Villanova University
The Graduate School
Department of Civil and Environmental Engineering
The Implications of the First Flush Phenomenon on Infiltration BMP Design
A Thesis in
Civil Engineering
by
Tom Batroney
Submitted in partial fulfillment
of the requirements
for the degree of
Master of Science in Water Resources
and Environmental Engineering
May 2007
Acknowledgements
Without the love and support of my father, Matthew, and my mother, Carol, this
thesis would not have been possible. I truly feel blessed to have been raised by such
caring individuals. They instilled virtues such as hard work, dedication, honesty, and
faith which provided the cornerstones in which to achieve my goals and aspirations in life.
I also hold Dr. Robert Traver in the same regard and see in him all the same
characteristics as my parents. His drive to make the VUSP, its members, and everyone
within the Villanova community (which includes me) the best they can be is truly
amazing. Combine his hard work with his expertise in everything regarding stormwater
and you have a perfect combination for success. Upon first meeting Dr. Traver when
visiting potential graduate schools, I knew I wanted to come to Villanova University immediately because I thought so highly of him then. Upon leaving Villanova I hold him in even higher regard and I feel forever in debt for everything he provided me.
Very special thanks also to Clay Emerson who provided so much help and insight into my questions and problems. I affectionately called him "the master" for a reason. It was amazing how quickly he could diagnose a situation and present a brilliantly simple solution.
I would also like to thank Dr. Bridget Wadzuk, Bill Heasom, George Pappas,
Nick Grosso, Mary Ellen Dukart, Hans Benford, Keisha Ricketts, David Salas, Krista
Hankins, Matt Machusick, Matt Gore, Erika Tokarz, Megan Vanacore, Erin Burke, and
Deniz Yurtserver for providing me support on both an academic and personal level.
They will forever have my eternal friendship.
3 Abstract
The stormwater runoff entering the Villanova University Stormwater Partnership (VUSP)
Infiltration Trench was sampled incrementally during storm events between September
2006 and May 2007 in order to observe the change in pollutant concentration with respect to storm depth. The drainage area (20,400 ft2) consists of a 100 percent impervious
parking deck used exclusively for Villanova University faculty and staff.
The intention of this sampling methodology was to ascertain the existence of an initial
high pollution concentration, otherwise known as the first flush phenomena. It was
determined that total suspended solids, total dissolved solids, dissolved copper, and
dissolved cadmium exhibited a first flush behavior which lasted until a rainfall depth of
1.0 inches was reached. The depth of 1.0 inches was further verified using Student's
statistical T-test. Total phosphorus, total nitrogen, nitrite, nitrate, phosphate, and
dissolved chromium did not exhibit a first flush.
Using the observed data, a theoretical pollutant capture percent on the VUSP Infiltration
Trench was determined. For all observed storm events since the trench's construction in
July 2004, the theoretical pollutant capture on the pollutants ranged from 43% to 15%.
Had the VUSP Infiltration Trench been constructed to the state recommended standard of
5:1 drainage area to footprint ratio, the theoretical pollutant capture percent would have ranged from 97% to 90%. The VUSP Infiltration Trench is intentionally undersized
(158:1 drainage area to footprint ratio) in order to accelerate lifespan processes.
4 TABLE OF CONTENTS
ACKNOWLEDGEMENTS...... 3 ABSTRACT ...... 4 TABLE OF CONTENTS...... 5 TABLE OF FIGURES ...... 8 TABLE OF TABLES ...... 10
CHAPTER 1 - INTRODUCTION ...... 11 1.1 INTRODUCTION...... 11 1.2 FIRST FLUSH AT THE VUSP INFILTRATION TRENCH...... 13 1.3 RESEARCH OBJECTIVES ...... 13
CHAPTER 2 - INFILTRATION TRENCH SITE DESCRIPTION...... 15 2.1 INTRODUCTION...... 15 2.2 SITE LOCATION...... 15 2.3 CONTRIBUTING DRAINAGE AREA ...... 17 2.4 SURROUNDING SOIL AND SUBSURFACE CONDITIONS ...... 19 2.4 INFILTRATION TRENCH DESIGN...... 21 2.5 INFILTRATION TRENCH CONSTRUCTION ...... 23 2.5.1 INVESTIGATION OF PRE-CONSTRUCTION SITE PLANS ...... 23 2.5.2 RETAINING WALL CONSTRUCTION ...... 24 2.5.3 TRENCH EXCAVATION PROCEDURES ...... 25 2.5.4 INSTALLATION OF SAFETY OVERFLOW MECHANISMS...... 27
CHAPTER 3 – LITERATURE REVIEW...... 31 3.1 STORMWATER BEST MANAGEMENT PRACTICES IN THE URBAN SETTING ...... 31 3.1.1 INFILTRATION TRENCHES AS BEST MANAGEMENT PRACTICES ...... 32 3.2 SUSTAINABILITY OF INFILTRATION TRENCH BMPS ...... 34 3.3 FIRST FLUSH PHENOMENON ...... 36 3.3.1 EMPIRICAL BASED FIRST FLUSH THEORY ...... 37 3.3.2 CONCENTRATION BASED FIRST FLUSH THEORY ...... 38 3.3.3 MASS BASED FIRST FLUSH THEORY...... 39 3.4 THE AMBIGUOUS FIRST FLUSH DEFINITION ...... 44
CHAPTER 4 – METHODS...... 48 4.1 INTRODUCTION...... 48 4.2 INFILTRATION TRENCH INSTRUMENTATION AND SETUP – QUANTITY ...... 49
5 4.2.1 RAINFALL MONITORING ...... 49 4.2.2 INFLOW MONITORING...... 50 4.2.3 STORAGE DEPTH/VOLUME MONITORING ...... 52 4.2.4 OVERFLOW MONITORING...... 53 4.2.5 DATA LOGGING AND RETRIEVAL ...... 57 4.3 INFILTRATION TRENCH INSTRUMENTATION AND SETUP – QUALITY ...... 66 4.3.1 INFLOW SAMPLING PROCEDURES AND INSTRUMENTATION...... 66 4.3.2 OVERFLOW SAMPLING PROCEDURES AND INSTRUMENTATION...... 71 4.4 INSTRUMENTATION CALIBRATION ...... 75 4.4.1 RAIN GAGE ...... 76 4.4.2 PS9800 PRESSURE TRANSDUCERS ...... 76 4.4.3 SIGMA 900 AUTOMATED SAMPLERS...... 78 4.5 LABORATORY TESTING AND PROCEDURES ...... 78 4.5.1 LABORATORY METHODS - NUTRIENTS ...... 79 4.5.2 LABORATORY METHODS – SOLIDS...... 80 4.5.3 LABORATORY METHODS – DISSOLVED METALS ...... 81 4.5.4 LABORATORY METHODS – LOW TRACE IONS ...... 81 4.6 WATER QUALITY DATA ANALYSIS...... 82 4.6.1 DETERMINATION OF FIRST FLUSH PHENOMENON...... 82 4.6.1.1 Student’s T-Test ...... 84 4.6.2 FIRST FLUSH CAPTURE ANALYSIS...... 87 4.6.3 THE INFLUENCE OF STORM CHARACTERISTICS ...... 89
CHAPTER 5 - RESULTS ...... 89 5.1 INTRODUCTION...... 90 5.2 DETERMINATION OF FIRST FLUSH ...... 90 5.3 POLLUTANT TRANSPORT CHARACTERISTICS...... 100 5.4 THEORETICAL POLLUTANT REMOVAL EFFICIENCY...... 109 5.5 INFLUENCE OF RAINFALL CHARACTERISTICS ...... 123
CHAPTER 6 – DISCUSSION ...... 128 6.1 INTRODUCTION...... 128 6.2 FIRST FLUSH DETERMINATION...... 128 6.2.1 POLLUTANTS WITH DECREASING CONCENTRATIONS...... 128 6.2.2 STATISTICAL T-TEST VERIFICATION...... 131 6.2.3 MASS/RAINFALL DIMENSIONLESS DISTRIBUTION CURVES...... 132 6.3 THEORETICAL POLLUTANT CAPTURE EFFICIENCY ...... 135 6.4 THE IMPORTANCE OF DRAINAGE TO FOOTPRINT AREA LOADING RATIO...... 139 6.5 INFLUENCE OF STORM CHARACTERISTICS ON THE FIRST FLUSH ...... 142
CHAPTER 7 – CONCLUSIONS...... 144
6 CHAPTER 8 – RECOMMENDATIONS FOR FUTURE RESEARCH ...... 146
APPENDIX A: CAD DRAWINGS OF VUSP INFILTRATION TRENCH...... 151 APPENDIX B: CRBASIC TRENCH[23].CR1 PROGRAMMING FILE...... 153 APPENDIX C: OBSERVED WATER QUALITY DATA ...... 158 APPENDIX D: DETAILED F-TEST RESULTS FOR POLLUTANTS EXHIBITING DOWNWARD MEAN CONCENTRATIONS ...... 162 APPENDIX E: T-TEST RESULTS FOR DOWNWARD DECREASING POLLUTANTS...... 169 APPENDIX F: CALCULATED DATA USED TO COMPILE MASS/RAINFALL PLOTS ...... 181 APPENDIX G: SAMPLE PLOTS OF STORM EVENTS ...... 186
7
TABLE OF FIGURES
