. To assess the influence of abiotic water quality parameters on phytoplankton biomass and community composition, Pond 1 and 6 data from the same sample locations was used (sourced from Chapter 6). Phytoplankton biomass within both ponds generally decreased with depth, as function of PAR. Owing to the consistent high concentrations of inorganic nitrogen and phosphorus in Ponds 1 and 6 and the lack of stratification within the ponds, phytoplankton biomass was found to be partly controlled on a temporal scale by inorganic molar N:P ratio. Phytoplankton community succession was absent within the BWC System, owing to the dynamic hydraulic behaviour system. Phytoplankton enumeration revealed many features of the phytoplankton community that would have been otherwise unknown through basic phytoplankton monitoring via chlorophyll a concentration. Only 1 cyanobacteria bloom occurred (Anabaena spp.) within the ponds, occurring in late summer/early autumn within Pond 1. Phytoplankton species commonly present within Ponds 1 and 6 were characteristic of eutrophic freshwaters, dominated by phytoplankton from families Euglenaceae, Scenedesmaceae, Oocystaceae and Desmidiaceae.
Keywords: Phytoplankton, Chlorophyll a, PAR, Community Composition, Abiotic
Page 162 of 376 7.2 Introduction
7.2.1 Phytoplankton communities and the importance of species succession.
The term “phytoplankton” simply refers to algae living within the pelagic zone water bodies. The name is of Greek origin, with phyto meaning plant, and plankton meaning wander (Boney 1975; Wetzel 2001). Phytoplankton are the most important group of primary produce’s on earth (Dawes 1981). Standing on the base line of the aquatic food web, phytoplankton’s dynamic behaviour is of significance in global and local biogeochemical cycling (Wilhelm et al. 2004). Europe and the U.S.A have long used phytoplankton and/or phytoplankton assemblages within freshwaters as environmental indicators, often being used for indicators of heavy metal presence, toxicity, and the eutrophication potential of a particular water body. Historically, phytoplankton assemblages of freshwater bodies were used as a criterion for the classical ‘oligotrophic-mesotrophic-eutrophic’ classification scheme (Naumann 1919 in Vymazal 1995;(Sutcliffe and Jones 1992). Phytoplankton community structure and dynamics within freshwater environments can also be an indication of ecosystem health, with some species from particular Divisions producing toxic exudates (Cyanophyta and Dinophyta), while other species can potentially indicate the trophic status of a given water body (species from the family Euglenaceae). On a more ecological sense, freshwater phytoplankton are important food sources for fish, crustaceans and zooplankton within the food web and are of critical significance as carbon-fixing and oxygenating organisms within an freshwater ecosystem (Entwisle et al. 1997).
Phytoplankton ecology in freshwater wetlands and shallow lakes provides a pathway for the investigation of changing environments (both aquatic and terrestrial), with results becoming applicable to communities of larger organisms that are not so readily or easily investigated (Moss et al. 2003). The life cycle of phytoplankton is relatively short. Thus in the space of a few weeks a phytoplankton community can undergo many generations, giving rise to the opportunity for the phytoplankton community to be appropriately ‘suited’ to their environment. Shifts in phytoplankton community structure and composition can be viewed as an outcome of the environment in which
Page 163 of 376 they live in, with shifts in species compositions and dominance often becoming reflective of certain abiotic and biotic aquatic conditions. For example, in a study on phytoplankton dynamics in a freshwater shallow lake, Moss et al. (2003) reported a number of observations in regards to phytoplankton biomass and species presence and ecosystem change. They reported that a water temperature increase of 3°C had little effect on total Chlorophyll a concentration and total phytoplankton biovolume, but significantly influenced community structure. Total biomass of species from the families Dinophyceae and Cryptophyceae decreased, along with cyanobacteria species while two genera of green algae increased in biomass. Moss et al. (2003) also reported that the presence of fish increased phytoplankton diversity, most likely through the removal of zooplankton grazers via fish predation – a result also reported by Rejas et al. (2005). Phytoplankton succession becomes an important aspect of freshwater ecosystems as they generally define the major microbial biomass within the environment and have a major impact on other freshwater biota (Sigee 2005). Factors that influence phytoplankton species succession include allogenic, autogenic and sequential factors. Allogenic factors being those environmental factors which the organisms have no control over. Autogenic factors being those that can be regulated by a significant degree by the phytoplankton themselves. And sequential factors being those such as translocations of populations, hydraulic influences and environmental modifications(Smayda 1983). Table 7-1 displays a list of the major Allogenic and Autogenic factors that control phytoplankton succession in freshwater environments.
Table 7-1: Specific allogenic and autogenic factors controlling phytoplankton community and species succession. Adapted from (Boney 1975; Smayda 1983; Horne and Goldman 1994; Wetzel 2001) Allogenic factors Autogenic factors Temperature Life cycle Light (PAR) Water quality Turbulence Ectocrines Anthropogenic substances Predation
Factors influencing phytoplankton succession are not mutually exclusive between the above groups, with one factor often influencing the other. For example, phytoplankton density will affect light penetration and distribution through the water column, as will phytoplankton grazing through the loss of DOM and soluble pigment residues. Furthermore, general water quality can be influenced by the hydraulic
Page 164 of 376 behaviour of shallow wetlands and ponds and environmental modifications and/or disturbances via water pollution.
The light environment within aquatic ecosystems is the most variable of all allogenic factors, having the ability to change on a scale of seconds (MacKenzie and Campell 2005). Light within the aquatic environment is measured using an irradiance sensor that measures the intensity of light between the 450 and 650nm wavelengths, and is reported as Photosynthetic Active Radiation (PAR). Due to the variability of light, it is common place to normalise data, presenting % PAR at specific depths (Kirk 1994). Phytoplankton response to PAR variability occurs on a number of different levels, dependant on species presence within an aquatic ecosystem and. Certain family groups of phytoplankton, like cyanobacteria, can metabolise under low light conditions out competing other phytoplankton species for nutrients. Alternately, low light conditions favour phytoplankton species that have alternative (non-oxygen involving) metabolism pathways. In contrast, high PAR can potential have a negative effect on phytoplankton populations by means of cell mortality via exposure to ultra violet radiation. Put simply, certain phytoplankton species are anatomically and physiologically adapted to low and high PAR conditions(Mur and Schreurs 1995). Light within the aquatic environment is linked intimately with water temperature. Water temperature not only controls the rate of metabolism within phytoplankton cells, but it can be of significance in the seasonal succession of phytoplankton communities (Raven and Geider 1988; Kirk 1994). In most freshwater environments, ambient water temperature fluctuates on a seasonal basis. The influence of seasonal temperature variation on phytoplankton populations has been reported by many authors, with major dominant taxa within a given water body shifting with increasing/decreasing water temperature. The effect of water temperature on phytoplankton succession can often be twofold. Firstly by changing the ambient water quality conditions, and secondly, by promoting water stratification and mixing within the water column. There is a general notion among early published scientific papers that phytoplankton species succession in temperate systems is temperature regulated – given other variables remain relatively constant (Goldman and Ryther 1976; Goldman 1977; (Smayda 1983; Sigee 2004).
Page 165 of 376 Water turbulence within freshwater ecosystems has been known to influence phytoplankton community structure and composition since the early 1970’s (White 1976; Schone 1970; Kemp and Mitch 1979). Recent studies have continued to report this, stating that the movement of water within an aquatic ecosystem has the likelihood of determining phytoplankton species dominance and succession (Leland et al. 2001; Leland 2003; Buyukates and Roelke 2005). Water turbulence effects on phytoplankton community not only include the mixing of the water column and associated changes in algal composition, but cellular damage caused by water sheering (i.e. loss of flagellates, cell rupture). In their study of a lowland, freshwater river-lake system, Bahnwart et al. (1999) reported that algal assemblages were dependant on water velocity and water depth and hypothesized that ecosystem recruitment of the phytoplankton species was more dependant on location and hydraulic state within the river/lake system than on individual algal species productivity. Buyukates and Roelke (Buyukates and Roelke 2005) reviewed many published papers on the influence of flow rates within given water bodies on phytoplankton biomass and assemblages noting the relationship between pulsed and continuous hydraulic inflow on zooplankton assemblages, water column nutrient concentrations and changes in phytoplankton community structure based on k- selected and r-selected species. In their review, and outcomes of their own research, Buyukates and Roelke (2005) concluded that pulsed hydraulic flows can alter phytoplankton dynamics by stimulating energy transfer up the food web, i.e. through greater zooplankton accumulation of phytoplankton biomass.
Certain species of phytoplankton and cyanobacteria are capable of secreting compounds (known as ectocrine substances) that are either favourable or unfavourable for growth. For example, in some cyanobacteria blooms in lakes, N2 fixation rates are high, increasing Fe demand and in turn inducing the secretion of hydroxamate siderchromes – a substance that binds Fe and suppresses other algal growth (Murphy 1976). Excretion of ectocrines is physiological and behavioural trait that has been reported in varies species from the Cyanobacterium, Bacillariophyta, Chlorophya and Dinophyta Divisions. Cellular excretion of ectocrines have varying influence over phytoplankton succession within freshwaters, with the amount of ectocrine produced often diluted and/or consumed by bacteria for there to be a species-succession-effect.
Page 166 of 376 The ability of any given water body to support phytoplankton growth will, in terms of basic water quality and nutritional characteristics, strongly affect what species occur there and to what degree they occur. Phytoplankton growth and cell maintenance requires a supply of inorganic nutrients, namely nitrogen, phosphorus, carbon, sulphur, potassium and magnesium and to a lessor extend calcium, iron, manganese, zinc, copper, sodium, molybdenum, cobalt, boron, silicon, vanadium, halogens (chlorine, iodine and bromine) and selenium. The concentrations of these inorganic nutrients have been shown to affect the structure and composition of phytoplankton communities on a number of differing levels. For example, increasing macronutrients, nitrogen and phosphorus, was shown to shift species dominance and overall biomass in mesocosom experiments conducted in Denmark (Gonzalez Sagrario et al. 2005), while phytoplankton assemblages in subtropical wetlands were found to significantly change in species composition and dominance but not in overall biomass with changing nutrient concentrations (Pan et al. 2000). Moving on from basic nutrient concentrations of given water bodies, the trophic state of a water body can influence phytoplankton species presence and succession. In O2 poor environments dominated by high organic loading (much like wastewater treatment ponds and wetlands) phytoplankton species capable of mixotrophic production can become dominant (Vymazal 1995).
Traditionally, predation of phytoplankton was generally thought to be the result of zooplankton grazing. However, studies in the past 10 years have provided detailed information demonstrating the viruses can, and do, have the ability to act as agents for phytoplankton mortality (Brussaard 2004). Phytoplankton mortality via viral lysis has not been extensively studied, and thus the role of viruses in the control phytoplankton populations in not known from an ecosystem standpoint. Predation loss via zooplankton grazing, however, has been extensively studied and is reported as one of the main primary factors in controlling/influencing phytoplankton growth (Kobayashi and Church 2003; Walks and Cyr 2004).
Page 167 of 376 7.2.2 Freshwater phytoplankton taxonomy
Phytoplankton communities are characterised by individual species presence and abundance. The presence or absence of individual phytoplankton species can be used as a basic indicator for environmental variables and ecosystem health. With regard to ecological consequences, phytoplankton species succession is the most fundamental variable in dealing with community succession (Smayda 1983). Phytoplankton species succession involves the pattern of occurrence of phytoplankton species over time of a particular waster body. Key factors in investigating phytoplankton community succession include species co-occurring, duration of time of occurrence of different species, species population maximum (=environmental carrying capacity), concurrent species population maximum, time from appearance to disappearance of species and the time of occurrence of differing species.
The taxonomic classification of algae at high levels of the classification system is somewhat ever-changing. When comparing algal species, taxonomic categories become useful from the Division level, and are relatively interchangeable with the changing high level classification schemes (Campbell et al. 1997; Entwisle et al. 1997). According to Day et al.’s (Day et al. 1995) system, there are 13 Divisions of algae present in Australian freshwaters. Of the 13 Division groups found within Australian freshwaters, 7 major Divisions were represented in the BWC System, as indicated by bold type in the list below, and introduced briefly following. • Bacillariophyta (Diatoms) • Chlorophya (Green algae) • Chrysophyta (Golden-Brown Algae) • Cryptophyta (Cryptomonads) • Cyanobacteria (Blue-green Algae) • Dinophyta (Dinoflagellates) • Euglenophyta (Eugleniods) • Glaucophyta • Phaeophyta (Brown Algae) • Prymnesiophyta (Haptophytes) • Raphidophyta • Rhodophyta (Red Algae)
Page 168 of 376 • Tribophyta (Yellow-green Algae)
Bacillariophyta (Diatoms) Diatoms exist as single cells, colonies or filaments always consisting of two overlapping silica shells (Plate 1). Having heavy silica cell walls, diatoms often face the problem of how to remain in the photic zone of waters, with many species evolving specific physical adaptations to enhance flotation (Plate 1). Numerous diatom species are known to possess heterotrophic metabolic capabilities, inhabiting environments of high organic content. A number of authors have found that eutrophic conditions can lead to a decreased diversity of diatoms, with cellular division
(mitosis) becoming very dependant on the concentration of dissolved silica (SiO2) within the environment.
Chlorophyta The ‘green algae’ (Plate 2) exist as single cells, colonies, filaments and complex structured plant-like organisms appearing ‘grass green’ in colour. The division Chlorophyta contains 560 genera and approximately 8000 species (although some authors state 20,000 species exist), most of which occupy almost all freshwater habitats.
Chrysophyta Previously termed Haptophyta, the Division Chrysophyta contains only one class, with the vast majority of species being unicellular flagellates (Plate 3). They almost exclusively exist in the pelagic zone of freshwater lakes, rivers and ponds. Chrysophyta algae are yellow and golden-brown, rarely being green in colour and have the unique characteristic of being able to form endogenous silicified resting stages (statospores). Chrysophyta algae are generally found in non-polluted habitats, being sensitive to changes in the water quality of their environment.
Page 169 of 376 Cryptophyta Almost all algae from the Cryptophyta Division are unicellular bi-flagellates being red, blue-green or olive-brown in colour (Plate 4). Cryptophyta within freshwaters are represented by 12 genera containing 100 species, and are particularly common in nutrient enriched ponds and lakes. Cryptophyta algal species possess an extremely efficient photosynthetic system, having numerous light harvesting pigments. Because of this, Cryptophyta algal biomass maxima are generally higher in winter months or in lower depths of the water column. Cryptophyta algal suffer form light stress and alter their position in the water column accordingly.
Cyanobacteria Cyanobacteria, often termed ‘Blue-Green Algae’, are single celled and colony forming organisms (Plate 5). By strict definition, cyanobacteria are more like bacteria as apposed to algae, having no membrane bound organelles, but are often termed ‘algae’ due to their ability to preform oxygenic (oxygen-evolving) photosynthesis. Cyanobacteria species inhabit a diverse range of habitats, with most species occurring in freshwaters. Key characteristics of cyanobacteria include the [some] species ability to fix atmospheric nitrogen, buoyancy regulation, and exude toxic compounds to combat resource competition and mortality via grazing. Slow moving surface waters are prime habitat for cyanobacteria.
Dinophyta Unicellular flagellates consisting of 2000 species inhabiting surface waters, approximately 90% of dinoflagellates (Plate 6) are restricted to the marine environment. Some 50% of all Dinophyta species do not have chloroplasts, and are thus heterotrophic. Additionally, many of the photosynthetic species of Dinophyta are facultatively heterotrophic and feed phagotrophically. Dense blooms of some species of Dinophyta can release toxins into the water environment, potentially killing fish and invertebrate species within the community.
Page 170 of 376 Euglenophyta Eugleniods are unicellular flagellates, generally green in colour, often with an obvious red ‘eyespot’ (Plate 7). Although many Eugleniods are photosynthetic, many have a tendency towards heterotrophic nutrition. Species from the genus Euglena can supplement photosynthesis via direct DOC uptake with other species reliant entirely on heterotrophic nutritional pathways (eg Peranema spp. and Entosiphon spp.). Eugleniods tend to dominant freshwater environments high in animal pollution and/or decaying organic matter.
7.2.3 Research aims, objectives and research questions The aim of this chapter was to identify dominant phytoplankton family groups within urban stormwater treatment ponds, and asses their spatial and temporal variability within the BWC System over a 12 month period.
Specific objectives of this chapter to; • Determine overall phytoplankton biomass change spatially and temporally within Ponds 1 and 6; • Determine phytoplankton community structure and composition within Ponds 1 and 6 of the BWC System, and investigate shifts in species dominance and community succession. • Investigate linkages between total phytoplankton biomass and community structure and composition succession with inorganic nitrogen, phosphorus and carbon concentration, PAR conditions within Ponds 1 and 6 and pond water pH, redox, temperature, and dissolved oxygen concentrations.
Key research questions were; • Are cyanobacteria present within the BWC System, and if so, do they pose a public health issue for urban stormwater treatment ponds? • What are the driving factors governing phytoplankton abundance within stormwater ponds? • Does the phytoplankton community affect the treatment of stormwater within the BWC System?
Page 171 of 376 7.3 Methodology
7.3.1 Field techniques
Phytoplankton biomass and community structure and composition was measured in Ponds 1 and 6 of the BWC System between Jan-04 and Jan-05. Phytoplankton biomass was measured on a fortnightly basis at six sites within the BWC System (Figure 7-1) to assess changes in phytoplankton communities within each pond on a spatial and temporal scale.
Pond 6 north Pond 1 west Pond 1 north Pond 6 west Pond 1 east Pond 6 east
Pond 1 Pond 6
Figure 7-1: Location of monitoring sites for phytoplankton biomass and species identification. Note: Biomass via direct cell counts and species identification was restricted to sites Pond 1 North and Pond 6 North (as indicated by yellow and red cross). Arrows indicate water flow through ponds.
In situ Fluorescence Percent fluorescence of water within Ponds 1 and 6 was measured at 20cm intervals down the depth profile at each of the 3 sites within the Ponds 1 and 6 (Figure 7-1) using an YSI SNODE 6600 multimeter equipped with a optical fluorescence probe (YSI 6025 Chlorophyll probe) Fluorescence probe. Each pond was sampled on the same day every fortnight, or thereabouts between January 2004 and January 2005. On each sampling fortnight, the YSI SNODE 6600 was lowered off the shaded side of an inflatable dingy, stopping at 20 cm depth interval for % Fluorescence measurements until the entire water column had been measured.
Page 172 of 376 Fluorescence probe calibration Fluorescence values obtained within Ponds 1 and 6 using the YSI SNODE 6600 were calibrated to field Chlorophyll a concentrations using 29 water samples taken randomly from the upper and lower water column of each monitoring site in Ponds 1 and 6 over the course of the sampling regime (Appendix C, Table A.C 1). Each water sample taken for Chlorophyll a analysis was obtained by lowering a 1L sampling bottle to the desired water depth and filling it with water. Upon collection, water was handled and processed in accordance to the methods outlined in Chapter 3. Each of the water samples collected for Chlorophyll a analysis were aligned with the corresponding % Fluorescence readings for that site and depth (at that time) taken using the YSI SNODE 6600. A correlation analysis was then preformed to assess the relationship between % Fluorescence and Chlorophyll a concentration within both Ponds 1 and 2 (Figure 7-2), with a significant correlation evident. The equation of the line-of-best-fit displayed on Figure 2 was then used to calibrate the YSI SNODE 6600 fluorescence probe to field conditions, and enable phytoplankton biomass to be reported as Chlorophyll a conFHQWUDWLRQ ȝJ&KOa L-1) as oppose to % Fluorescence .
Figure 7-2: YSI SNODE 6600 fluorescence probe calibration chart. Linear equation, y = 2 3.963x + 5.2988, r 0.0005(1), 27 = 0.867
Page 173 of 376 Phytoplankton cell counts In order to representatively assess the phytoplankton community within the water column of the BWC System, direct cell counts were undertaken in the upper and lower 20cm of the water column in Pond 1 North and Pond 6 North sampling sites. Each fortnight between Apr-04 to Jan-05, 1 L of water was collected (using water sampling method outlined in Chapter 3) and 100mL of water decanted into 120mL polycarbonate jars. 4mL of 25% glutaraldehyde was added to each jar (to achieve a final concentration of 1%) to act as a preservative for the phytoplankton cells contained within the water sample. Each water sample was then placed on ice in the field and transported back to the laboratory for storage in a refrigerator until analysis.
PAR measurement Photosynthetic Active Radiation (PAR) is the wavelength range between approximately 400-700nm and comprises of the visible spectrum of the solar radiation wavelength range and drives the photosynthetic activities of most phototrophic organisms (Kirk, 1995; Sigee 2005). PAR was measured at each of the three sampling sites within Ponds 1 and 6 of the BWC System using a LiCor light meter, with attached above water and below water spherical light sensors. PAR was measured at 20 cm intervals down the depth profile of all sampling sites, with displayed values being calculated as percent incident light using Equation 7-1.
PARa Io = ⋅ 100 (7-1) PARw
Where;
Io = Incident light at particular water depth, % -2 -1 PARa = PAR of air directly above water surface, µmols. quanta. m . sec -2 -1 PARw = PAR at specific water depths, µmols. quanta. m . sec
Both light sensors used were calibrated in December 2003 by LiCor, USA. Calibration is recommended every 2 years for accurate measurement of PAR.
Page 174 of 376 7.3.2 Laboratory techniques
Chlorophyll a extraction Chlorophyll a concentration was determined for the 29 water samples taken for the calibration of the YSI SNODE 6600 fluorescence probe. For specific details of the laboratory techniques undertaken for the determination of Chlorophyll a from waters of the BWC System refer to Chapter 3.
Phytoplankton enumeration The cell density of phytoplankton communities from Ponds 1 and 6 were determined by direct cell counts. The enumeration of the phytoplankton community was achieved using a Sedgwick-Rafter counting cell and a Nikon Alphaphot-2 YS2 compound microscope under 400x magnification. Upon identification, phytoplankton species were grouped according to Division and Family.
Preserved, refrigerated samples were agitated gently and 1mL extracted using a granulated pipette. Using 60mm glass cover slips placed diagonally across the Sedgwick-Rafter counting cell, 1mL of sample was slowly added and the cover slip moved to cover the entire counting cell. After 15 minutes settling time, phytoplankton were counted using the marketed squares on the Sedgwick-Rafter counting cell. A minimum of 100 cells from the one family group were counted, using Equation 7-2 to calculate the cell densities of the particular water sample, and Equation 7-3 was used to calculate the frequency of occurrence of phytoplankton within any given water sample from differing Family or Division groups.
C⋅ 1000mm3 cells/ mL = (7-2) ADF⋅ ⋅ where; C = number of organisms counted A = area of field, mm2 D = depth of field of Sedgwick-Rafter Counting Cell, mm F = number of fields counted
Page 175 of 376 n f.. f= ⋅ 100 (7-3) t where; f.f. = frequency of occurrence of phytoplankton family groups, % n = number of times specific phytoplankton family groups where represented in water sample, t = total number of phytoplankton family groups represented throughout duration of study.
7.3.3 Statistical analysis
Multivariate statistical analysis was performed on the data set generated in this chapter. Principal Component Analysis and Hierarchal Cluster Analysis were undertaken using statistical packages SPSS® 14.01 and SYSSTAT® 11. Total fortnightly stormwater flow entering Pond 1 and exiting Pond 6 from Chapter 4, along with fortnightly water quality results (TN, Org-N, NH4-N, NOx-N, TP, Other-P, PO4- P, DOC, water temperature, DO, Redox Potential and pH) from Chapter 6 are included in this analysis.
Page 176 of 376 7.4 Results
7.4.1 Fortnightly Chlorophyll a and PAR profiles
Chlorophyll a concentrations within Ponds 1 and 6 (derived from % Fluorescence readings using the YSI SNODE 6600 with calibration equation displayed in Figure 7- 2) are displayed in Table A.C 2a-b (located in Appendix C). Chlorophyll a concentration within Ponds 1 and 6 varied little between sampling sites, however varied substantially between sampling dates (Figure 7-3). Chlorophyll a FRQFHQWUDWLRQSHDNHGDWȝJ/-1 within Pond 1 in mid October, and approximately ȝJ/-1 in Pond 6 during March and June. Fortnightly variation of Chlorophyll a concentration was most evident between May and December in Pond 1 and February and June in Pond 6, as indicated by fluctuating concentrations between sequential sampling dates in Figure 7-3.
Figure 7-4 displays isopleth diagrams of Chlorophyll a concentration within Ponds 1 and 6 down the depth profile throughout the entire sampling regime. Depth variation with Chlorophyll a concentration in Ponds 1 and 6 generally followed a ‘decreasing with increasing depth’ trend, with the exception of two sampling dates in Pond 1 where Chlorophyll a concentration was greatest in the lower section of the water column (15-Oct-04 and 3-Nov-04).
PAR was measured down the depth profile at each sampling site within Ponds 1 and 6. Table A.C 3a-b (located in Appendix C) displays mean % incident PAR within Ponds 1 and 6 down the depth profile, with variance within each ponds displayed using S.E. Little difference was found between PAR at a particular water depth and the location within Pond 1 or 6. PAR did, however, vary between ponds and within ponds on a fortnightly basis. The data displayed in Table 6a and b is best viewed 3 dimensionally, using isopleth diagrams that display PAR change with depth over the sampling regime (Figure 7-4).
Page 177 of 376 Figure 7-3: Chlorophyll a concentrations (µg L-1), based on insitu fluorescence readings, within Pond 1 and 6 of the BWC System. Values shown represent mean of three sample locations within each pond taken on a fortnightly basis from January 2004 to January 2005. Shaded bars show total stormwater flow into Pond 1 and out of Pond 6 preceding phytoplankton sampling. Hydraulic data presented on graph has been taken from Chapter 4.
7.4.2 Total cell counts
Table A.C 4 (located in Appendix C) and Figure 7-5 displays total phytoplankton cell density per sample date in the upper and lower water stratum of Ponds 1 and 6 excluding cyanobacteria. Within Pond 1, direct cell counts of phytoplankton peaked at 594, 000 cells mL-1 during mid October and 264, 000 cells mL-1 during mid April in Pond 1. Overall direct cell counts showed a stronger correlation with Chlorophyll
Page 178 of 376 a concentrations in Pond 1 over that of Pond 6, however both correlations were significant (Figure 7-6).
Direct cell counts of cyanobacteria colonies were not conducted due to the small size of the cyanobacteria cells viewed. Colonies identified were typical of the families Nostoceace (Anabaena and Nodularia spp.), and Chroococcaceae (Microcystis spp.). Table A.C 5 (located in Appendix C) and Figure 7-7 displays the number cyanobacteria colonies present within Ponds 1 and 6 for the duration of the study period.
7.4.3 Phytoplankton family groups.
Tables A.C 6 and 7 display the raw phytoplankton cell count data from each water sample analysed for Ponds 1 and 6 respectively (located in Appendix C). Figures 7-9 and 7-10 graphically display the data presented in Tables A.C 6 and 7 respectively, showing the occurrence and abundance of each of the phytoplankton Family or Division groups identified over the sampling regime. In both ponds, phytoplankton of the Family Euglenaceae were present 100% of the time, with Desmidiaceae present during most sampling dates within Pond 1, and Scenedesmaceae in Pond 6 (Tables 7- 2 and 7-3). Figure 7-8 displays a chart graphing the frequency of presence of phytoplankton from each Family or division group represented throughout the sampling regime (April 2004 – January 2005).
Page 179 of 376 Figure 7-4: Isopleth diagrams of Chlorophyll a concentration in, % incident PAR within Pond 1 Pond 6. Thick black line on PAR isopleths represents 1% PAR incident light – or the photic depth of the water column.
Page 180 of 376 Figure 7-5: Pond 1 and 6 total phytoplankton cell counts. Values Figure 7-6: Correlation analysis between phytoplankton cell counts and exclude cyanobacteria. Chlorophyll a readings taken using fluorescence probe in situ.
Page 181 of 376 Figure 7-7 Cyanobacteria colonies present within Ponds 1 and 6 of the Figure 7-8 Frequency of phytoplankton family occurrence within Ponds 1 BWC System. and 6 of the BWC System.
Page 182 of 376 Table 7-2: Presence/absence of phytoplankton family groups within Pond 1. (+) indicated presence, (-) indicates absence. U = upper 20cm; L = lower 20cm of water column
April, 04 May, 04 June, 04 July, 04 August, 04 October, November, 04 December, 04 January, 05 Family group 04 U L U L U L U L U L U L U L U L U L U L U L U L U L U L U L U L U L Cyanobacteria + - + + + + + - - + - - - + + + - - + - - - - + + + + + + - + + + Chrysomonaceae + - - + - - - + - - + - - - + - - - + + - - - + + + + + + - - + - + Cryptomonceae + - + - - + + + ------+ - - + + - + ------Dinoflalgellate - - - - + - - - + ------+ ------Euglenaceae + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Diatom + + + - - + - - + + + + + + - - + - - + + - - + + + + + + + + - + Oedogoniaceae ------+ ------Desmidiaceae + + + + + + + + + + + + + + + + + + + - - + + + + + + + + + Scenedesmaceae + + + + - - + + + + + + + + + + + + + + + + + + + + + + + + - - + - Oocystaceae ------+ - + - - + + + + + + ------+ - + + + + - - - - Micractiniaceae - + - - - - - + - - - + - - - + - + ------Palmellaceae + + + + - + + + + - - + + - + + - + - - - - + - - + - - + - + + - + Botryococcaceae ------+ + + + + + + + + ------un id 1 ------un id 2 ------
Table 7-3: Presence/absence of phytoplankton family groups within Pond 1. (+) indicated presence, (-) indicates absence.
April, 04 May, 04 June, 04 July, 04 August, 04 October, November, 04 December, 04 January, 05 04 Family group U L U L U L U L U L U L U L U L U L U L U L U L U L C L U L U L U L Cyanobacteria + + - - - - + - - - + ------+ - + - + - + - + - + - - - - Chrysomonaceae - - - - + + - + + + + - - - - + - - + - + + + + - + - + - + - - - - Cryptomonceae - - + - + ------+ - - - v ------+ - - - Dinoflalgellate ------+ - + - - - - + - + - + ------+ - + - - - - Euglenaceae + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Diatom + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Oedogoniaceae ------+ ------Desmidiaceae + + + + + + + + + + - + + - + + - + - + + + + + + + + + - + + + Scenedesmaceae + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +- - - + + Oocystaceae - + - + + + + + + - - + + + + + + + + + - + + - + + + + + + - - - - Micractiniaceae - - - + + ------+ - - - - - + - + - - - - Palmellaceae + + + + + + + + - - + + - - - - + - - + - + + + + + ------Botryococcaceae + + - - - - + + + + + - - - - - + - - + - + + - + - + ------+ un id 1 ------+ + ------un id 2 ------+ ------+ - - -
Page 183 of 376 Figure 7-9: Presence and abundance of phytoplankton (cells mL-1) Family and Division groups identified within Pond 1 of the BWC System. n = upper 20cm of water stratum, n = lower 20cm of water stratum.
Page 184 of 376 Figure 7-10: Presence and abundance of phytoplankton (cells mL-1) Family and Division groups identified within Pond 6 of the BWC System. n = upper 20cm of water stratum, n = lower 20cm of water stratum.
Page 185 of 376 7.5 Discussion
In the discussion of phytoplankton succession in the BWC System, it is important for the factors determining community composition, structure and succession be investigated. These factors, presented in the introduction, will be discussed here in relation to the observed results of phytoplankton biomass change (as measured using Chlorophyll a) within and between Ponds 1 and 6 of the BWC System, and in relation to phytoplankton community structure and composition. Some of the items discussed in this section will draw on data from a previous Chapter investigating changes in water chemistry and quality within the BWC System (Chapter 6).
7.5.1 Spatial variation in phytoplankton biomass
The spatial variation of phytoplankton biomass within Ponds 1 and 6 is evident on a vertical scale (Figure 7-3 & 7-4). Presented and discussed in Chapter 6 (Factors governing water quality within Stormwater treatment ponds) the nutrient status and dynamics of Ponds 1 and 6 can be described as a dynamic eutrophic ecosystem. Water entering the BWC System is sourced from storm induced urban runoff, containing a vast array of inorganic and organic pollutants – many of which input large quantities of nitrogen and phosphorus into the system (as discussed in Chapter 5). Inorganic nitrogen and phosphorus are two macronutrients of great importance to phytoplankton nutrition, and can often be important in determining phytoplankton community structure, composition and succession through resource competition and limitation (Elser et al. 2002; Kobayashi and Church 2003).
As shown in Figure 6-12 and 6-14 (Chapter 6), inorganic nutrients displayed little variation between sampling depths, but substantial variation between sampling times. This would indicate that the vertical changes in phytoplankton biomass experienced within Ponds 1 and 6 of the BWC System are likely to be controlled by factors other than inorganic N or P, including PAR, water temperature and/or water turbulence.
Being an unstratified water body (temperature, see Chapter 6), vertical mixing within the water column will influence the abiotic characteristics of the water column and hence, the likely phytoplankton community present there. Vertical mixing within the
Page 186 of 376 water column is known to occur at a rate around the m.day-1 scale, whereas horizontal mixing occurs at a rate of km.day-1 (Harris 1986). This characteristic of the water column highlights the importance of vertical mixing in phytoplankton succession, as the water environment at a particular water depth may remain stable for periods in excess of the regeneration time for phytoplankton species. The vertical distribution of phytoplankton within Ponds 1 and 6 generally decreases within increased water depth, with the exception of Pond 1 during late August to mid November 2004 where phytoplankton biomass increased with increasing water depth (see Figure 7-4).
Light and temperature – two inseparable allogenic factors –influence phytoplankton production, and hence, phytoplankton community structure, composition and distribution within the water column (Wetzel 2001). At any given water depth, light and temperature will not only dictate the presence or absence of phytoplankton species but also determine the photosynthetic rate of the particular phytoplankton species present within the water column (Mur and Schreurs 1995; Kingston 1999; Moss et al. 2003; Schallenberg and Burms 2004). The intensity of PAR required for phytoplankton to reach photosynthesis maxima increases within increased temperature (Wetzel 2001). In other words, the intensity of PAR required for a particular species of phytoplankton to reach photosynthetic maxima is higher in waters of higher temperatures than that of waters of lower temperatures. Thus in relation to the BWC System, one could assume that the temperature characteristics of Ponds 1 and 6 would affect the photosynthetic characteristics of the phytoplankton community, and in turn influence community biomass, composition, structure and succession.
Having briefly presented the notion of the interrelated effects of PAR and temperature on phytoplankton communities, what are the likely outcomes of these interrelated allogenic factors controlling phytoplankton community biomass, structure, composition and succession? Firstly, the light environment within the depth profile of Ponds 1 and 6 will favour certain phytoplankton species. For example, some species of phytoplankton are capable of high photosynthetic rates at relatively low PAR irradiance’s while other species are adapted to achieve high photosynthetic rates at high PAR irradiance (Kirk 1994; Mur and Schreurs 1995; Vymazal 1995). Secondly, the light and PAR environment within freshwater bodies can, in extreme cases, cause
Page 187 of 376 photo inhibition – the point at which PAR exceeds that of the physiological photosynthetic requirement and excess PAR damages phytoplankton cells. Thirdly, daily temperature fluctuations can influence phytoplankton species circadian rhythm – that being the metabolic or behavioural cycles of a particular species over a period of approximately 24 hours (Sigee 2005). And finally, water temperature. Water temperature is one of the most important environmental factors governing the growth and survival of phytoplankton, with each species having their own characteristic growth range with minimum, optimal and maximum temperature values. As stated earlier, water temperature will effect photosynthetic saturation of phytoplankton (linked closely to light intensity), as well as regulating metabolic activities and in some cases determining actual species presence and dominance.
Table 7-4 displays the results from a correlation analysis between phytoplankton biomass and PAR within Ponds 1 and 6 on a fortnightly basis, showing that many significant, but differing, relationships exist between phytoplankton biomass and PAR. The relationships presented in Table 7-11 indicate that PAR is an important factor in determining/regulating phytoplankton biomass between and within Ponds 1 and 6. Its importance varies between fortnights, and the phytoplankton community response to PAR varies (as indicated by changing regression analyses preformed). Significant relationship patterns of the fortnightly PAR and Chlorophyll a data fell into one four relationship categories; 1. no relationship, 2. 2nd order polynomial, 3. Inverse 3rd order, or 4. exponential rise to maximum.
Over the course of the fortnightly sampling regime, PAR and Chlorophyll a were found to significantly correlate on 17 occasions in Pond 1 and 13 occasions in Pond 6 – 85% of the time for Pond 1 and 59% of the time for Pond 6. It is important to note here that although the relationship between PAR and Chlorophyll a exists ‘most-of- the-time’, the actual type of relationship changes, indicating that the influence of PAR on phytoplankton biomass within the BWC System changes. Much of Pond 1 data falls under a 2nd order polynomial relationship – meaning that with increased PAR, phytoplankton biomass (Chlorophyll a) increases in a 2nd order linear fashion with
Page 188 of 376 phytoplankton biomass continually increasing with increasing PAR. This type of relationship would indicate a phytoplankton community that has little limitation and is under some degree of regular disturbance – i.e. phytoplankton biomass not reaching maxima.
In contrast, much of the data from Pond 6 states an exponential rise to maximum relationship between the two variables, stating that phytoplankton biomass increase within increased PAR exponentially initially, then plateaus out at a given PAR. The exponential rise to maximum relationship is one that best describes the productivity of phytoplankton communities under non limiting conditions (Wetzel 2001) and is one that represents a community dominated by r-selected species of phytoplankton. An inverse 3rd order polynomial relationship, experienced a number of times in both Ponds 1 and 6, states that phytoplankton biomass is greatest at lower PAR values, thereafter decreasing with increased PAR, plateauing at a particular Chlorophyll a value with increasing PAR. This type of relationship could exist for many reasons (i.e. turbid water conditions, intense competiong by graziers, hyradulic disturbance), highlighting the influence other allogenic and autogenic factors like inorganic nutrient concentration and water turbulence.
7.5.2 Temporal variation in phytoplankton biomass
As early as the 1960’s, Margalef (1963) introduced the notion of phytoplankton succession being initiated and controlled by water turbulence (Harris 1986). He identified that only phytoplankton communities from hydraulically ‘stable’ environments could be preserved and built upon. Additionally, hydraulically ‘unstable’ water bodies can allow nutrient resources to become replenished, encourage the resuspension of settled particulate matter that influence PAR transmission through the water column, and translocates phytoplankton populations (Leland 2003; Walks and Cyr 2004; Rejas et al. 2005). The BWC System can be termed as hydraulically ‘unstable’ water body for the purpose of this discussion, receiving an erratic, and at times intense, hydraulic input (refer to Chapter 4). Figure 7-3 displays phytoplankton biomass within Ponds 1 and 6 in relation to storm induced flow into Pond 1 and through Pond 6 of the BWC System. This figure displays the occurrence and magnitude of stormwater flow into and through the BWC System,
Page 189 of 376 Table 7-4: Regression analysis comparing pond water chlorophyll a and % incident PAR within Pond 1 and Pond 6 of the BWC System. Small graphs at the base of table represent typical shapes of correlation lines according to analysis preformed. Sampling Pond 1 Pond 6 date r2 value Regression analysis r2 value Regression analysis preformed preformed 21-Jan-04 0.991 2nd order polynomial 0.818 2nd order polynomial 04-Feb-04 - No relationship 0.831 2nd order polynomial 18-Feb-04 0.859 Exponential rise to - No relationship maximum 10-Mar-04 0.977 2nd order polynomial 0.903 2nd order polynomial 24-Mar-04 0.996 2nd order polynomial 0.976 2nd order polynomial 08-Apr-04 0.975 2nd order polynomial - No relationship 22-Apr-04 0.994 2nd order polynomial 0.828 Exponential rise to maximum 10-May-04 0.869 2nd order polynomial 0.884 Exponential rise to maximum 19-May-04 - No relationship - No relationship 03-Jun-04 0.968 Inverse 3rd order 0.989 Inverse 3rd order polynomial polynomial 18-Jun-04 0.972 2nd order polynomial - No relationship 01-Jul-04 - No relationship - No relationship 12-Jul-04 0.933 2nd order polynomial - No relationship 11-Aug-04 - No relationship - No relationship 24-Aug-04 0.990 2nd order polynomial 0.993 Inverse 3rd order polynomial 15-Oct-04 1.000 Inverse 3rd order - No relationship polynomial 03-Nov-04 0.900 2nd order polynomial 0.833 Exponential rise to maximum 17-Nov-04 0.949 2nd order polynomial 0.963 Linear 01-Dec-04 0.800 2nd order polynomial 0.944 Exponential rise to maximum 16-Dec-04 - No relationship 0.906 2nd order polynomial 07-Jan-05 0.903 2nd order polynomial - No relationship 20-Jan-05 0.992 2nd order polynomial 0.995 Exponential rise to maximum
Exponential rise to maximum Inverse 3rd order polynomial 2nd order polynomial
Page 190 of 376 appearing to have some influence on the measured chlorophyll a concentrations in both Ponds 1 and 6. To recap the hydraulic behaviour of the BWC System, Pond 1 receives 100% of catchment derived stormwater runoff almost instantaneously, whereas Pond 6 receives a percentage of the catchment derived stormwater runoff at a time somewhat latter than that of Pond 1 – variables that change frequently as a result of storm intensity, duration and existing hydraulic nature of the BWC System. Having said that, phytoplankton community succession was not evident within either ponds from the phytoplankton enumeration data.
The influence of inorganic nitrogen and phosphorus concentrations on phytoplankton presence, biomass, community structure and succession has been widely studied. In a shallow eutrophic lake in Portugal, Abrantes et al. (2006) found that phytoplankton succession was generally controlled and regulated by nutrients (bottom-up effect), Domingues et al. (Domingues et al. 2005) also found a relationship between phytoplankton biomass and the N:P ratio of water column inhabited by the phytoplankton community, and Nuccio et al. (2003) suggested that their data indicated inorganic nitrogen and phosphorus depletion or availability plays a vital role in phytoplankton community succession. The obvious importance of inorganic nitrogen and phosphorus in phytoplankton community succession is the result of species competition for resources and resource limitation. When looking at phytoplankton community biomass, composition, structure and succession (or lack thereof) within the BWC System, inorganic nitrogen and phosphorus concentrations had little effect on the vertical distribution of phytoplankton, but appears to be influential to some degree on biomass on a temporal scale. Figure 7-11 displays a correlation analysis between phytoplankton biomass and the inorganic molar N:P ratio of water within Ponds 1 and 6. This figure displays a relationship of increasing phytoplankton biomass with a decreasing inorganic N:P ratio, meaning that phytoplankton biomass increases when nutrient concentrations within the ponds of the studied ponds of the BWC System increase to N:P ratio close to 1. This relationship would suggest that the phytoplankton community in Ponds 1 and 6 are somewhat N limited, in line with the common perception of freshwater phytoplankton communities being N limited (Vymazal, 1995; Wetzel, 2001; Sigee, 2005). This relationship is similar to that reported in the literature on studies of small, shallow eutrophic water bodies (Domingues et al. 2005; Abrantes et al. 2006).
Page 191 of 376 Figure 7-11: Analysis showing water column inorganic molar N:P ratio within Pond 1 (a) and Pond 6 (b). Annotated limitation lines from Guildford and Hecky (2000), where N: molar ratio < 20 = N limitation, >50 = P limitation and between 20-50 = N and P balance.
7.5.3 Phytoplankton community structure and composition.
7.5.4 Cyanobacteria
The occurrence of cyanobacteria cells and colonies within the BWC System occurred in both Ponds 1 and 6, but in much greater frequency and quantity in Pond 1 than in Pond 6 (Figure 7-7). On 22-Apr-04, cyanobacteria cells reached ‘bloom’ concentrations (within the upper and lower water stratums of Pond 1 (identified as being from the genus Anabaena). For the remainder of the study period, concentrations of cyanobacteria never exceeded 3000 colonies .mL-1, but were more commonly around the 1500-2000 colonies .mL-1 concentration. The occurrence of the autumn bloom of Anabaena within Pond 1 appears to be quite common in subtropical and tropical mesotrophic and eutrophic water bodies (Sigee, 2005). Late summer/early Autumn blooms of Anabaena (a K-selected species) are characteristic of eutrophic subtropical water bodies and tend to occur in a hydraulically stable environment relatively high in inorganic nutrients and low light penetration through the water column (Albert et al. 2005) – all factors which were present immediately before the 22-Apr-04 bloom of Anabaena. Cyanobacteria cell counts occur at both a higher and more consistent level in Pond 1 than Pond 6. Specific conditions required for cyanobacteria presence and blooms include high nutrient conditions (especially phosphorus), high light and pH, and resistance to zooplankton grazing (Mur and
Page 192 of 376 Schreurs 1995; Albert et al. 2005; Oberholster et al. 2006). The exact cause of the Anabaena bloom in Pond 1 on the 22-Apr-04 is not well understood. Pond 1 and Pond 6 are perfect for cyanobacteria blooms most of the time (being eutrophic subtropical water bodies). However, a bloom only occurred once, and concentrations of cyanobacteria were erratic and unpredictable. Only one conclusion can be made from this data. That is, that the dynamic hydraulic nature of the BWC System prevents cyanobacteria blooms from occurring. This notion is further substantiated when looking at the occurrence of the Anabaena bloom on 22-Apr-2004 being preceded by approximately 42 days of low flow hydraulic conditions into the BWC System (refer to Figure 7-3).
7.5.5 Total cell counts (excluding cyanobacteria)
A good correlation existed between phytoplankton cell counts and chlorophyll a concentration within each pond (Figure 7-6), meaning that chlorophyll a concentrations obtained from in situ fluorescence probe methodology is an adequate means of assessing phytoplankton biomass within the BWC System (using the methods undertaken). There was, however, a poor relationship between chlorophyll a concentration and total cell count including cyanobacteria. The relationships found between Chlorophyll a concentration and total phytoplankton cell counts of Ponds 1 and 6 demonstrates a number of features of the phytoplankton community within studied ponds of the BWC System;
1. The water column of Pond 1 over the course of the study period supports a fairly consistent and similar assemblage of phytoplankton in relation to species types, as indicated by the significant relationship between chlorophyll a concentration and total phytoplankton cell counts. For example, if the phytoplankton community was highly dynamic in terms of species change and succession a poor relationship between chlorophyll a concentration and total phytoplankton cell counts would occur – owing to the fact that not all phytoplankton species have a similar amount of Chlorophyll a contained within their cells. The outliers shown on Figure 7-6a & b occurred on the 15- Oct-04 – phytoplankton community dominated by a bloom of species from the Scenedesmaceae Family. Thus, showing an irregular occurrence in relation to
Page 193 of 376 the phytoplankton community of Pond 1 and not ‘fitting’ the remainder of the data set. 2. The water column of Pond 6, when compared to Pond 1, has a more dynamic phytoplankton community (as shown by the lower r values), but is nonetheless statistically consistent and similar over the course of the study period (ANOVA p = 0.067). Pond 6 is somewhat more hydraulically stable than Pond 1 and is a more nutrient limited environment (see Figures 7-3 and 7-11). These characteristics have possibly influenced the phytoplankton community of Pond 6 to be more diverse and erratic than Pond 1 and thus exhibit a lessor significant relationship between Chlorophyll a concentration and total phytoplankton cell counts.
A total of 12 family groups that were identified in the BWC System, Pond 6 supporting a more diverse assemblage of phytoplankton than Pond 1 (Figure 4). Some family groups were present more often than others, with phytoplankton from the family Euglenaceae being present 100% of the time in Pond 1 and Pond 6 and phytoplankton from the families Bacillariophyceae (Diatoms) and Scenedesmaceae being present 100% of the time in Pond 6. Phytoplankton from the family Euglenaceae have a strong tendency towards heterotrophic metabolism – even though most species within the family possess photosynthetic capabilities (van den Hoek et al. 1995). This tendency towards heterotrophic nutrition and the dominance of phytoplankton from the Euglenaceae family indicates that Ponds 1 and 6 are largely heterotrophic (a point that will be discussed more in Chapters 8, 9, and 10). Phytoplankton community dominance by Euglenaceae genera often reflects polluted conditions, and systems high in dissolved organic compounds. Thus, the dominance of phytoplankton from the family Euglenaceae within Ponds 1 and 6 is not surprising given that eutrophic nature of the ponds.
Phytoplankton of family Scenedesmaceae were present in Ponds 1 and 6 most sampling weeks, and are common inclusions in phytoplankton communities inhabiting nutrient rich environments, with some species capable of heterotrophic nutrition. Many of the phytoplankton identified from the Scenedesmaceae family during the enumeration of the phytoplankton communities within Ponds 1 and 6 possessed numerous bristles surrounding the cell walls. This physiological adaptation
Page 194 of 376 has been reported to discourage zooplankton grazing and aid in maintaining buoyancy. Both of which would enhance their frequency and abundance within the BWC System through the reduction of mortality (grazing and sedimentation) and enhancement of production (increased time within photic zone).
Like phytoplankton from the families Euglenaceae and Scenedesmaceae, phytoplankton of the Oocystaceae family are characteristics of nutrient rich small lakes and ponds, with some species also capable of heterotrophic nutrition. Phytoplankton of the Oocystaceae family were more abundant and frequent in Pond 6 than Pond 1, however no specific reason for this could be found.
Page 195 of 376 7.6 Conclusion
Pelagic phytoplankton biomass at any give time within Ponds 1 and 6 of the BWC System can be described as spatially similar on a horizontal plane and spatially differing on a vertical plane. The main characteristics of the phytoplankton community within the BWC System are; • Phytoplankton biomass within both ponds of the BWC System generally decreased with depth, and was generally a function of PAR. • When the water column N:P ratio was high, phytoplankton biomass (as measured by chlorophyll a) was low and conversely, when the N:P ratio was low phytoplankton biomass was high. This illustrates a phytoplankton community limited by phosphorus. • Phytoplankton community succession was absent within the BWC System, owing to the dynamic hydraulic behaviour system. • Cyanobacteria blooms within the BWC System seemed limited as a result of hydraulic behaviour of the ponds.
The direct phytoplankton enumeration allowed one to determine the effect of various allogenic factors on phytoplankton community structure. For example, the Scenedesmaceae bloom in Pond 1 on the 15 October 2004 occurred as a result of limited hydraulic flow into Pond 1 combined with a high N:P ratio in the lower section of the water column. Additionally, phytoplankton enumeration enabled the verification of chlorophyll a as an effective method for determining phytoplankton biomass change over time within the BWC System.
Page 196 of 376 8 Chapter8: The trophic status of urban stormwater ponds: pelagic and benthic carbon dynamics
Page 197 of 376 8.1 Abstract
The production and consumption of carbon within aquatic ecosystems determines its trophic status, and ultimately, the movement of nutrients within the system. Processes leading to the movement of carbon within aquatic ecosystems involve the production of organic carbon (primary production), the consumption of organic carbon (secondary production), the release of inorganic carbon (respiration), the consumption of inorganic carbon (primary production) and the allochthonous input of organic and inorganic carbon compounds. Given the importance of primary production in the role of ecosystem functioning within lakes, ponds, rivers and creeks, it has received little scientific attention in the context of urban waterways. With stormwater management fast becoming a managed and mitigated water quality issue, the importance of our understanding on how these ecosystem function increases. The aim of this chapter was to measure water column and benthic production within the BWC System. The productivity of the phytoplankton and bacterioplankton communities was measured directly using radioisotope 14C Sodium Bicarbonate and [methyl-3H] thymidine respectively. Benthos production was measured indirectly via perspex benthic incubations chambers equipped with DO probes. Phytoplankton production incubations within Ponds 1 and 6 indicated that the studied system behaved similarly to that of hyper eutrophic freshwater aquatic environments, calculated at a range between 50-1500 mgC. m-2. h-1. Bacterioplankton production was measured at a rate between 8-210 g C m-3. d-1, characteristic of that of eutrophic water bodies and at the high end of the spectrum, wastewater treatment plants. Benthos community production within Ponds 1 and 6 differed, with Pond 1 measured as being net heterotrophic during three out of the four incubations, while Pond 6 was measured as net autotrophic during all four incubations. Abiotic factors governing phytoplankton, bacterioplankton and benthic production within the BWC System include water turbulence, water temperature, PO4-P and DOC concentration and the time following storm events.
Keywords: Phytoplankton, Bacterioplankton, 14C Sodium Bicarbonate, [methyl-3H] thymidine, Benthic Metabolism, Autotrophic, Heterotrophic, carbon
Page 198 of 376 8.2 Introduction
Ecosystem response to altered biogeochemical cycles is probably best stressed by Sutcliff and Jones (1992), who estimated that 80% of all freshwater environments world wide are either impacted or threatened directly by eutrophication. More specifically, the global carbon cycle has been influenced greatly by changes in carbon fluxes, atmospheric chemistry and enhanced nutrient loading on aquatic ecosystems. The carbon cycle within freshwaters relies on the consumption and production of inorganic and organic carbon by a suite of micro and macro organisms, along with the autothalous and allochthonous inputs of carbon compounds into a ‘system’. Processes leading to the movement of carbon within aquatic ecosystems (Figure 6-4, Chapter 6) involve the production of organic carbon (primary production), the consumption of organic carbon (secondary production), the release of inorganic carbon (respiration), the consumption of inorganic carbon (primary production) and the allochthonous input of organic and inorganic carbon compounds. The movement of carbon within freshwater ecosystems will determine the trophic state of the ecosystem. The tropic state of water bodies influences, among other things, the processing of nutrients. In a stormwater management perspective, the trophic status of a freshwater pond/wetland will affect losses of nitrogen and phosphorus from the system and ultimately the quality of water entering recieving waters.
The trophic state of freshwater lakes and ponds is determined by the level of primary production verus the level of secondary production. Freshwater lakes and ponds can be either autotrophic dominated or heterotrophic dominated depending on the level of primary and secondary production occurring within the system.
Autotrophy Autotrophic organisms within freshwater ecosystems include macrophytes, phytoplankton and benthic macro algae (BMA). The dominant autotrophic organisms within ponds and lake are phytoplankton, and if pond depth allows PAR to penetrate to the benthos, BMA. In freshwater environments, phytoplankton and BMA play a critical role in ecosystem functioning and form the base of all aquatic food webs (Wehr and Deescy 1998). Specifically, phytoplankton and BMA within freshwaters provides a pathway for inorganic nutrient uptake and cycling, a food supply for
Page 199 of 376 heterotrophic organisms, and a oxygen source for low gradient and stagnant water bodies (Vymazal 1995; Wehr and Deescy 1998; Jassby et al. 2002). The ecological importance of phytoplankton and BMA primary production is reflected in the large volume of published research papers and their inclusion in whole ecosystem models and budgets.
In urban freshwater ecosystems receiving polluted runoff, phytoplankton and BMA production is an extremely important component of the ecosystem. It will determine and influence the structure of the higher food web, impact on the oxygen concentration of the water column, impact on the turbidity and biodiversity of the water column and help to reduce excess nutrients entering the system (Robertson et al. 1992; Boney 1975; Wetzel 2001; Vymazal 1996). Given the importance of primary production in the role of ecosystem functioning within lakes, ponds, rivers and creeks, it has received little scientific attention in the context of urban waterways. With stormwater management fast becoming a managed and mitigated water quality issue, the importance of our understanding on how these ecosystem function increases.
Heterotrophy Heterotrophic organisms inhabiting freshwater lakes and ponds include a wide variety of micro and macroscopic organisms. Fish, crustaceans, birds, and invertebrates call all be classed as heterotrophic organisms inhabiting freshwater lakes and ponds. It is, however, the micro component of the heterotrophic community that dictates whether the ecosystem operates as net heterotrophic or net autotrophic. This micro component of the heterotrophic community within freshwater lakes and ponds includes bacteria, protists, and fugal organisms – with bacteria being main heterotrophic producers. Bacteria (when in the benthos) and bacterioplankton (when suspended within the water column) refers to a suite of heterotrophic organisms living within the water column, including Archae, Mycobacteria and Actinomycets (Sorokin 1999). Bacteria, whether within the sediment or water column, of aquatic ecosystems are generally heterotrophic microscopic organisms typically ranging in size between 0.2-2µm (Sigee 2004). Natural freshwaters generally contain between 0.5 and 5x106 bacteria cells per mL, with mesotrophic and eutrophic water bodies concentrations typically in the range between 2x106 – 2x109 bacteria cells per mL (Bell and Kuparinen 1984; Kisand and Tammert 2000; Kisand and Noges 2004). Bacterioplankton communities
Page 200 of 376 are a fundamental component of aquatic ecosystems, through which major energy flows. They are the most abundant free-living biota in freshwater ecosystems; with superior opportunistic traits over that of other free-living biota (Robarts and Zohary 1993; del Giorgio et al. 1997). In recent years, the ‘microbial loop’ (Figure 8-1) has been increasingly viewed as a major component within aquatic ecosystems, and a major component in the cycling of carbon, of which bacteria play a pivotal role (Valiela 1995; Hader et al. 1998).
Inorganic Phytoplankton nutrients
Bacteria Higher trophic levels
DOM inc virus’ Protists
Figure 8-1: The Microbial Loop, shown in continuous arrows. Broken arrows show addition movement of energy within the ecosystem. Adapted from Hader et al. (Hader et al. 1998).
Page 201 of 376 8.2.1 Research aims, objectives and research questions The aim of the chapter was to investigate the trophic status within the water column and benthic zone of two ponds receiving urban stormwater runoff. Specific objective of the chapter were to; • Measure phytoplankton production within Pond 1 and 6 of the BWC System. • Measure bacterioplankton production within Pond 1 and 6 of the BWC System. • Measure benthic production within Pond 1 and 6 of the BWC System. • Investigate factor/s limiting primary and secondary production within the BWC System.
Key research questions were; • Is the BWC System autotrophic or heterotrophic dominated? • Does the trophic status of the ponds change over time?
Page 202 of 376 8.3 Methodology
The methodology of this chapter is divided into three sections; 1) Phytoplankton production, 2) Bacterioplankton production, and 3) Benthic production.
8.3.1 Phytoplankton production
Study sites Primary production within the water column was measured in Ponds 1 and 6 of the BWC System using radioactive Sodium Bicarbonate [14C] field incubations on the 25th of August and of 14th December 2004 (Figure 8-2). This method was developed and introduced by Steemann Neilsen in 1952 and is the most widely used technique for estimating carbon uptake in aquatic environments (Peterson 1980; Geider et al. 2001)
Pond 6 north
Pond 1 north
Figure 8-2: Location of sample sites within Ponds 1 and 6 of the BWC System.
Radioisotope incubation On each sampling date, 12 L of water was taken from the upper 20cm of the water column in each pond using one sterile water flagon submersed just below the water surface. A YSI SNODE 6600 multimeter was placed within each flagon to determine pond water pH, redox potential, temperature, and DO. Following this, each 12 litre flagon of water was homogenised by shaking the flagon of water for 10 minutes and used to fill 9 x 600mL PET clear plastic bottles. Three 30mL water samples were
Page 203 of 376 taken from each 12L flagon for the determination of DOC, TN and TP, and NH4-N,
PO4-P and NOx-N concentrations according to the methods detailed in Chapter 3. An additional 1 L of water from each flagon was also sampled for Chlorophyll a concentration (according to methods detailed in Chapter 3), and 100mL of water from each flagon sampled for alkalinity concentration. Alkalinity concentrations for each pond were determined using Winkler titrations conducted by the Queensland Department of Heath Scientific Services Laboratory. All water samples taken for the determination of nutrient, Chlorophyll a and alkalinity concentrations were filtered on site (where applicable), placed on ice in the dark and transport to the laboratory for immediate analysis (within the exception of DOC and nutrient samples which where frozen until analysis). Figure 8-3 displays a schematic diagram representing the distribution of water from each 12L flagon.
Each set of the 9, 600mL PET clear plastic bottles containing homogenised water from Pond 1 and Pond 6 were spiked with 1mL aliquots containing 20µCi/ ml of radioactive 14C (specific activity of 311 MBq/ mmol) in an sterile aqueous solution. Upon spiking, each 600mL PET bottle was assigned a light shading factor, and placed into corresponding shade bags (Figure 8-3). Shade bags where constructed from a differing grades of nursery shade cloth, constructed to achieve the predetermined shade percentage, determined using a LiCor L-1400 light meter equipped with a spherical PAR light sensor on the 10th July 2004. The 100% incubation was not placed within a shade bag, but left in full exposure to the sun light.
TOC, DOC, NH , NOx, TN, 12 L 4 PO4, TP, Chlorophyll a & Alkalinity
0% 0% 2% 4% 15% 15% 34% 70% 100%
Page 204 of 376 Figure 8-3: Schematic of 14C incubation undertaken. Note: this was replicated for both Ponds 1 and 6, equalling a total of 18 incubation jars under 8 differing light intensities.
To ensure that the initial spiked PET bottles were not subject to a longer incubation time than that of the final spiked bottles, each PET bottle was immediately stored in the dark upon spiking with the radioactive 14C solution. When all PET bottles were spiked and in their respective shade bags, they were taken out of the dark and placed within the upper 20cm of the water column in Pond 1 and left to incubate for 2 h. Upon completion of the 2 h incubation, all 600mL bottles were removed from the water, wrapped in aluminium foil and placed on ice in the dark for transportation back to the laboratory for analysis. A LiCor L-1400 meter equipped with a spherical light sensor was used to log the intensity of the sunlight throughout the duration of the incubation.
On arrival to the laboratory each sample was filtered using 40mm diameter polycarbonate filters at pore sizes of 180µm, 10µm, 2µm and 0.2µm to determine the size fractionation of productive phytoplankton. Additionally, 0.2µm Millipore™ filter cartridges were used to collect 1mL of 0.2µm filtrate for each sample. Filter papers and 0.2µm filtrate for each sample were then placed in 30mL scintillation vials and 500µL 2M HCl added to the top of each paper/filtrate and left for 12 h in a fume hood. 100µL of 18.10deionised water was then added to each vial prior to filling with 30mL of Beakman ReadySafe™ scintillation cocktail. All vials were then capped and shaken vigorously by hand for 2 min. Vials where then placed in a Packard BioScience Company™, TRI-CARB 2100TR Liquid scintillation analyser and analysed for 14C radioactivity using a 20 min per vial count time for 2 cycles. All laboratory work was undertaken in minimal light to suppress continued phytoplankton productivity.
Calculations The productivity, mgC. m-2. h-1, for each sample analysed was determined using Equation 8-1 (Vollenweider 1974). Productivity per µg Chlorophyll a was calculated using Equation 8-2.
14 12Cassimilated 12 Cassimilated=14 ⋅ C available ⋅ k 1 ⋅ k 2 Cavailable Page 205 of 376 (8-1) where; 12 C available = alkalinity in meqv . pHT factor . 12 (see Vollenweider 1974) 14C available = 14C activity added 14C assimilated = (filter counts – background) . 1.06 k1 = aliquot correction = Incubation volume – aliquot volume / volume filtered k2 = time correction = 1(h) / incubation time (h)
12 12 Cassimilated Cassimilated (.)µ gChl a = Chla (8-2) where; Chl a = Chl. a concentration of the water sample (µg L-1)
Phytoplankton productivity in each one of the series of shade bags can be plotted against a percentage of the average Photosynthetic Active Radiation (PAR) experienced throughout the incubation period, often termed a PI curve. Figure 8-4 displays a typical PI curve, with the associated photosynthetic characteristics displayed. Pmax being the point at which a given phytoplankton community reached its productivity maximum, with Ik being the amount or degree of PAR required to achieve Pmax (Vollenweider 1974).
Figure 8-4: PI curve. Phytoplankton primary production plotted against PAR irradiance.
Pmax, maximum rate of production; Ik apparent light intensity at which Pmax is achieved
Page 206 of 376 (photosynthetic saturation). Adapted from (Pollard and Greenway 1993; Pollard and Kogure 1993)
8.3.2 Bacterioplankton production
Study sites Bacterial production within Ponds 1 and 6 of the BWC System was undertaken on the 22-Apr-04, 25-Aug-04 and 14-Dec-04, concurrently with phytoplankton production measurements (14C Sodium Bicarbonate incubations) presented above. Dates were determined to represent the potential temporal change in bacterioplankton community over a 12 month period. Figure 8-5 displays the location of sample sites within the BWC System where water was extracted for the [methyl-3H] thymidine bacterioplankton incubations. Water from the upper and lower 20cm of the water column was sampled on each incubation date from each sample site, totalling 4 samples per incubation date.
Pond 6 north
Pond 1 north
Figure 8-5: Location of sample sites within Ponds 1 and 6 of the BWC System. Upper and lower 20cm of the water column was sampled at each site.
Radioisotope incubation In situ bacterial production measurements were undertaken in Ponds 1 and 6 of the BWC System using the [methyl-3H] thymidine technique. This technique for measuring bacterial productivity allows for the direct quantification of bacterial
Page 207 of 376 growth rates, without the incorporation of other cellular metabolic activities – cell growth, maintenance, respiration and motion (Pollard 1997). Once radioactively labelled [methyl-3H] thymidine is added to a water sample it is actively incorporated into the bacterioplankton cell, and ‘labels’ all DNA newly synthesized. It does not label the DNA of non growing cells (Pollard 2002 and Kemp 1993). Figure 8-6 displays a schematic representation of the bacterial growth process in the presence of [methyl-3H] thymidine.
DNA
Radioactive Cells elongate DNA
Cell walls form
Cells separate with all new cells labelled
Figure 8-6: Bacterioplankton growth in the presence of radioactively labelled thymidine, after Pollard (1997) and Pollard (2002).
Three variables critical to the success of the [methyl-3H] thymidine incubation are the volume of [methyl-3H] thymidine added to each incubation; the length of time incubations run for; and the specific activity of the added [methyl-3H] thymidine. Upon an extensive literature search, no published research papers or reports have measured bacterial production via the [methyl-3H] thymidine technique in stormwater ponds and wetlands. Thus, the volume of pond water to use in each incubation, the amount of [methyl-3H] thymidine to add to each incubation, and the length of time to incubate was unknown. On the 19-Apr-04, a simple set of laboratory experiments was conducted to determine these values. 1L of Pond 1 water was taken from the upper water stratum, placed on ice and transported directly back to the laboratory. Stock [methyl-3H] thymidine solution was purchased from MP Biomedicals, Inc. (USA) at a concentration of 1.0 mCi. mL-1 in a sterile aqueous solution with a specific activity of 2.0 Ci mmol-1.
Page 208 of 376 Using the pond water, a series of incubations were conducted to determine the optimal incubation time for pond water from the BWC System. This was achieved by running 5mL pond water incubations containing 20 µL of [methyl-3H] thymidine at 5, 10, 15, 20, 25, 30, 35, 40, 50, and 60 minute intervals. Incubations were stopped by adding 100µL of 37% formalin, and processed according to the laboratory methods outlined in the following section. Figure 8-7a displays the radioactivity of each sample with increased incubation time, with Figure 8-7b highlighting the initial linear phase of the graph. Optimal incubation time is determined by a balance between staying in the linear phase of the graph and practicality. Ten minutes was the optimal length of time for incubating pond water for the BWC System.
Figure 8-7a-b: Correlation analysis between radioactivity (dpm) and incubation time from pond water [methyl-3H] thymidine incubations. (a) displays entire exponential rise to maximum response curve, with (b) displaying the initial linear response.
The volume of [methyl-3H] thymidine added to each incubation vial was determined after establishing the linear incubation time. 10 vials containing 5mL of pond water were incubated with the addition of 5, 10, 15, 20, 25, 30, 35, 40, 50, and 60 µL of [methyl-3H] thymidine (specific activity, 2.0 Ci. mmol-1; concentration, 1.0 mCi. mL- 1) (Figure 8-5). Shown in Figure 8-8, increased volume of [methyl-3H] thymidine increased the radioactivity (dpm) of the sample in a linear fashion up to 35 µL of [methyl-3H] thymidine. From this point on the radioactivity of the sample began to ‘plateau’, indicating that the addition of [methyl-3H] thymidine above 35 µL had a saturating affect. The volume of [methyl-3H] thymidine was determined by a balance
Page 209 of 376 between staying in the linear phase of the graph and conserving the volume used of [methyl-3H] thymidine in each incubation. The optimal volume of [methyl-3H] thymidine to be added to each incubation vial is 10µL.
Figure 8-8: Correlation analysis between the volume of [methyl-3H] thymidine added to pond water and its associated radioactivity following a standard 10 min incubation.
The incubation protocol established for this investigation consists of 5mL of sample to which was 10µL of [methyl-3H] thymidine (Figure 8-9). After a 10 minute incubation time, samples were stopped by the addition of 100µL of 37% formalin, within the exception of the control incubations which were stopped directly after the addition of [methyl-3H] thymidine (Figure 8-9).
Page 210 of 376 1 2 10µL of [methyl-3H] thymidine Specific activity = 2.0 Ci.mmol-1 1L composite -1 water sample Concentration = 1.0 mCi. mL
3
10 minutes
C T1 T2 T3
5 ml 4
100µL 37% formalin Each sample conducted in triplicate with one control
Figure 8-9: Summary of bacterioplankton incubation performed on all incubations.
Field incubations Physicochemical water quality parameters of the sampled water stratums within Ponds 1 and 6 on each incubation date is displayed in Table 8-1, obtained using a YSI SNODE 6600 multi meter on the day prior to incubations. Measurements taken using the YSI SNODE 6600 were conducted in accordance to the methods outlined in Chapter 3. Additionally, the hydraulic and rainfall characteristics of Ponds 1 and 6 preceding each incubation date are displayed in Table 8-2, from data presented in Chapter 4.
On the 21-Apr-05, 25-Aug-04 and the 14-Dec-04, 1 L of water from the upper and lower 20cm of Ponds 1 and 6 was obtained using a graded water sampling pole. From each 1L water sample, 5mL was added to each of 4 10mL centrifuge vials (3 treatments, 1 control). Incubations were started by the addition of 10µL [methyl-3H] thymidine; left to incubate for 10 minutes; then stopped by adding 100µL 37% formalin (Figure 8-9). Upon addition of formalin, all samples were placed on ice and transported back to the laboratory for analysis.
Page 211 of 376 Each 1 L water sample was sub sampled for the determination of TN, TP, NH4-N,
NOx-N, PO4-P, and DOC concentration. Sub sampling for the nutrient concentrations was conducted in accordance with that of outlined in Chapter 3.
Table 8-1: Physicochemical water quality parameters within Ponds 1 and 6 on the day prior to thymidine incubations (data derived from Chapters 6 and 7). Incubation Chlorophyll pH Redox DO Temp. Light date a (µg L-1) (mV) (mg L-1) (°C) (% PAR) Apr. 21st, 05 Pond 1 upper 32.9 6.8 164 4.16 23.3 73.53 Pond 1 lower 13.7 6.7 163 0.88 22.9 0.75 Pond 6 upper 48.6 7.2 138 6.16 23.1 73.35 Pond 6 lower 41.5 7.1 151 4.37 22.5 1.18 Aug. 25th, 05 Pond 1 upper 27.2 6.7 -34 5.22 18.2 60.77 Pond 1 lower 11.9 6.3 -110 0.15 15.5 0.16 Pond 6 upper 8.9 7.1 186 2.89 17.3 86.26 Pond 6 lower 10.4 6.9 200 2.49 15.9 0.38 Dec. 16th, 05 Pond 1 upper 2.02 6.9 97 4.06 26.6 84.00 Pond 1 lower 2.1 6.6 -37 0.27 23.2 0.07 Pond 6 upper 4.7 7.0 -12 4.60 25.7 82.22 Pond 6 lower 4.4 6.9 -116 2.80 24.4 2.21
Table 8-2: Rainfall and hydraulic conditions of Ponds 1 and 6 during and on incubation dates (data derived from Chapter 4). Incubation date Last rainfall received Storm flow into Pond Storm flow out of in catchment 1 Pond 6 April 22nd, 2005 17/4/05 13:36 No Yes; 0.9 l/sec August 24th, 2005 18/8/05 10:54 No Yes; 0.30 l/sec December 16th, 2005 16/12/05 07:40 Yes; 1.4 l/sec Yes; 10.07 l/sec
Thymidine laboratory analysis Upon arrival to the laboratory all centrifuge vials were placed in the refrigerator and analysed within 24 h of the initial incubations. In a solution containing 1 mmol of non radioactive thymidine, 25mm Millipore 0.2µm polycarbonate filters where submerged for > 1 hour. Each sample vial was then acidified using 250µL of 100% trichloroacetic acid (TCA) solution and left to stand for 20 minutes. Filter papers that had been previously soaked in the 1 mmol non radioactive thymidine solution were then placed on a Millipore™ filter manifold, and 5ml of each sample added onto their corresponding filter paper. All samples were then filtered and ‘washed’ with 1mL of 5% TCA solution followed by 1mL of 80% ethanol solution. This was repeated 5 times. Filter papers were then removed from the filter block and pushed to the bottom of 5mL scintillation vials. To each vial, 100µL of 5M HCl was then added, ensuring
Page 212 of 376 it was applied directly onto the filter paper. Vials were then capped, shaken and let stand for 30 minutes. 100µL of 18.1GHLRQLVHGPILOWHUHGZDWHUZDVDGGHGWR each vial before filling with Beakman™ Ready Safe scintillation cocktail. All vials were then shaken, checked to ensure they had not developed into double phase and their [methyl-3H] thymidine radioactivity measured using a Packard BioScience Company™, TRI-CARB 2100TR Liquid scintillation analyser.
Calculations Radioactivity incorporated into bacterioplankton DNA during the incubations was calculated using Equation 8-3 (Pollard 2002).
−1 − 1 Radioacr rv, tdr[] nmol tdr⋅ mL ⋅ h = (8-3) Cdpm/ µ Ci ⋅ SA ⋅ t where; -1 -1 rv,tdr = radioactivity incorporated into bacterioplankton, nmol.tdr.mL .h
Radioacr = radioactivity incorporated into bacterial DNA, as measured by liquid scintillation counter, dpm.L-1 6 Cdpm/µCi = number of nmoles of thymidine, 2.2 x 10 dpm. µCi SA = Specific activity of the [methyl-3H] thymidine, 2.0 Ci. mmol t = incubation time, h
A conversion factor was then used to determine the rate of formation of new bacterioplankton cells from the rate incorporated labelled thymidine determined using Equation 8-3 (Equation 8-4). This factor converts nmoles of thymidine incorporated into bacterioplankton DNA to a volumetric bacterioplankton growth rate (cells per unit time). There is common agreement between these methods in the published literature (Fuhrman and Azam 1982 Chrost et al. 1988; Pollard 2002).
rvx= r v, tdr K tdr (8-4) where; -1 rvx = volumetric bacterioplankton growth rate, cells.mL 9 -1 Ktdr = Conversion factor, 2 x 10 cells.nmol thymidine
Page 213 of 376 The growth of bacterioplankton communities within Ponds 1 and 6 of the BWC System was then calculated by the volumetric increase in bacterioplankton cell numbers divided by the concentration of bacterioplankton, multiplied by 24 to convert h to d (Equation 8-5) (Pollard 2002).
−1 1 µ[]d= rv, x ⋅ ⋅ 24 (8-5) Ccells where; µ = bacterioplankton specific growth rate, day-1 -1 Ccells = concentration of bacterioplankton in sample, cells.L
Bacterioplankton population doubling is a value used to quantify the time it would take for the bacterioplankton community to double in size, and was calculated using Equation 8-6.
n() 2 t = l (8-6) d µ where; td = Bacterioplankton population doubling time, days.