Figure 1.1 The Villanova University Stormwater Partnership Infiltration Trench (August 2006)...... 11 Figure 2.1 Infiltration Trench Study Site (Trench Footprint in Green) ...... 16 Figure 2.2 Pre-Construction Photograph of the Infiltration Trench Site ...... 17 Figure 2.3 Aerial View of the Infiltration Trench Drainage Area (Blue Square) and VUSP Infiltration Trench (Red Square)...... 19 Figure 2.4 Picture of Excavated Trench with Geotextile Liner ...... 26 Figure 2.5 Picture of Trench Excavation Pit Along with L-Shaped Distribution Pipe and 3 to 6 Inch Washed Stone Aggregate Fill...... 27 Figure 2.6 Picture of Overflow Efficiency Device Installed Post-Construction...... 28 Figure 2.7 Picture of the Finished Infiltration Trench ...... 30 Figure 3.1 Development of M(V) Curve Demonstrated by Bertrand, et al. 1998 ...... 41 Figure 3.2 Typical First Flush Coefficient b Value with Respective M(V) Curves ...... 44 Figure 3.3 Typical TSS Concentrations Based Upon Land Use (Maestre and Pitt 2005) ...... 46 Figure 4.1 Stormwater Passing through the Pre-Treatment Bench and Entering the VUSP Infiltration Trench...... 48 Figure 4.2 Rain Gage at the VUSP Infiltration Trench ...... 49 Figure 4.3 V-Notch Weir Located at the Exit End of the Pretreatment Bench...... 50 Figure 4.4 Picture of the Overflow Palmer Bowlus Flume (Looking Upstream)...... 54 Figure 4.5 Turbulent Nape Caused by the Hose Clamp in the Overflow Flume ...... 56 Figure 4.6 Campbell Scientific Data Logging Equipment at the Infiltration Trench ...... 58 Figure 4.7 Loggernet Mainframe Window...... 64 Figure 4.8 Data Display Screen Number 1 on Loggernet...... 65 Figure 4.9 Location of the Inflow Sampling Point (Intake Strainer Location)...... 70 Figure 4.10 Overflow Hanger Sampler Located at the End of the Flume ...... 71 Figure 4.11 Overflow Sampling Reservoir for Intake Strainer (Strainer Inside)...... 73 Figure 4.12 Overflow Tarp...... 75 Figure 4.13 Calibration Plot of the Overflow Pressure Transducer...... 77 Figure 5.1 Observed Total Suspended Solids Concentrations during Storm Events...... 92 Figure 5.2 Observed Total Dissolved Solids Concentrations during Storm Events ...... 93 Figure 5.3 Observed Total Nitrogen Concentrations during Storm Events...... 93 Figure 5.4 Observed Total Phosphorus Concentrations during Storm Events...... 94 Figure 5.5 Observed Nitrite Concentrations during Storm Events ...... 94 Figure 5.6 Observed Nitrate Concentrations during Storm Events ...... 95 Figure 5.7 Observed Phosphate Concentrations during Storm Events ...... 95 Figure 5.8 Chloride Concentrations during Storm Events...... 96 Figure 5.9 Observed Dissolved Copper Concentrations during Storm Events...... 96 Figure 5.10 Observed Dissolved Lead Concentrations during Storm Events...... 97 Figure 5.11 Observed Dissolved Cadmium Concentrations during Storm Events...... 97 Figure 5.12 Observed Dissolved Chromium Concentrations during Storm Events ...... 98 Figure 5.13 Cumulative Mass/Rainfall Plot for Total Suspended Solids ...... 101 Figure 5.14 Cumulative Mass/Rainfall Plot for Total Suspended Solids ...... 101 Figure 5.15 Cumulative Mass/Rainfall Plot for Total Nitrogen ...... 102 Figure 5.16 Cumulative Mass/Rainfall Plot for Total Phosphorus...... 102 Figure 5.17 Cumulative Mass/Rainfall Plot for Nitrite ...... 103 Figure 5.18 Cumulative Mass/Rainfall Plot for Nitrate...... 103 Figure 5.19 Cumulative Mass/Rainfall Plot for Phosphate ...... 104 Figure 5.20 Cumulative Mass/Rainfall Plot for Chloride...... 104 Figure 5.21 Cumulative Mass/Rainfall Plot for Dissolved Copper ...... 105 Figure 5.22 Cumulative Mass/Rainfall Plot for Dissolved Cadmium ...... 105 Figure 5.23 Cumulative Mass/Rainfall Plot for Dissolved Chromium...... 106 Figure 5.24 Comparison between Averaged and Discrete Samples (TSS) April 14 ...... 107
8 Figure 5.25 Comparison between Averaged and Discrete Samples (TSS) April 25 ...... 107 Figure 5.26 Comparison between Averaged and Discrete Samples (TDS) April 14...... 108 Figure 5.27 Comparison between Averaged and Discrete Samples (TDS) April 25...... 108 Figure 5.28 Typical Water Level Readings within VUSP Infiltration Trench ...... 109 Figure 5.29 Depth of Rainfall Captured at the VUSP Infiltration Trench...... 111 Figure 5.30 Depth of Rainfall Captured at the VUSP Infiltration Trench...... 111 Figure 5.31 Total Rainfall Depth and Total Suspended Solids Load ...... 113 Figure 5.32 Total Rainfall Depth and Total Dissolved Solids Load...... 113 Figure 5.33 Total Rainfall Depth and Total Nitrogen Load ...... 114 Figure 5.34 Total Rainfall Depth and Total Phosphorus Load...... 114 Figure 5.35 Total Rainfall Depth and Total Ion Load ...... 115 Figure 5.36 Total Rainfall Depth and Total Chloride Load...... 115 Figure 5.37 Total Rainfall Depth and Total Dissolved Metals Load...... 116 Figure 5.38 Mass of Total Suspended Solids Captured (All Events) ...... 117 Figure 5.39 Mass of Total Suspended Solids Captured (Overflow Events Only) ...... 117 Figure 5.40 Rainfall Captured during Trench Fill Time and Total Observed Rainfall...... 118 Figure 5.41 Theoretical Pollutants Captured (Blue) by and Transported (Red) to the VUSP Infiltration Trench (Gram Units) ...... 119 Figure 5.42 Theoretical Pollutants Captured by and Transported to the VUSP Infiltration Trench (Pound Units)...... 120 Figure 5.43 Average Transport Curves with the Evolution of the Dimensionless Rainfall Parameter...... 122 Figure 5.44 October 27, 2006 Storm Event and Respective Sampling Locations...... 124
9
TABLE OF TABLES
Table 3.1 Low Trace Ions Tested Using HPLC...... 81 Table 5.1 Number of Events Observed for First Flush...... 91 Table 5.2 Number of Storm Events Observed Depending Upon Rainfall Depth ...... 91 Table 5.3 F-Test Comparison Results (Respective T-Test Used)...... 99 Table 5.4 T-Test Results for Decreasing Mean Pollutants (Above 90% in Bold)...... 100 Table 5.5 Total Theoretical Capture Efficiencies for Pollutants at the VUSP Infiltration Trench ...... 120 Table 5.6 Coefficient of Determination Values for Regression Analysis between 0.25 Inch TSS and TDS Samlpes with Various Storm Characteristics...... 125 Table 5.7 Coefficient of Determination Values for Regression Analysis between 0.50 Inch TSS and TDS Samlpes with Various Storm Characteristics...... 125 Table 5.8 Coefficient of Determination Values for Regression Analysis between 1.00 Inch TSS and TDS Samlpes with Various Storm Characteristics...... 126 Table 5.9 Coefficient of Determination Values for Regression Analysis between Greater Than 1.00 Inch TSS and TDS Samlpes with Various Storm Characteristics ...... 126 Table 5.10 Coefficient of Determination Values for Regression Analysis between Total Storm Event TSS and TDS Samlpes with Various Storm Characteristics ...... 127 Table 6.1 Comparison of Average Composite Overflow Sample to Average Inflow Sample during Overflow...... 136 Table 6.2 Total Theoretical Pollutant Capture Efficiency for a 5:1 Loading Ratio Infiltration Trench (Overflow Depth = 5.2 feet) ...... 141
10 Chapter 1 - Introduction
Figure 1.1 The Villanova University Stormwater Partnership Infiltration Trench (August 2006)
1.1 Introduction
The purpose of this research is to investigate the variation in inflow pollutant concentration with respect to rainfall at the infiltration trench best management practice
(BMP) on the campus of Villanova University. These pollutants include: total suspended solids (TSS), total dissolved solids (TDS), total nitrogen, total phosphate orthophosphorus, and dissolved metals in the form of zinc, lead, and copper with the central focus being the transport of solids within the infiltration trench. The
11 understanding of the relationship between pollutant concentration and rainfall depth can
allow designers and engineers to intercept volumes of runoff at the peak pollutant
concentration. The highest concentration is believed to occur at the beginning of the rain
event. This reoccurring phenomenon has become known as the “first flush.” Being able
to capture the first flush for improved downstream conditions has long been the focus for
many designers. Determining what percent runoff volume capture is required for a
respective percent removal of pollutants is often a challenge for designers. Without
actual data from the drainage area, determining when the first flush occurs is often
difficult.