Bacterioplankton enumeration From each 1L water sample taken in Ponds 1 and 6 on the 21-Apr-05, 25-Aug-04 and the 14-Dec-04, 10mL was taken for the determination of bacterioplankton cell concentration. Each 10mL water sample was preserved with 1mL of formalin in a 10mL centrifuge vial and stored in the refrigerator until analysis
Bacterioplankton cell counts were conducted by means of epifluorescence microscopy using a HBO 50 mercury lamp and blue incident light. Of the preserved samples, 1000µL was dispensed into 5mL eppendorfs wrapped in foil. To each eppendorf, 5µL of Syba-Green DNA stain was added, which was then capped, wrapped in foil, shaken and left to incubate for 30 minutes. Each sample was then filtered using a 0.22µm GTBP Millipore™ 25mm filter, removed from the filter block and placed on a microscope slide. One drop of 100x microscope oil was then applied directly to the
Page 214 of 376 filter paper, which was subsequently covered by a cover slip and another drop of 100x microscope oil applied. Bacterioplankton cells were then viewed (Figure 8-10).
Bacterioplankton
Phytoplankton
Figure 8-10: Photograph of one field of view used to count bacterioplankton. Scale bar equals 10µm in length.
Digital photographs of 10 fields of view from each sample filter paper were taken immediately following the staining procedure. Within each field of view, bacterioplankton were counted in 10 graticule squares, with an area that represented the fraction of the total filter area on which bacterioplankton had been counted. The total number of bacterioplankton on each filter was then calculated according to Equation 8-7. The number of bacterioplankton cells per L of mixed pond water was then calculated using Equation 8-8 (Pollard 1997).
ANsquare⋅ square −6 ifilter = ⋅ 10 (8-7) Afilter where; ifilter = total bacterioplankton on filter
Asquare = Area of square counted, 10 x 10 µm
Nsquare = number of graticule squares counted 8 Afilter = Area of filter, 2.83 x 10 µm
cells Ncells⋅ f dil 3 Ccells = ⋅ 10 (8-8) L ifilter⋅ V sample, dil
Page 215 of 376 where;
Ccells = concentration of bacterioplankton cells, L-1
Ncells = number of bacterioplankton cells counted fdil = sample dilution factor
Vsample, dil = sample dilution factor
8.3.3 Benthic Production
Study sites The benthic production within Ponds 1 and 6 of the BWC System was measured on four occasions during 2004. In Pond 1, chambers where placed in the south east corner, approximately 3m north of the eastern stormwater inlet channel (Figure 8-11). Within Pond 6, chambers were place in the eastern corner of the pond, approximately 1m north of the macrophyte burn dividing Pond 5 from Pond 6 (Figure 8-11). Both sites where chosen based on three factors; 1) water height (ease of installation), 2) proximity to the bank (for storing data loggers and batteries) and, 3) representative benthic area of the pond. The benthic zone of Pond 1 can be qualitatively described as being “rich in particulate organic matter”, while the benthic zone in Pond 6 can be described as “clayey”. Chambers were deployed within a water depth range between 30cm and 80cm, a factor dependant on the hydraulic conditions of the ponds prior to chamber deployment.
Figure 8-11: Location of incubation chambers within Ponds 1 and 6 of the BWC System.
Page 216 of 376 Benthic Production Incubation Benthic metabolism was measured in situ, using perspex benthic incubation chambers, (Figure 8-12 and Table 8-3), located within Ponds 1 and 6 the BWC System.
TPS 12 volt data battery logger Submersible pump
DO Probe
Figure 8-12: Schematic of the incubation chambers used for benthic metabolism calculations and nutrient flux between the benthos/water column interface. Small arrows indicate water flow.
Table 8-3: Perspex incubation chambers specifications. Specification/variable Ponds 1 and 6 –all deployment dates Chamber diameter 29.5 cm Chamber height 35 cm Chamber volume (litres) 6.15 Chamber PASA (m2) 0.0638 Chamber benthic contact surface area (m2) 0.648 DO probe YSI attached to TPS WD82 data logger DO logging frequency Every 15 mins Chamber nutrient sampling frequency Every 4 hrs Deployment time 24 hrs
Incubation chambers were deployed within the BWC System over a range of “days following storm events”. As shown in Figure 8-13, chambers were deployed in Ponds 1 and 6 on the 19-Jan-04, 19-Apr-04, 19-Jun-04 and the 25-Aug-04 corresponding to 1, 2, 9 and 7 days following rainfall events within the catchments respectively
Page 217 of 376 Figure 8-13: Daily rainfall within the BWC System catchment (data taken from Chapter 4), with red dots and drop lines indicating deployed incubation chambers within Ponds 1 and 6 for 24 hrs.
Calculations Benthic production within deployed incubation chambers was determined by monitoring the changes in dissolved oxygen (DO) concentration every 15 minutes within the chamber over a 24 hour period (using YSI probe connected to TPS data logger)). The amount of carbon produced (Gross Primary Production, GPP) was determined using Equations 8-9 and 8-10, and the amount of carbon consumed
(Respiration, R24) was determined using Equation 8-11. As an approximate measure of gross ecosystem metabolism, a production/respiration (p/r) ratio was calculated using Equation 8-12.
GPPdaytimeflux=∑( r 1 ⋅ t) (8-9) where;
GPPdaytimflux = Gross primary production during daylight hours -1 r1 = rate mean of dissolved oxygen production during daylight hours (mgL ) t = fraction of hour for time interval (decimal hour)
Page 218 of 376 ∑GPPdaytimeflux ⋅ V ⋅ 24 SA GPP = (8-10) 24 1000 where; -2 -1 GPP24 = Daily gross primary production (gC.m .day ) V = volume of dome (L) SA = surface area of dome (m2)
r2 ⋅ 0. 375 ⋅ V ⋅ 24 SA R = (8-11) 24 1000 where; -2 -1 R24 = Daily respiration (gC.m .day ) -1 r2 = rate mean of dissolved oxygen respiration (loss) during night time hours (mgL )
GPP p/ r = 24 (8-12) R24
Page 219 of 376 8.4 Results
8.4.1 Phytoplankton production
Ponds 1 and 6 of the BWC System were eutrophic during both incubation dates, according to the nutrient concentrations displayed in Table 8-4 (Schwoerbel 1987; Wetzel 2001). The abiotic water characteristics of Ponds 1 and 6 changed substantially between incubation dates, and to a lesser extent between ponds. In both incubations, Pond 6 water nutrient concentrations were generally less than that of Pond 1 – owing to the fact that Pond 6 is downstream of Pond 1 and water has received a certain level of ‘treatment’ before entering Pond 6. PO4-P was at greater concentration within Pond 6 than Pond 1 during the spring/summer incubation (Table 8-4). This could be due to the re-suspension and desorption of sediment bound phosphate during frequent spring/summer storm events, as depicted in Figure 4-4 (Chapter 4) and reported within the literature (Schallenberg and Burns 2004; Ogilive and Mitchell 1998).
Table 8-4: Water quality results from each pond on the 24th August and 14 December 14C incubations. Water quality results were obtained on samples or measurements taken on the 12 L homogenised flagon of water from each sample site Parameter 24-Aug 04 14-Dec 04 Pond 1 Pond 6 Pond 1 Pond 6 TN (mg L-1) 0.58 0.46 1.28 0.96 Org-N (mg L-1) 0.49 0.15 0.79 0.93 -1 NH4 (mg L ) 0.07 0.27 0.09 0.02 NOx (mg L-1) 0.01 0.03 0.40 0.02 -1 PO4 (mg L ) 0.16 0.06 0.23 0.35 Org-P (mg L-1) 0.52 0.17 0.26 0.26 TP (mg L-1) 0.68 0.23 0.49 0.60 Alkalinity (mg L-1) 48.00 62.00 61.00 50.00 Chlorophyll a µg 84.8 7.02 12.9 22.7 L-1) pH 6.67 7.07 7.00 6.86 Redox (mV) -34 186 -12 97 DO (mg L-1) 5.22 2.89 0.37 4.06 Temperature (°C) 18.16 17.28 25.66 26.58
Figures 8-14a-d display the photosynthetic carbon uptake by phytoplankton within Ponds 1 and 6 on both sample dates. From the PI curves displayed in Figures 8-14a- d, the photosynthetic characteristics of the phytoplankton community were calculated, and are displayed in Table 8-5. Primary production within Pond 6 displayed quite
Page 220 of 376 different results between incubation dates to that of Pond 1. Pond 6 displayed -2 -1 maximum productivity in the summer incubation at a Pmax of 473 mg C. m . day . -2 -1 Pmax during the winter incubation was substantially less at 65.6 mg C. m . day .
Again, the Ik of Pond 6 during both incubations was similar at 790 and 822 µmols. Quanta. m-2. s-1 for the winter and summer incubation respectively. The initial slope of the PI curves for Pond 6 display’s a more rapid photosynthetic rate during the summer than that of the winter, opposite to that of Pond 1.
Table 8-5: Photosynthetic characteristics of the phytoplankton community within the BWC System. 24-Aug 14-Dec Pond 1 Pond 6 Pond 1 Pond 6 -2 -1 Pmax (mg C.m . h ) 1500 58 50 540 -2 -1 Ik (µmol.Quanta.m .s ) 850 850 1000 800
Dividing the phytoplankton community in four size fractions, Figures 8-15a-d display the differing photosynthetic rates of phytoplankton from differing size fractions within Ponds 1 and 6. Based on this, the dominant primary producing phytoplankton within all incubations (with the exception of the Pond 1 winter incubation) was in the size range between 0.2-2.0µm – a size range known as picoplankton. Microplankton (size range between 20-200µm) was dominant in the autumn/winter incubation of Pond 1 water coinciding with an cyanobacteria bloom of Anabaena spp. recorded on the 22-Apr-04 (see Chapter 7). The Pmax of phytoplankton from the differing size fractions exhibited similar Pmax characteristics to that of the entire phytoplankton community (Table 8-6).
Page 221 of 376 Figure 8-14: Total phytoplankton community primary production in Ponds 1 and 6.
Table 8-6: Photosynthetic maxima (Pmax) size fractionation for BWC System during winter and summer. Phytoplankton 24-Aug 14-Dec size fraction (µm) Pond 1 Pond 6 Pond 1 Pond 6 0.2-2.0 504 35.6 24.6 250 2.0-10.0 473 20.64 11.65 145 10.0-180.0 614 9.03 13.2 143 >180.0 21 0.35 0.97 7.8
Page 222 of 376 Figure 8-15: Production of differing size fractions of phytoplankton within Pond 1 and 6. 2nd order Polynomial line of best fit shown for each size fraction on each graph.
8.4.2 Bacterioplankton Production All [methyl-3H] thymidine bacterioplankton productivity incubations conducted in the BWC System were, with the exception of the April incubation, done concurrently with primary productivity assays detailed in Chapter 8. Table 8-7 displays total and dissolved N, P, and C concentrations within pond water used for each [methyl-3H] thymidine bacterioplankton productivity incubation, showing extreme variation in concentrations between all three incubation dates. Dissolved inorganic P ranged between a low of 0.003 mg L-1 in Pond 6 during the April incubation to a high of 0.345 mg L-1 in Pond 1 during the December incubation. Dissolved inorganic N was
Page 223 of 376 dominated by NOx-N in both ponds during the April incubation, by NH4-N in the August and December incubations.
Table 8-7: Nutrient results from incubation water used for each [methyl-3H] thymidine incubation. Italic values represents detection limit of methods used for analysis.
+ 3- Incubation TN NH4 NOx TP PO4 DOC date (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) 21-Apr-05 Pond 1 upper 0.47 0.04 0.10 0.74 0.03 3.25 Pond 1 lower 0.29 0.04 0.10 0.49 0.02 2.77 Pond 6 upper 0.34 0.03 0.10 0.45 0.00 8.27 Pond 6 lower 0.31 0.05 0.10 0.42 0.00 5.88 25-Aug-05 Pond 1 upper 0.58 0.07 0.01 0.68 0.16 8.90 Pond 1 lower 0.52 0.01 0.02 1.12 0.33 10.00 Pond 6 upper 0.39 0.33 0.04 0.19 0.04 9.75 Pond 6 lower 0.47 0.25 0.02 0.24 0.04 8.93 16-Dec-05 Pond 1 upper 0.96 0.02 0.02 0.60 0.35 20.43 Pond 1 lower 2.10 1.75 0.00 1.57 0.88 20.18 Pond 6 upper 1.28 0.09 0.40 0.49 0.23 10.18 Pond 6 lower 1.23 0.27 0.00 0.57 0.25 15.21
Table 8-8 displays the bacterioplankton concentration and productivity results derived from the thymidine assays and bacterioplankton cell enumerations for Ponds 1 and 6 of the BWC System in April, August and December of 2004. Bacterioplankton cell counts for Pond 1 and Pond 6 varied between ponds, sampling depths and sampling dates, with the highest recorded bacterioplankton populations in the upper water stratum of Pond 1 during the August incubation, and the lowest recorded population measured in lower water stratum of Pond 6 during the April incubation (Figure 8-16). The specific growth rate of bacterioplankton is a measure of the amount of new bacterial cells synthesised per day (Figure 8-17). Within the BWC System, the bacterioplankton population specific growth rate was highest in both ponds at all sampling depths on the 16 December. The lower water stratum of Pond 1 during the August incubation exhibited the lowest recorded specific growth rates at 0.01 µ d-1.
Page 224 of 376 Table 8-8: Bacterioplankton concentration and production values for Ponds 1 and 6 of the BWC System.
Incubation Site Concentration Specific growth Pop. doubling Total date rate time production (106 cell. mL-1) (µ d-1) (d-1) (mgC m-3. h-1) 21-Apr-05 Pond 1 Upper 4.26 6.64 0.05 2494 Pond 1 Lower 6.77 5.61 0.05 3345 Pond 6 Upper 3.52 1.10 0.27 341 Pond 6 Lower 2.30 4.94 0.06 1000 25-Aug-05 Pond 1 Upper 9.66 3.42 0.20 2912 Pond 1 Lower 4.89 0.01 7.00 42.6 Pond 6 Upper 6.43 1.03 0.67 584 Pond 6 Lower 5.05 1.09 0.64 484 16-Dec-05 Pond 1 Upper 5.30 18.76 0.02 8768 Pond 1 Lower 4.52 16.90 0.02 6743 Pond 6 Upper 4.56 17.74 0.02 7137 Pond 6 Lower 4.28 19.40 0.02 7322
Figure 8-16: Bacterioplankton cell counts of Pond 1 and Pond 6 water immediately prior to [methyl-3H] thymidine incubation in the upper and lower water stratums.
Figure 8-17: Bacterioplankton specific growth rate for Pond 1 and Pond 6 in the upper and lower water stratums.
Page 225 of 376 Bacterioplankton population doubling time reflects the specific growth rate of the bacterioplankton population, with the grow rates corresponding to high population doubling time. The bacterioplankton population doubling time within the BWC System was greatest within the August incubation across all ponds at all depths, reaching a maximum of 6.99 days in the lower water stratum of Pond 1 (Figure 8-18). The lowest calculated population doubling time for both ponds and water stratums was exclusively restricted to the December incubations, with both ponds showing similar values: averaging at 0.01 days (Figure 8-18).
The productivity of the bacterioplankton community within the BWC System reached a maximum rate of 8768 mg. m-3. h-1 in the upper water stratum of Pond 1 in the December incubation, falling to a low of 42.6 mg. m-3. h-1 in the lower water stratum of Pond 1 during the August incubation (Figure 8-29). Pond 6 generally had a reduce rate of bacterioplankton productivity compared to that of Pond 1, with Pond 6 exceeding Pond 1 productivity on two occasions only.
Figure 8-18: Bacterioplankton population doubling time for Pond 1 and Pond 6 in the upper and lower water stratums.
Page 226 of 376 Figure 8-19: Bacterioplankton productivity for Pond 1 and Pond 6 in the upper and lower water stratums.
8.4.3 Benthic Production
The graphical representation of the rate of DO consumption and production within each incubation dome on each deployment date is displayed in Figure 10-4. Various problems were encountered with the methodology used, and can be seen on Figure 10-4 where the concentration of DO exhibited rapid ‘spikes’ and ‘falls’ and where the DO concentration was measured at 0 mg L-1 over a long periods of time that dominated the sampling period. When the concentration of DO within the incubations exhibited problems, specific sets of values within the individual data set/s was used to calculate production and respiration rates. For example, a 2 h section of data within a 24 h data set was used to calculate production rates as the remaining data within the set was reported at less than 0 mg L-1. Using the DO concentrations displayed in Figure 8-20, the rate of production and respiration occurring within the Benthic Metabolism Chambers was calculated, and a p/r ratio given to each chamber in each pond for all deployment dates (Table 8-9). Within Pond 1 DO ranged between 0.00 and 7.95 mg L-1, with three out of the 4 incubations defining the benthic system as being net heterotrophic (based on a p/r ratio less than 1). With a p/r ration of 1.34 during the January incubation, Pond 1 was defined as being net autotrophic, indicating that the autotrophic production of organic C exceeded that of the heterotrophic consumption of organic C. Showing marked difference to Pond 1, the
Page 227 of 376 benthic environment within Pond 6 was classed as net autotrophic during all four incubations, showing a DO range between 0.91 to 6.32 mg L-1.
Table 8-9: Benthic production and respiration rates measured in Ponds 1 and 6 in the BWC System in 2004.
GPP24 R24 p/r ratio DO Temp (gC m-2 day-1) (gC m-2 day-1) (mg L-1) (°C) Pond 1 1.10 0.79 1.34 0.00-0.85 20.4-26 19-Jan-04 Pond 6 0.81 0.75 1.07 0.91-4.84 25.8-28.4 Pond 1 0.37 0.38 0.95 3.71 - 7.95 27.2 - 34.3 19-Apr-04 Pond 6 0.71 0.42 1.67 3.47 - 7.31 27.0 - 36.5 Pond 1 0.50 0.70 0.71 0.00 - 3.25 14.3 - 17.3 19-Jun-04 Pond 6 0.62 0.23 2.69 3.79 - 6.09 12.9 - 16.8 Pond 1 0.02 0.79 0.03 0.00 - 1.39 17.4 - 19.2 25-Aug-04 Pond 6 1.51 0.28 5.39 2.24 - 6.31 15.0 - 24.0
Page 228 of 376 Figure 8-20: DO concentration within Benthic Metabolism Chambers deployed in Ponds 1 and 6 of the BWC System during 2004.
Page 229 of 376 8.5 Discussion
8.5.1 Phytoplankton production
One of the most important differences between Ponds 1 and 6 and between the winter and summer incubation dates was the concentration of Chlorophyll a – an indication of phytoplankton biomass and a dominant factor influencing 14C incubations. Primary production, as determined by the 14C incubations, within the BWC System changed dramatically between ponds and between seasons. The Pmax in Pond 1 was 1500 and 58 mg C. m-3. h-1 for the winter and summer incubation respectively. Comparatively, phytoplankton community production was 32 times greater in the autumn/winter incubation than the spring/summer incubation, regardless of the higher inorganic nutrient concentration present in the spring/summer incubation. During both incubations in Pond 1, the PAR Ik similar (Table 8-5), indicating that the phytoplankton community of Pond 1 achieved maximum production under the same or similar light conditions during both incubations. Displayed in Figure 8-14, the initial slope of the PI curves for Pond 1 was much higher in the winter incubation than that in the summer incubation, indicating that phytoplankton community production was not only greater, but increased more dramatically in response to increased light intensity.
The dominant primary producing phytoplankton, based on size fraction, within aquatic environments results from a combination of abiotic factors within the water column, light, water turbulence, species flotation mechanisms and inorganic nutrient concentrations (Ogilvie and Mitchell 1998; Burford and Rothlisberg 1999; Elser et al. 2002; Jassby et al. 2002; Schallenberg and Burms 2004). In two of his research papers, Fogg (1991 and 1995) found that when phytoplankton reduced in size to less than 20µm, nutrient uptake was almost entirely by molecular diffusion (in place of passive diffusion), making them comparatively more efficient at nutrient uptake than larger sized phytoplankton. As a rule of thumb, the relative size of phytoplankton within freshwater environments increases with the increased bioavailability of inorganic nutrients within the water body. Generally, smaller size phytoplankton species are dominant in oligotrophic waters, and larger phytoplankton species are dominant in mesotrophic or eutrophic water bodies (Wetzel 2001). In the case of this
Page 230 of 376 investigation, picoplankton productivity exceeded 50% of the total community productivity in all incubations except in the autumn/winter Pond 1 incubation – where microplankton was the dominant producer. Although this data does not fit into the commonly accepted pattern of phytoplankton size productivity in relation to the trophic state of an ecosystem, it highlights the unique abiotic and biotic environment of stormwater treatment ponds and highlights the importance of other abiotic parameters governing phytoplankton community composition and structure. For example, water flow into, within and out of Ponds 1 and 6 of the BWC System is restricted to the time of catchment rainfall events. Thus, water movement within Ponds 1 and 6 is either non existent or at relatively low velocities for the majority of time (see Chapter 4). This could, potentially, enhance the sedimentation of larger phytoplankton species from the water column. Additionally, small phytoplankton species have a faster physiological response time to that of larger phytoplankton species. As such they tend to out compete and dominate in aquatic ecosystems that undergo rapid and constant change or those which are subject to regular disturbances (Sigee 2004).
Differences between Pond 1 and 6. Splitting this data into Pond 1 and Pond 6, primary production (per µg Chl. a h-1) was greatest in Pond 1 during winter and Pond 6 during summer. Keeping in mind that this result is based on modelled data from two data points, the question rises: why is primary productivity higher in the winter within Pond 1, and higher in the summer within Pond 6? Major limitations to pelagic algal production are light, temperature, availability of inorganic nutrients and flotation mechanisms and water turbulence (Wetzel 2001).
Due to the presence of high inorganic nutrient concentrations in all incubations, the limitation of phytoplankton productivity due to nutrient depletion in unlikely. The temperature difference between each incubation date, displayed in Table 8-4, is approximately 7°C, with the minimum and maximum incubation temperatures 17.3°C and 26°C respectively. As a general rule in phycology, pelagic phytoplankton production increases exponentially with increased temperature up to an optimal temperature range (varying depending on algal species), thereafter declining (Sorokin
Page 231 of 376 and Krauss1962; Smayda 1969; Raven and Geider 1988). So, it is likely that water temperature had an influence on the productivity rates calculated on the winter and summer incubations of Ponds 1 and 6 water. However, this temperature related influence appears quite minimal based on the greater productivity rates measured during the colder winter incubation in Pond 1. Probably the best explanation to the lowered phytoplankton production in Pond 1 during the summer incubation can be attributed to the storm event within the BWC catchment three days preceding the incubation date (refer to Chapter 4). Table 8-4, showing the physicochemical water parameters of Ponds 1 and 6 on the incubation dates, lists low Chlorophyll a concentration and low redox potential for the December incubation – both basic indicators of recent storm flow into Pond 1 (see Chapter 4). The storm flow into Pond 1 would have caused the phytoplankton community to be ‘disturbed’, likely carrying much of the pre-existing phytoplankton biomass downstream. Low redox potential within Pond 1 reflects a reducing and oxygen starved environment – possibly caused by the breakdown of organic matter entering Pond 1 during the recent storm event.
Comparison to other systems To gain a better understanding of the primary production within Ponds 1 and 6 of the BWC System in a global context, comparison to other types of freshwater ecosystems is necessary. Table 8-6 displays primary production in a range of freshwater ecosystems, ranging from oligotrophic pristine environments to eutrophic polluted environments. Phytoplankton primary production rates displayed for this study have been normalised to mg C m-2 d-1 for direct comparison to the other studies presented in Table 8-10.
The classification of an aquatic ecosystem in terms of its trophic state, as displayed in Table 8-10, is based on the availability of nutrients for phytoplankton productivity (Boney 1975). This particular classification system was introduced in 1919, refined during the years of 1920-1930, and has now gained wide spread acceptance for many years (Naumann 1929 in Wetzel 2001). Eutrophic aquatic systems characteristically have a large supply of plant nutrients and potentially support high phytoplankton productivity, while oligotrophic aquatic systems are the opposite. Mesotrophic aquatic system fall in between oligotrophic and eutrophic systems, and
Page 232 of 376 hypereutrophic systems exceed that of eutrophic systems. Factors other than nutrients can control phytoplankton production in freshwaters, including pH, iron and light, and can cause confusion when classing particular water bodies based solely on the availability of plant nutrients.
The BWC System is a stormwater treatment wetland receiving stormwater runoff from a completely urbanised catchment. Pond 1 acts as the primary treatment pond, designed as a sedimentation basin and Pond 6 acts as a final polishing pond. Given the nature of water entering both Ponds 1 and 6, it is not surprising that, when compared to other freshwater aquatic systems, phytoplankton production is very high. According to Table 8-10, Ponds 1 and 6 can be classed hypereutrophic, with maximum production rates exceeding all those reported in the literature. Having said this, there is very limited published data on primary production in wetlands and small lakes receiving stormwater effluent.
With high productivity at the base of the aquatic food web within Ponds 1 and 6 of the BWC System, energy movement up the trophic cascade to influence the trophic status to the ponds is highly likely. In eutrophic and hypereutrophic ecosystems the risk of ecosystem imbalance is high. When looking at the response of an aquatic ecosystem in enhanced nutrient loading and high phytoplankton production rates, two scenarios arise. The first is termed a homeostatic ecosystem, which any increase in phytoplankton production is matched by the increased production of all higher trophic organisms (zooplankton and fish) (Sigee 2004). The second is termed a non- homeostatic ecosystem, and is the result of a homeostatic ecosystem breaking becoming unbalanced (Sigee 2004). A break down in a homeostatic ecosystem is generally the result of the growth of dense populations of cyanobacteria which inhibit zooplankton grazing via toxins and, when they form dense mats, size. This gives rise to a series of ecosystem level impacts such has hypoxia, harmful algal blooms and loss of aesthetic and amenity values (Sutcliffe and Jones 1992; Sommarunga and Robarts 1997; Kemp et al. 2005; Davis and Koop 2006; Smith et al. 2006). Given the two scenario, Ponds 1 and 6 appeared to be a homeostatic ecosystem, where by increased phytoplankton production was matched up the trophic food chain. This can be concluded by very limited cyanobacteria blooms being recorded within the ponds
Page 233 of 376 (see Chapter 7), lack of any dense algal mats forming and zero fish kills events over the entire study regime and general aesthetic nature of the ponds.
Table 8-10: Comparison of freshwater pelagic phytoplankton production. Values given are mean or actual with the range given in parentheses. Trophic Location Ecosystem Productivity Reference state (mgC m-2 day-1) Oligotrophic Mertta, N.W.T., Lake polluted by 8.5 (0-170) Kalff and Canada sewage Welch 1974 Lawrence, Michigan, Small hardwater 99.3 (5-497) Wetzel 2001 USA lake Various Reservoirs – 151 (67-235) Kimmel et mean of 10 al. 1990) Mesotrophic Erkren, Sweden Large, deep lake 285 (40-2,205) Wetzel 2001 Clear, California , Large, shallow 438 (2-2,440 Wetzel 2001 USA lake Foreso, Denmark Large, deep, lake. 463 (0-1,380) Wetzel 2001 Many macrophytes Simpson River, Costal river 207 (119-362) Ferguson Australia system 2002 Lachlan River Flood plain 500 Robertson et Floodplain, Australia wetland al. 1999 Various Reservoirs – 570 (260-940) Kimmel et mean of 36 al. 1990) Eutrophic Bremer River, Urban river 1,100 (400-1,500) Pollard Australia system 2004 Brisbane river, Urban river 1,920 Pollard Australia system 2001 Brunswick River, Costal river 2,003 (1,605- Ferguson Australia system 3,660) 2002 Ovens River Flood plain 3,000 Robertson et Floodplain, Australia wetland al. 1996 Kis-Balaton Water supply 7,200 Voros et al. Reservoir, Hungry reservoir 2003 Various Reservoirs – 2,019 (1,125- Kimmel et mean of 21 3,975) al. 1990) This Pond 1, BWC Stormwater 606-19,308 This study studyàà System , Brisbane, treatment Australia detention pond Pond 6, BWC Stormwater 787-5,676 This study System, Brisbane, treatment Australia pond/wetland
Page 234 of 376 8.5.2 Bacterioplankton production Table 8-11 is a review of bacterioplankton productivity studies undertaken during the last 20-25 years in a range of marine, estuarine and freshwater environments. Although the ponds studied in this investigation are not marine or estuarine, it is useful to know what end of the bacterioplankton productivity spectrum they lie. For example, the bacterioplankton productivity rates calculated for Ponds 1 and 6 of the BWC System are substantially higher than all those reported elsewhere in natural water bodies within the literature, but somewhat similar to that of the polluted and eutrophic waters (i.e. Bremer River, Subtropical Australia; Hyper eutrophic reservoir, South Africa).
In almost all incubations, the concentration of bacterioplankton was highest in Pond 1, reaching a maximum concentration of 9.66x106 cells. mL-1 in the upper water stratum of Pond 1 during the August incubation. The lowest bacterioplankton concentration recorded occurred in the lower water stratum of Pond 6 during the 20-Apr-05 incubation (2.3x106 cells. mL-1) (Figure 8-16). Comparatively to other published studies, the concentration of bacterioplankton in Ponds 1 and 6 of the BWC System was at the mid to higher end of the spectrum. As stated earlier, there has been little to no published research papers that investigate bacterioplankton in stormwater treatment ponds and/or wetlands. Thus, comparisons are best made with eutrophic water bodies and municipal wastewater treatment systems. Kisand and Tammert (2000) found a similar bacterioplankton concentration to that of this study in a shallow eutrophic lake directly following a cyanobacterium bloom, as did Boon (Boon 1991) who found bacterioplankton concentrations between 1.0x106 – 1.6x107 in the Australian Murray River floodplain wetlands. Interestingly, Pollard (2002) and Pollard and Greenfield (1997) found bacterioplankton concentrations in an activated sludge wastewater treatment plant and anaerobic digesters to be 0.3-0.7x1010 and 0.04-1.1x1010 cell ml-1 respectively. To put the bacterioplankton cell counts stated into more perspective, oligotrophic lakes and rivers generally support a bacterioplankton community of between 0.1 and 2x106 cell ml-1 (Bell and Kuparinen 1984; Coveney and Wetzel 1992, 1995; Castillo et al. 2004). As a general rule of thumb, bacterioplankton numbers increase along a trophic gradient from oligotrophic to eutrophic (Figure 8-21).
Page 235 of 376 Bacterioplankton concentration (x106 cells mL-1) 0.5 1.0 3.7
Oligotrophic Mesotrophic Eutrophic
Figure 8-21: Increase in bacterioplankton concentration with increases in the trophic status of the ecosystem. Numbers displayed are means calculated by a review in Wetzel (2001) of 54 published research papers.
The specific growth rate of bacterioplankton communities refers to the volumetric increase in cell numbers divided by the number of bacterioplankton cells. Thus, the specific growth rates, µ, calculated in Table 8-8 show the growth rate for the entire bacterioplankton community, including bacterioplankton that are dormant (not dividing), and/or slowly growing (Figure 8-17) (Pollard and Greenfield 1997). The specific growth rate of a bacterioplankton community is an indicator of the dynamics and stability of aquatic ecosystems (Wetzel 2001). The bacterioplankton specific growth rates measured in Pond 1 and 6 of the BWC System were highly variable, ranging between 0.045 and 19.40 µ (d-1), with lower values comparable to activated sludge studies undertaken by Pollard (1997) and Pollard and Greenfield (1997). Higher specific growth rates reported within Ponds 1 and 6 of the BWC System are comparable to that of two subtropical river systems (Pollard, 2001), and series of shallow eutrophic lakes located in Denmark (Theil-Nielsen and Sondergaard 1999).
Page 236 of 376 Table 8-11: Review of published bacterioplankton productivity studies. Trophic state Location Ecosystem type Productivity Reference (g C m-3 d-1) Oligotrophic SW Michigan, USA Oligotrophic hardwater lake 49-124 Convey and Wetzel (1995) Mirror Lake, USA Freshwater lake 3.0 Cole (1988) Middle Orinoco Basin, Venezuela Freshwater river system 0.09-0.28 Castillo et al. (2004) South eastern Aust. Australian floodplain wetlands 0.006-0.07 Boon (1991) Lake 0.07-1.07 (Sommarunga 1995) Mesotrophic Sweden Freshwater lake 1.2x106 -1.7x106 Bell and Kuparine (1984) Southern Quebec, Canada Freshwater lakes, differing TP concentration 0.004-0.047 Rooney and Kalff (2003) Germany Shallow wetland 440 Marxsen (1999) Georgia, USA Swamp 88 Murray and Hodson (1985) Biwa, Japan Lake 100-1200 Nagata (1984) Sub tropical Australia Estuarine/freshwater River 1.5-2.74 Pollard (2001)
Eutrophic Lake Mendota, USA Freshwater lake 23.2 Cole (1988) Kis-Balaton Reservoir, Hungry Water supply reservoir 0.51-12 Voros et al. (2003) Mendota, Wisconsin, USA Lake 592-1123 Pedros-Alio and Brock (1982) Humboldt Lake, USA Lake 0.032 Sommarunga and Robarts (Sommarunga and Robarts 1997) Eastern Germany Freshwater, shallow lake 23.1* Nixdorf and Jander (2003) Xolotlan, Nicaragua Polymictic tropical lake 600-1200 Erikson et al. (1998) Mikolakskie, Poland Eutrophic lake ~2500 Gajewski and Chrost (1995) South Africa Hypereutrophic reservoir 7.2-168 Robarts and Wicks (1990) Bremer River Eutrophic subtropical river 2.5 Pollard (2004)
This Pond 1, BWC System, SE Qld. Aust Eutrophic small pond (inlet) 59-210 This study studyàà Pond 6, BWC System, SE Qld. Aust Eutrophic shallow pond (outlet) 8-175 This study
Page 237 of 376 Factors influencing bacterioplankton production The productivity of the bacterioplankton community in Ponds 1 and 6, as discussed earlier, was highly variable between both ponds and sample times, and was at the higher end to that reported in the published literature. When comparing the observed bacterioplankton production rates with that of some external factors likely to influence their production, one can determine what is likely to limit the productivity of bacterioplankton.
Temperature Probably the most obvious of external factors likely to limit bacterioplankton production in the BWC System is water temperature. Figure 8-22 displays a correlation analysis between bacterioplankton production measured in Ponds 1 and 6 and the temperature of the water column. This figure displays that with increased water column temperature, bacterioplankton productivity increases. Numerous authors have found a similar trend in bacterioplankton production, stating that bacterial productivity increases with increase temperature for a temperature range between 5-60 °C (Coveney and Wetzel 1995; Wetzel 2001; Sigee 2004)
Figure 8-22: Increased bacterioplankton production with increased water column temperature in Ponds 1 and 6 of the BWC System.
Page 238 of 376 Nutrients Ponds, lakes, rivers and reservoirs subject to an increasing inorganic nutrient load (either autochthonous or allochthonous sourced) are generally more productive, both on a secondary and primary level, than that of systems not (Coveney and Wetzel 1992; Carr et al. 2005; der Gucht et al. 2005). Australian waterways are generally phosphorus limited, indicating that ecosystem response from nutrient addition is likely to occur more often from phosphorus than that of other inorganic macronutrients such as ammonia, nitrate, potassium and calcium (Davis and Koop 2006). Figure 8-23 displays a correlation between bacterioplankton production and
PO4-P concentration, showing a non-linear exponential rise to maximum trend. Given that the data presented in Figure 8-23 was not designed to determine PO4 influence on bacterioplankton production, strong conclusions from this data can not be made. However, it appears that bacterioplankton production increases exponentially with increased PO4-P concentration, reaching a maximum production rate thereafter levelling out regardless of increased PO4-P concentration. The observed relationship between PO4 concentration and bacterioplankton productivity was similar to that reported by Currie and Klaff (1984b), in their study of the ability of freshwater bacteria to acquire and attain phosphorus.
Figure 8-23: Correlation analysis between PO4-P concentration and bacterioplankton productivity and chlorophyll a concentration. Chlorophyll a data taken from Table 9-2.
Also shown in Figure 8-23 is the change in Chlorophyll a concentration with increasing PO4-P concentration, displaying an opposite relationship to increasing PO4- P concentration and increasing bacterioplankton production. Competition between
Page 239 of 376 bacteria and algae for inorganic nutrients, particularly phosphorus, has been demonstrated by Rooney and Kalff (2003), indicating that bacterioplankton can, and often do, out compete phytoplankton. This appears to occur in Ponds 1 and 6 of the BWC System, shown in Figure 8-23, with bacterioplankton productivity increasing with increasing PO4-P concentration coupled with decreasing Chlorophyll a concentration. This relationship is revealed further using an XYZ scatter plot showing increasing bacterioplankton production with increased PO4 concentration and decreased chlorophyll a concentration (Figure 8-24).
Figure 8-24: Bacterioplankton production increase with increased PO4 concentration and decreased Chlorophyll a concentration.
In a series of papers published in 1984, Currie and Kalff (1984; 1984) investigated the ability and importance of freshwater bacterioplankton and phytoplankton in PO4-P uptake in a large oligotrophic lake in south western Canada. They found that bacterioplankton were markedly superior competitors for natural PO4-P concentration to that of phytoplankton, and that bacterioplankton are responsible for most of the
PO4-P uptake in most freshwater lakes. Currie and Kalff’s conclusions are consistent with numerous other published research papers (Rigler 1956; Schindler 1975; Berman and Stiller 1977; Coveney and Wetzel 1992; Kuparinen and Heinanen 1993; Harris 2001; Vadstein et al. 2003; Elmetri and Bell 2004; Davis and Koop 2006). In relation to the findings of this investigation, the fate of pelagic borne PO4-P within Ponds 1 and 6 of the BWC System appears to be strongly influenced by the productivity of the
Page 240 of 376 bacterioplankton community. This situation gives rise to the question of pelagic PO4-
P removal via phytoplankton uptake, and what importance it has the fate of PO4-P within stormwater treatment ponds. Bacterioplankton exudates containing nonorthophosphates may be an important source of inorganic P for phytoplankton, as reported by Currie and Kalff (1984) and/or the ability of phytoplankton to retain inorganic phosphorus (via luxury uptake) may be important. Never the less, it appears that the fate of PO4-P within the pelagic zone of Ponds 1 and 6 is in part driven by bacterioplankton and complex interactions with phytoplankton.