Villanova University is host to the Villanova Urban Stormwater Partnership (VUSP),
whose stated mission is to advance the evolving comprehensive storm water management
field and to foster the development of public and private partnerships through research on
innovative storm water management BMPs, directed studies, technology transfer and education. The unique role of the VUSP allows it to manage research on a variety of
storm water management BMPs on Villanova University’s campus in Villanova,
Pennsylvania.
The infiltration trench is one of the several BMPs in the VUSP that is intensely being
studied both in terms of water quantity and water quality. The Infiltration Trench was
constructed in July of 2004 with funding from the Pennsylvania Department of
Environmental Protection 319 Program NPS Pollution Program. The Infiltration Trench
was specifically designed and built to reduce runoff volumes through soil infiltration.
12
A previous study has been performed on the Infiltration Trench (Dean 2005). The focus
of Dean’s research was on water quantity and modeling procedures for trench design.
This thesis focuses primarily on water quality in order to assess the transport of pollutants
(with added emphasis on total suspended and dissolved solids) entering and leaving the
infiltration trench with respect to the first flush phenomena.
1.2 First Flush at the VUSP Infiltration Trench
In order to better understand the transport of the pollutants entering the VUSP Infiltration
Trench, a first flush study was performed and is the main focus of this thesis. First flush studies consist of taking several individual samples throughout a storm event in order to gain an understanding of when entering pollutants are at their highest concentrations.
Typically if pollutant concentrations are at their highest during the onset of the storm event then the pollutant is said to exhibit a first flush. Prior to this thesis, it was suspected that the first flush phenomenon is present at the VUSP Infiltration Trench and this work investigates this hypothesis.
1.3 Research Objectives
The purpose of this research is to characterize the relationship between pollutant discharge and rainfall depth at the VUSP Infiltration Trench, with the intent of determining whether or not a first flush exists.
13
The questions to be answered upon the completion of this research include:
• Is there a first flush phenomenon at the VUSP Infiltration Trench?
• What quantity of runoff depth capture is necessary to capture the first flush?
• What portion of the first flush is the VUSP Infiltration Trench capturing?
• What potential impact would this capture have on the pollutant transport within
the trench?
• Do rainfall characteristics play a role in the possible first flush phenomenon at
the VUSP infiltration trench?
14 Chapter 2 - Infiltration Trench Site Description
2.1 Introduction
This chapter describes more in depth the VUSP Infiltration Trench Best Management
Practice (BMP). The issues discussed in this chapter include: site location, contributing drainage area, surrounding soil and subsurface conditions, the design of the trench, and the actual construction process.
2.2 Site Location
The VUSP infiltration trench BMP on the campus of Villanova University is located in
Radnor Township, Delaware County, Pennsylvania. The infiltration trench is located in the headwaters of the Mill Creek watershed with Mill Creek being a part of the greater
Schuylkill River watershed. Mill Creek is classified by the Pennsylvania Department of
Environmental Protection’s Chapter 93 Water Quality Classification as a Trout Stocked
Fishery (TSF) (Pennsylvania 2005). Mill Creek is also listed on the Pennsylvania
Department of Environmental Protection’s 303d List of Streams Impaired by Pollution not requiring a TMDL for urban runoff/storm pollution/flow variation (Pennsylvania
DEP 2004).
The infiltration trench is a retrofit that exists in a small area between an academic building (St. Augustine Center) and an adjacent parking garage. A picture of the trench,
15 along with the academic building and parking garage, is shown in Figure 2.1. The edge
of the parking garage is can be seen on the left hand side of the picture.
Figure 2.1 Infiltration Trench Study Site (Trench Footprint in Green)
Both existing structures are greater than eight feet away and up gradient from the infiltration trench, preventing the possibility of flooding subsurface areas. Additionally, there are no potable water supply wells within 100 feet of the trench, alleviating any chance of intrusion of infiltrated water into a well used for drinking water. The site had previously provided a picnic area to students and staff for many years, however prior to construction there was a lack of landscaping and maintenance and it was considered unsightly by many. Additionally, the pre-construction site was subject to erosion from the accompanying hillside. The finished site not only fulfills its stormwater management function but also provides an attractive area for visitors and reduces unwanted erosion
16 into the nearby stormwater system. A picture of the pre-construction site area is shown in
Figure 2.2.
Figure 2.2 Pre-Construction Photograph of the Infiltration Trench Site
2.3 Contributing Drainage Area
The drainage area for the infiltration trench is 100% impervious and is equal to approximately 20,400 square feet (just under 0.5 acre.) The total drainage area consists of less than one half of the upper level concrete parking garage that is exclusively used by the Villanova University staff in the adjacent St. Augustine Center. Since the parking deck is dedicated to parking purposes only and no large truck activity occurs on the upper level of the garage, the probability of experiencing any type of hazardous spill is extremely low.
17
Prior to the construction of the trench, the entire 44,800 square foot lot was drained by a closed pipe storm system where it ultimately entered the campus underground stormwater sewer system. Nearly half of the pipes that carry the runoff from the upper deck of the parking garage were re-routed to discharge into the inlet structure of the infiltration trench. Due to the relatively flat nature of the parking lot drainage area, it was important to observe the flow patterns during rain events. Delineation of the upper portion of the parking garage that drains into the trench was determined by Dean (2005) through the use of a ‘dye tracer method.’ During a rain event, red food coloring was dropped along what was assumed to be the border line for the drainage area. Upon observing the flow of the red food coloring during the rain event, the area that enters the infiltration trench was measured using aerial photography and was determined to be approximately 159 feet by
128 feet (20,400 ft2). Figure 2.3 depicts an aerial view of the drainage area (dashed line) with respect to the area of the infiltration trench (small square in bottom right corner).
18
Figure 2.3 Aerial View of the Infiltration Trench Drainage Area (Blue Square) and VUSP Infiltration Trench (Red Square)
2.4 Surrounding Soil and Subsurface Conditions
Stormwater infiltration design depends highly on properties of the soil that exists at a proposed site. In the initial phases of the design process, a site investigation and feasibility test was performed to determine suitability for the installation of an infiltration system. Technical observation was provided by Mr. Bill Heasom of the Villanova Urban
Stormwater Partnership for a test pit and percolation test performed in June 2004.
Additionally, a review of published geologic data was performed to characterize the subsurface conditions at anticipated location of the infiltration trench. The parameters
19 investigated during the review included: types of material, uniformity, depth to bedrock,
and depth to groundwater conditions. All pre-construction field work was performed by
excavation subcontractors N. Abbonizio Inc.