Dissolved organic carbon Being predominantly heterotrophic organisms, most bacterioplankton rely on an external carbon source to fuel cellular metabolism. In freshwater ecosystems, DOC acts as the carbon source for heterotrophic bacterioplankton metabolism, potentially limiting production if concentrations are low (Wetzel 2001). There was a correlation between bacterioplankton production and DOC concentration within the water column of Ponds 1 and 6 at the time of the [methyl-3H] thymidine incubations (Figure 8-25). This correlation states that bacterioplankton productivity increases with increased DOC concentration within the water column, a trend generally supported throughout the literature (Coveney and Wetzel 1992; Voros et al. 2003; Kisand and Noges 2004; der Gucht et al. 2005; Pearce 2005). Presented in Chapter 5, water column DOC concentration was found to increase with increase stormwater flow into 2 Pond 1 (linear, r 0.0005(1),52 = 0.48), but not Pond 6. Thus, bacterioplankton productivity would be expected to increase with increase stormwater flow into Pond 1. Most likely due to the increased input of energy rich dissolved organic substrates for bacterial decomposition by the Pond 1 bacterioplankton community.
Page 241 of 376 Figure 8-25: Increased bacterioplankton production with an increase in water column DOC concentration.
The effect of depth on bacterioplankton concentration, specific growth rates, population doubling time and productivity were neither strong nor consistent between sample dates. Although differences between the upper and lower water stratums were measured, they were not consistently significant (according to ANOVA; all p values >0.05) during all [methyl-3H] thymidine incubations undertaken in Ponds 1 and 6 of the BWC System. This is, in part, in agreement with Convey and Wetzel’s (1995) (Coveney and Wetzel 1995) research that found similar bacterioplankton numbers with increasing depth in a large oligotrophic lake in Canada. In relatively shallow, small pond systems like that of the BWC System, it appears that the bacterioplankton community are more influenced by water temperature, PO4-P and DOC concentration (as discussed above) than that of water depth.
8.5.3 Benthic production - heterotrophy vs. autotrophy.
The incubation chambers deployed in Ponds 1 and 6 of the BWC System assessed benthic community production – or – the amount of organic carbon produced and consumed within benthic zones of the Ponds 1 and 6 ecosystems. Looking at the data presented in Table 8-9, the gross benthic community production and respiration rates for Ponds 1 and 6 during four incubations have been calculated. During all four incubation times, benthic community production within Pond 1 was different to that
Page 242 of 376 of Pond 6 (ANOVA; p = <0.01), with Pond 6 exhibiting consistently higher Gross daily Primary Production (GPP24) rates. Additionally, Pond 1 daily Respiration (R24) was consistently greater than that of the GPP24 of the same time period, and displayed a much lower and narrower DO range. As an approximate means of classifying Pond 1 and 6 ecosystems (autotrophic verus heterotrophic) at the time of the benthic metabolism incubations, the ratio between GPP24 and R24 can be calculated. Table 8-9 displays GPP24/R24 ratios for Pond 1 and 6 of the BWC System during the benthic metabolism incubations. A ratio value of 1.0 would describe a system where carbon is produced and consumed at the same rate. A ratio less than 1.0 indicates a system that consumes more carbon than is being internally produced, and a value greater that 1.0 indicates a system that produces carbon greater than is being consumed. Pond 1 of the BWC System, in three out of the four incubations, had a GPP24/R24 ratio of < 1, decreasing from a high of 1.39 in the January incubation to 0.95 in the April incubation, then to 0.71 in the June incubation and then reaching the lowest value of 0.06 during the August incubation. So, carbon consumption within the benthic environment within Pond 1 increases as we move from a summer subtropical wet climate through to a winter/autumn subtropical dry climate. In contrast to that of
Pond 1, Pond 6 GPP24/R24 ratio increases, suggesting that the benthic environment is producing organic carbon at an increasingly higher rate than is being consumed by heterotrophic organisms. Looking at the rainfall pattern within the BWC System catchment displayed in Figure 8-13, the deployment of the chambers roughly follows a pattern of decreasing rainfall frequency and intensity within the BWC System catchment. This would in turn result in a decreasing pollutant load into the BWC System. Combining the data presented in Table 8-9 and Figure 8-13, Figure 8-26 displays the relationship found between the GPP24/R24 ratio and time since last rainfall within the BWC System catchment.
Page 243 of 376 Figure 8-26: GPP24/R24 ratio versus total 2 week rainfall preceding the deployment of the chambers within Ponds 1 and 6.
The basic relationship shown in Figure 8-26 states that with increased time since the last rainfall event within the BWC System catchment, the Pond 1 GPP24/R24 ratio decreases. The behaviour of Pond 6 was contrary to that of Pond 1 with the
GPP24/R24 ratio within Pond 6 increasing with increased time since the last rainfall event within the BWC Catchment, consistently staying above 1 – or within the autotrophic production dominated range. Given this information, the benthic zone within Ponds 1 and 6 appear to behave differently with the advent of storm flow entering the BWC System. Thus each pond needs to be discussed as different ecosystems. Pond 1, by and large, remains a heterotrophic system, increasing the consumption of C with increased time since the last rainfall event. Pond 6 remains an autotrophic dominated system, increasing carbon production with increased time since last rainfall event. With increased time since last rainfall event influencing both systems to function close to a GPP24/R24 ratio of 1 – where the production of carbon equals the consumption of carbon or autotrophic production equals heterotrophic respiration. Thinking of the benthic environment within Ponds 1 and 6, and that of stormwater entering and moving through the BWC System the benthic metabolism patterns displayed and discussed above could be explained by a number of reasons.
Page 244 of 376 Firstly, the flow of increased stormwater from increased rainfall within the BWC Catchment will transport with it particulate organic matter ranging from grass clippings and vegetative debris to benthic particulate matter (Brinkmann 1985; Butler and Davies 2000; Lee et al. 2002; Taebi and Droste 2004). Upon entering Pond 1, much of this particulate organic matter would settle out and accumulate in the submerged benthic environment. It would be expected that with increased time, the rate of breakdown of settled particulate matter would increase, as shown by Gulis and Suberkropp (2003) and Carlisle and Clements (2005), consuming oxygen in the process. The relationship shown between time since last rainfall within the BWC Catchment and the GPP24/R24 ratio could be the result of the increased breakdown of settled particulate organic matter with increased time since last rainfall event – thus reducing the
GPP24/R24 ratio.
Secondly, assuming that the majority of particulate organic matter is removed in Pond 1 of the BWC System (or bypasses Ponds 2-6 via the high flow bypass channel) water entering Pond 6 would have a considerably less particulate organic matter load. Thus, the oxygen demand on the breakdown of gross particulate organic matter within Pond 6 is likely to be far less than that within Pond 1. Given that Pond 6 was consistently within the GPP24/R24 autotrophic range, the increased production of organic matter, as opposed to consumption of, would indicate that Pond 6 benthic metabolism is highly influenced by autotrophic organisms. From Chapter 4, it was shown the water is held within Pond 6 for up to 8 days following a storm event. This long retention time would allow substantial opportunity for the autotrophic benthic microbial community to access inorganic nutrients direct from the water column to fuel production. Thus, one may expect that with low competition of heterotrophic bacteria, fungi and protozoans combined with inorganic nutrients and adequate light conditions, autotrophic benthic microbes will produce until a certain growth factor becomes limited. This may well be the onset of a flood event driven by rainfall within the BWC Catchment that would raise the water level of the pond and decrease the PAR reaching the benthic environment and associated autotrophic microbes.
Page 245 of 376 The rate and type of the trophic state (heterotrophic or autotrophic) of freshwaters can be greatly influenced by the level of nutrients occurring both within the water column and the benthic zone (Wetzel 2001). The initial concentration of dissolved NOx-N
PO4-P and DOC within each chamber during all four incubations was found to significantly influence the trophic state, as measured using GPP24/R24 ratios, of the benthic environment within Ponds 1 and 6. The concentration of DOC was shown to decrease with an increasing GPP24/R24 ratio, indicating a reduction of DOC with an increasing autotrophic system (inverse linear relationship). This, on the contrary, defies logic. Assuming that autotrophic production is a combination of pelagic and benthic algae, increased algal production increases levels of DOC within a given water body (Vymazal 1995; Wetzel 2001). What this relationship probably best demonstrates is the lack of importance that bacteria population have on DOC exudates from autotrophic sources. Commonly, a strong relationship exists between phytoplankton and benthic micro algae production and bacteria production in freshwaters, where an increase in phytoplankton production results in an increase in bacteria production (Almeida et al. 2005). However, a number of freshwater studies have indicated that DOC sourced from phytoplankton and benthic micro algae excretion account for only a small fraction of what they consume. With the remainder sourced from the breakdown of organic material within the ecosystem or transported to the system from an external source (Coveney 1982; Coveney and Wetzel 1995; Gulis and Suberkropp 2003). The inverse linear relationship shown between DOC and the GPP24/R24 ratio within Ponds 1 and 6, suggests that bacteria within the benthic zone are consuming DOC that is predominantly allochthonously sourced (i.e. from higher terrestrial plant and animal matter, leaf litter and particulate organic matter arriving via a urban runoff), relying little on that sourced from within the system (i.e. from autotrophic production).
Increasing NOx-N concentration within the incubation chambers deployed in Ponds 1 and 6 followed an exponential rise to maximum non- linear trend with an increasing
GPP24/R24 ratio. Being a product from an aerobic microbial reaction, NOx (NO2 &
NO3) the expectation that the concentration of NOx-N increases as an environment becomes more aerobic is normal (i.e. from algal production of oxygen) (Kadlec et al. 2000). The lowest NOx concentration within the study data set occurred in Pond 1 on th the 25 August 2004, when the GPP24/R24 ratio was 0.06 and the maximum DO
Page 246 of 376 concentration for the incubation period was 1.39 mg L-1 – both indicators of an extremely oxygen deprived environment that would reduce the production of the + aerobic bacteria responsible for the oxidation of NH4 to either NO3 or NO2
(collectively NOx). A strong positive linear regression between increased GPP24/R24 ratio and PO4-P concentration, suggests that the PO4-P concentration within the incubation chambers may be based on the limiting nutrient concept. In most freshwater ecosystems it is well documented that phytoplankton and algae are limited predominantly by inorganic phosphorus and there is intense competition for inorganic phosphorus between bacteria, benthic algae and pelagic phytoplankton (Currie and Kalff 1984, 1984; Vymazal 1995; Wetzel 2001). Making the assumption that phosphorus is most likely the limiting nutrient to both heterotrophic and autotrophic production within Ponds 1 and 6 of the BWC System, increases in PO4-P concentration would be expected to increase autotrophic production, like that displayed in the significant linear regression between increased GPP24/R24 ratio and
PO4-P ratio.
In summary, the influence of nutrient concentrations within Ponds 1 and 6 appears to have a significant effect on the trophic status of ponds. High DOC concentrations -1 reduce the GPP24/R24 ratio, with concentrations over 12 mg L turning the systems to net heterotrophic. Low PO4-P concentrations likely enhance the competition between bacteria and phytoplankton/benthic micro algae, with bacteria potentially out competing and dominating, as reported by (Currie and Kalff 1984, 1984). The concentration of NOx-N within the incubation chambers is probably a result of the trophic status of the ponds, rather than a cause, with low concentration present in heterotrophic conditions, thereafter increasing with increased autotrophy of the system/s.
Comparison to other systems When comparing the productivity data measured in this study with those of others, it is important to establish what was actually measured using the incubation chambers and what and how other studies measured benthic productivity. The productivity and respiration results presented in this chapter relate to the water column directly overlying the benthos in < 0.5m of water depth plus that of the benthic environment.
Page 247 of 376 As the domes involve manual deployment, water depth is restrictive and thus results cannot be assumed to equal those of deeper areas. Table 8-12 displays a comparison of GPP24 and R24 values obtained in this study to those of other investigations using incubation chambers, benthic core incubations and a number of other DO based methods.
Table 8-12: Comparison of GPP24/R24 rates measured in this study to those of other studies.
Location Ecosystem GPP24 R24 GPP24/R24 Reference (gC m-2 day- (gC m-2 day-1) ratio 1) Johnstone Subtropical 0.164 0.264 0.62 (Bunn et River forested river al. 1999) Mary River Subtropical 0.086 0.168 0.51 (Bunn et forested river al. 1999) Kyabra Arid 2.61 1.68 1.55 (Bunn et Creek waterhole al. 2003) Mayfield Arid 3.46 2.46 1.40 (Bunn et River waterhole al. 2003) Barcoo- Arid 1.86 1.03 1.81 (Bunn et Welford waterhole al. 2003) River Lake Shallow 1.4 - - (Qu et al. Illawarra coastal 2003) lagoon Pond 1 Stormwater 0.11-0.50 0.38-1.61 0.06-0.95 This study treatment detention pond Pond 6 Stormwater 0.62-1.5 0.23-0.42 1.67-5.38 This study treatment pond/wetland
From the comparative data present in Table 8-12, Pond 1 and 6 production and respiration rates (as measured by the dissolved oxygen method) seem to be within the range expected in subtropical and temperate freshwater environments. Pond 1 p/r calculated in this study was the lowest of that reported, indicating that the system is highly and uncharacteristically heterotrophic. Most likely the influence of the huge pollutant loads entering the system during storm events. Pond 6 metabolism appears to more ‘in line’ with that of other subtropical and temperate freshwater environments, possibly due to the treatment of stormwater from preceding ponds within the BWC System (Ponds 1, 2, 3, 4, and 5).
Page 248 of 376 8.6 Conclusion
To date, no published research papers have investigated the fate or behaviour of carbon within constructed stormwater treatment pond systems. Within the BWC System, the production and consumption of carbon: • by the phytoplankton community was comparable to that of hyper eutrophic freshwater aquatic environments. • by the bacterioplankton community was comparable to that of biological wastewater treatment plants. • within the benthos was net heterotrophic within Pond 1 and net autotrophic in Pond 6 – at rates comparable to other freshwater ecosystems within subtropical Australia.
Abiotic factors governing the production and consumption of carbon within the BWC System where not measured directly, however a number of basic conclusions can be drawn; • Water turbulence – an important factor governing phytoplankton community production (as opposed to light and nutrients). In all but one of the four productivity incubations conducted within the BWC System, the dominant producing phytoplankton (based on size) was that between the 0-2-2.0 µm. Another result suggested to be attributed to the hydraulic nature of the ponds. • Water temperature – increased water temperature increase bacterioplankton productivity
• PO4-P and DOC concentration – increased bacterioplankton production. • Time following storm event – increase benthos heterotrophy within Pond 1 and benthos autotrophy within Pond 6
Page 249 of 376 9 Chapter 9: Cycling of inorganic nitrogen and phosphorus in the pelagic and benthic zone
Page 250 of 376 9.1 Abstract Although stormwater ponds are being used as a management tool to reduce the concentration of nutrients within urban stormwater, the extent of and processes leading to nutrient reduction within the pond ecosystem remains largely unknown and unquantified. The aim of this chapter was to assess the nutrient removal capabilities of epiphyton, submerged macrophyte and phytoplankton communities inhabiting the pelagic and littoral zone, and investigate nutrient fluxes across the benthos/water column interface. Field bioassay lasting 7 days where conducted to assess the fate of
NH4-N, NOx-N and PO4-P within a; 1) phytoplankton community, 2) Ceratophyllum demersum/epiphyton community, 3) attached epiphyton community and 4), a Potamogeton javanicus/epiphyton community. Each community was assessed using a series of control/treatment jars, with a nutrient ‘spike’ containing dissolved N and P compounds added to each treatment jar. Water samples where taken daily from each jar for 7 days, with one bioassays conducted in Sept-04 and Jan-05. Nutrient fluxes across the benthos/water column interface was measured using dome shaped perspex benthic chambers (29.5cm diameter; 35cm height) deployed in two locations on four dates within Ponds 1 and 6 of the BWC System. Each incubation chapter allowed for the manual extraction of water for the analysis of TN, NH4-N, NOx-N, TP, PO4-P and DOC every 4 hours for a 24hr day/night period. The Ceratophyllum demersum/epiphyton complex community was superior at removing PO4-P and NH4- N from the water column during the Sept bioassay, with the Potamogeton javanicus/epiphyton community superior in PO4-P reduction during the Jan bioassay.
NH4-N within most incubation jars was exhausted prior to NOx-N, indicating a biological preference of NH4-N over that of NOx-N. A substantial movement of PO4-
P, NH4-N, and DOC from the benthos to the overlying water column was measured in
Pond 1, with a substantial movement of PO4-P and NH4-N from the water column to the benthos measured in Pond 6.
Keywords: Phytoplankton, Epiphyton, Submerged Macrophyte, Nitrogen, Phosphorus, Benthic
Page 251 of 376 9.2 Introduction Within the pond environment, benthic, planktonic and epiphytic algae and bacteria communities play a crucial role in the uptake, cycling and immobilization of inorganic forms of nitrogen and phosphorus (Coveney and Wetzel 1992; Drakare et al. 2003). In addition, the shallow water zones, often fringing the ponds, may support a submerged macrophyte/epiphyton community and a productive BMA community (Greenway 2002; Jenkins and Greenway 2005): both of which have been reported to have significant influence on the concentration of inorganic nutrients within pond and wetland environments (Havens et al. 1999; McCormick et al. 2001; DeBusk et al. 2004; Gaiser et al. 2004; Larned et al. 2004; Pietro et al. 2006). While there is much literature on the role benthic and planktonic algae and bacteria communities within freshwater environments (Wetzel 1993, 2001; Rejas et al. 2005; Pietro et al. 2006; Smith et al. 2006), limited research has been conducted in the context of stormwater treatment ponds (Johengen and LaRock 1993; Newman and Pietro 2001; Knight et al. 2003; Taylor et al. 2004). With stormwater ponds being increasingly used throughout our urban landscape as a means for ameliorating urban runoff, an understanding of the role these communities play in inorganic nitrogen and phosphorus removal and transformation is required.
Epiphyton
Epiphyton communities can play a key role in the direct PO4, NOx and NH4 reduction from the water column in ponds and wetlands (McCormick and Scinto 1999; Pietro et al. 2006), with biomass measurements providing a indicator for the rapid assessment of ecosystem health. Within the epiphyton biomass, bacteria and algae can co exist in ‘micro habitats’ thereby increasing the biodiversity of micro-organisms and enhancing inorganic nitrogen processing (Kadlec 1999; Dodds 2003; Scinto and Reddy 2003; DeBusk et al. 2004). Due the diverse array of organisms within an epiphytic community, epiphyton can be highly productive (1338-2863 mg C m-2 d-1), with rates often exceeding that of phytoplankton in open water systems (Robertson et al. 1996). In one such study, Wetzel (1964) reported that epiphyton can contribute up to 45% of total lake productivity, even given that the amount of area available for epiphyton growth was limited to the vegetation fringing the banks of the lake. Other studies have reported similar results, proving epiphyton attached to fringing
Page 252 of 376 vegetation of lakes and ponds to be major, and often dominant, producer in freshwater lakes (McCormick et al. 1998; Wetzel 2001).
Submerged macrophytes Submerged macrophytes, with attached epiphyton communities, compete directly with phytoplankton for inorganic nutrients and reduce the PAR within the water column – both factors can limit the occurrence of algal blooms and shift aquatic ecosystems from turbid phytoplankton boom/crash dominated ecosystems to more steady state macrophyte dominated ecosystems (van Donk and van de Bund 2002; Rodriguez et al. 2003; Ivor Norlin et al. 2005). In the past 5 years, a number of researchers have published results that have began to quantify the role and significance of submerged macrophytes in the cycling of inorganic nutrients, and their ecological role within pond/wetland environments (Engelhardt and Ritchie 2001; Joglekar et al. 2001; Rooney and Kalff 2003; Fritioff et al. 2004; Ivor Norlin et al. 2005; James et al. 2005; Pietro et al. 2006).. These studies have all concluded that, depending on the species, submerged macrophytes are a key element in overall aquatic ecosystem health. They also stated that submerged macrophytes have the ability to significantly remove inorganic nutrients directly from the water column and sediment zone (if species are rooted), reduce turbidity and the occurrence of algal blooms (including cyanobacteria blooms) and provide a more steady state ecosystem.
Phytoplankton Phytoplankton have been introduced and discussed in detail in Chapters 7 and 8, thus a major introduction to phytoplankton in not needed here. Briefly, phytoplankton communities within subtropical climates can be highly productive, consuming anywhere between 500 and 3000 mg C m-2 day-1 in eutrophic water bodies, with annual productivity often exceeding 100 g C m-2 yr-1 (Wetzel 2001). The highly productive nature of phytoplankton within freshwater lakes within the subtropical climate can strongly influence the loss of inorganic nutrients from the water column. Inorganic nutrient uptake by phytoplankton has been well studied, and is reported that;
Page 253 of 376 • phytoplankton nutritional requirements generally correlate to that of the nutrient loading into a particular system (rather than the classic Redfield ratio (Elser et al. 2002). + • phytoplankton will generally exhaust NH4 within the water column - - before NO3 or NO2 (collectively NOx) (Berman et al. 1984; Staehr and Sand-Jensen 2006). • phosphorus most often limits phytoplankton growth in Australian freshwater ecosystems (Eyre 2000; Harris 2001; Davis and Koop 2006).
Benthic zone The transfer of nutrients across the benthic/water interface in lake and wetland systems forms a key compartment in quantifying entire system nutrient dynamics (DeLaune et al. 1981; Boynton and Kemp 1985; Moore JR. et al. 1992; Kadlec and Knight 1996; Amus et al. 1998; Khoshmanesh et al. 1999). The importance of benthic communities in ecosystem processing is highlighted by their common inclusion in whole ecosystem studies throughout the literature (Ferguson 2002). Nutrient fluxes from submerged sediments can become an important component of ecosystem functioning due to their ability to substantially influence water quality. Specifically, benthic zones within ponds are known to be significant sources, and sinks, of bio available carbon, nitrogen and phosphorus. The benthic zone of lakes, ponds and wetlands are also major sites for the microbial mineralization of organic matter delivered to it from allochthonous (urban runoff) and autochthonous (phytoplankton production) sources. In zones within ponds where PAR can penetrate to the benthos, Benthic Micro Algae (BMA) can be a dominate trophic group within the ecosystem, utilizing nutrients sourced from both the overlying water column and that from within in the benthos itself (Aller and Yingst 1980; Boon et al. 1986; Khoshmanesh et al. 1999).
9.2.2 Research aims, objectives and research questions The aim of this chapter was to investigate the cycling of inorganic nitrogen and phosphorus within the pelagic and benthic zones of urban stormwater treatment ponds.
Page 254 of 376 Specific objectives of this chapter were to; • assess the inorganic nitrogen and phosphorus removal capabilities of epiphyton, submerged macrophytes and phytoplankton communities within Pond 1 and 6 of the BWC System., and • measure nutrient movement across the benthic/water column interface within Pond 1 and 6 of the BWC System. Specific research questions were; • What pelagic biotic community is most efficient at removing inorganic N and P from the water column? • Is there a difference in the movement of nutrients across the benthos/water column interface between Ponds 1 and 6? • Does the nutrient regeneration between the benthos/water column interface and benthic community metabolism appear to be significantly and functionally influenced from stormwater flow
Page 255 of 376 9.3 Methodology
9.3.1 The pelagic zone To measure the cycling of inorganic N and P within the pelagic zone of urban stormwater treatment ponds, 7-day field based bio-assays were employed. Field bio- assays have been used extensively to measure the cycling of nitrogen and phosphorus in a wide range of freshwater and marine environments (Dennison and Abal 1999; Piehler et al. 2004; Schallenberg and Burms 2004). This particular approach to conducting bio-assays allows the particular targeted biotic community to be assessed under ‘field conditions’ (light regime, water temperature). Bioassays lasting 7 days allow one to sufficiently measure the response of the targeted biotic community to the addition of inorganic N and P compounds.
Bio-assays lasted 7 complete days and were conducted on the 3- Sept-04 and the 15- Jan-05 within Pond 1 of the BWC System. Three biotic communities were assessed, with the 15-Jan-05 bioassay including a fourth community; • The phytoplankton community; • The epiphyton community colonising submerged stems of Schoenoplectus validus stems; • The Ceratophyllum demersum/epiphyton complex; and • The Potamogeton javanicus epiphyton complex (15-Jan-05 bioassay only).
On the day of incubation, 15L of water was taken from the upper 20cm of the water column from Pond 1 (termed stock water), homogenised in a 20L pre rinsed pickle barrel, and 1.5L decanted into 21 (or 27 on the 15-Jan-05 incubation) 2L clear plastic PET jars. Figure 9-1 displays a schematic diagram of the bioassay’s deployed in Pond 1 of the BWC System. An initial water sample of the homogenised ‘stock water’ was taken for the determination of NOx-N, NH4-N and PO4-P concentration (refer to Chapter 3 on dissolved nutrient sampling, storage and analysis procedures). This sample was then used as Day 1 nutrient concentrations within each jar of all control incubations.
Page 256 of 376 Incubation length = 7 days Volume of water extracted from each jar daily = 10mL **YSI SNODE 6600 reading taken daily
Phytoplankton Phytoplankton Epiphyton Epiphyton CeratophyllumCeratophyllum Base treatment treatment** control** treatment control treatment control / control**
Water column Potamogeton Potamogeton treatment control
15-Jan-05 assay only
Figure 9-1: Schematic of 7 day field bioassay’s undertaken in Pond 1 on the 3-Sept- 04 and 15-Jan-05.
It is important to note here that the January bioassay was affected by a flood event at Day 3. All incubations bottles were intact following the flood event, however some were carried into nearby macrophyte stands where they laid for up to 20 h. The data from this bioassay are presented, but not discussed in great detail due to the likely inaccuracy of the data. As such, much of the investigation is based around the September incubation only.
As displayed in Figure 9-1, each set of 3 jars represent one biotic community subject to treatment/control experiments. Each treatment jar was given a nutrient cocktail
‘spike’, representing the mean concentration of dissolved NH4-N, NOx-N and PO4-P entering a stormwater treatment system in subtropical Australia (Table 9-1) (Greenway 2002; Greenway and Jenkins 2004). The nutrient ‘spike’ was created by dissolving a known weight of laboratory grade reagents (KH2PO4 for PO4, KNO3 for
NO3 and NH4Cl for NH4) in 18.1 GHLRQLVHG ZDWHU WR FUHDWH D SSP VWRFN solution, which was then diluted to achieve the desired final concentration within the
Page 257 of 376 incubation jars (Table 9-1). Each control jar was left ‘un spiked’, at ambient conditions. The initial concentration for each control jar was that determined by the nutrient concentrations of the ‘stock water’, while the initial concentration for each treatment jar was that of the nutrient concentrations of the ‘stock water’ plus the concentration of nutrients within the nutrient cocktail.
Table 9-1: Nutrient spiking cocktail. Sourced from average inorganic nutrient inputs from raw, untreated stormwater (Greenway 2002; Greenway and Jenkins 2004). In organic Final nutrient conc of pond nutrient water within each ‘treatment’ incubation jar (mg.L-1) NH4 0.26 NOx 0.64
PO4 0.30
The ‘Base treatment/control’ jars displayed in Figure 9-1 was incubated in the dark for the length of the bioassay, thus providing an assessment of the inorganic nutrient processing of the heterotrophic community within the ‘stock water’. This Base control/treatment was used in the calculation of nitrogen and phosphorus loss from the range of biotic communities investigated, thus taking into account the processing of inorganic nutrients within the incubation jars from the heterotrophic component of the ‘stock water’.
Table 9-2 briefly outlines the methods undertaken for each treatment and control for all bioassays undertaken, stating the frequency of water sampling and in situ water quality measurements taken using a YSI SNODE 6600 (refer to Chapter 3 for methodology on calibration and use of the YSI SNODE 6600 multi probe). In more detail; • submerged macrophytes used in the bioassay’s, Ceratophyllum demersum and Potamogeton javanicus, were harvested from Pond 6 of the BWC System and a local pond (Golden Pond, Calamvale) respectively on the morning of each bioassay. Submerged macrophytes were gently washed with 18.1XOWUDILOWHUHG water to remove sediment, blot dried and weighed. 10g w/w of each submerged
Page 258 of 376 macrophyte species was added to 2L clear plastic PET jar of the Ceratophyllum demersum and Potamogeton javanicus treatment/controls. • epiphyton communities colonising Schoenoplectus validus stems within Pond 1 were harvested from the north facing Schoenoplectus validus stand by cutting 10cm submerged lengths of stem in the morning of each bioassay. Cut stems were selected based on having a greater than 60cm length above the water column. Thus indicating the submerged section of the stem had been submerged for an adequate length of time (> 2 weeks) for a productive community of epiphyton to establish. Based on the qualitative approximated stem density of the of Schoenoplectus validus stand, two cut, 10cm lengths of Schoenoplectus validus stems with attached epiphyton community were placed in 2L clear plastic PET jars of the Epiphyton treatment/control incubations. • phytoplankton community within the stock sampled water of Pond 1 was initially measured using a fluorescence probe attached to a YSI SNODE 6600 multimeter (according to Chapter 3). 1.5L of stock water containing the phytoplankton community of Pond 1 at the time of water collection was then added to 2L clear plastic PET jars for the Phytoplankton treatment/control incubations. The base control/treatment incubations consisted of 1.5L of stock water.
• water samples for the determination of NH4-N, NOx-N and PO4-P were taken from each incubation jar using a sterile syringe and processed on-site in accordance to Chapter 3. Chlorophyll a concentration was measured an a daily basis by submerging the YSI SNODE 6600 multimeter equipped with a fluorescence probe within phytoplankton treatment/control and base treatment/control jars. Fluorescence readings were then converted µg Chl a using Figure 7-2, Chapter 7.
Potamogeton javanicus biomass measurements In October 2004, large stands of Potamogeton javanicus were observed growing within Pond 6. As such, it was decided to include Potamogeton javanicus within the 15-Jan-05 bioassay (thus Potamogeton javanicus was not included in the initial 3- Sept-04 bioassay). Due to the extensive coverage of Potamogeton javanicus within Pond 6 by December 04, biomass measurements where taken along a depth profile of Pond 6. Biomass measurements where obtained by collecting all above and below
Page 259 of 376 ground biomass of Potamogeton javanicus within 0.25m2 quadrats spaced every 50cm along a depth transect within Pond 6. In each quadrat, Potamogeton javanicus and associated epiphytic community was collected, bagged and the depth of water column noted. Within the laboratory, the Potamogeton javanicus and associated epiphytic community was gently washed in 18.1XOWUDSXUHZDWHUWRUHPRYHSDUWLFXODWHPDWWHU blot dried, placed in brown paper bags and left to dry within an oven set at 60oC for 48 h. Bags were then removed from the oven, let cool in a desiccator and weighed. Values were reported in Dry Weight (DW) per m2 sediment.
Table 9-2: Summary of methods used in bioassays. Treatment / control Method Data collection (daily) Phytoplankton Unfiltered pond water left in jars, nutrient NOx-N, NH4-N cocktail added to treatment (3 jars), control left and PO4-P, and under ambient conditions (3 jars). Chl. a Epiphyton 2 x 10cm Schoenoplectus validus stems cut NOx-N, NH4-N from littoral region of Pond 1 colonised with and PO4-P epiphyton and added to each jar containing 1.5 L of unfiltered pond water. nutrient cocktail added to treatment (3 jars), control left under ambient conditions (3 jars). Ceratophyllum 10g w/w of Ceratophyllum spp. taken from a NOx-N, NH4-N demersum/epiphyton local pond and added to each jar containing 1.5 and PO4-P complex L of unfiltered pond water. Nutrient cocktail added to treatment (3 jars), control left under ambient conditions (3 jars). Potamogeton 10g w/w of Potamogeton javanicus spp. NOx-N, NH4-N javanicus/epiphyton taken from Pong 6 of the BWC System and and PO4-P complex added to each jar containing 1.5 L of unfiltered pond water. Nutrient cocktail added to treatment (3 jars), control left under ambient conditions (3 jars). Base treatment / Unfiltered pond water added to 3 jars, spiked NOx-N, NH4-N control with nutrient cocktail and wrapped in foil to and PO4-P, and exclude light. Chl. a
Nutrient processing calculations The calculation of nutrient processing within each treatment/control was achieved by subtracting the results obtained in each control jar from that of each treatment jar for each time step (24 hours). It is important to note here that the initial concentration of nutrients within each treatment jar is equal to that of the initial nutrient concentration of the pond water plus that added to it in the spike (Table 9-1). Nutrient data for each time step obtained in the Base control/treatment incubation jar was then subtracted
Page 260 of 376 from the adjusted treatment results, and a final nutrient concentration obtained. Thus results obtained and displayed in the following sections are; • Phytoplankton – processing of inorganic nutrients via phytoplankton (Equation 9-1) • Epiphyton - processing of inorganic nutrients via epiphyton community within the littoral zone of wetlands/ponds (Equation 9-1). • Ceratophyllum demersum/epiphyton and Potamogeton javanicus/epiphyton complex. - processing of inorganic nutrients via the submerged macrophyte complex’s within littoral zone of wetlands/ponds (Equation 9-1).
As the displayed results will reflect the changes in inorganic nutrient concentration within the incubation jars, the change per time step (24 hours) was calculated to account for the accumulated reduction in inorganic nutrient concentration within the jars over the 7-day incubation period (Equation 9-2).
Conc =() T − C − D (9-1) where; Conc = Concentration, mg L-1 T = mean of 3 replicate treatments, mg L-1 C = mean of 3 replicate controls, mg L-1 D = mean of 3 Base control/treatments, mg L-1
c c Nutloss= Conx − Con y (9-2) where; Nutloss = nutrient concentration within jar resulting from accumulated nutrient loss per time step, mg L-1 c -1 Con x = nutrient concentration at incubation 0 hours, mg L c -1 Con y = nutrient concentration at given hours since incubation began, mg L
These equations were created to display data resulting from the biological response by the epiphyton, Ceratophyllum demersum/epiphyton and Potamogeton
Page 261 of 376 javanicus/epiphyton complex or phytoplankton community to the ‘nutrient spike’ added or alternatively, a supply of inorganic nutrients from a rainfall event within the BWC System catchment. The initial inorganic nutrient conditions within the control jars of each treatment where assumed to be background, all falling below the background range experienced within the BWC System (Refer to Chapter 6). The change in phytoplankton biomass within the phytoplankton treatment/control jars over the course of the bioassays was calculated using Equation 9-3. Equations 9-4 and 9-5 were used to calculate the rate of inorganic nutrient loss from each incubation jar using mg L-1 and % of initial concentration loss respectively
Phytobiomass= Inital Chla −() Chla y − Chla x (9-3) Where; -1 Phytobiomass = Phytoplankton biomass at any give time, µg L -1 InitialChla = Phytoplankton concentration at beginning of bioassay, µg L -1 Chlay = Phytoplankton concentration at time y, µg L -1 Chlax = phytoplankton concentration at time x, µg L
Conc− Con c Nutloss = x z (9-4) rate d
Where; -1 Nutlossrate = nutrient concentration loss rate, mg L d c -1 Con z = Concentration of nutrient at 144 hours (end of incubation), mg L c d = number of days for concentration to reach Con z, d
()Conc− Con c x z ⋅100 Conc Nutloss = x (9-5) % d
Where; -1 Nutloss% = gross nutrient concentration reduction, %. d
Page 262 of 376 9.3.2 The benthic zone
Study sites The nutrient flux across the benthos/water column interface within Ponds 1 and 6 of the BWC System was measured on four occasions during 2004. In Pond 1, chambers where placed in the south east corner, approximately 3m north of the eastern stormwater inlet channel (Figure 9-2). Within Pond 6, chambers were place in the eastern corner of the pond, approximately 1m north of the microphyte burn dividing Pond 5 from Pond 6 (Figure 9-2). Both sites where chosen based on three factors; 1) water height (ease of installation), 2) proximity to the bank (for storing data loggers and batteries) and, 3) representative benthic area of the pond. The benthic zone of Pond 1 can be qualitatively described as being “rich in particulate organic matter”, while the benthic zone in Pond 6 can be described as “clayey”. Chambers were deployed within a water depth range between 30cm and 80cm, a factor dependant on the hydraulic conditions of the ponds prior to chamber deployment.
Incubation chambers were deployed within the BWC System over a range of “days following storm events”. As shown in Figure 9-3, chambers were deployed in Ponds 1 and 6 on the 19-Jan-04, 19-Apr-04, 19-Jun-04 and the 25-Aug-04 corresponding to 1, 2, 9 and 7 days following rainfall events within the catchments respectively
Figure 9-2: Location of incubation chambers within Ponds 1 and 6 of the BWC System.
Page 263 of 376 Figure 9-3: Daily rainfall within the BWC System catchment (data taken from Chapter 4), with red dots and drop lines indicating deployed incubation chambers within Ponds 1 and 6 for 24 hrs.
In-situ incubation chambers The nutrient flux across the benthos/water column interface was measured in situ, using perspex benthic incubation chambers, (Figure 9-4 and Table 9-3), located within Ponds 1 and 6 the BWC System (Figure 9-2).
Table 9-3: Perspex incubation chambers specifications. Specification/variable Ponds 1 and 6 –all deployment dates Chamber diameter 29.5 cm Chamber height 35 cm Chamber volume (litres) 6.15 Chamber PASA (m2) 0.0638 Chamber benthic contact surface area (m2) 0.648 DO probe YSI attached to TPS WD82 data logger DO logging frequency Every 15 mins Chamber nutrient sampling frequency Every 4 hrs Deployment time 24 hrs
Page 264 of 376 12 volt battery
Submersible pump
DO Probe Water in Water out
Figure 9-4: Schematic of the incubation chambers used for benthic metabolism calculations and nutrient flux between the benthos/water column interface. Small arrows indicate water flow.
The nutrient flux across the benthos/water column interface within the chambers was measured by extracting water out of the chambers every 0, 4, 8, 12, 16, 20 and 24 hours after chamber deployment. Using a sterile syringe, 100mL of water was extracted from each dome at each time interval, with 50mL filtered through 0.45µm millepore filter units for DOC, NH4-N, NOx-N and PO4-P analysis and 50mL left unfiltered for TOC, TN, and TP analysis (Refer Chapter 3 for full methodology on
DOC, NH4-N, NOx-N, PO4-P TOC, TN, and TP sample collection, storage and analysis). Pond water was then injected back into the dome to compensate for the water extracted.
The initial and final nutrient concentration for each incubation chamber within each pond on all four deployment dates was used to calculated the net flux of nutrients across the benthos/water column interface (Equation 9-5).
(conc− conc ) NF = 0 24 (9-5) SA
Page 265 of 376 where; NF = nutrient flux across benthos/water column interface (mg.m-2.day-1) -1 conc0 = nutrient concentration within chambers at 0 hours (mgL ) -1 conc24 = nutrient concentration within chamber at 24 hours (mgL ) SA = surface area of dome/s
Page 266 of 376 9.4 Results
Any in-situ based experiments conducted in a heterogenous environment, like that of a natural pond, has the problem of yielding results that are not 100% representative. Alternatively, lab based incubations have the ability to statistically better represent the targeted environment. However, the issue of what is measured in the lab compared to what is happening in the field arises. Given this paradox, the results presented in this chapter can be viewed as an estimation of the behaviour of pelagic and benthic environment within stormwater ponds and highly loaded or polluted freshwater environments.
9.4.1 The pelagic zone The raw inorganic nutrient results from the 7-day field bioassay conducted on the 3- Sept-04 and 15-Jan-05 are displayed in Tables A.D 1 and 2 respectively, within Appendix D. Tables A.D 1 and 2 display both the mean and SE for each treatment and control incubation conducted in this experiment. The mean values displayed in Tables A.D.1 and 2 are used in Equations 9-1 through to 9-5, thus Figures and Tables displaying these results do not display SE values (as the results have been calculated based on mean numbers). Using the mean concentration of inorganic N and P within each treatment and control incubation jar (Equation 9-1), Equation 9-2 calculated the reduction of NOx-N, NH4-N and PO4-P within the each bioassay as a result of the ‘treatment’ biotic communities (phytoplankton, epiphyton, Ceratophyllum demersum/epiphyton and Potamogeton javanicus/epiphyton communities). Raw Chlorophyll a readings taken in the Phytoplankton Treatment and Control jars, along with the Base Treatment/Control jars are also displayed in the Appendix D, with Table 9-4 displaying the calculated response (using Equation 11-3) of the phytoplankton community to inorganic nutrient addition.
Table 9-5 displays the calculated inorganic nutrient concentrations within incubation jars resulting from the biotic interactions with the added ‘nutrient spike’ during 3- Sept-04 and 15-Jan-05 assays – as determined using Equation 9-1.
Page 267 of 376 Phytoplankton community Inorganic nutrient losses resulting from the phytoplankton community during each bioassay showed significant variation between assay dates (T-Test, p = 0.02). Figure 11-2 displays the behaviour of inorganic N and P (as a result of the phytoplankton community) within the water column during both the 3-Sept-04 and 15-Jan-05 bioassays. Within both assays, NH4-N was rapidly removed from the water column, with the 15-Jan-05 assay exhausting PO4-P concentration by hour 96 of the incubation. Phytoplankton biomass increased gradually during the 3-Sept-04 assay, show in exponential growth characteristics. During the 15-Jan-05 assay, SK\WRSODQNWRQ JURZWK ZDV LQLWLDOO\ UDSLGO\ UHDFKLQJ D PD[LPXP RI ȝJ Chlorophyll a L-1 then crashed after h 48 of the incubation.
Table 9-4: Calculated response of the phytoplankton community within incubation jars of each bioassay date. Values calculated using mean Chlorophyll a measurements displayed in Table A.D 3, Appendix D using Equation 11-8QLWVDUHȝJ&KOa-1.
Incubation time (h) Date 0 24 48 72 96 120 144 3-Sept-04 20.7 4.8 7.1 10.4 22.6 38.3 69.2 15-Jan-05 15.9 29.1 50.8 0 7.4 9.2 9.9
As can be seen in Figure 9-5, the September incubation results appear more coherent than that of the January results. Using the September results only, the changing phytoplankton biomass within the incubation jars was found to significantly alter the concentration of NOx-N, NH4-N and PO4-P (Figures 11-3, 11-4 and 11-5 respectively) (p = 0.024). In reference to Figure 9-6, increased phytoplankton biomass resulted in a decrease concentration of NH4-N, exhibiting an exponential decay relationship. The story was much the same for phytoplankton biomass and
PO4-P concentration within in the bioassay’s (Figure 9-7). Increased phytoplankton biomass was also correlated with decreased NOx-N concentration within the bioassay jars, following a negative linear relationship (Figure 9-8). This relationship can be further analysed to include the influence of time of NOx-N concentration within the bioassay jars (Figure 9-9).
Page 268 of 376 Table 9-5: Calculated inorganic nutrient concentrations resulting from the biotic interactions with the added ‘nutrient spike’ during 3-Sept-04 and 15-Jan-05 assays. Units in mg L-1. Displayed value determined using Equation 11-1.
Time Ceratophyllum Potamogeton Nutrient (h) Phytoplankton Epiphyton demersum javanicus
3-Sept-04 NOx 0 0.69 0.65 0.65 n/d 24 0.29 0.60 0.28 n/d 48 0.30 0.58 0.28 n/d 72 0.31 0.56 0.28 n/d 96 0.30 0.57 0.17 n/d 120 0.31 0.51 0.15 n/d 144 0.31 0.42 0.22 n/d
NH4-N 0 0.28 0.27 0.27 n/d 24 0.31 0.16 0.12 n/d 48 0.36 0.03 0.25 n/d 72 0.40 0.00 0.29 n/d 96 0.43 0.00 0.27 n/d 120 0.42 0.00 0.27 n/d 144 0.48 0.00 0.27 n/d
PO4-P 0 0.38 0.39 0.39 n/d 24 0.37 0.23 0.12 n/d 48 0.34 0.25 0.11 n/d 72 0.32 0.27 0.09 n/d 96 0.36 0.21 0.15 n/d 120 0.34 0.21 0.15 n/d 144 0.36 0.18 0.18 n/d 15-Jan-05 NOx 0 0.65 0.65 0.65 0.65 24 0.21 0.24 0.45 0.20 48 0.36 0.40 0.29 0.35 72 0.53 0.55 0.11 0.50 96 0.45 0.45 0.20 0.44 120 0.24 0.26 0.38 0.27 144 0.17 0.14 0.47 0.21
NH4-N 0 0.27 0.27 0.27 0.27 24 0.00 0.00 0.37 0.10 48 0.00 0.00 0.32 0.18 72 0.00 0.00 0.27 0.27 96 0.00 0.00 0.29 0.23 120 0.00 0.00 0.28 0.27 144 0.00 0.00 0.27 0.27
PO4-P 0 0.39 0.39 0.39 0.39 24 0.22 0.15 0.16 0.00 48 0.17 0.09 0.24 0.00 72 0.11 0.02 0.33 0.21 96 0.00 0.00 0.42 0.27 120 0.00 0.00 0.40 0.33 144 0.00 0.00 0.40 0.33
Page 269 of 376 Figure 9-5: Reduction of NH4-N, NOx-N and PO4-P via phytoplankton community.
Figure 9-6: NH4-N reduction verus Chlorophyll a concentration within incubation chambers during the September incubation only.
Page 270 of 376 Figure 9-7: PO4-P reduction verus Chlorophyll a concentration within incubation chambers during the September incubation only.
Figure 9-8: NOx-N reduction verus Chlorophyll a concentration within incubation chambers during the September incubation only.
Figure 9-9: Reduction in NOx concentration with increased incubation length within the Phytoplankton community incubation jars during the September incubation only.
Page 271 of 376 Epiphyton community Between bioassay dates, the epiphytic community colonising the 10cm lengths of Schoenoplectus validus stems exhibited differing inorganic nutrient reduction characteristics. In both assays, PO4-P was reduced rapidly, within the first 48 h of the bioassay (Figure 9-10). NH4-N reduction within the incubation jars showed a similar pattern between incubation dates, but was exhausted in the 15-Jan-05 incubation (Figure 9-10). Results indicate that the epiphyton community showed similar inorganic nutrient reduction behaviour between assay dates with PO4-P (T-test, p =
0.027), but not that of NOx-N or NH4-N.
Figure 9-10: Reduction of NH4-N, NOx-N and PO4-P via epiphytic community attached to 2 x 10cm lengths of Schoenoplectus validus stems.
Page 272 of 376 Ceratophyllum demersum/epiphyton community The Ceratophyllum demersum/epiphyton community showed marked differences in inorganic nutrient reduction between incubation dates (Figure 9-10). In the 3-Sept-04 incubation, PO4-P and NH4-N were rapidly exhausting concentrations (within the initial 24 h), however in the 15-Jan-05 incubation PO4-P was not reduced to any significant level (single factor ANOVA; p = 0.064) nor did NH4-N concentrations follow a similar pattern to that of the 3-Sept-04 incubation (Figure 9-11).
Interestingly, at the 72 h mark of the 15-Jan-05 incubation, NH4-N concentration reached a ‘low’ then proceeded to increase for the remainder of the incubation.
Figure 9-11: Reduction of NH4-N, NOx-N and PO4-P via Ceratophyllum demersum/epiphyton complex.
Potamogeton javanicus/epiphyton community Nutrient reduction from the water column via the Potamogeton javanicus/epiphyton community was only assessed during the 15-Jan-05 incubation. All inorganic
Page 273 of 376 nutrients exhibited an initial strong decline in concentration within the water column (first 24 h), thereafter increasing in concentration for the reminder of the incubation
(Figure 9-12). With the exception of NH4-N, which decreased again after reaching a peak at 72 h.
Figure 9-12: Reduction of NH4-N, NOx-N and PO4-P via Potamogeton javanicus/epiphyton complex.
Potamogeton javanicus/epiphyton complex biomass results are summarized in Table 9-6 by grouping in accordance to water depth classes of 10cm. Greatest biomass values of the Potamogeton javanicus/epiphyton complex was measured within the 30- 40cm depth range, with no biomass measured below 1m water depth (Figure 9-13)
Table 9-6: Potamogeton javanicus/epiphyton community biomass measurements in Pond 6 along a depth transect (g DW m-2).
Depth class n min max mean S.E. S.D. 0-10 2 26.2 30.2 28.2 2.0 2.9 11-20 1 30.2 30.2 30.2 - - 21-30 4 21.1 127.2 74.0 28.6 57.2 31-40 9 102.9 153.8 133.0 5.2 15.6 41-50 13 20.8 141.4 98.0 11.1 40.1 51-60 9 18.3 123.3 56.3 13.2 39.5 61-70 8 18.3 134.7 69.0 14.9 42.0 71-80 7 16.7 60.2 38.2 6.2 16.3 81-90 4 17.1 26.8 21.3 2.1 4.3 91-100 1 17.1 17.1 17.1 - -
Page 274 of 376 Figure 9-13: Potamogeton javanicus/epiphyton community biomass measurements in Pond 6 along a depth transect, bars graph mean of replicate depth samples (see above table for n values), with SE bars.
Inorganic nutrient reduction rates Applying Equation 9-3 to the mean inorganic nutrient loss values calculated and presented in Table 9-7, the rate of inorganic nutrient processing over the course of each bioassay was assessed (Table 9-7). During the 3-Sept-04 bioassay, all finial concentrations within the bioassay jars were less than that of the initial concentration. During the 15-Jan-05 bioassay this was not the case. As such, to calculate nutrient reduction rates in bioassay where the final concentration was greater than the initial concentration, the value less than that of the initial concentration was used – as opposed to that of the final concentration. In situations where concentrations were never less than that of the initial concentration (in one case only) nutrient regeneration was assumed. Inorganic P reduction rates calculated for the Ceratophyllum demersum/epiphyton community on the 3-Sept-04 and the Potamogeton javanicus/epiphyton community during the 15-Jan-05 where the same, owing the massive reduction in PO4-P within the incubation jars within the initial 2 days of each bioassay.
Using bar charts to diagrammatically present these results (Figure 9-14), the most striking feature is the rate of PO4-P loss within the submerged macrophyte bioassay jars. During the Sept bioassay, the Ceratophyllum demersum/epiphyton community
Page 275 of 376 reduced the concentration of PO4-P at rates far exceeding that of the other treatments.
During the Jan bioassay, PO4-P reduction was greatest in the Potamogeton javanicus/epiphyton community.
Table 9-7: Mean inorganic nutrient reduction rates calculated (using Equation 9-4) for the four bioassays preformed. Units displayed in mg. L-1. d-1
Ceratophyllum Potamogeton Phytoplankton Epiphyton demersum/ javanicus/ community community epiphyton community epiphyton community 3-Sept-04 NOx-N 0.032 0.060 0.029 n/d NH4-N 0.091 0.074 0.272 n/d PO4-P 0.029 0.030 0.387 n/d 15-Jan-05 NOx-N 0.068 0.073 0.026 0.062 NH4-N 0.272 0.272 0.000 0.173 PO4-P 0.055 0.052 0.226 0.387
Figure 9-14: Graphical representation of mean inorganic nutrient reduction rates calculated for all four bioassay’s.
9.4.2 The benthic zone The nutrient flux between the benthos/water column interface in Ponds 1 and 6 of the BWC System during the four deployment dates varied considerably. Table 9-3 displays the raw nutrient concentration data measured in Ponds 1 and 6 over the four deployment dates. No significant trends were measured in reference to day/night behaviour of nutrients within the chambers, nor were Ponds 1 or 6 consistently similar or dissimilar. Summarising this data, Table 9-4 displays calculated mean (and SE)
Page 276 of 376 values of the nutrient flux across the benthos/water column interface within Ponds 1 and 6. Using data displayed in Table 9-4, a significant correlation was found between incubation time and NOx-N concentration and NH4-N concentration within Pond 1 (Figure 9-15). This figure displays a reduction in NOx-N concentration with increased incubation time, with a contrasting increase in NH4-N concentration. Again using the mean results displayed in Table 9-4, NOx-N concentrations were significantly related to DOC concentrations within both Ponds 1 and 6, exhibiting a highly significant inverse linear relationship (Figure 9-16). This correlation states that increased DOC concentration within the incubation chambers results in a decreased NOx-N concentration, with data grouped according to Pond.
Given the data presented in Tables 9-8 and 9-9, one can calculate the final movement of nutrients across the benthos/water column interface. It is important to note at this point, that the variation among the data sets (i.e. Pond 1 and Pond 6 nutrient movent during all 4 sampling dates) is large – as indicated by the SE values shown in Table 9- 9. Thus, the calculation of the movement of nutrients across the benthos/water column interface over the 24 hour incubation period should not be seen as an absolute number, rather an indication of the approximate direction of nutrient movement. Table 9-10 displays calculated nutrient movement across the benthos/water column interface in Ponds 1 and 6 from the 0 and 24hr mean nutrient concentration displayed in Table 9-9. Nutrient movement within Pond 1 was generally from the benthos to the water column, with the exception of Org-N and NOx-N. TN, TP, and DOC were ‘released’ from the benthos to the overlying water column at a rate of 0.732, 0.366 and 0.744 mg. m-2. d-1. The release of TN and TP was dominated by Org-N and Other-P compounds. Within Pond 6, nutrient movement was calculated to move from the overlying water column to the benthos, with the exception of DOC. TN and TP were reduced within the incubation chambers by a rate of 0.243 and 0.031 mg. m-2. d- 1 , with Org-N dominating the measured TN decrease and both Other-P and PO4-P contributing equally to the measured TP decrease.
Page 277 of 376 Table 9-8: Raw nutrient concentration within incubation chamber for the four deployment dates within Ponds 1 and 6, recorded at 4 hourly intervals. All values in mg L-1. Time Pond Nutrient Date 0 4 8 12 16 20 24 TN 0.838 0.826 0.610 0.504 0.945 1.109 1.120 Org-N 0.629 0.562 0.356 0.149 0.577 0.674 0.718 + NH4 0.128 0.205 0.249 0.320 0.368 0.412 0.392 Pond 1 NOx 0.081 0.059 0.005 0.035 0.000 0.023 0.010 TP 0.216 0.192 0.193 0.279 0.504 0.433 0.516 Org-P 0.124 0.108 0.065 0.125 0.350 0.294 0.344 - PO4 0.092 0.084 0.128 0.154 0.154 0.139 0.172 19-Jan-05 DOC n/d n/d n/d n/d n/d n/d n/d TN 3.325 2.137 3.251 1.360 3.123 3.008 2.018 Org-N 2.663 1.527 2.777 0.891 2.779 2.740 1.692 + NH4 0.210 0.187 0.079 0.151 0.064 0.070 0.099 NOx 0.452 0.423 0.395 0.318 0.280 0.198 0.227 Pond 6 TP 0.275 0.232 0.222 0.201 0.259 0.215 0.204 Org-P 0.089 0.068 0.068 0.057 0.122 0.080 0.060 - PO4 0.186 0.164 0.154 0.145 0.137 0.135 0.144 DOC n/d n/d n/d n/d n/d n/d n/d TN 0.820 0.600 0.381 0.464 0.547 0.698 0.730 Org-N 0.241 0.189 0.137 0.125 0.113 0.233 0.176 + NH4 0.500 0.326 0.152 0.253 0.354 0.388 0.475 Pond 1 NOx 0.078 0.085 0.091 0.086 0.080 0.077 0.079 TP 1.816 1.310 0.804 1.144 1.483 1.942 1.981 Org-P 1.793 1.269 0.745 1.096 1.447 1.917 1.964 - PO4 0.023 0.041 0.058 0.047 0.037 0.025 0.017 19-Apr-04 DOC 15.17 11.42 7.67 8.62 9.56 13.53 9.44 TN 0.420 0.426 0.432 0.443 0.454 0.404 0.376 Org-N 0.342 0.338 0.333 0.338 0.343 0.285 0.278 + NH4 0.000 0.009 0.018 0.025 0.033 0.040 0.018 NOx 0.078 0.080 0.082 0.079 0.077 0.079 0.080 Pond 6 TP 0.617 0.662 0.707 0.800 0.894 0.640 0.585 Org-P 0.604 0.646 0.688 0.788 0.888 0.638 0.580 - PO4 0.013 0.016 0.019 0.013 0.006 0.002 0.005 DOC 9.05 10.41 11.76 11.81 11.85 11.98 8.84 TN 0.078 0.249 0.215 0.254 0.293 0.283 0.309 Org-N 0.000 0.134 0.095 0.121 0.148 0.111 0.161 + NH4 0.000 0.037 0.042 0.054 0.066 0.094 0.070 Pond 1 NOx 0.078 0.079 0.078 0.079 0.080 0.078 0.079 TP 0.010 0.217 0.199 0.266 0.334 0.368 0.566 Org-P 0.000 0.217 0.193 0.255 0.317 0.346 0.557 - PO4 0.010 0.000 0.006 0.011 0.017 0.023 0.009 19-Jun DOC 5.11 6.24 7.48 7.37 7.25 6.75 7.48 TN 0.390 0.411 0.500 0.495 0.489 0.485 0.482 Org-N 0.002 0.200 0.063 0.107 0.151 0.116 0.098 + NH4 0.297 0.129 0.351 0.301 0.252 0.276 0.288 NOx 0.091 0.081 0.087 0.087 0.086 0.093 0.096 Pond 6 TP 0.195 0.155 0.165 0.140 0.114 0.173 0.307 Org-P 0.173 0.131 0.150 0.123 0.096 0.166 0.290 - PO4 0.022 0.024 0.016 0.017 0.018 0.007 0.017 DOC 7.22 6.51 8.02 6.68 5.35 6.77 6.38 TN 0.423 0.321 0.441 0.640 0.840 0.773 0.706 Org-N 0.349 0.256 0.366 0.549 0.733 0.672 0.612 + NH4 0.057 0.049 0.059 0.074 0.090 0.085 0.081 Pond 1 NOx 0.016 0.016 0.016 0.017 0.018 0.016 0.013 TP 0.627 0.731 0.994 1.352 1.711 1.800 1.608 Org-P 0.486 0.516 0.717 1.030 1.343 1.400 1.253 - PO4 0.141 0.215 0.277 0.323 0.368 0.400 0.355 25-Aug DOC 15.67 16.47 20.00 20.25 20.50 20.86 21.22 TN 0.276 0.377 0.439 0.362 0.285 0.368 0.451 Org-N 0.027 0.146 0.240 0.147 0.055 0.163 0.271 + NH4 0.214 0.199 0.173 0.189 0.205 0.175 0.144 Pond 6 NOx 0.036 0.032 0.027 0.026 0.025 0.030 0.036 TP 0.300 0.155 0.196 0.171 0.146 0.100 0.154 Org-P 0.259 0.119 0.175 0.144 0.112 0.070 0.126 - PO4 0.041 0.036 0.021 0.027 0.034 0.030 0.028
Page 278 of 376 Table 9-9: Mean and SE (italic numbers) values for measured nutrients within incubation chambers of Ponds 1 and 6. n=4.
+ 3- Pond 1 TN Org-N NH4 NOx TP Org-P PO4 DOC 0 hrs 0.540 0.305 0.171 0.063 0.667 0.601 0.067 11.983 0.181 0.130 0.113 0.016 0.404 0.411 0.031 3.440 4 hrs 0.499 0.285 0.154 0.060 0.613 0.528 0.085 11.377 0.133 0.096 0.069 0.016 0.264 0.262 0.047 2.953 8 hrs 0.412 0.239 0.126 0.048 0.548 0.430 0.117 11.717 0.082 0.071 0.048 0.022 0.207 0.176 0.059 4.142 12 hrs 0.466 0.236 0.175 0.054 0.760 0.627 0.134 12.080 0.080 0.105 0.066 0.017 0.285 0.254 0.070 4.101 16 hrs 0.656 0.393 0.220 0.045 1.008 0.864 0.144 12.437 0.147 0.155 0.082 0.021 0.345 0.307 0.081 4.086 20 hrs 0.716 0.423 0.245 0.049 1.136 0.989 0.147 13.713 0.170 0.147 0.090 0.017 0.426 0.401 0.089 4.074 24 hrs 0.716 0.417 0.255 0.045 1.168 1.030 0.138 12.713 0.166 0.145 0.105 0.019 0.370 0.367 0.081 4.291 Pond 6 0 hrs 1.103 0.759 0.180 0.164 0.347 0.281 0.066 8.410 0.741 0.640 0.063 0.097 0.093 0.113 0.041 0.596 4 hrs 0.838 0.553 0.131 0.154 0.301 0.241 0.060 9.193 0.433 0.327 0.043 0.090 0.122 0.136 0.035 1.344 8 hrs 1.156 0.853 0.155 0.148 0.323 0.270 0.053 9.883 0.699 0.644 0.073 0.084 0.129 0.141 0.034 1.080 12 hrs 0.665 0.371 0.167 0.128 0.328 0.278 0.051 9.147 0.233 0.181 0.057 0.065 0.158 0.171 0.032 1.484 16 hrs 1.088 0.832 0.139 0.117 0.353 0.305 0.049 8.413 0.680 0.652 0.053 0.056 0.183 0.195 0.030 1.886 20 hrs 1.066 0.826 0.140 0.100 0.282 0.239 0.044 9.367 0.648 0.639 0.054 0.035 0.122 0.135 0.031 1.504 24 hrs 0.832 0.585 0.137 0.110 0.313 0.264 0.049 8.630 0.396 0.371 0.057 0.041 0.096 0.116 0.032 1.243
Figure 9-15: Mean NOx-N and NH4-N concentration versus increased incubation time within Pond 1. Trend line represents 2nd order polynomial linear regression analysis. n=4
Page 279 of 376 Figure 9-16: Mean DOC vs. NOx-N concentration within incubation chambers within Ponds 1 and 6. n=14.
Table 9-10: Nutrient movement across the benthos/water column interface in Ponds 1 and 6. Increase refers to an increase in the nutrient concentrations within the chamber (gross flux from benthos to water column) while decrease refers to the decrease in the nutrient concentrations within the chamber (gross flux from water column to benthos), units mg. L-1. Rate values refer to the rate of nutrient flux across the benthos/water column interface, units mg. m-2. d-1.
TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Decrease 0.031 0.036 Pond Increase 0.466 0.020 0.233 0.206 0.028 0.474 1 Rate 0.732 0.049 0.031 0.057 0.366 0.323 0.044 0.744 Decrease 0.155 0.099 0.024 0.031 0.020 0.010 0.010 Pond Increase 0.110 6 Rate 0.243 0.155 0.038 0.049 0.031 0.016 0.016 0.173
Page 280 of 376 9.5 Discussion Inorganic nutrient cycling within lakes, ponds and wetlands occurs via a range of physical, chemical and biological mechanisms. The data presented in the above section provides information on the cycling of inorganic nitrogen and phosphorus within the pelagic and benthic zone of urban stormwater treatment ponds.
9.5.1 The pelagic zone
Phytoplankton community There were marked differences in the processing of inorganic N and P by the phytoplankton community between incubations conducted on the 3-Sept-04 and 15- Jan-05. The rapid growth phase of phytoplankton within the incubation chambers corresponded roughly with the removal of NH4-N from the water column by the phytoplankton community. In an early study on the characteristics of NH4 and NO3 uptake by phytoplankton in a freshwater lake, Berman et al. (1984) reported preferential uptake of NH4 by phytoplankton to that of NO3, stating that uptake of
NH4 increases with increased water column concentration in an exponential rise to maximum relationship. Given that the data presented in Figure 9-6 does not take into account phytoplankton productivity or direct measurement of phytoplankton NH4 uptake, a direct comparison to Berman’s study is somewhat speculative. However, the loss of NH4-N within the bioassay jars attributed to phytoplankton does influence the concentration of Chlorophyll a (as seen in Figure 9-6). Figure 9-6 describes that
NH4-N reduction within the incubation chambers follows an increase in Chlorophyll a concentration – a non linear exponential decay trend. Using Chlorophyll a as an indicator for algal biomass, the phytoplankton community appear to rapidly utilize
NH4-N within the water column thereafter the community increases in biomass with little to no additional increase in NH4-N uptake. Chlorophyll a, a photosynthetic pigment present in all species of phytoplankton, absorbs blue-violet and red wavelengths of light and produces photons that are then ultimately used as an energy source for the creation of organic matter in the photosynthesis process (Campbell, Mitchell et al. 1997; Gregor and Marsalek 2004). Vymazal (1995) states that nitrogen uptake by algae is indirectly associated with photosynthesis, and hence the productive characteristics of the algal community. Phytoplankton have the ability to assimilate
Page 281 of 376 nitrogen, or other essential nutrients, in excess of that required to meet productive demands. This physiological adaptation is particularly common in times of excess nutrient supply (Vymazal 1995); Sigee 2004) and may explain the rapid loss of NH4- N from the incubation jars followed by an increase in phytoplankton biomass.
In truly pelagic freshwater ecosystem NOx-N is reduced by two main processes. Denitrification and algae uptake. In eutrophic wetlands, lakes and ponds, denitrification in the major NOx-N removal mechanisms occurring at rates dependant on acidity, temperature, redox potential, and organic carbon presence (Howard- Williams 1985; Braskerud 2002; Dodds et al. 2004). It has been reported by many authors over the years that NH4-N presence can, and often does, suppress the uptake of NOx-N by phytoplankton. Given this, NOx-N reduction within the incubation jars is most likely a function of denitrification, with increased denitrification occurring with increased incubation time and Chlorophyll a concentration. One possible scenario explaining the NOx-N dynamics within the incubation jars includes the theory of carbon limitation of denitrification (Narkis et al. 1979; Burgoon 2000). Phytoplankton release a large variety of dissolved organic compounds extracellularly, increasing the amount of organic carbon available to heterotrophic metabolism (Bell and Sakshaug 1980; Wetzel 2001). These extracellular dissolved organic compounds leached out of phytoplankton cells have been proven to provide a significant source of carbon for pelagic heterotrophic communities, given that the pelagic zone in freshwaters is often referred to as a desert in relation to nutrient supply (Bell and Sakshaug 1980; Shackle et al. 2000; Aota and Nakajima 2001; Cole et al. 2002).
PO4-P reduction within the water column of the phytoplankton bioassay jars showed similar characteristics to the epiphyton community, reducing total concentration by 54% and 100% over the 7 day incubation period during September and January incubations respectively. Phosphorus loss within ponds is driven by hydrologic, soil and biotic processes (Kadlec 1999), however in the bioassay jars studied removal pathways are restricted to predominantly biological processes. Throughout the incubation period the reduction in PO4-P was correlated to with increasing
Chlorophyll a concentration (Figure 9-7). PO4-P uptake by pelagic algae predominantly occurs via active transport across the cell wall, generally increasing within the presence of light and production rate of algae (Cembella et al. 1983, 1984).
Page 282 of 376 Thus, algae PO4-P uptake is predominantly driven by metabolic processes and would expect to increase under increased metabolic activity by algae. However the direct relationship between algal PO4-P uptake under varying abiotic conditions still needs to receive major research attention (Vymazal 1995). Given that PO4-P loss is significantly correlated with an increase in Chlorophyll a concentration within the incubation jars, increased phytoplankton productivity (and thus increasing
Chlorophyll a concentration) can be assumed to increase PO4-P removal from the water column.
Epiphyton community The epiphyton community attached to the Schoenoplectus validus stem sections showed similar NH4-N and NOx-N reduction characteristics to that of the
Ceratophyllum demersum/epiphyton complex. PO4-P reduction within the epiphyton incubation jars was less than that of the Ceratophyllum demersum/epiphyton complex incubation jars, calculated at 0.03 and 0.05 mg L-1 day-1 for September and January incubations respectively – similar to that of the phytoplankton incubation jars. NH4-N reduction by the epiphyton community attached to the Schoenoplectus validus stem section was initially rapid, occurring within the initial 24 hours of the incubation experiment. Interestingly, the NH4-N concentration within the September incubation increased after 24 hours, as did NOx-N in the January incubations. This increase in inorganic nutrient concentration within the incubation jars could be the result of inorganic nutrient ‘lysing’ from the cut Schoenoplectus validus stem and/or from a stressed epiphyton community (due movement).
As stated earlier, NH4 is often the preferred inorganic nitrogen source for most aquatics plants, algae and bacterial uptake as no energy is needed for its assimilation nor is there any induction period of enzymes required for assimilation (Sigee 2004).
The favouritism of NH4 over the oxidised form of inorganic nitrogen (NO2 and NO3 – collectively NOx) by freshwater algae and bacteria would explain the rapid drop in
NH4-N concentration not only within the epiphyton community attached to the Schoenoplectus validus stem section, but within the Ceratophyllum demersum/epiphyton complex bioassay jar and phytoplankton community bioassay jar also.
Page 283 of 376 Epiphyton communities have the ability to assimilate phosphorus, alter the hydrodynamics and modify the local chemical environment in ways that can ultimately influences phosphorus retention by aquatic ecosystems (Dodds 2003;
Scinto and Reddy 2003). When looking at the mechanisms behind PO4-P assimilation by epiphyton, active transport is the key factor in how epiphyton communities acquire nutrients. As such epiphyton attached to substrates that have a high surface area to volume ratio can have phosphorus uptake rates of up to two times that of epiphyton of more prostrate glutinous forms (Dodds 2003; Scinto and Reddy 2003). This factor could help explain the increased PO4-P loss within the Ceratophyllum demersum/epiphyton complex incubation jars than that of the epiphyton community attached to the Schoenoplectus validus stem section. As no direct measurement of epiphyton biomass was made on either the Ceratophyllum demersum/epiphyton complex or the epiphyton community attached to the Schoenoplectus validus stem section direct conclusions on biomass uptake difference can not be made – only implied. However, PO4-P loss form the water column within the epiphyton incubation jars was calculated to be an order of magnitude less than that of the Ceratophyllum demersum/epiphyton and Potamogeton javanicus/epiphyton complex and similar to the of the phytoplankton community. In a study conducted in 1988, Moeller et al. found that epiphyton colonising macrophyte hosts can obtain up to 60% of their nutritional requirements from the host itself. Additionally, Moeller et al. (1988) stated that algal and bacterial communities within the epiphyton matrix simply obtain nutrients from their host as diffusion through the cell wall is quicker than that of diffusion through the complex epiphytic matrix. Of particular relevance to this investigation, Wetzel (Wetzel 1990, 1993) found that when macrophytes enter dormancy phases or senescence, releases of macrophyte tissue phosphorus is readily used by the attached epiphyton community. Given that the Schoenoplectus validus stems were cut and placed directly within the incubation chambers, it is possible that tissues within the stem of the Schoenoplectus validus would leach out, whereby the epiphyton community would readily utilize it – and thus altering the rate of PO4-P loss measured from the water column within the bioassay jars.
Page 284 of 376 Ceratophyllum demersum/epiphyton complex The behaviour of inorganic nutrients within the Ceratophyllum demersum/epiphyton complex was very different between the September and January incubation dates. Unfortunately, much of the data derived from the January incubation may be unrepresentative thus the majority of the discussion is based around the data collected during the September incubations.
Shown in Figure 9-14, the Ceratophyllum demersum/epiphyton complex removed
PO4-P and NOx-N from the water column within the incubation jars at a more rapid rate than that of the epiphyton or Phytoplankton community during the September -1 -1 incubation. NOx-N and PO4-P were reduced by 0.26 and 0.16 mg L day respectively by the Ceratophyllum demersum/epiphyton complex with concentration reduction efficiencies being 100% and 46% per day for NOx-N and PO4-P respectively. Little variation between treatments was found with NOx-N reduction by the Ceratophyllum demersum/epiphyton complex, however PO4-P reduction was somewhat variable between the treatments. Given these values, the Ceratophyllum demersum/epiphyton complex can be viewed as an extremely efficient community at reducing the concentration of NOx-N and PO4-P from the water column within stormwater ponds and wetlands. Mitch and Gosselink (2000) view inorganic nutrient uptake and storage by aquatic vegetation as an integral part of the biogeochemical cycles within natural wetland ecosystems, with submerged aquatic macrophytes having the ability to assimilate nutrients directly from the water column. In microcosm experiments undertaken in wetlands within the Florida Everglades, Pietro et al. (2006) found that PO4-P loss from the water column was related to the presence of submerged macrophytes, and not a result of physical or chemical processes in the water column and/or sorption of PO4-P onto the inside surface of the microcosm. The observed results of this study further indicate this through the noticeable reduction of
PO4-P during both incubations. The rapid loss of PO4-P from incubated pond water by the Ceratophyllum demersum/epiphyton complex experienced in this study follows a similar trend to other studies that have focused on the removal, or uptake, of PO4 by a Ceratophyllum demersum/epiphyton complex, reporting areal P uptake rates between 0.6 and 32.8 mg P m-2 d-1 (DeBusk et al. 2004; Pietro et al. 2006). According to Wetzel (2001), submerged macrophytes with attached epiphyton
Page 285 of 376 communities have the ability to absorb and retain vast amounts of PO4 directly from the water column – a phenomena called luxury consumption.
The reduction of NH4-N by the Ceratophyllum demersum/epiphyton complex was complete, occurring rapidly within the first 24 hours of the incubation. The reduction of NH4-N observed within the incubation jars would have likely occurred via nitrification and/or direct uptake by the Ceratophyllum demersum/epiphyton complex.
NH4-N is an energy-efficient source of nitrogen for aquatic plants (both uni and multicellular), and is the preferred species of inorganic nitrogen for uptake (Berman, Sheer et al. 1984; Vymazal 1995; Vymazal and Richardson 1995; Wetzel 2001). Due to the ability of many submerged aquatic species to exchange photosynthetic gases between the leaf surfaces and surrounding water, submerged aquatic vegetation supports a rich epiphyton community, with nitrification and dentification bacteria abundant within the epiphyton matrix (Eriksson and Weisner 1999). Eriksson and Weisner (1999) found that submerged macrophytes within wastewater treatment wetlands can enhance nitrogen removal through the encouragement of the nitrification and denitrification processes – providing an anaerobic surface area with aerobic micro-sites that support both nitrifying and denitrifying bacteria. The immense surface area of some submerged macrophyte species, like that of the Ceratophyllum demersum researched in this investigation, provide a high surface area for epiphytic growth and the possible enhancement of NH4-N reduction via direct uptake and nitrification.
NOx-N loss from the water column within the Ceratophyllum demersum/epiphyton complex incubation chambers occurs either by plant uptake or denitrification. As stated earlier, NH4 is the preferred nitrogen species for submerged macrophyte uptake (along with epiphyton and algae(Vymazal and Richardson 1995)), thus NOx-N reduction is more than likely dominated by denitrification when appreciable concentrations of NH4-N are available for algae and plant uptake. Comparing NOx-N reduction in each bioassay jar (Figure 11-10), there is little difference between the Ceratophyllum demersum/epiphyton complex, phytoplankton and epiphyton treatments. This further indicates that denitrification of NOx-N is the predominant pathway for NOx-N reduction (in place of plant/algae uptake), with the limitation of NOx-N reduction likely to be influenced by light, carbon supply and concentration of
Page 286 of 376 dissolved oxygen within the water column (Narkis, Rebhun et al. 1979; Rodgers, Breen et al. 1991; Ingersoll and L.A. 1998; Bachand and Horne 2000; Burgoon 2000).