Based upon a review of the United States Department of Agriculture (USDA) Soil
Conservation Service (SCS) Soil Survey for Chester and Delaware Counties (1963), the
soils in the area of the trench consist of made land, schist and gneiss materials. These
soils are described in the survey as “well drained to moderately well drained, mixed
coastal plain materials, three to eight feet thick; underlain by unconsolidated coastal plain
deposits of clay, silt, sand and gravel ranging from four to 40 feet or more in thickness”
(USDA 1963). These types of soils comprise approximately 13.2 % (15,650 acres) of
Delaware County according to the survey.
A test pit was dug to a depth from four to six feet below existing grade. The test pit
encountered a layer of disturbed topsoil, approximately 18 inches thick, followed by an
equally thick layer of schist. The bottom 36 inches of the soil profile was dominated by a
tan, silt-sand layer. Laboratory testing was performed on selected soil samples obtained
from the subsurface exploration to assess the grain size characteristics of encountered soils and verify field soil classifications. A mechanical grain size (sieve analysis)
analysis determined that the soil at the site consists of 73% sand, 23% silt, and 4% clay.
This type of soil is classified as loamy sand according to the US SCS Soil Texture
Triangle, and a type ‘B’ soil according to the curve number method. A summary of the
lab results, including calculations, are presented in Appendix A of Dean (2005). The
depth to bedrock in the original test pit was noted as approximately six feet. No signs of
20 mottling were encountered while digging the test pit. Due to the small distance to
bedrock in the original test pit, as well as the presence of an existing underground conduit,
the location of the infiltration trench was changed slightly. A new test pit was dug to a
total depth of ten feet (six feet to the bottom of the trench plus an additional 4 feet that
was hand augured). At the new location no bedrock was encountered. The depth to
groundwater at the site of the trench was estimated at approximately 15 feet. This
estimation is based on the difference between the known elevation of a nearby gaining
stream and the known elevation of the trench.
A percolation test was performed using a constant-head infiltrometer. The infiltrometer
consists of a six inch diameter metal ring that is hammered into the soil to a depth of
three inches. A graduated water supply tube stands on top of the ring and maintains a
constant six inches of head on the soil surface. The flow rate was calculated directly using the graduations and a stop watch. The percolation results showed that the soil
absorbs 8.1 inches per hour. However, it should be noted that this rate is not exclusively
due to one dimensional vertical flow, but also consists of three-dimensional horizontal
flow.
2.4 Infiltration Trench Design
Stormwater infiltration is the primary design objective of the infiltration trench.
However, there were design components specific to the site’s monitoring and
demonstration purposes. For the purposes of describing the design of the system, the
21 infiltration trench will be divided into three main entities; the inflow conveyance system,
the inlet structure, and the trench itself.
Inflow to the trench is carried via a system of interconnecting four inch diameter
polyvinyl chloride plastic (PVC) pipes. The existing pipes were part of the parking
garage’s former stormwater collection system and were re-routed to carry flow under the
second story parking lot, through the inlet structure, and into the trench.
The inlet structure serves as an intersecting point for the pipes coming from the parking
lot. The structure is a rectangular box that is 9.33 feet long by 3 feet wide and
constructed of pressure treated 6-inch by 6-inch pine garden ties. The top of the inlet
structure consists of nine “Trex” brand decking boards fastened together to form a lid.
There are two locks on the inlet structure to prevent tampering of any type. The inside of
the inlet structure consists of a wire screen to separate out large particles that enter along
with the inflow, a energy dissipating solid block, a baffle to create more uniform flow,
and a V-notch weir. More specific information on the V-notch weir can be found in
Chapter 4.
The trench provides an effective storage capacity of 180 ft3, equivalent to approximately
0.12 inches of rainfall over the drainage area; this volume also takes into account the stone aggregate that was used to fill the trench. The storage capacity of the trench was determined by an analysis of the stage-storage relationship between the depth of water and the storage volume. The volume was calculated by using simple geometric formulas
22 expressed as a function of depth. For more information on the construction of stage-
storage rating curve can be found in the research performed by Dean (2005).
2.5 Infiltration Trench Construction
The following sections will discuss the evolution of the infiltration trench construction process. This process included four steps: investigation of pre-construction site plans, construction of safety retaining wall, trench excavation procedures, and installation of safety overflow mechanisms.
2.5.1 Investigation of Pre-Construction Site Plans
In addition to the preliminary investigation of the surrounding soil and subsurface
(previously discussed in 2.3), preliminary construction tasks included the collection and analysis of existing Villanova University site plans. These plans were reviewed in an effort to determine the locations of various utility lines that were known to exist at the proposed site. Plans were found to be available for both the parking garage and the academic building, but no plans were found that were specific to the common area between the two structures, at the proposed infiltration trench site. Therefore, an on-site investigation was relied upon for determining exact locations for major utilities.
The onsite investigation found that the area contained three large electrical conduits which were encased in concrete, one single telephone line, two stormwater conduits, and
23 an existing stormwater inlet; this amount of infrastructure is dense for an area only approximately 45 feet wide. The locations of these utilities, along with the shallow depth to bedrock that had been observed in the original test pit, ultimately determined the exact size and orientation of the trench. Based on the findings of the utility investigation, the final location of the trench is approximately 10 feet away from the test pit that was initially dug. This new location is directly downhill along what was likely the original slope of the site. Therefore, it was assumed that the depth to bedrock at this particular location would be larger. Ultimately, there was no contact with bedrock during the excavation of the trench or during the installation of the monitoring instruments.
2.5.2 Retaining Wall Construction
Construction on the site took place during the months of May and June of 2004. The first phase of the process was not directly part of the infiltration trench project and funding, but was necessary to ensure that the finished infiltration trench would be safe and successful. This part of the process involved building a retaining wall prior to the excavation of the trench to prevent potential compaction and the migration of sediment into the newly constructed trench. Additionally, the retaining wall was built to alleviate the erosion of the steep slope that existed adjacent to the location of the future trench. An eroding slope existed prior to the construction of the retaining wall (Figure 2.2).
24
2.5.3 Trench Excavation Procedures
The second phase of the construction process involved marking out the boundaries of the infiltration trench to the required depth and dimensions, and physically digging the trench location. The trench was excavated to a final depth of approximately six feet. No bedrock was encountered by hand auguring to an additional depth of four feet within the base of the trench, to a depth of ten feet total. The final length of the trench is approximately 13 feet, and the final width at the surface is approximately ten feet. The excavation process was performed so that no heavy equipment came in contact with the undisturbed soil, resulting in compaction. This was done to help ensure the infiltration capacity of the underlying soil.
After trench excavation, a four-inch overflow pipe was installed between the north sidewall of the trench and the existing storm sewer inlet, located approximately two feet away. The invert of the overflow pipe is at an elevation of 5.2 feet from the bottom of the trench. The overflow pipe was intended to carry flow from the trench once the water surface elevation within the trench met the invert elevation of the outflow pipe. Two four-inch diameter PVC monitoring wells were installed in the base of the trench. Each well was installed with a pair of soil lysimeters, one at two feet and the other four feet beneath the bottom of the trench in the undisturbed subsoil. The soil lysimeters allow for the sampling and water quality analysis for parameters such as nutrient concentrations, dissolved metals, pH, conductivity, and total dissolved solids. One of the monitoring wells was instrumented with an INW PS9800 pressure transducer, enabling the depth in
25 the trench to be monitored. The excavated trench, along with the installed geotextile liner
and monitoring wells are shown as Figure 2.4.
Figure 2.4 Picture of Excavated Trench with Geotextile Liner
The geotextile shown in Figure 2.4 was used to line the base, sides, and top of the entire infiltration trench. The geotextile liner was used for controlling sediment transport into the trench, which otherwise may clog and cause a decrease in the effective storage.
Within the geotextile liner was laid a bed of large, clean, washed stone aggregate, approximately three to six inches in diameter to a depth of approximately 2 feet below the top of the trench. A 12-inch diameter corrugated ‘L’ shaped distribution pipe was
then positioned in the center of the stone bed and the remaining trench area was filled
with stone. The geotextile liner was then wrapped around the top. Figure 2.5 shows the
26 excavated trench filled with aggregate along with the monitoring wells and the distribution pipe.
Figure 2.5 Picture of Trench Excavation Pit Along with L-Shaped Distribution Pipe and 3 to 6 Inch Washed Stone Aggregate Fill
2.5.4 Installation of Safety Overflow Mechanisms
Not shown in Figure 2.5 is the 6-inch outflow pipe placed between an existing storm sewer inlet and the infiltration trench. The invert of the overflow pipe is at an elevation of 5.2 feet from the trench bottom. It was included in the construction of the trench to carry flow from the trench into the storm sewer, located two feet away from the trench, when depths within the trench exceed 5.2 feet. Flow through the overflow pipe is
27 measured using a Palmer-Bowlus flume. More information on Palmer-Bowlus type
flumes can be found in Chapter 3 of this research.
Post construction, the overflow pipe was observed to be restricted by the stone aggregate
fill. An efficiency flow device was installed on the overflow pipe within the infiltration trench to help correct the lack of flow traversing through the pipe. The addition of this device helped increase the efficiency of the overflow by allowing the water to enter the overflow pipe more effectively and efficiently. The efficiency flow device attached to the overflow pipe can be seen in Figure 2.6.
Figure 2.6 Picture of Overflow Efficiency Device Installed Post-Construction
To complete the construction of the infiltration trench, a two-inch deep layer of choker
stone was placed on the top of the geotextile liner along with EP Henry brand ‘Eco-
Pavers’. The porous pavers are linked blocks that evenly space apart upon installation.
They were specifically designed by the manufacturers to allow infiltration on walkways.
28 Ironically, the porous pavers are being implemented in the complete opposite intention of the design manufacturer’s intent; at the infiltration trench the porous pavers allow water to seep upwards and out during extreme events when the overflow pipe capacity is exceeded. The fully constructed and linked together porous pavers provide approximately 17.4% open space according to the manufacturer’s specification sheet.
The open space between the porous pavers was then filled with small choker stone to complete the porous paver installation.
Finally, six-inch by six-inch timbers were used to outline the porous pavers, and to create a more attractive presentation. The pavers used in the project not only provide a durable and attractive surface to the trench, but they permit overflow in periods of intense rainfall when the trench fills with runoff and the capacity of the overflow pipe is exceeded. The resulting overflow from the top of the trench travels to the existing storm drain two feet away. The completed trench is shown as Figure 2.7.
A complete computer aided drawing of the infiltration trench can be viewed in Appendix
A. Both the cross sectional and plan view of the trench instrumentation and design are shown in Appendix A.
29
Figure 2.7 Picture of the Finished Infiltration Trench
Chapter 3 – Literature Review
3.1 Stormwater Best Management Practices in the Urban Setting
Recently, stormwater management strategies for urban development such as the
Pennsylvania Stormwater Best Practices Manual (2007), the Maryland Stormwater
Design Manual (2000), and The New Jersey Stormwater Best Management Practices
Manual (2004), have shifted the focus of urban stormwater design to include volume reduction through innovative Best Management Practices (BMPs). “Urban sprawl,” a common phrase used to describe the migration of the city populous to the outer undeveloped and suburban regions, led to the construction of thousands of dry detention ponds throughout the United States during the mid to late 20th century due to the increased runoff from development and added impervious surface. The sole function of dry detention ponds is to attenuate the increased peak flow in order to protect the receiving streams and water bodies. However, dry detention ponds do not account for the increased volume generated from the added impervious area. Research has since concluded that volume should also be considered a primary factor (along with peak flow rate) to the overall health of the receiving water body (Traver and Chadderton 1983;
Emerson, et al. 2005). Findings from research such as these have brought about the importance of volume reduction in the design of urban stormwater management systems.
Switching from the established dry detention pond to the less known, and innovative
BMP will take research and understanding in order to understand their function and win
acceptance. Through progressive state agencies and partnerships, such as the VUSP, the
future of stormwater management through BMPs is heading in a positive direction toward
including volume reduction. The issue of peak flow reduction and volume reduction is
being conjunctively addressed in order to better preserve the integrity of the world’s
water bodies.
3.1.1 Infiltration Trenches as Best Management Practices
One of the many BMP strategies implemented for managing peak flow and volume
reduction are infiltration trenches. Constructed below the ground surface, these structural
BMPs allow for the reduction of stormwater runoff volume through infiltration processes
into the surrounding soil layer. The resulting infiltration volume travels down to the
groundwater aquifer where it can slowly enter the receiving water body as baseflow.
Depending on the size of the infiltration trench storage volume and contributing drainage
area, they can also serve as an effective peak flow reduction device as well.
Since the surrounding soil layer is the medium in which the infiltration takes place, soil
properties, such as hydraulic conductivity and depth to groundwater table and bedrock,
are the most crucial design aspects for assessing the feasibility of an infiltration trench
BMP. Poorly drained soils with low hydraulic conductivity that are highly compacted
due to anthropogenic activity are not conducive for infiltration applications, such as infiltration trenches. Nearby groundwater wells for human consumption and man-made structures downgrade to the groundwater flow path are also important factors when
32 considering the implementation of an infiltration trench. Care must be taken because of
the potential risk of contaminating the groundwater is elevated due to the increased
volume being infiltrated. Likewise added structural risk on the nearby and down gradient
man-made structures would also be of concern due to the possible temporary rise in
groundwater table during wet periods from the increased volume from the infiltration
trench. These are just some of the factors that need serious investigation before
considering volume reduction BMPs, such as an infiltration trench. Many state agencies have set guidelines regarding these factors. For instance, in the construction of infiltration trenches the 2006 Pennsylvania Best Management Practice Manual requires a
depth to water table of at least 2 feet (from the bottom of the bed), a distance of at least
50 feet from water supply wells, and a site location of 10 feet down gradient from uphill
structures and 100 feet up gradient from downhill structures. Additionally, the manual
requires a soil investigation and percolation test of the surrounding soils and an
infiltration rate of the surrounding soils of 0.1 to 10 inches per hour,
As alluded to previously, infiltration trenches situated in high risk areas for hazardous spills may allow for heavily polluted stormwater runoff to enter and contaminate the surrounding soil and groundwater (Barraud, et al. 1999; Clark and Pitt 2007). Provided that the receiving groundwater is not being utilized for human consumption, the process of infiltrating the polluted runoff is highly advocated due to the pollutant removal capabilities of the underlying soil. Infiltration BMPs, such as infiltration trenches, have shown to accumulate pollutants in various settings. In a study in France, four urban stormwater infiltration basins subsoil layers were analyzed for pollutants through core
33 sampling techniques (Barraud, et al. 2005). The ages of the four basins ranged from 10-
25 years. All metals tested (lead, cadmium, zinc, and copper), including hydrocarbons,
were shown to be higher than the safety target levels based on Dutch Standards in the
upper 40 cm of the soil layer beneath the structures. Below 40 cm depths, the pollutant concentrations of the soil layers improved drastically and were within Dutch Standards.
In Australia, a large infiltration basin (30 m x 13 m x 1.5 m) was investigated for removal efficiency of urban stormwater pollutants through auto-sampling techniques of the inlet
and outlet (Birch, et al. 2005). The removal efficiency value was based upon the
weighted average concentration of the inflow and outflow, not mass based (described
later in section 3.3). The Australian infiltration basin showed a significant removal
efficiency of suspended solids, total phosphorus and metals copper, lead, and zinc
(greater than 50%.)
3.2 Sustainability of Infiltration Trench BMPs
Under many stormwater management guidelines, including the 2006 Pennsylvania
Stormwater Best Management Practice Manual, several infiltration BMPs are to be specifically designed to capture incoming pollutants. For example, the 2006
Pennsylvania Stormwater Best Management Practice Manual states that an infiltration trench should be designed to remove 85% of total suspended solids, 85% of total phosphorus, and 30% of the nitrate (NO3) from the annual inflow.
34 Despite the advantages of removing pollutants along with some stormwater volume, the sustainability of infiltration BMPs remains questionable. The accumulation of particles and pollutants plays a conflicting role with regards to the soil interface on the bottom of the trench and infiltration capabilities. The continual and intentional buildup of pollutants in an underground, enclosed and non-restorable location raises some concerns about clogging and long-term function. Various research studies have shown that the clogging of the soil interface will result in a reduction of infiltration rates (Dechesne, et al.
2005). In order to effectively remove pollutants from the primary infiltration basin, often
BMPs in series (Lin, et al. 2006) or pretreatment mechanisms, such as channelized riprap
(Heasom, et al. 2006), are employed to capture some of the inflow pollutants. The VUSP
Infiltration Trench has a small pre-treatment box used for capturing incoming pollutants; however, its efficiency at capturing pollutants has not yet been determined.