Potamogeton javanicus/epiphyton complex Within Pond 6 of the BWC System Potamogeton javanicus was found to inhabit a depth range of 0-100cm, reaching peak biomass between a water depth of 30-40cm. The extensive growth of Potamogeton javanicus throughout Pond 6 was noticed in October 2004, and lead to its inclusion in the January incubation. However, the results of the bioassay, shown in Figure 11-8, maybe somewhat unrepresentative due the a flood event midway through the incubation. Nonetheless, if we take the data displayed the Potamogeton javanicus/epiphyton complex reduced all inorganic nutrients considerably in the initial 24 h of incubation, thereafter increasing. Based on inorganic nutrient reduction rates calculated, the Potamogeton javanicus/epiphyton complex was the most efficient at reducing PO4-P when compared to phytoplankton, epiphyton and Ceratophyllum demersum/epiphyton complex incubations, having similar NOx-N removal rates to that of the phytoplankton and epiphyton incubations.
In terms of NH4-N reduction, the Potamogeton javanicus/epiphyton complex showed reduction a rate less than that of the phytoplankton and epiphyton incubations, but substantially higher than that of the Ceratophyllum demersum/epiphyton complex. Due to the likely inaccuracy of the data, no major conclusions can be drawn about inorganic nutrient processing resulting from the Potamogeton javanicus/epiphyton complex.
Page 287 of 376 9.5.2 The benthic zone
The rate and type of the trophic state (heterotrophic or autotrophic) of freshwaters can be greatly influenced by the level of nutrients occurring both within the water column and the benthic zone (Wetzel 2001). Drawing on data from Chapter 8, the initial concentration of dissolved NOx-N PO4-P and DOC within each benthic chamber during all four incubations was found to significantly influence the trophic state, as measured using GPP24/R24 ratios, of the benthic environment within Ponds 1 and 6.
The concentration of DOC was shown to decrease with an increasing GPP24/R24 ratio, indicating a reduction of DOC with an increasing autotrophic system (inverse linear relationship). This, on the contrary, defies logic. Assuming that autotrophic production is a combination of pelagic and benthic algae, increased algal production increases levels of DOC within a given water body (Vymazal 1995; Wetzel 2001). What this relationship probably best demonstrates is the lack of importance that bacteria population have on DOC exudates from autotrophic sources. Commonly, a strong relationship exists between phytoplankton and benthic micro algae production and bacteria production in freshwaters, where an increase in phytoplankton production results in an increase in bacteria production (Almeida et al. 2005). However, a number of freshwater studies have indicated that DOC sourced from phytoplankton and benthic micro algae excretion account for only a small fraction of what they consume. With the remainder sourced from the breakdown of organic material within the ecosystem or transported to the system from an external source (Coveney 1982; Coveney and Wetzel 1995; Gulis and Suberkropp 2003). The inverse linear relationship shown between DOC and the GPP24/R24 ratio within Ponds 1 and 6, suggests that bacteria within the benthic zone are consuming DOC that is predominantly allochthonously sourced (i.e. from higher terrestrial plant and animal matter, leaf litter and particulate organic matter arriving via a urban runoff), relying little on that sourced from within the system (i.e. from autotrophic production).
Increasing NOx-N concentration within the incubation chambers deployed in Ponds 1 and 6 followed an exponential rise to maximum non- linear trend with an increasing
GPP24/R24 ratio. Being a product from an aerobic microbial reaction, NOx (NO2 &
NO3) the expectation that the concentration of NOx-N increases as an environment
Page 288 of 376 becomes more aerobic is normal (i.e. from algal production of oxygen) (Kadlec et al. 2000). The lowest NOx concentration within the study data set occurred in Pond 1 on th the 25 August 2004, when the GPP24/R24 ratio was 0.06 and the maximum DO concentration for the incubation period was 1.39 mg L-1 – both indicators of an extremely oxygen deprived environment that would reduce the production of the + aerobic bacteria responsible for the oxidation of NH4 to either NO3 or NO2
(collectively NOx). A strong positive linear regression between increased GPP24/R24 ratio and PO4-P concentration, suggests that the PO4-P concentration within the incubation chambers may be based on the limiting nutrient concept. In most freshwater ecosystems it is well documented that phytoplankton and algae are limited predominantly by inorganic phosphorus and there is intense competition for inorganic phosphorus between bacteria, benthic algae and pelagic phytoplankton (Currie and Kalff 1984, 1984; Vymazal 1995; Wetzel 2001). Making the assumption that phosphorus is most likely the limiting nutrient to both heterotrophic and autotrophic production within Ponds 1 and 6 of the BWC System, increases in PO4-P concentration would be expected to increase autotrophic production, like that displayed in the significant linear regression between increased GPP24/R24 ratio and
PO4-P ratio.
In summary, the influence of nutrient concentrations within Ponds 1 and 6 appears to have a significant effect of the trophic status of ponds. High DOC concentrations -1 reduce the GPP24/R24 ratio, with concentrations over 12 mg L turning the systems to net heterotrophic. Low PO4-P concentrations likely enhance the competition between bacteria and phytoplankton/benthic micro algae, with bacteria potentially out competing and dominating, as reported by (Currie and Kalff 1984, 1984). The concentration of NOx-N within the incubation chambers is probably a result of the trophic status of the ponds, rather than a cause, with low concentration present in heterotrophic conditions, thereafter increasing with increased autotrophy of the system/s.
As reported in numerous other studies world wide (Boynton and Kemp 1985; Miller- Way et al. 1994; Van Luijn et al. 1995; Berelson et al. 1998; Ferguson 2002; Cook et al. 2004), the flux of nutrients across the benthos/water column interface is an important compartment in total system dynamics. The regeneration of nutrients
Page 289 of 376 within Pond 1 and the loss of nutrients from the water column to the benthos in Pond 6 at the rates displayed in Table 9-10, provides link to the importance of the benthic environment (and the microbial community inhabiting them) in the reduction and generation of nutrients within urban stormwater ponds.
Page 290 of 376 9.6 Conclusion
The cycling of inorganic N and P within stormwater ponds has received limited attention within the published scientific literature. In terms of inorganic N and P loss within the pelagic zone; • The Ceratophyllum demersum/Epiphyton complex was superior at removing
PO4-P and NH4-N from the water column • The epiphyton and phytoplankton community displayed similar characteristics
in PO4-P concentration reduction from the water column, however rates where an order of magnitude less that that of the Ceratophyllum demersum/Epiphyton complex. • NOx-N losses from all incubation treatments were similar
Within the benthic zone;
• A substantial movement of PO4-P, NH4-N, and DOC from the benthos to the overlying water column was measured in Pond 1 – most likely due to the breakdown of allochthonous organic matter entering the pond from recent storm events.
• In Pond 6, a movement of PO4-P, NH4-N, and DOC from the overlying water column to the benthos was measured - most likely due to the presence of a productive BMA community sequesting essential nutrients from the water column.
Page 291 of 376 Section 3: Conclusions
Page 292 of 376 10 Chapter 10: A series of conceptual models
Page 293 of 376 Building a conceptual model In order gain a more detailed understanding on how the BWC System operates as a stormwater treatment device, various components of the research presented in Chapters 4 through to 9 can be used to create conceptual models. The use of conceptual models that investigate and display data can be beneficial in portraying basic water quality data in a way that summarises the behaviour/characteristics of systems in a broad context. Like all models, conceptual models are only as good as the data that is used to create them, and the validity of any assumptions made in their creation. Thus, the conceptual models presented in this chapter should be viewed as an approximate representation of what is occurring within the BWC System. Using the data presented in Chapters 4 – 9, models investigating aspects of the BWC System have been built to conceptualise; • the yearly load reduction of stormwater derived N, P and C within the BWC System; • phytoplankton production and associated inorganic nutrient uptake; • bacterioplankton production and associated inorganic nutrient uptake; and • benthic fluxes of N, P and C.
Yearly N, P and C load reduction – data derived from Chapters 3, 4, and 5 Using the catchment surface area, hydraulic flow and EMC data from Chapters 3, 4 and 5, the total yearly load of N, P, and C exported from the BWC Catchment, removed by the BWC System and that entering receiving waters was calculated using Equations 10-1 and 10-2.
CatchLoad= EMCPond1 inlet ⋅ Q yearly ⋅ SA Catchment (10-1) where; CatchLoad = total catchment yearly load of N, P and/or C, ton yr-1 -1 EMCPond1inlet = Event Mean Concentration of Pond 1 inlet, mg L 3 Qyearly = yearly volume of stormwater flow entering Pond 1, m
Nutloadx= EMC x ⋅ Q yr, x
Page 294 of 376 where; -1 Nutloadx = Nutrient load (N, P or C) at location x, ton yr -1 EMCx = Event Mean Concentration at location x, mg L 3 Qyr,x = yearly volume of stormwater flow passing location x, m
Phytoplankton production and inorganic N, P, and C uptake - data derived from Chapters 7 and 8 Phytoplankton primary production within Ponds 1 and 6 was modelled every fortnight between January 2004 and January 2005 based on the relationship between calculated phytoplankton production rates standardised to mgC-1.µg.Chl a-1 and PAR during both Sodium Bicarbonate [14C] radioisotope incubations within Ponds 1 and 6 (Chapter 8). Equation 10-3 was used to determine the annual amount of C fixed by the phytoplankton community within Pond 1 and 6. This calculation relies on the assumption that the total productivity rate for both Ponds 1 and 6 equals that of the mean productivity rate down the depth profile multiplied by the average base volume of the pond (data taken from Chapter 4). In specific reference to Pond 6, productivity data was extrapolated to cover the entire volume of Ponds 2-6.
In order to assess inorganic N and P uptake by phytoplankton on an annual basis, productivity data and Redfield ratio calculations were used (Equations 10-4 and 10-5 respectively). Redfield’s ratio calculations were based on a 41:7.2:1 C:N:P molar ratio of phytoplankton as determined by Redfield and reported in many texts and published literature.
d ∑ ⋅V ⋅ t ⋅ t 1 2 n Phyto = (10-3) PPannual 1,, 000 000 where;
PhytoPPannual = total yearly carbon fixed via phytoplankton production per given pond, kg. yr-1 d = modelled phytoplankton primary production at fortnightly depth intervals of given pond, mgC m-3. h-1
Page 295 of 376 V =Volume of given pond, m3 t1 = hour to day time conversion factor, 24 t2 = length of time between sampling dates within modelled data, days
d ∑ ⋅V ⋅ t ⋅ t 1 2 n 1,, 000 000 Phyt= ⋅ 7. 2 (10-4) inorg Nuptake 41 where;
PhytoInorgNuptake = total yearly inorganic N assimilation via phytoplankton production per given pond, kg. yr-1
d ∑ ⋅V ⋅ t ⋅ t 1 2 n 1,, 000 000 Phyt= ⋅ 1 (10-5) inorg Puptake 41 where;
PhytoInorgPuptake = total yearly inorganic P assimilation via phytoplankton production per given pond, kg. yr-1
Bacterioplankton production and inorganic N, P, and C uptake data derived from Chapter 8 In order to assess the amount of inorganic N and P uptake associated with bacterioplankton production within the BWC System a C:N:P ratio of 12.4:2.9:1 was adopted. Although the relative inorganic nutrient uptake ratio by bacterioplankton will change in relation to the growth phase of the bacterioplankton (exponential or stationary), this ratio has been reported in the literature as an approximate estimation for bacterioplankton during their exponential growth phase (Kroer 1994; Eccleston- Parry and Leadbeater 1995; Vrede et al. 2002). As the bacterioplankton productivity was measured during their exponential growth phase, this ratio is assumed to be a satisfactory estimate of bacterioplankton inorganic N and P uptake.
Page 296 of 376 Gross yearly production of bacterioplankton was estimated in Pond 1 from measured productivity values within the pond. Pond 6 bacterioplankton productivity values were extrapolated to include Ponds 2, 3, 4 and 5 due to their hydraulic connection. Equation 10-6 was used to calculate gross yearly production in the BWC System, with reported value then used in estimate inorganic N and P uptake according to the ratio stated in above paragraph (Equations 10-7 and 10-8). The volume of the given ponds was assumed to be at base volume (shown in Chapter 4).
d ∑ ⋅V ⋅ t ⋅ t 1 2 n Bacterio = (10-6) Pannual 1,,, 000 000 000 where;
BacterioPannual = total yearly carbon fixed via bacterioplankton production per given pond/s, ton. yr-1 d = measured bacterioplankton production, mgC m-3. h-1 V =Volume of given pond/s, m3 t1 = hour to day time conversion factor, 24 t2 = day to year conversion factor, 365
d ∑ ⋅V ⋅ t ⋅ t 1 2 n 1,,, 000 000 000 Bacterio= ⋅ 2. 9 (10-7) inorg Nuptake 12. 4 where;
BacterioInorgNuptake = total yearly inorganic N assimilation associated with bacterioplankton production per given pond, ton. yr-1
d ∑ ⋅V ⋅ t ⋅ t 1 2 n 1,,, 000 000 000 Bacterio= ⋅ 1 (10-8) inorg Puptake 12. 4
Page 297 of 376 where;
BacterioInorgPuptake = total yearly inorganic P assimilation associated with bacterioplankton production per given pond, ton. yr-1
Benthic fluxes – data derived from Chapters 4 and 9 The movement of nutrients across the benthos/water column interface was modelled over the bottom surface area of the BWC System. It was decided to model nutrients
PO4-P, NH4-N and DOC concentration changes within the BWC System due to the significant changes in concentration occurring within the chambers deployed in Ponds 1 and 6 (Chapter 9). TN, TP, Org-N, Other-P, and NOx-N data was not used, as changes in the concentration of these nutrients were either not substantial (NOx) or not deemed to be truly reflective of the nutrient flux across the benthos/water column interface (i.e. Org-N and Other-P fractions possible contain high levels or bacteria or phytoplankton). Using the BWC system bathymetry presented in Chapter 4, the approximate surface area of the benthic zone for each pond under at a mean water level (4.5 AHD) was calculated and used to model the flux of PO4-P, NH4-N and DOC across the benthos/water column interface and an annual basis (using Equation 10-9).
NFyr = NF ⋅ t ⋅ SA (10-9)
-1 NFyr = yearly nutrient flux, kg. yr NF = calculated nutrient flux, mg.m2.d-1 SA = Surface area of pond, m2 t = day to year conversion factor, 365
Page 298 of 376 The conceptual models
Using the methods outlined above, basic conceptual models have been created that summarise; • the total export of N, P and C from the BWC Catchment and that removed by the BWC System; • the inorganic N and P uptake by the phytoplankton and bacterioplankton communities within the BWC System; and • the flux of N, P and C across the benthos/water interface within the BWC System.
Yearly N, P and C load reduction Based on the conceptual model presented in Figure 10-1, the BWC System provided some level of treatment to stormwater produced within the BWC Catchment. As shown in this model, the BWC Catchment delivered a yearly load of 1.2, 0.3 and 5.4 ton of N, P, and DOC respectively to the BWC System. As a result of the high percentage of stormwater bypassing Ponds 2-6 of the BWC System (via the high flow bypass channel, see Chapter 4), only 0.3, 0.04 and 1.5 ton of N, P, and DOC entered Ponds 2-6 of the BWC System to receive treatment prior to entering receiving waters.
Looking at the efficiency of the BWC System at reducing yearly N, P, and DOC loads from the BWC Catchment, a substantial amount of N was removed (51.5%), with negligible amounts of DOC removed (Figure 10-1). Surprisingly, there was a calculated gross increase in TP load (8.5%), resulting from the production of TP within the BCW System over the modelled period (Jan -04 to Jan-05).
Page 299 of 376 Catchment runoff 1.2 tonne N 0.3 tonne P 5.4 tonne DOC High flow bypass channel 0.9 tonne N 0.26 tonne P Pond 1 3.9 tonne DOC Nutrient reduction, with the exception of TP and Other-P
Ponds 2-6 Flow out of Pond Flow into Ponds 2-6 Reduction of TP and 6 0.3 tonne N Other-P only 0.17tonne N 0.04 tonne P 0.06 tonne P 1.5 tonne DOC 1.3 tonne DOC
Catchment runoff nutrient Nutrient removal within the reduction BWC System 51.5% N 51.5% N -8.5% P -29% P 4.7% DOC 16% DOC Figure 10-1: Diagrammatic representation of 2004/2005 yearly load reduction of TN, TP and DOC within the BWC System and from catchment runoff loads.
Phytoplankton productivity and N & P uptake The production rate of the phytoplankton within Pond 1 and Ponds 2-6 of the BWC System over the study period was estimated, based on modelled data, to be 6.1 and 8.98 ton C yr-1; collectively totalling 15.08 ton C yr-1. Based on this calculation, the amount of inorganic N and P consumed by the phytoplankton community over the study year was estimated, using Redfield’s Ratio, and can be used to infer a gross pathway for inorganic N and P loss within the BWC System via phytoplankton uptake (Figure 10-2). Given the high productivity rates, the loss of inorganic forms of N and P via phytoplankton production within the BWC System over the course of the study period was estimated at 2.64 ton of N and 0.065 ton of P per yr.
Page 300 of 376 The BWC System
Phytoplankton production = 15.1 ton C yr-1
Inorganic N and P uptake N 2.6 ton yr-1 associated with phytoplankton production P 0.07 ton yr-1
Figure 10-2: Modelled N and P removal within the BWC System via phytoplankton uptake.
Bacterioplankton productivity and N & P uptake The production rate of the bacterioplankton community within the BWC System over the study period was estimated, based on modelled data, to be 222 ton C yr-1. Based on this calculation, the amount of inorganic N and P consumed by the bacterioplankton community over the study year was estimated and can be used to infer a gross pathway for inorganic N and P loss within the BWC System via bacterioplankton uptake (Figure 10-3). This loss of inorganic forms of N and P from the water column within the BWC system was calculated at 77 ton of N and 17.9 ton of P per yr – substantially greater than that of the phytoplankton community.
The BWC System
Bacterioplankton production = 222 ton C yr-1
Inorganic N and P uptake N 77 ton yr-1 associated with bacterioplankton production P 17.9 ton yr-1
Figure 10-3: Modelled N and P within the BWC System via bacterioplankton uptake.
Flux of nutrients across the benthos/water column interface
Within Pond 1, there was a net flux of PO4-P, NH4-N and DOC from the benthos to the overlying water column, calculated at 55.6, 40.6 and 952 kg yr-1 respectively
(Figure 10-4). The movement of PO4-P and NH4-N across the benthos/water column interface within Ponds 2-6 was opposite to that in Pond 1, with 85.7 and 218 kg yr-1 of
PO4-P and NH4-N lost for the water column to the benthos respectively (Figure 10-5).
Page 301 of 376 The BWC System Pond 1 Ponds 2-6
Water column Water column -1 NH4-N 40.6 kg yr -1 -1 PO4-P 55.6 kg yr -1 NH4-N 218 kg yr -1 DOC 1587 kg yr -1 DOC 952 kg yr PO4-P 85.7 kg yr
Benthos Benthos
Figure 10-4: Modelled yearly flux of nutrients across the benthos/water column interface in the BWC System. Calculated values are based on the mean flux rate from four incubation experiments. Pond 6 data was extrapolated across Ponds 2, 3, 4 and 5.
The fate of N, P and DOC on an annual time scale…
Combining all of the above models, Figure 10-5 presents an interesting story exploring the fate of N, P and DOC within the BWC System on an annual time scale. Via the radioisotope incubation experiments and basic modelling, the uptake of inorganic N and P via phytoplankton and bacterioplankton was estimated. These values, displayed in Figure 10-4, provide an insight into the amount of inorganic N and P these microtrophic organisms consume, on an annual basis. Comparing these values to that of the input catchment loads of N, P and DOC (Figure 10-4), there appears to be some unbalance. Total DOC entering the BWC System equalled 5.4 ton, but the total production of the phytoplankton and bacterioplankton communities equalled 237.51 ton. The estimated inorganic N and P uptake via phytoplankton and bacterioplankton production also far exceeded that of the measured N and P loads entering the BWC System. Assuming the calculations are within an order of magnitude of being correct, the question arises; Where are all the nutrients coming from to fuel such a productive microbial community? One possible source that could contribute to N, P and C loads into the BWC System not accounted for the above budget is the input of N, P and C from the break down of particulate organic matter delivered to the system in the form of grass clippings, organic debris and the desorption of sediment bound compounds. Plate 10-1 displays a photograph taken of Pond 1 following a storm event showing the large amounts of gross organic matter on
Page 302 of 376 the outlet structure to proceeding ponds. Although this photograph only shows the amount of gross organic matter on top of the outlet structure of Pond 1, one can comprehend the amount of organic matter lying on the benthos within Pond 1.
The BWC System Pond 1 Ponds 2-6
Catchment runoff Flow into Ponds 2-6 Outflow from Pond 6 1.2 ton N 0.3 ton N 0.3 ton P 0.04 ton P 0.17ton N 5.4 ton DOC 1.5 ton DOC 0.06 ton P 1.3 ton DOC Phytoplankton and bacterioplankton production C 237.51 ton yr-1; N 79.65 ton yr-1; P 18.01 ton yr-1
-1 NH4-N 0.004 ton yr Water column -1 -1 PO4-P 0.055 ton yr -1 NH4-N 0.22 ton yr -1 DOC 1.59 ton yr -1 DOC 0.952 ton yr PO4-P 0.085 ton yr
Benthos
Figure 10-5: Gross conceptual model, incorporating all modelled parameters and communities. Red numbers indicate input to the system, Blue numbers represent loss from the system, and Black numbers represent outflow from the system/s.
The theory of major inorganic nutrient inputs into the BWC System was assessed and modelled using benthic metabolism chambers (Figure 10-4). Although the modelled values did not correspond well to the difference between measured inputs into the BWC System and calculated uptake by phytoplankton and bacterioplankton, they did display the movement of nutrients across the benthic water interface, and classify the trophic nature of the benthic environment (heterotrophic in Pond 1; autotrophic in Pond 6). Given that there was a gross movement of inorganic N and P from the benthos to the water column within Pond 1 and the benthic environment was constantly heterotrophic, the theory of major inorganic nutrient inputs into the BWC System from degrading particulate matter seems likely.
Page 303 of 376 Plate 10-1: Gross organic matter within Pond 1 of the BWC System.
Although the use of the models presented is somewhat speculative, they allow one to obtain a ‘feel’ for the removal and fate of N, P and DOC within the BWC System on an annual basis. As the BWC System solely receives hydraulic input via stormwater (see Chapter 3 and 4), the results of this modelling suggests that the BWC Catchment is producing at least 237.51, 79.65 and 18.01 ton of C, N, and ,P and delivering it to the BWC System on an annual basis to drive the modelled rates of phytoplankton and bacterioplankton production. Or, there is a complex nutrient regeneration pathway within the system involving the microbial communities within the benthos, water column and those attached to submerged substrates.
Page 304 of 376 11 Chapter 11: Conclusion and design implications for stormwater treatment ponds
Page 305 of 376 11.1The problem
Point and non point sources of pollution within our urban and rural landscape are widely recognised as an ecological determinant to the health and productivity of aquatic ecosystems (Wetzel 2001). By the nature of their production and treatment, point sources of pollution are relatively easy to quantify, treat and monitor. With an effective legislation framework (and supporting enforcement) in place, point sources of pollution and their associated impact on a receiving environment can be relatively well controlled and monitored (ANZECC 1992; USEPA 2000). It can be speculated that with the advancement in point source pollution control technology in the last century, coupled with intensive urban development, we have not only began to realise the polluting effects of non point sources of pollution, but taken steps to ameliorate them as well. Comparatively, non point sources of pollution are more difficult to quantify, predict, monitor and control than that of point sources of pollution (Herricks 1995; Ferguson 1998; Butler and Davies 2000).
Government agencies at all levels are becoming increasing concerned about the quality of urban runoff, due to the reported non desirable effects of many urban stormwater pollutants. In response to State legislative documents (The Queensland Environmental Protection Act 1994 and the Environmental Protection [Water] Policy 1997) local council authorities and private land developers within Brisbane have been installing stormwater quality improvement devices for the last 5 years (Browning et al. 2004). These policies require the local council to manage the impact of stormwater on receiving waters and develop management plans for every stormwater system in their jurisdiction. The improvement of urban stormwater (volume and pollutant reduction) within urban catchments can potentially occurs at three distinct levels - source control, transport within the catchment and actual treatment technologies/systems (Wong et al. 1999; Somes et al. 2000; Coombes and Kuczera 2001; Kuczera and Coombes 2001). This thesis has examined the use of a constructed pond system (BWC System) to reduce catchment N, P, and C loads entering the receiving waters of Norman Creek, a tributary of the Brisbane River which flows into Moreton Bay.
Page 306 of 376 To lead on from this, water quality guidelines for the State in which the monitored system is located (Queensland, Australia) can be used as a reference point to monitor and assess waterways and provide a ‘target’ for waterway health restoration and pollutant reduction (Table 11-1) (Webb 2000; BCC 2003). The BWC System was constructed to help meet the stated guidelines for the Bridgewater Creek and Norman Creek waterways. Figure 11-1 displays a bar chart graphing the frequency of measured water quality parameters within the BWC System falling at or below that displayed in Table 11-1.
Table 11-1: Freshwater water quality objectives for the assessment of water quality in Brisbane’s waterways set by the Queensland EPA (Webb 2000). Indicator Units Upper limit Org-N mg. L-1 0.5 + -1 NH4 mg. L 0.035 NOx mg. L-1 0.13 TN mg. L-1 0.65 - -1 PO3 mg. L 0.035 TP mg. L-1 0.07
Figure 11-1: Frequency of measurements falling bellow water quality guidelines within the BWC System (n = 44).
From Figure 11-1, water quality within the BWC System does not meet the Water Quality Guidelines 100% of the time for any one of the parameters measured. Some parameter guidelines are met more often than others, with TP and Chl a concentrations rarely meeting guideline concentrations in either Pond 1 or Pond 6. NOx-N concentration was the most consistent parameter meeting the guidelines at 72% and 83% of the time in Ponds 1 and 6 respectively. It must be stressed, however,
Page 307 of 376 that the water samples taken within Ponds 1 and 6 are that of the water quality within the stormwater treatment system – not that of water exiting the system. It would be safe to assume that the ability of the BWC System to achieve the water quality guidelines would be somewhat dependant on the hydraulic loading of the system. Based on the water quality guidelines displayed in Table 11-1, Figure 11-2 graphically displays the influence of hydraulic loading into Ponds 1 and 6 of the BWC System during the two weeks prior to water sampling on whether or not measured nutrients meet these water quality guidelines.
Figure 11-2: Frequency of pond water achieving water quality guidelines as a function of hydraulic loading (m3) into Ponds 1 and 6. The occurrence of a filled coloured dot represents that the water quality guideline was meet, with the colour representing specific nutrient (see key). Open dot represents measurement exceeding of water quality guidelines.
Figure 11-2 diagrammatically highlights the occurrence of nutrient concentrations within Ponds 1 and 6 meeting the Water Quality Objectives (Table 11-1) as a function of hydraulic loading into the system. As can be seen, an increase in hydraulic loading generally dictates a failure in meeting the Water Quality Objectives.
Page 308 of 376 11.2Broad research conclusions Looking back at Figure 1-3, represented here (Figure 11-3), this thesis has addressed some of the key micro-trophic groups within stormwater treatment ponds, and enhanced the knowledge of how these groups remove/process N, P and C compounds within the stormwater pond ecosystem. This thesis has attempted to open the ‘black box’ of stormwater treatment ponds, and assess the biotic pathway for N, P, and C removal within these systems. It has identified and quantified particular areas of a pond environment that biologically processes nitrogen, phosphorus and carbon compounds within the water column. This thesis has found that; • bacterioplankton and phytoplankton communities are capable of massive rates of N, P and C uptake direct from the water column; • submerged macrophytes have the ability to reduce water column N and P concentrations rapidly, to back ground concentrations; and • gross particulate matter entering stormwater ponds during storm events can potentially release large amounts of inorganic and organic N, P, and C to the water column upon mineralization.
Urban stormwater runoff Flow to receiving waters
P P N, P & C N, P & C
N, P & C
N, P & C
Key Stormwater flow rates and CP & W hydraulic Pelagic phytoplankton community efficiency.
N, P & C movement between benthic/water Periphyton/biofilm attached to P interface macrophyte hosts
Benthic metabolism and nutrient movement across the benthos/water column interface
Bacterioplankton community N, P & C N, P & C fluxes within CP & W's
Figure 11-3: Diagrammatic representation of the key micro-trophic groups in stormwater treatment ponds and wetlands.
Page 309 of 376 11.3The solution - design of ponds for the treatment of urban runoff
Although this thesis has justified the use of constructed ponds for the treatment of urban runoff, the research presented on the key micro-trophic groups identified in Figure 11-3 (along with hydrology information) has resulted in the development of five main design and management issues that will enhance their function to aim at reducing catchment N and P loads to receiving waters; Size ponds effectivelyà A general rule of thumb when designing ponds and wetlands for the treatment of urban stormwater is the final pond size should be between 3 and 6% of the total catchment area draining into it (Lawrence and Breen 1998). The BWC System occupies only 0.43% of the catchment area draining into it – 85% less area than the reported minimum area. The small size of the pond system, and the use of a high flow bypass channel in the design, has resulted in a measured 48% of all urban runoff bypassing the system on an annual basis. A larger pond system would allow for a greater water holding capacity and thus a greater amount of the urban runoff would receive treatment prior to entering receiving waters.
Water retention timeà The length of time wastewaters spend in a treatment system generally increases the treatment efficiency of the system (Crites and Tchobanoglous 1998). Constructed ponds for stormwater treatment are no exception. As discussed earlier, water is retained within Ponds 2-6 for up to 9 days following the peak flood level within the ponds. From a water treatment point of view this may appear beneficial. However, prolonged periods of flooded waters have been shown to have a negative impact on plant growth and establishment (Ewing 1996; Casanova and Brock 2000; Greenway et al. 2006). This appears to occur within Ponds 2-6 of the BWC System, with macrophyte die back and poor recruitment occurring throughout the ponds (Greenway et al. 2006). The loss of significant macrophytes stands (or lack there of) within ponds can enhance sediment re-suspension and increase the likelihood of algal blooms, both of which should be avoided in maintaining urban waterway health (Barko and Smart 1986; Ogilvie and Mitchell 1998). Water retention time within constructed stormwater treatment ponds becomes
Page 310 of 376 a balance between retaining water long enough for it the receive the desired level of ‘treatment’ while ensuring the ponding water is not detrimental to the pond ecosystem.
Increase macrophyte biomassà As reported in the literature and demonstrated in this thesis, submerged macrophytes are both effective and efficient at reducing water column inorganic nutrients concentrations (Takamura et al. 2003; Pietro et al. 2006). As such, submerged macrophyte zones should be ‘designed’ into stormwater ponds (turning them into wetlands) with their sole purpose to enhance nutrient reduction from the system. Emergent macrophytes should be both abundant and productive within a pond system to ensure stream bank stability, reduce sediment re- suspension, reduce the velocity of flood waters and enhance biodiversity (Brix and Schierup 1989; Brix 1997; Schutten et al. 2005; Pietro et al. 2006). Productive macrophyte communities within stormwater ponds will promote the movement of nutrients up the trophic cascade of the pond ecosystem and increase the overall resilience of the system.
Pond configurationà Stormwater treatment ponds can be configured a number of ways, but can be simply grouped into a ‘meandering system’ or ‘treatment train system’. The BWC System is a meandering pond system. Both approaches have there advantages and disadvantages. For example, a meandering pond system provides a longer hydraulic flow path, thus enhancing its potential treatment via increased detention time. However, if such a system is poorly designed, short-circuiting can easily occur and thus reducing the overall treatment area of the system. Stormwater ponds designed using the ‘treatment train’ approach ensures that all water delivered to it passes through the system. Again, if not effectively designed this approach can result in high velocity flood waters moving through the system, resulting in the retention time of water within the system being low and increase the risk of stream bank erosion. Given both designs, a meandering pond system is probably best suited to a temperate climate where rainfall intensities are, on average, less than that of subtropical and tropical climates. The design of stormwater treatment ponds using the ‘treatment train’ approach is probably
Page 311 of 376 best suited to subtropical and tropical climates forcing all generated stormwater to move through the treatment system, allowing little opportunity for hydraulic short circuiting.
Reduce gross organic matterà It was suggested in Chapter 10 that gross organic matter played a significant role in fuelling pelagic secondary production (bacterioplankton). It was also suggested that the gross organic matter entering Pond 1 of the BWC System could be releasing large quantities of inorganic and organic nutrients into the system via the mineralisation of the organic matter. Given this result, it is recommended that the urban stormwater treatment systems be designed to remove gross organic matter prior to it entering a pond/wetland system. This can be achieved via gross pollutant traps and correct and careful design. However, it is important to clean and maintain these traps regularly and following storm events so as to minimise the mineralisation of organic matter within the pond system and the input of excessive quantities of nutrients.
11.4Future research questions To further enhance our understanding of how biotic communities within stormwater treatment ponds (bacterioplankton, phytoplankton, submerged macrophytes and epiphyton) interact with stormwater derived N, P, and C compounds, more detailed investigations of each of the identified communities would prove useful. It is suggested that such investigations be conducted in a way to would allow the ‘scaling up’ of results. For example, submerged macrophyte beds within a stormwater treatment wetland can remove x amount of N from the water column per ha. Leading on from this, it would be beneficial to investigate the ultimate fate of N and P compounds within a stormwater treatment pond system, and answer question like; • Does N taken from the water column by epiphyton communities return to the water column via death and decay? • When P is adsorbed onto sediment within the pond benthos, how long does it stay there, and under what conditions would it be released to the water column?
Page 312 of 376 Another possible research topic arising from this thesis would be to investigate the mineralization of gross organic matter within stormwater ponds, and the subsequent release of dissolved and organic nutrients to the water column. This could result into an investigation on the maintenance of gross pollutant traps within stormwater pond systems and the influence it has on water quality within and exiting a stormwater pond system.
Page 313 of 376 12 Chapter 12: Appendixes
Page 314 of 376 Appendix A – non essential data from Chapter 5
Table A.A-1: Storm 1, raw nutrient results for sampling sites. Units in mg. L-1-
Date Time TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Pond 1 in 18/04/2004 07:30am 0.364663 0.364663 0.20233 4.74 19/04/2004 09:30am 0.341258 0.0493615 0.0654105 0.226486 0.482001 0.453785 0.028216 5.71 12:30am 0.361494 0.100095 0.056308 0.205091 0.339411 0.307425 0.031986 3.81 06:00pm 0.357548 0.236305 0.015926 0.105317 0.268682 0.247643 0.021039 4.61 20/04/2004 07:30am 0.355085 0.1019 0.133691 0.119494 0.380863 0.288212 0.092651 6.52 21/04/2004 12:00am 0.340021 0.114252 0.081979 0.14379 0.394843 0.31657 0.078273 5.68 22/04/2004 08:30am 0.366097 0.165715 0.087665 0.112717 0.4988 0.371323 0.127477 4.50 Pond 1 out 18/04/2004 07:30am 0.3378 0.0377 0.1329 0.1671 0.5074 0.4234 0.0841 2.69 19/04/2004 09:30am 0.3896 0.0764 0.1629 0.1503 0.4539 0.3645 0.0895 2.81 12:30am 0.3960 0.0788 0.1622 0.1550 0.4884 0.4070 0.0814 2.78 06:00pm 0.4060 0.1574 0.0989 0.1496 0.7588 0.6949 0.0639 2.97 20/04/2004 07:30am 0.3090 0.0476 0.1475 0.1139 0.4345 0.3543 0.0802 3.17 21/04/2004 12:00am 0.3033 0.1434 0.0513 0.1085 0.5679 0.5229 0.0450 3.36 22/04/2004 08:30am 0.3033 0.1622 0.0381 0.1030 0.3547 0.3038 0.0509 3.30 Pond 6 in 18/04/2004 07:30am 0.4096 0.2800 0.0276 0.1021 0.6936 0.6866 0.0070 3.47 19/04/2004 09:30am 0.2860 0.1647 0.0209 0.1004 0.3767 0.3711 0.0057 4.47 12:30am 0.2641 0.1369 0.0294 0.0978 0.3670 0.3622 0.0048 3.99 06:00pm 0.2189 0.0989 0.0196 0.1004 0.3995 0.3889 0.0106 4.47 20/04/2004 07:30am 0.3013 0.1564 0.0444 0.1005 0.4193 0.4148 0.0046 4.04 21/04/2004 12:00am 0.2897 0.1468 0.0451 0.0979 0.3907 0.3880 0.0027 4.26 22/04/2004 08:30am 0.3147 0.2105 0.0292 0.0750 0.5582 0.5529 0.0054 3.77 Pond 6 out 18/04/2004 07:30am 0.3621 0.2183 0.0446 0.0992 0.5870 0.5726 0.0144 4.51 19/04/2004 09:30am 0.3604 0.2178 0.0405 0.1020 0.5198 0.5085 0.0113 5.18 12:30am 0.2877 0.1579 0.0314 0.0984 0.4019 0.3886 0.0133 4.71 06:00pm 0.3671 0.2401 0.0245 0.1025 0.5594 0.5465 0.0129 4.64 20/04/2004 07:30am 0.3462 0.2491 -0.0034 0.1005 0.2872 0.2833 0.0039 4.35 21/04/2004 12:00am 0.2716 0.1404 0.0311 0.1000 0.3797 0.3769 0.0028 3.09 22/04/2004 08:30am 0.3222 0.2016 0.0210 0.0996 0.4897 0.4782 0.0115 3.58
Page 315 of 376 Table A.A-2: Storm 2, raw nutrient results for sampling sites. Units in mg. L-1-
Date Time TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Pond 1 in 9/05/2004 08:45am 0.4774 0.1911 0.0487 0.2376 0.3296 0.3085 0.0211 9.67 18:30pm 0.4108 0.1420 0.0476 0.2213 0.3112 0.2631 0.0482 8.43 10/05/2004 07:45am 0.4991 0.2520 0.0148 0.2323 0.2011 0.1744 0.0267 9.35 18:30pm 0.3098 0.1276 0.0514 0.1308 0.2978 0.2436 0.0542 6.85 11/05/2004 08:10am 0.6615 0.3122 0.0941 0.2552 0.2111 0.1955 0.0156 9.78 12/05/2004 08:30am 0.5947 0.3799 0.0325 0.1824 0.1993 0.1678 0.0315 12.14 13/05/2004 08:30am 0.8149 0.3851 0.0752 0.3546 0.1113 0.0875 0.0238 9.32 Pond 1 west in 9/05/2004 08:45am 0.7970 0.1115 0.0248 0.6607 0.4055 0.2174 0.1881 7.07 Pond 1 out 9/05/2004 08:45am 0.2890 0.0979 0.0495 0.1417 0.3434 0.2753 0.0681 4.18 18:30pm 0.2395 0.1042 0.0230 0.1123 0.3467 0.3034 0.0433 3.73 10/05/2004 07:45am 0.1971 0.0221 0.0548 0.1202 0.2738 0.2151 0.0587 3.40 18:30pm 0.1833 0.0366 0.0348 0.1119 0.2819 0.2384 0.0435 3.59 11/05/2004 08:10am 0.2241 0.0590 0.0584 0.1067 0.3634 0.2980 0.0654 4.89 12/05/2004 08:30am 0.2789 0.1158 0.0654 0.0976 0.4616 0.4120 0.0497 2.79 13/05/2004 08:30am 0.8856 0.7667 0.0280 0.0909 0.9253 0.8750 0.0503 3.17 Pond 6 in 9/05/2004 08:45am 0.1437 0.0415 0.0019 0.1004 0.1928 0.1764 0.0165 3.88 18:30pm 0.3200 0.2056 0.0125 0.1019 0.3750 0.3492 0.0258 2.94 10/05/2004 07:45am 0.2365 0.1473 -0.0047 0.0939 0.2702 0.2523 0.0180 3.47 18:30pm 0.2233 0.1273 0.0176 0.0784 0.2980 0.3171 -0.0191 4.26 11/05/2004 08:10am 0.2650 0.1234 0.0555 0.0861 0.3917 0.3824 0.0093 4.00 12/05/2004 08:30am 0.2608 0.1594 0.0229 0.0785 0.3301 0.3465 -0.0165 4.34 13/05/2004 08:30am 0.2860 0.2057 0.0014 0.0789 0.3887 0.4141 -0.0253 3.27 Pond 6 out 9/05/2004 08:45am 0.2114 0.1258 -0.0016 0.0871 0.2170 0.2170 0.0000 4.12 18:30pm 0.0944 0.0093 -0.0044 0.0895 0.2294 0.2294 0.0000 4.57 10/05/2004 07:45am 0.2022 0.0911 0.0243 0.0869 0.2405 0.2390 0.0015 4.46 18:30pm 0.1637 0.0411 0.0402 0.0824 0.1743 0.1684 0.0059 3.06 11/05/2004 08:10am 0.2784 0.1844 0.0127 0.0813 0.3443 0.3441 0.0002 4.61 12/05/2004 08:30am 0.1676 0.0737 0.0140 0.0799 0.2022 0.2022 0.0000 2.53 13/05/2004 08:30am 0.1985 0.0981 0.0212 0.0791 0.3123 0.3123 0.0000 4.05
Page 316 of 376 Table A.A-3: Storm 3, raw nutrient results for sampling sites. Units in mg. L-1-
Date Time TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Pond 1 in 18/08/2004 08:30am 0.7357 0.3877 0.1905 0.1575 0.6343 0.4261 0.2082 8.72 06:00pm 1.1394 0.6269 0.0785 0.4340 0.5185 0.3191 0.1994 19.28 19/08/2004 07:30am 0.4881 0.3441 0.0493 0.0948 0.4012 0.2538 0.1474 13.54 11:00am 0.6665 0.5231 0.0283 0.1151 0.3746 0.2333 0.1413 16.10 23/08/2004 02:00pm 0.6014 0.4495 0.0860 0.0660 0.2891 0.1966 0.0924 24/08/2004 08:00am Pond 1 west in 18/08/2004 08:30am 0.5913 0.1809 0.2129 0.1976 0.3930 0.1847 0.2083 7.66 06:00pm 1.8578 1.1463 0.0292 0.6824 0.3509 0.2414 0.1096 6.67 Pond 1 out 08:30am 0.4321 0.1173 0.1892 0.1257 0.6176 0.4560 0.1617 7.66 19/08/2004 06:00pm 0.4782 0.0595 0.1834 0.2353 0.3736 0.1868 0.1868 10.38 07:30am 0.4244 0.1518 0.0932 0.1794 0.4130 0.2520 0.1610 10.74 23/08/2004 11:00am 0.6908 0.4799 0.0292 0.1818 0.5518 0.3895 0.1623 11.52 24/08/2004 02:00pm 0.2030 0.1261 0.0612 0.0156 0.3763 0.2221 0.1542 7.46 Pond 6 in 18/08/2004 08:30am 0.8907 0.0767 0.7212 0.0928 0.2235 0.1693 0.0543 12.20 06:00pm 0.6628 0.3560 0.2070 0.0998 0.4748 0.3520 0.1229 11.82 19/08/2004 07:30am 0.5470 0.1881 0.2467 0.1122 0.3392 0.2556 0.0837 11.36 11:00am 0.4561 0.2218 0.1305 0.1038 0.3018 0.2217 0.0802 11.91 23/08/2004 02:00pm 0.5225 0.2118 0.2793 0.0314 0.1833 0.1111 0.0722 8.12 24/08/2004 08:00am 0.5904 0.2589 0.2946 0.0369 0.2021 0.1368 0.0653 9.50 25/08/2004 07:30am 0.3977 0.0894 0.2790 0.0293 0.1730 0.1303 0.0428 26/08/2004 07:30am 0.3302 0.0559 0.2395 0.0348 0.1660 0.1353 0.0308 Pond 6 out 18/08/2004 08:30am 0.8734 0.2130 0.5788 0.0815 0.0972 0.0708 0.0264 8.83 06:00pm 1.0522 0.6980 0.2860 0.0682 0.3617 0.2759 0.0858 5.75 19/08/2004 07:30am 0.6349 0.3912 0.1638 0.0799 0.2583 0.2005 0.0578 12.27 11:00am 0.5635 0.3006 0.1333 0.1296 0.1615 0.0790 0.0824 11.83 23/08/2004 02:00pm 0.6418 0.3355 0.2646 0.0417 0.2172 0.1628 0.0544 8.65 24/08/2004 08:00am 0.7648 0.4509 0.2803 0.0336 0.1922 0.1481 0.0442 7.31 25/08/2004 07:30am 0.4387 0.1182 0.2809 0.0396 0.2026 0.1577 0.0449 26/08/2004 07:30am 0.3918 0.1066 0.2396 0.0457 0.1554 0.1101 0.0453
Page 317 of 376 Table A.A-4: Storm 4, raw nutrient results for sampling sites. Units in mg. L-1-
Date Time TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Pond 1 in 10/17/04 06:30am 7.7600 4.0020 1.6930 2.0650 0.9700 0.2790 0.6910 11.65 10/18/04 08:30am 6.9276 4.6879 0.2257 2.0140 1.0286 0.3948 0.6338 13.69 10/19/04 07:30am 5.9680 0.7753 0.0293 5.1634 0.2364 0.0884 0.1479 10/20/04 O8:00am 4.2922 1.9269 0.3628 2.0026 0.3468 0.0773 0.2696 7.49 10/21/04 07:45am 2.2510 1.6176 0.0667 0.5667 0.2771 0.2061 0.0710 6.46 10/22/04 08:15am 1.6115 0.7900 0.0559 0.7655 0.1888 0.1174 0.0714 8.88 Pond 1 west in 10/17/04 06:30am 4.5330 0.9580 0.0790 3.4960 0.1710 0.0300 0.1410 11.93 10/18/04 08:30am 4.3744 0.9120 0.3468 3.1156 0.8348 0.1515 0.6833 11.11 10/19/04 07:30am 2.3456 0.1054 0.2368 2.0033 0.5932 0.1695 0.4237 9.22 10/20/04 O8:00am 1.4346 0.4167 0.1268 0.8911 0.3516 0.1875 0.1641 7.33 10/21/04 07:45am 3.0478 0.5684 0.1260 2.3533 0.2988 0.1327 0.1661 10/22/04 08:15am 5.2830 0.7250 0.3155 4.2425 0.7982 0.5523 0.2459 Pond 1 out 10/17/04 06:30am 1.2790 1.1520 0.0030 0.1240 0.1520 0.1430 0.0090 7.92 10/18/04 08:30am 2.2392 0.6525 0.2206 1.3661 0.5954 0.1367 0.4587 10/19/04 07:30am 2.0110 -1.0293 0.0003 3.0400 0.5748 0.1836 0.3911 12.51 10/20/04 O8:00am 1.6467 0.8954 0.2248 0.5265 0.5768 0.4317 0.1451 7.43 10/21/04 07:45am -0.6844 0.1926 0.4918 0.5977 0.4560 0.1417 11.28 10/22/04 08:15am 1.3494 0.6751 0.3051 0.3692 0.6345 0.4692 0.1653 9.96 Pond 6 in 10/17/04 06:30am 0.9624 -0.0532 0.3412 0.6744 0.4490 0.1845 0.2644 8.77 10/18/04 08:30am 1.3240 -0.6735 0.0433 1.9542 0.5352 0.2181 0.3170 9.92 10/19/04 07:30am 1.0547 1.0490 0.0285 -0.0229 0.4239 0.1220 0.3018 7.80 10/20/04 O8:00am 0.7474 0.6636 0.0666 0.0172 0.3762 0.0523 0.3239 5.63 10/21/04 07:45am 0.9864 0.8746 0.1209 -0.0091 0.4800 0.2134 0.2666 9.20 Pond 6 out 10/17/04 06:30am 0.0446 0.0084 0.4574 0.2739 0.1834 8.60 10/18/04 08:30am 0.9047 -0.2009 0.3100 0.7957 0.4374 0.2037 0.2336 7.72 10/19/04 07:30am 1.2367 -0.7496 0.0471 1.9393 0.4791 0.1365 0.3426 7.69 10/20/04 O8:00am 1.1643 1.0946 0.0608 0.0089 0.4353 0.2279 0.2074 9.58 10/21/04 07:45am 1.0200 0.9760 0.0418 0.0022 0.4496 0.1886 0.2610 8.58 10/22/04 08:15am 0.9428 0.5505 0.3397 0.0526 0.4562 0.1029 0.3534 8.33
Page 318 of 376 Table A.A-5: Storm 5, raw nutrient results for sampling sites. Units in mg. L-1-
Date Time TN Org-N NH4-N NOx-N TP Other-P PO4-P DOC Pond 1 in 7/11/2004 06:30am 5.914 0.909 0.090 4.915 0.185 0.011 0.174 17.04 8/11/2004 07:00am 4.179 1.389 0.100 2.690 0.078 0.007 0.071 13.68 9/11/2004 07:15am 5.207 1.140 0.066 4.001 0.119 0.003 0.116 16.94 10/11/2004 O6:30am 3.419 0.432 0.065 2.921 0.111 0.006 0.105 19.84 11/11/2004 07:00am 3.302 1.087 0.103 2.112 0.065 0.016 0.049 15.95 12/11/2004 07:00am 2.591 0.454 0.099 2.039 0.102 0.046 0.056 16.54 13/11/2004 07:15am 2.385 0.679 0.089 1.617 0.095 0.045 0.050 13.94 14/11/2004 08:00am 2.178 0.905 0.079 1.194 0.087 0.043 0.044 13.825 15/11/2004 07:30am 1.627 0.813 0.067 0.746 0.100 0.073 0.028 13.71 Pond 1 west in 7/11/2004 06:30am 4.850 1.164 0.100 3.585 0.090 0.008 0.082 20.45 8/11/2004 07:00am 4.449 1.084 0.081 3.284 0.112 0.005 0.107 13.56 9/11/2004 07:15am 3.818 1.020 0.074 2.724 0.132 0.033 0.099 10.21 10/11/2004 O6:30am 3.239 1.508 0.091 1.640 0.620 0.575 0.045 19.54 Pond 1 out 7/11/2004 06:30am 5.889 1.596 0.161 4.133 0.356 0.012 0.344 13.260 8/11/2004 07:00am 4.698 1.248 0.127 3.323 0.082 0.005 0.077 14.39 9/11/2004 07:15am 4.770 1.365 0.072 3.333 0.213 0.013 0.200 19.97 10/11/2004 O6:30am 3.819 1.441 0.074 2.304 0.165 0.028 0.137 18.52 11/11/2004 07:00am 3.481 1.223 0.058 2.201 0.150 0.007 0.143 20.32 12/11/2004 07:00am 2.911 0.816 0.048 2.047 0.150 0.062 0.088 19.45 13/11/2004 07:15am 2.241 0.608 0.000 1.633 0.142 0.046 0.097 17.49 14/11/2004 08:00am 2.011 0.960 0.056 0.995 0.143 0.062 0.081 12.9 15/11/2004 07:30am 2.025 1.704 0.061 0.260 0.206 0.181 0.025 Pond 6 in 7/11/2004 06:30am 4.295 1.624 0.150 2.521 0.623 0.018 0.605 14.26 8/11/2004 07:00am 3.104 1.277 0.116 1.711 0.345 0.007 0.338 10.33 9/11/2004 07:15am 4.002 1.477 0.145 2.381 0.400 0.017 0.383 20.06 10/11/2004 O6:30am 3.099 1.409 0.088 1.602 0.390 0.026 0.364 20.09 11/11/2004 07:00am 3.341 1.418 0.173 1.751 0.324 0.005 0.319 16.08 12/11/2004 07:00am 2.840 1.185 0.199 1.457 0.318 0.018 0.300 19 13/11/2004 07:15am 2.337 1.362 0.093 0.881 0.327 0.044 0.284 22.1 14/11/2004 08:00am 1.683 1.520 0.008 0.155 0.328 0.205 0.124 22.23 15/11/2004 07:30am 1.400 1.291 0.065 0.044 0.284 0.190 0.094 22.64 16/11/2004 07:15am 1.118 0.461 0.255 0.401 0.240 0.094 0.146 22.57 17/11/2004 O6:30am 1.173642 1.046 0.119605 0.008501 0.597709 0.344 0.254206 21.19 Pond 6 out 7/11/2004 06:30am 3.536 1.426 0.175 1.935 0.350 0.000 0.390 11.88 8/11/2004 07:00am 3.953 1.814 0.187 1.953 0.310 0.000 0.360 12.45 9/11/2004 07:15am 3.847 1.608 0.094 2.146 0.330 0.000 0.390 17.96 10/11/2004 O6:30am 2.800 0.938 0.423 1.439 0.270 0.000 0.380 18.04 11/11/2004 07:00am 3.514 1.650 0.188 1.677 0.240 0.000 0.350 19.83 12/11/2004 07:00am 2.794 1.126 0.185 1.483 0.361 0.076 0.285 14.46 13/11/2004 07:15am 2.344 1.091 0.251 1.003 0.389 0.079 0.310 20.34 14/11/2004 08:00am 1.853 1.646 0.055 0.152 0.364 0.204 0.160 22.03 15/11/2004 07:30am 1.223 1.110 0.071 0.042 0.248 0.144 0.103 21.29 16/11/2004 07:15am 0.593 0.475 0.087 0.031 0.132 0.085 0.047 21.87 17/11/2004 O6:30am 1.232177 0.986 0.206 0.040 0.660238 0.359 0.301 12.5
Page 319 of 376 Appendix B – non essential data from Chapter 6
Table A.B 1: Temperature data set for (a) Pond 1, and (b) Pond 6. Values displayed are means of the 3 sampling sites in each pond. Units in C°. SE shown in parentheses. a) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 27.77 26.88 26.05 25.57 25.25 25.06 24.97 24.78 24.59 0.145 0.032 0.131 0.115 0.090 0.047 0.029 - - 04-Feb-04 25.48 24.59 24.31 24.1 23.98 23.91 23.86 23.83 23.83 0.191 0.148 0.088 0.080 0.039 0.038 0.015 0.005 0.005 18-Feb-04 29.43 28.65 28.38 28.24 28.18 28.05 27.94 27.58 27.22 0.067 0.056 0.034 0.044 0.020 0.032 0.045 0.395 - 10-Mar-04 28.08 27.47 26.74 26.26 25.83 25.39 25.21 24.83 24.45 0.277 0.184 0.098 0.035 0.076 0.040 0.108 - - 24-Mar-04 24.17 23.6 23.29 23.08 22.98 22.95 22.87 22.87 22.88 0.167 0.130 0.088 0.072 0.055 0.065 0.075 - - 08-Apr-04 24 23.38 23.04 22.91 22.76 22.69 22.65 22.62 22.55 0.000 0.052 0.075 0.052 0.039 0.041 0.049 0.038 - 22-Apr-04 23.96 23.51 23.26 23.13 23.01 22.97 22.95 22.93 22.9 0.043 0.134 0.038 0.023 0.018 0.007 0.007 0.003 - 10-May-04 20.7 19.95 19.7 19.61 19.52 19.48 19.44 19.44 19.44 0.297 0.043 0.015 0.032 0.035 0.029 0.040 - - 19-May-04 20.84 20.44 20.29 20.04 19.86 19.8 19.76 19.78 19.81 1.517 1.405 1.484 1.545 1.568 1.580 1.590 1.574 - 03-Jun-04 18.07 17.72 17.62 17.48 17.16 17.03 16.95 16.88 16.89 0.067 0.009 0.042 0.057 0.025 0.023 0.075 - - 18-Jun-04 17.12 16.78 16.54 16.37 16.31 16.27 16.18 16.1 16.03 0.196 0.040 0.017 0.015 0.015 0.021 0.045 0.072 - 01-Jul-04 17.32 16.05 15.62 15.44 15.29 15.22 15.13 15.09 15.15 0.118 0.133 0.132 0.047 0.050 0.032 0.050 0.033 - 12-Jul-04 14.25 14.21 14.06 13.94 13.83 13.78 13.73 13.73 13.76 0.546 0.410 0.277 0.307 0.264 0.231 0.191 0.174 - 11-Aug-04 17.98 16.05 15.74 15.35 15.22 15.08 15.04 15.04 14.71 0.225 0.093 0.116 0.260 0.237 0.259 0.274 0.315 - 24-Aug-04 18.13 16.71 16.03 15.89 15.74 15.66 15.62 15.63 15.63 0.803 0.633 0.093 0.064 0.047 0.012 0.057 0.068 0.045 15-Oct-04 24.98 24.12 23.83 23.71 23.5 23.24 23.09 22.55 22.22 0.270 0.125 0.107 0.038 0.046 0.112 0.044 0.100 - 03-Nov-04 26.23 24.6 24.19 23.19 22.53 22.24 22.08 21.88 21.68 0.445 0.291 0.208 0.110 0.041 0.020 0.040 - - 17-Nov-04 28.11 27.28 26.76 26.55 25.92 25.41 25.02 24.71 23.5 0.454 0.217 0.179 0.214 0.138 0.022 0.115 0.265 - 01-Dec-04 27.97 26.48 25.86 25.21 24.6 23.91 23.48 21.88 20.28 1.179 1.018 0.803 1.014 1.030 0.823 0.698 - - 16-Dec-04 27.34 26.43 25.89 25.57 25.35 24.94 24.56 24.23 24.19 1.557 0.737 0.522 0.395 0.335 0.072 0.223 0.316 - 07-Jan-05 27.65 26.68 26.39 26.22 26.14 26.1 26.09 25.95 25.81 0.504 0.079 0.069 0.053 0.040 0.026 0.035 0.015 - 20-Jan-05 26.19 25 24.72 24.58 24.43 24.4 24.29 24.25 24.09 0.426 0.055 0.022 0.040 0.012 0.010 0.040 0.085 -
Page 320 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 28.39 26.59 25.76 25.57 25.13 24.79 24.44 24.1 28.39 1.243 0.217 0.074 0.07 0.11 0.315 04-Feb-04 24.86 24.25 23.67 23.49 23.37 23.32 23.28 23.2 24.86 0.296 0.177 0.01 0.015 0.009 0.012 0.038 18-Feb-04 27.63 26.73 26.36 25.4 25.27 25.2 25.19 25.17 27.63 1.081 1.027 1.246 1.765 1.765 1.81 1.815 10-Mar-04 28.29 27.71 27.28 26.31 25.5 24.91 24.43 24.31 28.29 0.266 0.023 0.187 0.48 0.25 24-Mar-04 25.83 25.08 24.49 24.58 24.27 22.75 21.24 19.72 25.83 1.146 1.399 1.197 1.69 1.525 08-Apr-04 24.6 23.91 23.69 23.54 23.37 23.25 23.25 23.25 24.6 0.472 0.267 0.074 22-Apr-04 23.81 23.46 23.15 22.58 22.52 22.45 22.44 22.43 23.81 0.398 0.375 0.01 10-May-04 21.83 21.09 20.89 19.68 19.64 19.56 19.54 19.53 21.83 1.093 1.083 1.246 0.015 0.05 19-May-04 18.83 18.42 18.5 18.45 18.4 17.11 15.82 14.53 18.83 0.805 0.805 0.187 1.275 1.26 03-Jun-04 17.93 17.69 16.62 15.97 15.86 15.85 15.84 15.83 17.93 0.431 0.329 1.197 18-Jun-04 16.66 16.49 16.62 15.43 15.39 15.35 15.31 15.27 16.66 0.631 0.526 0.141 01-Jul-04 14.63 14.24 13.02 12.98 12.93 12.91 12.89 12.87 14.63 0.703 0.763 0.332 12-Jul-04 15.41 15.3 15.78 15.75 15.63 15.6 15.59 15.58 15.41 0.808 0.79 1.104 11-Aug-04 16.83 16.46 15.92 14.93 14.74 14.61 14.61 14.61 16.83 0.675 0.499 1.305 24-Aug-04 17.07 16.58 15.95 16.03 15.92 15.89 15.86 15.83 17.07 0.143 0.079 . 15-Oct-04 22.23 20.46 20.39 23.64 23.52 23.47 23.42 23.37 22.23 2.132 3.62 0.92 03-Nov-04 26.86 24.2 24.66 24.45 24.1 24.03 23.96 23.89 26.86 1.434 0.85 . 17-Nov-04 27.27 26.26 25.92 24.91 23.93 23.3 22.67 22.04 27.27 0.355 0.243 . 01-Dec-04 26.74 26.13 25.98 25.23 24.98 24.67 24.67 24.67 26.74 0.116 0.226 0.493 16-Dec-04 27.05 26.24 26.31 26 25.77 23.9 23.23 22.56 27.05 0.287 0.156 0.175 0.095 07-Jan-05 28.11 27.35 27.12 27.64 27.52 27.42 26.93 25.55 28.11 0.753 0.662 3.615 0.025 20-Jan-05 26.38 26.07 25.78 25.62 24.46 24.4 24.36 24.32 26.38 0.926 0.925 0.907
Page 321 of 376 Tables A.B 2a-b: DO data set for (a) Pond 1, and (b) Pond 6. Values displayed are means of the 3 sampling sites in each pond. Units in mg. L-1. SE shown in parentheses. a) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100- 120- 140- 160- 120 140 160 180 21-Jan-04 1.93 1.54 0.99 0.80 0.65 0.58 0.54 0.32 6.06 1.03 0.82 0.48 0.39 0.26 0.20 0.17 . 04-Feb-04 9.33 8.15 7.13 6.74 6.53 6.43 6.47 6.23 0.12 0.44 0.33 0.30 0.15 0.17 0.21 0.19 0.17 0.08 18-Feb-04 1.73 1.18 0.99 0.65 0.36 0.20 0.14 0.12 0.39 0.16 0.17 0.16 0.06 0.03 0.03 0.02 10-Mar-04 2.52 2.08 0.70 0.29 0.16 0.13 0.13 0.10 0.29 0.51 0.30 0.08 0.02 0.01 0.01 . 24-Mar-04 3.25 2.46 1.46 1.08 0.60 0.27 0.16 0.10 0.12 0.25 0.18 0.11 0.20 0.05 0.07 0.01 . 08-Apr-04 6.06 5.39 4.35 3.14 2.11 1.50 0.69 0.45 0.58 0.29 0.22 0.20 0.19 0.20 0.20 0.19 22-Apr-04 5.00 4.96 3.92 2.85 1.98 1.36 1.11 0.88 0.87 0.62 0.50 0.28 0.22 0.16 0.13 . 10-May-04 3.90 3.54 3.20 3.06 2.89 2.93 2.78 2.37 0.56 0.50 0.23 0.23 0.22 0.23 0.28 . 19-May-04 4.86 4.26 3.70 3.32 2.87 2.46 1.46 1.00 0.13 0.88 0.28 0.27 0.33 0.40 0.57 0.04 0.09 03-Jun-04 7.33 6.25 6.48 5.00 2.70 1.12 0.39 0.20 0.85 0.33 0.31 0.30 0.28 0.60 0.30 0.03 . 18-Jun-04 4.16 3.48 3.23 2.30 1.87 1.50 1.35 1.44 3.02 0.42 0.12 0.05 0.31 0.18 0.07 0.05 0.23 01-Jul-04 3.50 3.41 3.39 3.37 3.31 3.19 3.25 3.55 0.39 0.10 0.06 0.06 0.06 0.06 0.02 0.13 0.46 . 12-Jul-04 8.52 7.46 6.41 5.50 5.03 3.88 3.53 1.57 6.62 1.27 1.49 1.53 1.83 1.92 1.86 1.84 0.77 . 11-Aug-04 11.62 11.02 10.94 10.01 9.27 8.43 7.84 7.22 0.06 0.78 0.69 0.55 0.36 0.23 0.50 0.43 0.08 0.31 24-Aug-04 4.82 3.49 2.81 2.09 1.26 0.39 0.13 0.07 0.20 0.29 0.38 0.36 0.27 0.48 0.09 0.01 0.01 15-Oct-04 7.02 6.71 6.51 6.21 5.20 3.63 1.74 0.32 0.46 0.31 0.41 0.31 0.25 0.90 0.57 0.07 03-Nov-04 18.84 12.92 6.15 2.01 0.70 0.56 0.41 0.36 0.25 2.24 3.56 2.38 0.69 0.05 0.02 0.05 . 17-Nov-04 26.21 25.35 22.34 17.15 15.95 5.96 1.02 0.45 0.63 0.97 1.63 1.77 3.34 2.05 0.39 . 01-Dec-04 15.12 13.38 9.44 5.14 2.44 0.50 0.34 0.36 0.71 0.61 1.55 1.15 0.98 0.07 0.09 16-Dec-04 5.66 5.47 4.67 3.50 1.87 0.44 0.29 0.26 5.17 5.00 4.27 3.17 1.56 0.15 0.01 0.03 07-Jan-05 4.64 4.26 3.68 3.56 3.31 3.00 2.46 2.49 0.18 0.44 0.48 0.31 0.28 0.28 0.38 0.21 20-Jan-05 2.10 1.83 1.61 1.52 1.66 1.84 2.73 2.20 0.46 0.40 0.28 0.04 0.17 0.24 0.39
Page 322 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 7.33 6.40 4.80 3.18 2.23 1.79 1.23 1.59 0.87 0.69 0.11 0.43 0.31 . 04-Feb-04 5.39 4.94 4.76 4.42 4.30 4.07 3.60 3.84 3.38 0.31 0.19 0.27 0.41 0.35 0.17 0.25 18-Feb-04 3.10 2.78 2.48 1.27 0.58 0.46 0.48 0.08 0.40 . 10-Mar-04 12.31 9.56 6.48 2.68 1.51 0.55 0.17 0.91 0.16 0.57 0.80 24-Mar-04 6.58 6.10 5.30 3.53 2.92 2.81 0.51 0.37 0.24 . 08-Apr-04 8.55 8.06 7.51 6.13 5.21 4.56 4.23 4.09 0.45 0.14 0.28 . 22-Apr-04 6.36 6.69 5.84 4.71 4.37 4.12 0.33 0.24 0.36 . 10-May-04 5.94 5.76 4.89 4.26 4.39 4.29 4.09 3.99 0.22 0.11 0.16 0.58 0.02 19-May-04 5.57 5.87 4.13 3.82 3.68 3.60 0.53 0.53 . 03-Jun-04 7.67 7.78 8.33 4.92 2.20 1.25 0.71 0.62 18-Jun-04 4.62 4.37 3.83 3.81 3.75 3.73 0.64 0.47 01-Jul-04 6.86 6.89 6.17 6.15 6.15 6.11 6.06 0.42 0.44 . 12-Jul-04 5.09 5.13 4.84 4.60 4.40 4.12 3.95 0.31 0.30 0.17 11-Aug-04 7.63 7.27 7.17 6.31 6.27 6.19 6.14 0.69 0.48 0.64 24-Aug-04 3.39 3.26 2.71 2.60 2.60 2.49 0.25 0.17 15-Oct-04 3.89 3.83 2.97 2.85 2.76 2.68 0.40 0.66 03-Nov-04 15.52 12.89 6.94 6.13 6.94 7.02 2.00 4.21 17-Nov-04 13.44 12.71 9.85 8.73 3.61 1.54 0.94 0.13 01-Dec-04 9.30 7.50 4.10 2.40 0.90 0.80 0.99 0.35 16-Dec-04 4.39 3.66 1.94 0.83 0.50 0.30 0.27 0.63 0.15 0.41 07-Jan-05 2.94 1.96 0.63 0.58 0.34 0.31 0.29 0.29 0.73 0.71 0.05 0.10 20-Jan-05 0.93 0.78 0.57 0.42 0.25 0.25 0.27 0.31 0.24 0.20 0.17
Page 323 of 376 Tables A.B 3a-b: Redox potential data set for (a) Pond 1, and (b) Pond 6. Values displayed are means of the 3 sampling sites in each pond. Units in mV SE shown in parentheses. a) Sample Water depth (cm) date 0-20 20-40 40-60 60-80 80-100 100- 120- 140- 160- 120 140 160 180 21-Jan-04 -11.7 -25.3 -32.3 -58.7 -79.7 -95.7 -107.0 -147.0 24.9 28.0 30.2 41.3 40.3 35.9 30.9 . 04-Feb-04 60.3 72.0 92.6 97.2 101.2 104.4 151.2 144.0 96.8 32.1 38.9 47.1 48.6 50.3 51.1 22.8 4.0 9.3 18-Feb-04 -37.2 -57.7 -55.2 -59.3 -90.9 -108.5 -202.5 -218.5 41.5 44.2 47.5 48.1 46.3 56.6 11.5 14.5 10-Mar-04 35.9 31.2 10.8 -14.8 -60.2 -89.5 -112.3 -188.0 33.1 24.2 14.3 11.5 31.2 46.8 57.2 24-Mar-04 99.1 94.4 86.4 85.0 90.5 80.5 -4.5 57.4 54.5 48.2 47.9 42.9 59.5 53.5 08-Apr-04 122.3 120.8 121.7 121.3 117.2 103.0 82.5 32.3 -97.0 60.4 59.9 60.9 60.9 59.4 55.4 49.0 19.6 22-Apr-04 120.0 119.6 120.3 121.2 121.6 122.6 120.3 173.0 59.7 59.6 60.8 61.4 62.0 62.5 61.6 24.0 10-May-04 127.7 125.1 125.7 126.6 127.2 128.5 182.0 152.0 64.7 63.4 63.5 63.7 64.0 62.5 14.0 . 19-May-04 134.7 135.5 137.2 139.8 140.8 141.8 95.7 -10.4 68.3 69.1 70.2 71.4 71.9 72.4 96.3 11.7 03-Jun-04 42.0 46.2 54.6 59.5 63.4 51.1 26.1 -1.0 -47.0 27.6 28.1 30.1 31.7 33.3 25.7 12.5 . 18-Jun-04 47.6 55.6 56.8 60.6 62.5 64.6 43.6 33.6 28.1 31.7 31.8 33.9 34.5 35.2 24.7 26.1 01-Jul-04 92.8 93.5 93.5 93.5 93.6 92.2 89.1 68.8 128.0 47.5 47.8 47.6 47.6 47.3 46.5 44.9 60.2 12-Jul-04 130.5 132.1 133.2 134.3 135.5 134.6 129.7 121.8 149.0 65.6 66.3 66.7 67.5 67.7 66.9 63.3 58.7 11-Aug-04 145.6 144.9 145.2 146.5 147.4 146.4 142.0 203.0 187.0 72.1 73.7 73.9 74.6 75.2 74.4 71.3 2.0 24-Aug-04 26.3 29.2 35.2 38.2 14.1 -25.8 -72.1 -65.7 -146.0 44.4 43.6 47.3 49.7 27.3 15.8 36.9 69.3 15-Oct-04 113.0 119.4 124.5 130.0 88.1 33.8 -18.6 -161.0 -176.0 83.3 84.2 84.8 84.0 103.9 72.4 57.4 03-Nov-04 -21.3 -5.3 -33.7 -89.3 -113.7 -130.5 -113.0 -226.0 25.4 34.5 22.9 49.1 60.6 69.1 118.0 . 17-Nov-04 134.7 138.0 142.5 143.6 135.2 77.5 -6.8 -61.5 -164.0 78.8 80.0 80.0 83.2 84.3 44.5 29.8 44.5 01-Dec-04 49.0 39.2 28.0 -1.8 -50.3 -87.1 -104.7 -226.0 89.6 77.6 71.7 93.3 75.4 68.5 67.8 16-Dec-04 42.6 49.6 49.9 46.6 -3.2 -31.5 -44.7 -129.0 37.7 40.9 40.8 38.4 5.9 24.9 31.3 07-Jan-05 111.7 123.8 133.9 140.0 144.7 148.0 151.1 67.0 54.6 59.3 64.2 67.1 69.3 71.1 69.4 20-Jan-05 68.5 63.9 59.9 53.9 47.6 29.4 18.2 -10.5 34.7 32.8 31.0 29.5 26.6 19.7 16.9 7.5
Page 324 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140- 160- 160 180 21-Jan-04 198.3 226.7 238.0 223.0 187.5 162.0 39.1 13.6 7.5 8.0 44.5 04-Feb-04 126.3 132.0 136.0 139.0 142.0 143.0 132.0 118.5 141.0 15.6 13.3 11.7 11.0 9.1 8.1 18-Feb-04 95.0 97.0 100.0 111.0 113.0 115.5 116.5 5.7 6.1 7.0 0.0 0.0 0.5 13.6 10-Mar-04 121.3 149.3 162.0 164.7 136.5 103.0 58.0 18.0 45.4 48.2 45.5 41.8 43.5 . 1.5 23.5 24-Mar-04 144.0 160.3 172.3 167.5 126.0 181.0 9.0 4.4 3.2 13.5 62.0 -10.6 08-Apr-04 152.0 156.0 148.5 168.0 170.0 172.0 170.0 154.0 15.9 18.2 14.5 . . 22-Apr-04 138.0 125.3 138.5 146.0 148.0 149.0 151.0 152.0 2.5 11.8 3.5 . . 10-May-04 193.3 193.7 194.7 195.7 160.0 198.0 199.0 194.0 1.7 1.3 0.9 0.9 37.0 19-May-04 145.7 147.0 230.0 233.0 234.0 235.0 38.2 39.1 . 03-Jun-04 107.3 109.3 168.0 169.0 173.0 175.0 159.0 30.5 29.3 18-Jun-04 125.7 125.7 136.0 134.0 133.0 131.0 7.5 6.4 01-Jul-04 110.7 112.0 149.0 149.0 149.0 148.0 146.0 24.4 23.9 12-Jul-04 160.3 164.7 173.5 179.0 182.0 185.0 189.0 7.0 5.9 4.5 11-Aug-04 196.3 197.7 250.0 253.0 253.0 254.0 254.0 29.2 28.2 24-Aug-04 153.7 154.0 189.0 195.0 199.0 200.0 199.0 31.8 32.5 15-Oct-04 126.3 164.0 171.0 179.0 186.0 192.0 28.8 03-Nov-04 176.0 238.5 260.0 266.0 275.0 281.0 35.9 11.5 17-Nov-04 151.3 165.0 245.0 249.0 66.0 0.0 42.2 45.2 01-Dec-04 261.0 272.7 282.0 291.0 74.0 38.0 1.0 0.7 6.0 16-Dec-04 103.0 103.3 104.3 37.0 -4.0 -37.0 6.5 11.1 17.4 19.0 07-Jan-05 87.7 80.7 46.0 36.0 -2.0 -44.0 -97.0 -137.0 15.6 14.3 7.5 5.5 20-Jan-05 39.7 44.3 30.7 -26.5 -47.5 -42.0 -49.0 18.4 18.0 22.0 19.5 12.5