Questions about the longevity of infiltration trenches sometimes come about whenever infiltration trench implementation is being considered. However, the often used term
“longevity” may not be applicable because this assumes an eventual complete termination of the trench’s infiltration capabilities. Up until the completion of this research, studies have yet to produce an infiltration trench that has completely extinguished its “longevity;” infiltration rates through infiltration trenches such as the ones in France, have proven functional even after 20 years of continual operation.
Infiltration structures that are over 100 years old are also known to exist and still be in operation in various European countries, such as Denmark (Warnaars, et al. 1999), as well as at Villanova University (Gore 2007).
35
3.3 First Flush Phenomenon
Studying the transport process of stormwater runoff pollutants has long been a challenge
for designers and engineers due to the seemingly unlimited amount of environmental and
anthropogenic factors for a given storm event and drainage area. Some examples of the
environmental and anthropogenic factors that have an effect on pollutant loading and
distribution include: rainfall intensity, antecedent dry time, season/temperature, land use,
street sweeping frequency, and slope of the drainage area and conveyance system (Chin
2000.) Despite all the uncertainties and variation when trying to understand the pollutant
transport throughout a single rain event, one such reoccurring phenomenon for several
different types of pollutants is known to exist regardless of environmental and
anthropogenic factors. Often referred to as the “first flush phenomenon”, this catchphrase describes the disproportionately large amount of transported pollutants at the
onset of the storm event as compared to the latter part of the storm event.
The first flush phenomenon and the knowledge of how pollutants from stormwater runoff
are distributed throughout a single storm event is valuable information for stormwater
design. This is especially true for infiltration BMP systems that utilize pollutant removal
for achieving recommended performance guidelines and optimal performance. The
capture and diversion of the first flush from infiltration BMPs can help sustain shorter
drainage times through the prevention of clogging; thus improving the long-term and
overall performance. There is a delicate balance between achieving the recommended
36 guidelines for pollutant removal from stormwater manuals and maintaining the most
optimal drainage times for infiltration BMPs. Capturing and diverting the high
concentration pollutants from the first flush phenomenon may be a feasible solution for
accomplishing this somewhat contradictory balance.
First flush studies in the past have focused on three main theoretical spectrums: Empirical
based first flush theory, Concentration based first flush theory, and Mass based first flush theory. All three theories have their advantages and disadvantages for designers and engineers. The following sections will describe each theory and how they are utilized for design purposes.
3.3.1 Empirical Based First Flush Theory
Empirical based first flush theory derives from multiple linear regressions of several parameters from observed data. Most often these parameters include, but are not limited to, rainfall intensity, rainfall duration, and antecedent dry time (Gupta and Saul 1996).
Through linear regression of several of the above described parameters, Gupta and Saul formed an equation for the mass of the first flush pollutants for a single storm event. The following is the Gupta Saul first flush equation:
β γ δ Mff = αd i max ANT (equation 3.1)
37 Where: Mff is the mass of the first flush in kilograms, d is the rainfall duration in minutes,
ANT is the antecedent dry time in hours, i is the maximum rainfall intensity in mm/hr, and α, β, δ, and γ are empirical numerical coefficients for the landuse in question. Gupta and Saul point out that standard numerical coefficients for many different types of land usages have yet to be defined, and more research is required before this equation can become applicable for design purposes on a broader scale.
In addition to multiple linear regression technique, several studies in the past have formed an empirical first flush theory by observing the connection between rainfall/runoff depth and the first flush duration. Some of the relationships correlate the first flush with rainfall depth, while others implement runoff depth. Some examples of rainfall depth first flush empirical theory include: the runoff volume produced by the first 0.5 inch (1.27 cm) of rainfall over the drainage area (Shaver 1986), and the runoff volume produced by the first 0.75 inch (1.9 cm) of rainfall (California 2001). Some examples of runoff depth first flush empirical theory include: the first 0.5 inch (1.27 cm) of runoff per impervious acre (Schueler 1987), and the first 0.5 inch (1.27 cm) of runoff from contributing drainage area (Grisham 1995).
3.3.2 Concentration Based First Flush Theory
Concentration based first flush theory is often considered the most general theory of the three first flush theories. The concentration first flush theory can be summarized in three progressions during a single storm event. These progressions include: 1.) an initial high
38 concentration in pollutant runoff at the onset of the storm event, 2.) a proceeding rapid decline in concentration in pollutant runoff once the initial load of pollution first flush buildup has been exhausted from the drainage area, and 3.) a relatively low and constant concentration in pollutant runoff throughout the remaining duration of the storm event.
The United States Environmental Protection Agency (EPA 1993) has established an often critiqued definition based upon concentration based first flush theory. The U.S. EPA established its definition based upon the comparison of two water quality parameters of single event stormwater runoff pollutant concentration, the event mean concentration
(EMC) and the partial event mean concentration (PEMC). The EMC is defined as the sum of the total pollutant mass of the storm event divided by the sum of the total runoff volume of the storm event. While the PEMC is defined as any discrete point along the corresponding storm event pollutograph. According to U.S. EPA (1993), the first flush phenomenon exists until PEMC is less than the EMC for a single storm event.
3.3.3 Mass Based First Flush Theory
Mass based first flush theory is the analysis of pollutant mass distribution throughout a single storm event with respect to volume. The principal advantage to mass based first flush theory is that it allows the comparison of multiple storm events through dimensionless representation of mass and volume. Visual comparison, as well as mathematical comparison, for individual storm event pollutant distribution can be
39 achieved by developing mass/volume curves [ M(V) Curves ] through calculation of dimensionless cumulative mass and volume values.
The development of M(V) curves is best illustrated by Bertrand-Krajewski, et al. 1998 in
Figure 3.1. The following paragraph is taken from that study.
The drawing of one M(V) curve is shown in Figure 3.1. Arrows indicate how to draw each point of the M(V) curve from both the hydrograph and the pollutograph. The example is given for t = 26 minutes. At this time, the flow rate is 170 m3/h (graph 2) and the discharged volume since t = 0 is 43 m3 (graph 4). The total discharged volume during the storm event being 123 m3 (graph 4), the ratio of cumulative volume to total volume is X = 43/123 = 0.35 (graph 6). This value X = 0.35 is transferred onto the co-ordinate axis on graph 7. At the same time, the total suspended solids (TSS) concentration is 750 mg/L (graph 1) and the TSS cumulative mass since t=0 is 35 kg (graph 3), the ratio of cumulative mass to total mass is Y = 35/71 = 0.49 (graph 5). This value Y = 0.49 is transferred onto the ordinate axis on graph 7; the result is the point (X, Y) = (0.35, 0.49). The same process is applied for all times t during the storm event to draw the whole M(V) curve.
There are several advantages in creating M(V) curves for analyzing the first flush phenomenon. Creating normalized dimensionless M(V) curves allows direct comparison of multiple storm events on one graph. Concentration plots on the other hand are often widely varying making analysis sometimes difficult for multiple storm events.
Additionally, M(V) curves also allow analysis of the distribution of the pollutant by investigating the relationship of the curve to the bisector. If the M(V) curve is above the
45 degree bisector, the pollutant mass distribution is dominant at the onset of the runoff volume/storm event. Concurrently, if the M(V) curve is below the 45 degree bisector
40
Figure 3.1 Development of M(V) Curve Demonstrated by Bertrand, et al. 1998
41
the pollutant mass distribution is dominant at the conclusion of the runoff volume/storm event. Finally, if the M(V) curve follows the 45 degree bisector, the pollutant mass is evenly distributed throughout the runoff volume/storm event.
Another significant advantage to mass based first flush theory is the design capabilities of
M(V) curves. By implementing M(V) curves, it can be determined how much runoff volume is needed to transport an amount of pollutants. For design purposes, this can be very valuable information because highly concentrated pollutants can be captured and diverted when the pollutant mass to runoff volume transport relationship is understood.
Many previous studies have focused on defining this relationship with a numerical nomenclature describing the mass to volume transport. This numerical nomenclature to describe the mass based first flush is typically shown as an x mass to y volume ratio. For example, Bertrand-Krajewski, et al. (1998) suggested that a first flush exists when 80%
(or greater) of the pollutant mass is transported in the first 30% of runoff volume, thus, the numerical nomenclature would be an 80/30 first flush. Other studies have suggested that the first flush only exists for 50/25 (Wanielista and Yousef 1993) and 80/20 mass to volume ratio (Stahre and Urbonas 1990).
Another major advantage to mass based theory and M(V) curve development is that the
M(V) curve can be mathematically represented approximately by a power function
(Bertrand-Krajewski, et al. 1998; Lee, et al. 2002; Sansalone and Cristina 2004; Renwick, et al. 2005).
42
F(X) = Xb (equation 3.2)
where b is the first flush coefficient and X is the dimensionless rainfall. The value of the first flush coefficient is computed by a simple linear regression of the dimensionless mass and volume data points, as demonstrated below.
Ln(F(X)) = b · ln(X) (equation 3.3)
where: the first flush coefficient (b) is the slope of the log-normal of relationship. From equations 3.2 and 3.3 it can be seen that b is inversely proportional to the strength of the first flush (i.e. the lower the b coefficient, the higher the M(V) curve will be above the 45 degree bisector). Figure 3.2 shows typical b coefficient values with respective M(V) curve for each value.
43 1.0 io t 0.8 b = 0.1 b = 0.5 b = 3 0.6 b = 10 45 Degree Bisector
0.4 ive Dimensionless Mass Ra t 0.2 Cumula
0.0 0.00.20.40.60.81.0 Cumulative Dimensionless Volume Ratio
Figure 3.2 Typical First Flush Coefficient b Value with Respective M(V) Curves
The one inherent disadvantage to using M(V) curves for first flush analysis is the actual quantity of pollutant mass. Concentration based theory analysis can only provide such an understanding of determining actually “how much” pollutant is transported. Mass based theory and M(V) curves are best for determining the correlation between runoff volume transport and the respective pollutant mass transport for storm events.
3.4 The Ambiguous First Flush Definition
The most evident issue from the previous sections is that the first flush has many different definitions depending upon the author. The wide range in definitions is most
44 likely due to the fact that all of these definitions were constructed using vastly different study sites. Drainage area and watershed properties are arguably the most influential factor in the strength of the first flush. Pollutant availability due to anthropogenic factors, such as impervious area and traffic volume, has proven to be the most important factors in urban stormwater quality. Pavement has shown to account for nearly 40-50% of the total solids generated from urban landscapes, while tire interaction from automobiles has shown to account for another 20-30% of the total solids generated (Kobriger and
Geinopolos 1984).
The variability in first flush definitions is due to the wide range in possible pollutant concentration values from a given drainage area. Total suspended solids are highly variable depending upon several environmental factors, such as antecedent dry time, season, and land use. For example, Maestre and Pitt (2005) compiled data from the U.S.
EPA’s National Pollutant Discharge Elimination System during the period of 1992 to
2002 in order to perform a national water quality assessment. In their assessment,
Maestre and Pitt state that the typical range in concentration of TSS (mg/L) can vary nearly an order of magnitude depending on land use. A box plot of the typical TSS values reported by Maestre and Pitt is shown in Figure 3.3.
45
Figure 3.3 Typical TSS Concentrations Based Upon Land Use (Maestre and Pitt 2005)
The difficulty in determining pollutant transport makes predicting the first flush all the more challenging to designers and engineers. Until standard values to the Gupta and Saul
(equation 3.1) coefficients or standard practice b values are determined for several different land uses, obtaining actual water quality data for the study site or selecting one of the many definitions are currently the only options for designers. Additionally, obtaining water quality data from stormwater infrastructure is often a long, arduous, and expensive process due to the fact water quality data should be a long term commitment in order to gather a significant and sufficient amount of data points. With that being said, more significant research and data is needed over a wide range of conditions in order to
46 advance first flush capturing methods for improving receiving water bodies and infiltration BMPs.
47 Chapter 4 – Methods
Figure 4.1 Stormwater Passing through the Pre-Treatment Bench and Entering the VUSP Infiltration Trench
4.1 Introduction
The following chapter will outline the methods performed in order to collect data for the purpose of this thesis. Included in this section is quantity monitoring instrumentation, quality monitoring instrumentation, instrumentation calibration, and laboratory water quality testing protocols utilized for the data management for the VUSP Infiltration
Trench. Data collection for the purpose of this thesis was conducted between September
2006 and May 2007.
48 4.2 Infiltration Trench Instrumentation and Setup – Quantity
Section 4.2 will outline the instrumentation implemented for collection of water quantity data at the VUSP Infiltration Trench. The methods used for monitoring rainfall, inflow, storage, overflow, and data logging will be covered in this section. The rainfall, inflow, and storage sections are adapted from Dean (2005).
4.2.1 Rainfall Monitoring
Rainfall is measured through the use of a tipping-bucket rain gage located directly in the drainage area of the infiltration trench, as shown in Figure 4.2.
Figure 4.2 Rain Gage at the VUSP Infiltration Trench
49 Care was taken during the installation of the rain gage to ensure that the location of the gage was such that the rain gage accurately reflects the rainfall over the drainage area.
Specifically, the gage is located on top of one of the parking lot support columns and is not near any existing trees or tall structures that might contribute to errors in rainfall measurements.
4.2.2 Inflow Monitoring
Flow that enters the infiltration trench through the parking garage piping system is measured using an INW PS9800 pressure transducer in conjunction with a V-notch weir, which is located within the inlet structure, as shown in Figure 4.3. The weir was machined from a 1/4" aluminum plate as per ASTM standards (D 5242-92).
Figure 4.3 V-Notch Weir Located at the Exit End of the Pretreatment Bench
50
The pressure transducer measures the height of water before the weir. The flow rate was calculated using the measured vertical distance from the crest of the weir to the water surface elevation along with the V-notch weir equation, as given by (Munson B. 1994) in
Equation 4.1:
8 θ Q = C (2g)1 / 2 tan H 5 / 2 (equation 4.1) 15 d 2
In which θ = 45 Degrees; Q = Flow Rate [L3T-1]; Cd = Discharge coefficient (0.58); and
H = Height of water over the weir [L]. The Cd value was chosen based upon recommendations for triangular V-notch weirs and calibration procedures were not performed to determine its accuracy.
The compartment which houses inlet weir pressure transducer is located in a sump below the minimum elevation for which V-notch weir flow can occur. This requires regular examination of the pressure transducer/V-notch weir “zero point.” The zero point is the depth on the pressure transducer when flow first occurs on the V-notch weir. Since the zero point value has known to fluctuate by a half inch on the pressure transducer the value should be monitored and recorded at least once a month. The values are recorded and are located in the faculty network folder for future reference and data mining.
Since the pressure transducer is located in standing water in between storm events, during times of freezing temperatures the sump compartment freezes into a solid block. During winters of 2005 and 2006, the V-notch weir pressure transducer was removed during cold
51 temperatures to prevent icing damage to the highly sensitive pressure sensor; no data for the inlet weir is recorded during this time. Heaters were installed in the sump area that houses the pressure transducer for the winter of 2006-2007 (see Figure 3.3). The heating unit consists of roofing dicing wire wrapped around a PVC pipe fitted for the sump compartment. The wiring is plugged into an outlet located inside of the cage along the trench. The heating unit proved to work sufficiently at preventing icing during the winter of 2006-2007 where temperatures dropped into the single digits during several periods.
Since the heating device is an electrical unit operating in a pool of water, extreme care should be taken due to possible risks of electrocution. Maintenance and inspection of wiring for fraying, cracking and other abnormalities should be performed before the start of each cold season and implementation.
4.2.3 Storage Depth/Volume Monitoring
An INW 9800 pressure transducer is used to measure the depth and volume within the infiltration trench. The pressure transducer is located at the bottom of the trench within a
4-inch diameter PVC monitoring well. Increases in depth within the trench occur as the result of runoff from the drainage area. Additionally, a small amount of inflow into the trench also occurs through the porous pavers on the top surface area of the trench; this relatively small amount is not considered in the evaluation of the quantity data since the foot print area is insignificant compared to the area of the contributing drainage area.
52 4.2.4 Overflow Monitoring
Overflow from the trench occurs in two manners. One is through a 6 inch diameter PVC overflow pipe located at 5.2 feet from the bottom of the trench bed. And the second is through the surface of the trench porous eco-pavers, when the overflow capacity is exceeded. It is estimated that the porous eco-pavers are not implemented until the trench storage depth is in excess of 6 feet; however, this has not been confirmed in the field. On most occasions the overflow pipe is sufficient enough to handle the overflow capacity.