Page 325 of 376 Tables A.B 4a-b: pH data set for (a) Pond 1, and (b) Pond 6. Values displayed are means of the 3 sampling sites in each pond. SE shown in parentheses. a) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 6.74 6.73 6.71 6.68 6.68 6.69 6.71 0.02 0.01 0.01 0.02 0.02 0.02 04-Feb-04 7.47 7.40 7.35 7.31 7.28 7.24 7.29 7.27 7.16 0.06 0.05 0.05 0.04 0.06 0.08 0.05 0.04 0.10 18-Feb-04 6.94 6.93 6.93 6.92 6.93 6.92 6.91 6.89 6.51 0.03 0.01 0.01 0.01 0.01 0.01 0.01 0.01 10-Mar-04 7.06 7.04 7.00 6.95 6.89 6.81 6.79 6.65 0.03 0.01 0.03 0.02 0.03 0.04 0.08 24-Mar-04 7.83 6.81 6.78 6.75 6.73 6.74 6.71 6.71 0.98 0.04 0.04 0.02 0.02 0.02 0.03 08-Apr-04 7.04 6.93 6.84 6.78 6.71 6.67 6.64 6.53 0.03 0.01 0.02 0.03 0.04 0.03 0.03 0.07 22-Apr-04 6.83 6.83 6.80 6.76 6.74 6.71 6.70 6.69 6.65 0.05 0.05 0.05 0.04 0.03 0.02 0.02 0.02 10-May-04 6.66 6.66 6.62 6.61 6.60 6.58 6.59 6.56 0.07 0.07 0.04 0.03 0.03 0.02 0.02 0.01 19-May-04 6.91 6.85 6.79 6.75 6.72 6.69 6.63 6.55 0.20 0.14 0.08 0.06 0.05 0.05 0.00 0.04 03-Jun-04 7.03 7.01 6.96 6.89 6.84 6.82 6.82 6.79 0.05 0.03 0.03 0.03 0.03 0.02 18-Jun-04 7.02 6.99 6.97 6.94 6.92 6.90 6.89 6.87 0.06 0.04 0.03 0.03 0.02 0.02 0.02 0.01 01-Jul-04 6.87 6.86 6.86 6.86 6.86 6.85 6.85 6.86 6.85 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 12-Jul-04 7.15 7.10 7.05 7.02 6.98 6.94 6.93 6.92 6.96 0.07 0.04 0.02 0.02 0.02 0.03 0.04 0.04 11-Aug-04 7.34 7.34 7.30 7.25 7.19 7.15 7.13 7.12 7.08 0.06 0.04 0.05 0.04 0.03 0.03 0.02 0.00 24-Aug-04 6.63 6.65 6.56 6.52 6.45 6.38 6.24 6.25 6.22 0.03 0.01 0.02 0.01 0.02 0.02 0.06 0.00 15-Oct-04 7.16 7.15 7.10 7.01 6.85 6.55 6.34 6.20 0.06 0.06 0.07 0.07 0.05 0.11 . 03-Nov-04 7.17 7.08 6.94 6.77 6.57 6.42 6.24 6.03 0.07 0.11 0.08 0.06 0.02 0.05 0.04 17-Nov-04 7.77 7.78 7.60 7.39 7.28 7.12 6.92 6.78 0.08 0.09 0.04 0.06 0.06 0.06 0.08 0.13 01-Dec-04 7.15 7.10 7.04 6.91 6.78 6.52 6.35 6.03 0.06 0.10 0.11 0.11 0.13 0.10 0.09 16-Dec-04 7.00 6.96 6.94 6.88 6.81 6.73 6.63 6.33 0.02 0.04 0.06 0.10 0.14 0.20 0.22 0.30 07-Jan-05 6.94 6.91 6.82 6.73 6.62 6.54 6.47 6.51 0.18 0.14 0.15 0.12 0.06 0.01 0.02 20-Jan-05 6.82 6.80 6.75 6.72 6.70 6.69 6.63 0.04 0.06 0.05 0.03 0.03 0.04 0.02
Page 326 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 6.91 6.84 6.72 6.65 6.64 6.75 6.74 0.06 0.03 0.05 0.04 0.03 04-Feb-04 7.01 7.02 7.00 6.98 6.96 6.95 6.93 6.88 6.86 0.01 0.01 0.01 0.02 0.02 0.01 0.02 18-Feb-04 7.20 7.13 7.11 7.05 7.02 6.99 6.98 0.15 0.09 . 10-Mar-04 8.08 7.60 7.35 7.02 7.06 6.89 6.80 6.66 0.22 0.05 0.03 0.15 24-Mar-04 6.88 6.81 6.71 6.74 6.62 6.54 0.17 0.10 0.08 08-Apr-04 7.25 7.19 7.08 7.02 6.98 6.94 6.93 6.91 0.12 0.09 0.02 22-Apr-04 4.78 7.17 7.16 7.11 7.08 7.07 7.06 7.05 2.39 0.01 0.02 10-May-04 7.01 6.86 6.75 6.70 6.68 6.74 6.71 6.68 6.66 0.25 0.17 0.09 0.08 0.09 19-May-04 6.91 6.91 6.96 6.87 6.81 6.78 0.20 0.11 03-Jun-04 7.29 7.24 7.62 7.46 7.39 7.31 7.15 0.28 0.23 18-Jun-04 7.09 7.07 7.13 7.08 7.06 7.04 7.03 0.06 0.06 01-Jul-04 6.85 6.86 6.83 6.82 6.82 6.82 6.82 0.02 0.02 12-Jul-04 7.01 7.00 7.00 7.03 7.01 7.00 6.98 0.04 0.03 0.05 11-Aug-04 7.15 7.14 7.03 6.98 6.98 6.97 6.98 6.98 0.09 0.08 24-Aug-04 6.94 6.93 7.03 6.96 6.91 6.88 0.06 0.08 15-Oct-04 7.57 7.56 7.37 7.33 7.28 7.26 0.10 0.18 03-Nov-04 8.73 8.12 7.22 7.10 7.01 6.98 0.55 0.67 17-Nov-04 7.50 7.48 7.27 7.22 7.08 6.95 0.15 0.12 01-Dec-04 7.66 7.56 7.35 7.28 7.13 7.09 0.10 0.04 16-Dec-04 6.90 6.90 6.82 6.82 6.79 6.68 6.55 0.05 0.03 0.04 0.05 07-Jan-05 7.17 7.14 7.06 7.02 7.01 6.97 6.93 6.78 0.01 0.01 0.01 0.01 20-Jan-05 6.65 6.63 6.61 6.52 6.57 6.51 6.46 0.06 0.04 0.03 0.13
Page 327 of 376 Appendix C – non essential data from Chapter 7
Table A.C 1: Calibration table for YSI SNODE 6600 % Fluorescence probe. Sample Date Sample location Chlorophyll a % Fluorescence concentration (mg L-1) 10-May-04 Pond 1 North upper 8.70 1.2 10-May-04 Pond 1 North lower 10.68 0.9 10-May-04 Pond 1 East upper 13.35 1.5 10-May-04 Pond 1 East lower 2.67 0.3 10-May-04 Pond 1 West upper 5.00 1.5 10-May-04 Pond 1 West lower 18.69 1.3 10-May-04 Pond 6 North upper 56.07 11.3 10-May-04 Pond 6 North lower 21.36 3.7 10-May-04 Pond 6 West upper 53.40 12.0 10-May-04 Pond 6 West lower 18.69 4.3 10-May-04 Pond 6 East upper 40.05 5.8 10-May-04 Pond 6 East lower 24.27 4.6 22-Jan-04 Pond 1 North lower 29.37 4.9 23-Jan-04 Pond 6 North upper 29.37 4.9 24-Jan-04 Pond 6 West upper 53.40 10.5 25-Jan-04 Pond 1 East upper 13.35 2.2 26-Jan-04 Pond 1 West upper 32.04 3.1 17-Nov-04 Pond 1 North upper 69.42 14.6 17-Nov-04 Pond 1 North lower 39.16 6.6 17-Nov-04 Pond 1 East upper 74.76 16.6 17-Nov-04 Pond 1 East lower 14.24 3.0 17-Nov-04 Pond 1 West upper 51.62 14.4 17-Nov-04 Pond 1 West lower 30.26 6.6 17-Nov-04 Pond 6 North upper 28.48 8.8 17-Nov-04 Pond 6 North lower 33.82 9.2 17-Nov-04 Pond 6 West upper 23.14 5.1 17-Nov-04 Pond 6 West lower 12.46 4.8 17-Nov-04 Pond 6 East upper 17.80 5.4 17-Nov-04 Pond 6 East lower 17.80 5.0
Page 328 of 376 Table A.C 2a-b: Pond 1 Chlorophyll a concentration with increasing depth. Values shown are the mean of 3 replicates from the sampling sites shown in Figure 1, with S.E. shown in parentheses.. Units = µg L-1. a) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 17.37 14.87 12.79 11.84 10.98 10.68 11.24 0.15 1.02 0.64 0.59 0.38 0.28 0.15 04-Feb-04 16.31 16.59 17.00 16.68 16.89 16.47 16.60 16.56 16.98 1.08 0.48 0.03 0.23 0.39 0.32 0.05 0.00 0.00 18-Feb-04 29.58 29.86 32.53 28.77 25.00 21.01 22.44 21.98 1.86 0.54 4.05 1.53 2.54 1.35 5.95 11.10 10-Mar-04 28.12 22.82 15.75 13.71 12.18 12.46 12.32 12.60 1.99 3.54 1.60 0.56 0.58 0.23 0.00 0.00 24-Mar-04 28.19 25.48 19.50 15.20 13.92 11.77 11.45 12.25 2.03 2.76 0.72 1.55 1.17 0.60 1.05 0.00 08-Apr-04 35.59 33.57 22.82 18.60 15.98 15.84 15.33 15.82 1.56 1.73 2.49 1.00 0.43 1.27 0.81 0.69 22-Apr-04 40.66 40.24 31.95 25.11 19.62 17.42 16.19 14.75 4.61 6.19 7.25 5.40 3.73 2.29 1.93 0.30 10-May-04 11.19 10.77 10.82 9.50 8.80 8.71 7.42 8.01 0.75 0.56 0.37 0.74 0.39 1.29 0.45 1.50 19-May-04 26.69 26.76 26.66 29.19 26.15 28.29 20.11 22.05 10.12 8.57 8.31 11.63 9.46 11.37 0.40 1.40 03-Jun-04 33.59 33.34 30.00 32.62 37.60 43.12 50.52 49.65 4.03 2.99 1.37 6.39 1.96 3.57 11.85 0.00 18-Jun-04 28.47 27.06 25.67 22.61 21.29 20.52 20.25 20.99 1.83 0.89 0.25 0.55 0.99 0.92 0.53 1.24 01-Jul-04 8.68 8.78 9.98 10.68 11.30 10.75 10.58 10.14 10.41 0.43 0.61 0.38 0.15 0.81 0.44 0.26 0.98 2.35 12-Jul-04 38.78 32.85 28.70 27.08 25.97 25.76 24.97 28.97 25.67 5.05 1.71 2.80 3.91 3.14 2.84 4.00 9.45 0.00 11-Aug-04 33.31 31.46 32.53 32.11 29.54 28.38 27.57 28.73 24.21 4.22 1.07 2.68 1.29 0.70 1.89 0.78 5.20 0.00 24-Aug-04 21.89 13.85 13.02 12.23 13.09 12.25 12.00 11.56 16.01 3.72 1.05 1.40 1.07 0.75 0.95 1.27 0.10 0.00 15-Oct-04 100.51 113.21 103.87 114.23 131.26 159.06 161.08 7.92 7.07 7.31 18.40 22.36 25.36 17.10 03-Nov-04 61.86 68.42 74.49 101.53 109.68 109.94 103.54 105.12 8.83 6.30 10.96 7.78 6.74 10.53 21.01 0.00 17-Nov-04 58.22 64.41 55.37 48.95 29.86 30.99 30.39 30.33 0.92 3.09 5.68 15.90 1.28 0.05 0.30 0.00 01-Dec-04 35.19 41.33 40.68 36.51 37.30 32.04 32.32 0.98 2.19 1.42 2.15 1.22 1.55 6.24 16-Dec-04 13.18 12.74 12.55 12.14 12.28 12.14 12.79 13.16 0.18 0.46 0.13 0.13 0.48 0.35 0.35 0.50 07-Jan-05 13.27 13.74 12.48 9.89 9.31 9.84 10.12 9.96 0.80 2.23 1.89 0.15 0.23 0.34 0.58 0.00 20-Jan-05 14.85 14.27 12.04 11.12 10.24 9.84 10.58 10.79 1.17 1.46 0.32 0.29 0.25 0.20 0.47 0.00
Page 329 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 28.27 32.83 22.19 17.00 13.50 22.19 23.58 -- -- 2.13 2.19 3.51 0.96 0.42 8.55 04-Feb-04 18.55 18.23 16.70 16.35 15.77 15.64 16.10 16.91 15.45 0.67 0.20 0.56 0.46 0.23 0.06 0.72 1.39 18-Feb-04 18.55 18.23 16.70 16.35 15.77 15.64 16.10 16.91 15.45 0.60 0.86 0.72 10-Mar-04 42.17 37.53 38.30 36.86 37.97 33.80 25.46 -- -- 1.51 7.94 5.88 4.34 5.60 24-Mar-04 75.30 70.57 53.24 37.46 36.42 26.02 37.83 20.59 -- 0.88 0.52 1.53 4.69 08-Apr-04 37.33 34.31 30.07 25.08 17.40 17.81 ------1.56 3.23 3.90 22-Apr-04 41.09 39.94 40.17 42.21 42.49 42.21 44.23 46.17 -- 0.31 0.28 1.79 10-May-04 44.39 45.62 45.27 46.94 44.09 42.63 41.10 40.40 -- 0.67 0.95 0.87 0.66 1.77 3.16 19-May-04 27.33 26.46 25.41 23.93 24.56 24.38 19.62 19.34 24.35 1.36 1.62 1.63 03-Jun-04 48.80 47.76 47.01 49.44 57.64 66.82 72.45 -- -- 1.85 1.35 1.95 18-Jun-04 8.24 7.90 11.38 7.32 8.29 8.57 8.22 -- -- 0.06 0.28 2.54 01-Jul-04 7.34 7.78 7.11 7.46 6.90 6.55 7.53 -- -- 0.68 0.85 12-Jul-04 6.71 7.43 7.28 6.97 7.32 6.97 6.69 -- -- 0.23 0.48 0.03 11-Aug-04 6.81 6.44 7.16 6.76 6.97 7.04 7.66 8.64 7.53 0.09 0.27 0.60 24-Aug-04 9.59 9.70 9.92 10.45 9.68 10.17 ------0.47 0.26 0.10 15-Oct-04 10.82 13.57 9.12 9.89 10.10 10.38 ------2.26 5.70 03-Nov-04 11.84 9.06 7.25 8.99 9.75 7.60 ------2.64 17-Nov-04 29.82 1.60 28.22 39.99 40.20 36.86 ------4.37 5.69 5.25 01-Dec-04 18.39 18.14 19.00 15.87 15.94 17.54 ------2.69 2.46 2.57 16-Dec-04 25.38 22.68 18.74 2.68 18.00 17.12 15.52 -- -- 1.69 0.66 0.41 07-Jan-05 17.84 21.34 17.93 29.28 16.77 16.56 18.09 24.97 -- 2.50 6.11 2.97 10.41 20-Jan-05 19.13 20.18 20.15 20.77 21.85 21.78 22.54 -- -- 1.91 2.91 2.47 1.49 0.28
Page 330 of 376 Table A.C. 3a-b: % incident PAR down the depth profile of Pond 1. Values displayed are the mean of the 3 sample locations within Pond 1, with S.E. shown in parentheses. a) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 51.61 23.50 11.12 5.25 3.26 2.00 1.64 2.55 2.58 2.33 1.20 0.43 0.27 0.20 04-Feb-04 49.79 26.10 14.08 7.78 4.12 2.55 1.55 0.54 0.40 5.84 3.86 1.34 1.04 0.59 0.43 0.22 0.29 0.05 18-Feb-04 35.16 18.09 9.10 4.76 2.50 1.46 0.67 4.20 2.33 1.38 0.76 0.59 0.72 0.63 10-Mar-04 35.08 18.09 9.10 4.98 2.56 0.74 0.02 4.27 2.33 1.38 0.65 0.54 24-Mar-04 35.01 18.10 9.11 5.20 2.62 1.46 1.30 4.34 2.32 1.37 0.60 0.49 0.73 08-Apr-04 80.33 31.90 17.71 7.77 4.35 3.15 0.76 3.74 5.66 2.36 2.44 1.61 1.28 22-Apr-04 73.53 28.53 16.15 6.90 3.84 2.04 0.76 6.48 2.93 1.14 1.58 1.13 0.17 10-May-04 66.74 25.16 14.58 6.04 3.33 1.87 0.76 12.74 2.80 1.38 0.74 0.69 19-May-04 75.29 51.87 31.81 18.92 12.13 7.14 5.16 3.15 7.26 5.98 3.08 2.75 1.29 0.86 1.04 0.66 03-Jun-04 54.69 31.66 19.01 7.77 4.68 2.38 1.79 1.19 6.06 3.28 3.52 0.56 0.73 0.41 18-Jun-04 73.81 51.33 37.57 22.59 16.51 12.00 8.00 6.62 5.05 3.28 6.08 2.29 0.34 0.33 0.25 0.48 0.43 01-Jul-04 92.94 71.00 56.12 37.42 28.35 21.61 15.41 12.84 9.45 0.68 8.91 4.27 0.87 0.96 0.52 1.05 1.23 12-Jul-04 64.29 22.35 8.39 3.08 2.10 0.88 0.45 0.24 0.27 1.66 3.90 1.61 0.42 0.95 0.39 0.22 0.12 11-Aug-04 52.02 22.82 12.22 6.78 4.08 2.58 1.29 0.49 1.54 1.12 1.39 0.46 0.23 0.13 0.02 0.16 24-Aug-04 60.78 20.01 8.28 3.08 1.29 0.36 0.16 1.57 1.94 1.97 0.68 0.33 0.02 0.01 15-Oct-04 86.75 43.38 18.94 5.69 2.36 0.83 0.27 0.05 4.00 1.71 1.09 1.13 0.74 0.30 0.10 03-Nov-04 65.28 23.86 7.93 2.57 0.76 0.23 0.06 0.03 9.07 3.77 1.24 0.64 0.23 0.05 0.02 0.02 17-Nov-04 80.49 46.25 21.08 11.02 4.89 2.79 1.60 1.33 9.81 16.63 5.46 2.34 1.06 1.00 0.25 01-Dec-04 80.53 31.07 15.17 6.64 3.09 1.51 0.61 3.48 8.44 3.33 1.19 0.59 0.36 0.13 16-Dec-04 84.05 31.73 13.41 6.04 2.61 1.17 0.47 0.07 0.60 2.89 1.09 0.41 0.26 0.16 0.06 0.01 07-Jan-05 87.57 32.40 11.65 5.43 2.13 0.84 0.34 0.15 4.13 3.37 1.14 0.38 0.08 0.04 0.02 0.02 20-Jan-05 17.75 6.20 2.36 1.02 0.48 0.23
Page 331 of 376 b) Sample date Water depth (cm) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 21-Jan-04 63.94 37.44 20.60 11.09 5.36 4.65 2.23 1.41 2.36 4.49 5.94 2.44 0.86 04-Feb-04 32.35 13.82 5.80 2.92 1.45 0.83 0.39 0.13 0.061 9.61 3.57 1.50 0.65 0.37 0.08 0.07 18-Feb-04 49.41 31.03 25.68 18.13 3.66 2.10 8.41 5.93 7.99 11.36 10-Mar-04 49.41 32.61 26.68 16.81 3.66 2.11 8.41 6.36 8.45 6.70 24-Mar-04 49.41 34.19 27.67 21.53 3.65 2.13 8.40 7.13 8.97 7.45 08-Apr-04 71.32 41.77 28.60 16.91 8.14 1.50 0.87 14.83 11.33 10.19 4.81 1.88 22-Apr-04 73.35 44.66 25.63 14.76 6.75 2.61 1.19 8.61 7.88 6.34 3.22 0.87 0.52 10-May-04 75.38 47.55 22.66 12.61 8.08 4.47 1.50 0.87 3.59 6.23 2.62 1.92 1.39 1.79 19-May-04 72.43 31.59 9.10 0.48 0.10 0.03 11.29 9.49 3.45 03-Jun-04 80.84 50.86 15.89 9.07 0.53 0.19 8.49 13.55 5.38 7.10 18-Jun-04 94.90 59.81 21.78 10.30 6.94 4.17 3.35 9.18 6.44 5.27 2.22 01-Jul-04 108.96 68.76 41.50 23.08 13.35 8.16 6.70 10.24 5.47 7.25 12-Jul-04 70.41 52.07 39.47 21.42 12.87 9.39 7.17 3.92 4.41 5.71 11-Aug-04 91.28 66.88 56.57 20.11 10.14 7.91 11.54 11.74 12.79 24-Aug-04 86.26 35.46 15.19 3.47 1.11 0.48 0.38 4.60 2.41 2.90 15-Oct-04 84.38 55.73 39.08 25.98 15.38 8.34 4.70 6.94 03-Nov-04 96.40 49.00 30.86 17.90 18.63 11.84 10.18 3.63 9.65 10.25 9.31 17-Nov-04 84.56 30.52 15.31 6.87 2.54 1.08 5.39 3.85 2.02 01-Dec-04 88.64 41.27 26.87 13.19 5.40 3.61 2.40 4.15 7.27 4.49 2.89 1.83 1.81 1.53 16-Dec-04 82.22 34.76 18.05 8.77 4.69 3.25 2.22 3.41 4.17 1.62 0.58 07-Jan-05 75.80 28.24 9.23 4.35 2.83 1.07 0.50 0.28 3.06 2.93 2.08 2.04 0.69 20-Jan-05 18.54 3.08 1.89 0.72 0.27 0.11 0.07 0.07
Page 332 of 376 Table A.C 4: Total cyanobacteria colony counts within Ponds 1 and 6 of the BWC System over the study period spanning April 2004 to January 2005. Noting ‘*’ where the large concentration of colonies within Pond 1 on the 22 April were from the Family Nostoceace (Anabaena spp.). Units in colonies.mL-1. Sample date Pond 1 Pond 6 Upper water Lower water Upper water Lower water stratum stratum stratum stratum 08-Apr-04 556 0 1500 2333 22-Apr-04 6667 24000 0 0 10-May-04 333 714 0 0 19-May-04 1000 1400 2667 0 03-Jun-04 0 1750 0 0 18-Jun-04 0 0 143 0 01-Jul-04 0 143 0 0 12-Jul-04 333 250 0 0 11-Aug-04 0 0 0 0 24-Aug-04 500 0 0 1500 15-Oct-04 0 0 0 1000 03-Nov-04 0 3000 0 250 17-Nov-04 0 2000 0 500 01-Dec-04 600 2000 0 1000 16-Dec-04 625 2500 0 1250 07-Jan-05 0 1500 0 0 20-Jan-05 2000 1500 0 0
Table A.C 5: Total phytoplankton cell counts within Ponds 1 and 6 of the BWC System over the study period spanning April 2004 to January 2005. Units in cells.mL-1. Sample date Pond 1 Pond 6 Upper water Lower water Upper water Lower water stratum stratum stratum stratum 08-Apr-04 27000 33800 153500 98667 22-Apr-04 50000 36750 198000 264000 10-May-04 17833 17000 39167 58750 19-May-04 48333 31200 146667 222000 03-Jun-04 38750 49000 135000 206000 18-Jun-04 20143 24857 22714 30000 01-Jul-04 15000 12714 16125 18000 12-Jul-04 51667 42000 84000 14100 11-Aug-04 45400 43250 58000 40750 24-Aug-04 46000 60500 37250 93000 15-Oct-04 421000 594000 62333 56167 03-Nov-04 75000 105000 49750 47000 17-Nov-04 71000 55333 58500 61000 01-Dec-04 27200 37000 65000 54500 16-Dec-04 15500 30250 68500 47000 07-Jan-05 24333 40750 25143 54667 20-Jan-05 184000 18250 10688 75000
Page 333 of 376 Table A.C 6: Phytoplankton family groups identified in Pond 1. Each number represents individual phytoplankton cell. Sampling Water date stratum depth Chrysomonaceae Cryptomonceae Dinoflalgellate Euglenaceae Bacillariophyta (Diatoms) Oedogoniaceae Desmidiaceae Scenedesmaceae Oocystaceae Micractiniaceae Palmellaceae Dictyosphaeriaceae 8-Apr-04 upper 778 222 0 5000 111 0 667 11333 0 0 8889 0 lower 0 0 0 21200 200 0 2800 200 0 6200 3200 0 22-Apr-04 upper 0 10500 0 17000 167 0 167 7167 0 0 15000 0 lower 4250 0 0 25750 0 0 1500 4250 0 0 1000 0 10-May-04 upper 0 0 167 16667 0 0 1000 0 0 0 0 0 lower 0 1857 0 14143 571 0 143 0 0 0 286 0 19-May-04 upper 0 1333 0 34333 0 2667 5000 333 667 0 4000 0 lower 1200 1200 0 20600 0 0 2600 3400 0 600 1600 0 3-Jun-04 upper 0 0 0 28750 500 0 1000 5500 1000 0 2000 0 lower 0 0 250 29000 750 0 2000 2500 0 0 0 14500 18-Jun-04 upper 429 0 0 14429 143 0 286 4286 0 0 0 571 lower 0 0 0 14429 1286 0 143 4714 571 571 2429 714 1-Jul-04 upper 0 0 0 8364 364 0 1000 818 727 0 2182 1545 lower 0 0 0 7357 357 0 286 1571 143 0 0 3000 12-Jul-04 upper 1000 0 0 37667 0 0 2000 2667 1333 0 7000 0 lower 0 0 0 25000 0 0 1000 4000 0 2250 1000 8750 11-Aug-04 upper 0 0 0 22000 800 0 800 4600 10600 0 0 6600 lower 0 0 0 25750 0 0 2250 1000 5750 2250 6250 0 24-Aug-04 upper 1750 0 0 27500 0 0 250 6000 0 0 0 10500 lower 1000 0 0 51000 1000 0 0 5500 0 0 0 2000 15-Oct-04 upper 0 3000 0 15000 1000 0 0 402000 0 0 0 0 lower 0 0 0 26000 0 0 0 568000 0 0 0 0 3-Nov-04 upper 0 0 0 6000 0 0 0 66000 0 0 3000 0 lower 6500 1500 500 39000 500 0 3000 54000 0 0 0 0 17-Nov-04 upper 1000 500 0 47500 3000 0 2000 15500 1500 0 0 0 lower 1000 0 0 38333 333 0 0 9000 0 0 6667 0 1-Dec-04 upper 400 200 0 21000 800 0 600 3400 800 0 0 0 lower 500 0 0 28250 250 0 0 4750 750 0 2500 0 16-Dec-04 upper 250 0 0 14250 125 0 125 250 500 0 0 0 lower 0 0 0 27500 0 0 0 2500 250 0 0 0 7-Jan-05 upper 0 0 0 19167 500 0 500 0 0 0 4167 0 lower 1250 0 0 34250 250 0 1250 0 0 0 3750 0 20-Jan-05 upper 0 0 0 181000 0 0 3000 0 0 0 0 0 lower 1250 0 0 11250 250 0 1500 250 0 0 3750 0
Page 334 of 376 Table A.C 7: Phytoplankton family groups identified in Pond 6. Each number represents individual phytoplankton cell. Sampling Water date depth lariophyta lariophyta Chrysomonaceae Cryptomonceae Dinoflalgellate Euglenaceae Bacil (Diatoms) Oedogoniaceae Desmidiaceae Scenedesmaceae Oocystaceae Micractiniaceae Palmellaceae Dictyosphaeriaceae 8-Apr-04 upper 0 0 0 55500 17500 0 15000 44500 0 0 3500 17500 lower 0 0 0 30333 12000 0 6333 22333 3333 0 12333 12000 22-Apr-04 upper 0 4000 0 18000 0 0 2000 136000 0 0 38000 0 lower 0 0 0 58000 27000 0 5000 100000 7000 1000 66000 0 10-May-04 upper 1333 0 0 11667 667 0 333 19500 1833 1333 2500 0 lower 1000 250 0 16250 2000 0 3500 27500 3000 0 5250 0 19-May-04 upper 0 0 0 40000 11333 0 2667 67333 6667 0 16000 2667 lower 1000 0 0 100000 8000 0 8000 81000 9000 0 4000 11000 3-Jun-04 upper 3000 0 500 25500 500 0 0 77000 1000 0 0 27500 lower 3000 0 0 8000 2000 0 1000 150000 0 0 0 42000 18-Jun-04 upper 143 0 143 14429 1143 0 286 4000 0 0 0 2571 lower 0 0 0 17667 500 0 0 8333 1833 0 1667 0 1-Jul-04 upper 0 0 0 12250 1000 0 375 1000 1000 0 500 0 lower 0 0 0 14375 375 0 0 2875 375 0 0 0 12-Jul-04 upper 0 0 0 63000 500 0 500 16000 4000 0 0 0 lower 500 0 100 10300 1100 0 0 1200 500 0 400 0 11-Aug-04 upper 0 0 1333 36000 1333 0 1000 11000 1333 0 4667 1333 lower 500 0 0 26250 2250 0 0 8500 3250 0 0 0 24-Aug-04 upper 750 250 0 25000 2500 0 500 7500 250 0 500 0 lower 0 0 500 65000 7500 0 1000 3000 3000 0 10000 3000 15-Oct-04 upper 333 0 0 2000 3000 0 4333 4667 0 0 0 0 lower 333 0 0 4333 3000 1000 833 1000 1000 1000 7333 1000 3-Nov-04 upper 250 0 0 25500 1750 0 2250 14500 750 0 1250 3500 lower 500 0 0 28250 750 0 750 10750 0 0 6000 0 17-Nov-04 upper 0 0 0 20000 1250 0 1250 25250 1500 0 2250 7000 lower 750 0 0 31250 1250 0 1250 21500 1250 0 3750 0 1-Dec-04 upper 0 0 0 33333 1333 0 1000 21333 1333 0 2000 4667 lower 1250 0 250 29000 1500 0 750 13250 1750 4750 2000 0 16-Dec-04 upper 0 0 0 53500 1000 0 0 13500 500 0 0 0 lower 1750 0 500 26500 1500 0 250 5000 2250 9250 0 0 7-Jan-05 upper 0 143 0 7143 1000 0 1286 571 0 0 571 0 lower 0 0 0 51333 2333 0 333 667 0 0 0 0 20-Jan-05 upper 0 0 0 7188 250 0 563 2125 0 0 563 0 lower 0 0 0 52500 1500 0 0 14000 0 0 0 7000
Page 335 of 376 Appendix D – non essential data from Chapter 9
Table A.D 1: NOx, NH4 and PO4 concentration in incubation jars reported every 24 hours for 7 days beginning on the 3-Sept-04. Numbers displayed are means of 3 replicates, with the SE in parentheses. Units mg L-1, n = 3. Time Base control Phytoplankton Epiphyton Ceratophyllum demersum Nutrient (h) Control Treatment Control Treatment Control Treatment Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE NOx 0 0.692 0.002 0.052 0.002 0.692 0.002 0.052 0.002 0.692 0.002 0.052 0.002 0.692 0.002 24 0.294 0.005 0.048 0.001 0.290 0.005 0.031 0.001 0.261 0.017 0.028 0.001 0.252 0.005 48 0.303 0.007 0.039 0.002 0.274 0.015 0.026 0.001 0.241 0.006 0.024 0.001 0.201 0.012 72 0.311 0.010 0.030 0.003 0.257 0.032 0.020 0.000 0.221 0.006 0.021 0.002 0.150 0.024 96 0.301 0.007 0.017 0.002 0.245 0.013 0.021 0.001 0.116 0.014 0.020 0.001 0.083 0.025 120 0.305 0.000 0.015 0.001 0.182 0.025 0.019 0.001 0.036 0.011 0.020 0.002 0.058 0.017 144 0.314 0.014 0.019 0.001 0.106 0.010 0.019 0.006 0.016 0.002 0.021 0.002 0.043 0.017 + NH4 0 0.276 0.001 0.016 0.001 0.276 0.001 0.016 0.001 0.276 0.001 0.016 0.001 0.276 0.001 24 0.311 0.008 0.032 0.001 0.228 0.010 0.032 0.004 0.096 0.004 0.010 0.001 0.021 0.003 48 0.355 0.004 0.019 0.001 0.135 0.012 0.019 0.002 0.056 0.002 0.010 0.001 0.016 0.002 72 0.400 0.005 0.007 0.000 0.042 0.015 0.007 0.000 0.016 0.002 0.009 0.001 0.010 0.001 96 0.433 0.032 0.006 0.001 0.008 0.000 0.012 0.001 0.054 0.041 0.010 0.001 0.012 0.001 120 0.419 0.015 0.010 0.004 0.007 0.000 0.009 0.001 0.014 0.003 0.010 0.001 0.015 0.003 144 0.484 0.041 0.005 0.001 0.009 0.001 0.022 0.007 0.015 0.001 0.007 0.000 0.012 0.002 3- PO4 0 0.384 0.000 0.084 0.000 0.384 0.000 0.084 0.000 0.384 0.000 0.084 0.000 0.384 0.000 24 0.369 0.011 0.076 0.004 0.324 0.006 0.070 0.005 0.328 0.009 0.037 0.001 0.099 0.011 48 0.343 0.012 0.074 0.001 0.317 0.011 0.069 0.002 0.315 0.003 0.025 0.002 0.075 0.007 72 0.317 0.017 0.072 0.005 0.309 0.017 0.067 0.003 0.301 0.004 0.013 0.003 0.051 0.004 96 0.357 0.003 0.065 0.002 0.285 0.011 0.062 0.001 0.270 0.004 0.012 0.002 0.040 0.004 120 0.344 0.006 0.051 0.002 0.256 0.010 0.043 0.005 0.242 0.005 0.013 0.005 0.035 0.004 144 0.359 0.004 0.046 0.005 0.241 0.019 0.022 0.008 0.210 0.005 0.021 0.004 0.031 0.005
Page 336 of 376 Table A.D 2: NOx, NH4 and PO4 concentration in incubation jars reported every 24 hours for 7 days beginning on the 15-Jan-05. Numbers displayed are means of 3 replicates, with the SE in parentheses. Units mg L-1, n = 3. Base control Phytoplankton Epiphyton Ceratophyllum demersum Potamogeton javanicus Time Control Treatment Control Treatment Control Treatment Control Treatment Nutrient Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE NOx 0 0.647 0.001 0.007 0.001 0.647 0.001 0.007 0.001 0.647 0.001 0.007 0.001 0.647 0.001 0.007 0.001 0.647 0.001 24 0.403 0.045 0.058 0.034 0.029 0.007 0.017 0.007 0.016 0.006 0.039 0.008 0.051 0.006 0.060 0.013 0.020 0.009 48 0.264 0.064 0.045 0.013 0.021 0.005 0.010 0.004 0.023 0.005 0.034 0.002 0.036 0.005 0.065 0.015 0.032 0.002 72 0.125 0.083 0.008 0.001 0.013 0.005 0.006 0.003 0.030 0.005 0.030 0.012 0.022 0.005 0.070 0.024 0.049 0.003 96 0.201 0.067 0.014 0.008 0.019 0.008 0.008 0.002 0.014 0.003 0.030 0.011 0.033 0.010 0.058 0.008 0.049 0.005 120 0.391 0.034 0.027 0.007 0.008 0.005 0.017 0.004 0.022 0.007 0.040 0.005 0.015 0.003 0.040 0.006 0.055 0.001 144 0.490 0.058 0.017 0.006 0.028 0.003 0.041 0.019 0.019 0.005 0.036 0.004 0.023 0.002 0.000 0.000 0.053 0.004 + NH4 0 0.272 0.013 0.024 0.004 0.272 0.013 0.012 0.013 0.272 0.013 0.024 0.004 0.272 0.013 0.012 0.013 0.272 0.013 24 1.725 0.097 0.026 0.002 0.995 0.079 0.008 0.001 0.209 0.099 0.035 0.008 0.498 0.156 0.027 0.006 0.002 0.002 48 1.476 0.062 0.015 0.004 0.501 0.038 0.009 0.002 0.111 0.049 0.021 0.004 0.262 0.085 0.025 0.008 0.020 0.001 72 1.226 0.219 0.011 0.001 0.007 0.004 0.011 0.003 0.013 0.005 0.007 0.001 0.025 0.020 0.023 0.008 0.039 0.001 96 1.286 0.251 0.014 0.002 0.024 0.014 0.019 0.007 0.064 0.029 0.018 0.003 0.033 0.018 0.035 0.004 0.023 0.006 120 1.265 0.212 0.006 0.002 0.009 0.002 0.007 0.002 0.020 0.011 0.022 0.004 0.005 0.003 0.037 0.010 0.026 0.004 144 1.265 0.219 0.021 0.006 0.024 0.011 0.044 0.018 0.029 0.020 0.032 0.000 0.009 0.002 0.000 0.000 0.028 0.005 3- PO4 0 0.387 0.002 0.087 0.002 0.387 0.002 0.087 0.002 0.387 0.002 0.087 0.002 0.387 0.002 0.087 0.002 0.387 0.002 24 0.654 0.015 0.070 0.007 0.561 0.007 0.043 0.005 0.460 0.003 0.011 0.009 0.146 0.063 0.241 0.069 0.411 0.023 48 0.590 0.005 0.065 0.007 0.436 0.011 0.025 0.002 0.314 0.027 0.013 0.005 0.088 0.035 0.281 0.043 0.409 0.002 72 0.527 0.005 0.059 0.007 0.310 0.017 0.006 0.003 0.167 0.054 0.015 0.010 0.029 0.011 0.320 0.060 0.368 0.043 96 0.606 0.021 0.066 0.005 0.276 0.012 0.011 0.001 0.120 0.038 0.003 0.001 0.024 0.006 0.373 0.041 0.273 0.028 120 0.596 0.008 0.057 0.007 0.057 0.004 0.006 0.002 0.061 0.013 0.002 0.001 0.011 0.002 0.261 0.064 0.382 0.051 144 0.636 0.016 0.064 0.003 0.285 0.033 0.007 0.003 0.069 0.015 0.012 0.004 0.023 0.003 0.003 0.003 0.403 0.039
Page 337 of 376 Table A.D 3: Chlorophyll a concentration within Base control/treatment and phytoplankton incubation jars. Unit in µg L-1 , n = 3. Time Base control Phytoplankton control Phytoplankton treatment (h) mean se mean se mean se 03-Sept-04 0 20.66 0.00 20.66 0.00 20.66 0.00 24 17.10 1.36 21.25 0.59 22.44 0.51 48 15.31 1.78 22.44 0.89 24.22 0.52 72 13.53 2.24 24.22 1.78 27.49 0.30 96 11.15 2.83 33.13 0.89 46.20 1.95 120 10.27 0.59 45.61 1.78 73.53 1.19 144 9.97 1.36 52.73 1.54 111.24 2.38 15-Jan-05 0 15.91 0.00 15.91 0.00 15.91 0.00 24 38.18 5.11 42.04 3.37 93.42 3.65 48 60.45 10.57 11.16 0.30 106.49 5.20 72 40.26 7.42 6.70 0.60 28.38 1.07 96 19.18 0.78 5.51 0.52 16.20 4.02 120 10.56 1.07 2.84 0.00 6.70 2.32 144 9.97 0.51 1.89 0.21 5.81 1.95
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