In order to obtain overflow volumes being conveyed through the 6 inch PVC overflow pipe, a 6 inch diameter Palmer-Bowlus open channel flume was installed at the end of the pipe. A free fall at the outlet is ideal for implementing these types of flumes. Since the outlet end of the overflow pipe is located above an approximately six foot drop into the existing storm drain, implementing a Palmer-Bowlus flume was preferable. As often is the case in designing engineering systems, adjustments to the original design of the flume needed to be performed in order to properly install the flume into the confined area of the storm drain. The storm drain is only 24 inches in length from the outlet of the pipe to the adjacent wall. However, the specifications for a 6 inch diameter flume were 24 inches long as well. In order to allow for sufficient space to account for the free fall of the conveyed water and prevent backwatering effect, the flume was shortened on the outlet side by 4 inches by the manufacturer. This adjustment has no impact on the mathematical rating curve and overall accuracy of the flume.
53 In order to secure the flume safely above the six foot deep storm drain, metal iron brackets were fastened to the walls of the storm drain using concrete anchors. The flume uses the brackets to support itself above the drop. The convenient design aspect of the iron bracket supports is that the flume can be easily removed and re-installed if need be; the flume simply rests upon the brackets. In order to prevent leakage from the connection of the overflow pipe to the flume, polymer is used to seal the joint. A picture of the
Palmer Bowlus flume is shown in Figure 4.4.
Figure 4.4 Picture of the Overflow Palmer Bowlus Flume (Looking Upstream)
54
The basic concept in measuring the flow through the flume is that the contraction at the end of the flume forces critical conditions in order to accurately produce a rating curve in the laboratory calibration process and flume construction. The rating curve equation can be seen below.
Q = 0.00005y4-0.0007y3+0.0175y2+0.0038y+0.0001 (equation 4.2)
Where: Q is flow in cfs and y is depth in inches behind the contraction minus the sump height.
By measuring the depth behind the weir contraction, a respective flow rate can be determined through the flume. The measuring point should be 1 inch behind the contraction of the flume for a 6 inch diameter flume. At the infiltration trench, the depth behind the contraction is measured using a PS9800 INW 5 psig pressure transducer.
The flume was installed on April 24, 2006 and measurements from the weir started on
May 10, 2006. Issues with the accuracy of the weir persisted for several months.
Originally a stainless steel hose clamp was used to fasten the pressure transducer in the weir. However it was discovered that this clamp produced a turbulent nape around the probe causing inaccurate measurements as shown in Figure 4.5.
55 Flow Direction
Area of Turbulence
Figure 4.5 Turbulent Nape Caused by the Hose Clamp in the Overflow Flume
Eventually the clip was removed completely in favor a thin piece of copper wire used to tie the probe to the bottom. Even after removing the clip, inaccurate readings were still occurring when larger flow rates were occurring through the flume. It was suggested by the manufacturer that the velocities through the flume were too large for accurate measurements. In order to slow down the incoming velocities, a wooden baffle was installed into the overflow pipe several inches from the start of the flume. This greatly improved the results. The installation date of the overflow baffle was November 15,
2006. From this date forward the overflow values were significantly more reliable.
56 Also of note, is that the flume contraction results in a 1 inch deep sump where water collects and stands between storms. In order to prevent freezing of this standing water during cold temperatures, a small hole is punctured into the bottom of the flume to allow the standing water to drain. This is done in order to protect the pressure transducer. If the pressure transducer were to become frozen within a solid block of ice it could damage the sensor and render it useless. When the weather becomes warm the hole can be patched with the same polymer that is used to connect the flume to the overflow pipe.
Also of importance with regards to the sump, when using the rating curve the 1 inch sump value should be subtracted from the actual depth measurement.
4.2.5 Data Logging and Retrieval
During June of 2006, the infiltration trench data logging equipment underwent an entire changeover to Campbell Scientific data logging equipment. The influence behind the overhaul of the data logging equipment is the ability for Campbell Scientific to send output signals based upon pressure transducer readings.
A Campbell Scientific CR1000 Measurement and Control System were chosen as the new data logging devices. Along with the actual data logging device, a CR1000KD
Campbell Scientific keyboard display for viewing real-time measurement values, a
COM210 Campbell Scientific telephone modem in order to connect from the laboratory, and an ACC battery supply kit was purchased. Each individual component is housed
57 within a weather resistant box specifically designed for Campbell Scientific equipment.
Each component is shown in Figure 4.6.
CR1000 Data Logger
Crydom Relay Telephone Modem
ACC Adapter
Keyboard Display
Figure 4.6 Campbell Scientific Data Logging Equipment at the Infiltration Trench
Before installation of the equipment could proceed, a grounding stake was needed to ensure the electrical integrity of the equipment should any power surge or failures occur.
The grounding stake consisted of a five foot long piece of heavy duty copper metal. The stake was inserted into the ground, leaving 3 inches above the ground surface for connection to the data logging equipment. With the equipment properly grounded, the programming and installation process of the equipment could begin. The following
58 section will discuss the various features available using the Campbell Scientific CR1000 data logger.
Short Cut Programming Tool:
Using the “Short Cut” programming tool provided by Campbell Scientific, wiring diagrams as well as pin assignments on the CR1000 unit for each field instrument can be obtained. Short Cut also creates a basic programming language data file used to run the instrumentation in the CR1000. All recognizable programming files used by the CR1000 contain a “.CR1” extension file name. The very first programming file created using the
Short Cut programming tool was labeled TRENCH[1].CR1; as more adjustments were made to the programming language, additional files were created by increasing the numeric within the brackets. For example, there have been 22 edits to the trench programming language, therefore TRENCH[23].CR1 programming file is currently in use within the CR1000 as of March 14, 2007.
Upon starting a new program in Short Cut, the program asks the user to enter the time interval “scan rate”. The scan rate is defined as the frequency in which the data logger will record an internal measurement. The scan rate for the CR1000 was designated at 5 seconds (the minimum allowable scan rate).
Once within the Short Cut programming sensor screen, a wide array of different instrumentation can be selected from to setup a working program within the CR1000.
59 Almost all of them require Campbell Scientific instrumentation in order to utilize the program. However, other equipment from different manufacturers can also be utilized within Short Cut under the “Generic Measurements” tab. The “Generic Measurements” tab was utilized for three Instrumentation Northwest PS9800 pressure transducers. All
INW PS9800 pressure transducers implement “differential voltage” within the “Generic
Measurements” tab. When “differential voltage” is selected, Short Cut will ask you to define the instrumentation characteristics. These characteristics include: range of sensor voltage, measurement integration, “reverse inputs to cancel offsets?”, settling time, multiplier, and offset. For the PS9800 pressure transducers, all of these values were left as the default values. However, the multiplier and offset values will not remain the default values. These values will be changed in future and vary depending upon calibration results. The determination of the multiplier and offset values is discussed further in the “Instrumentation Calibration” section of this chapter. Once all instrumentation is entered, the wiring diagram can be viewed by clicking on “Wiring
Diagram.”
Each PS9800 pressure transducer connects to an exclusive “high” and “low” port on
CR1000. For example, the inlet weir pressure transducer would connect to 1H and 2L; the storage/depth transducer would connect to 3H and 4L; and the outlet weir transducer would connect to 5H and 6L. Pressure transducers must be connected in sequence for each port (i.e 1H and 2L, 7H and 8L, and so on. 5H and 11L would be incorrect wiring.)
60 All rain gages are located under the “Meteorological” tab. The tipping bucket setup screen is very straight forward, where the only options the user can change are the units
(mm or inches) and depth of rain per tip. Wiring for the rain gage is assigned to the P
(pulse) ports, not on the high/low ports. When the rain gage tips, the constant running circuit is closed sending the pulse to the data logger, where it is recorded.
The “Sensors” screen is now complete. On the third screen, “Outputs”, the instrument data logging intervals and measurement processing are assigned. PS9800 pressure transducers are recorded on 1 minute intervals and the “average value” is processed over this interval. An “Average Value” measurement takes all of the scanned 5 second measurements and averages them over the 1 minute period. For example, the CR1000 will internally record 12 measurements and calculate the average value for each pressure transducer.
Likewise the rain gage also uses 1 minute intervals; however it uses “total value” over this interval (i.e the total of the 5 second scans are summed over a 1 minute period).
Within this screen the “units” are also displayed for the pressure transducers, but they cannot be edited within the Short Cut tool. The units can only be edited within the actual programming editor called CRBasic.
With the “Outputs” screen complete, the Short Cut tool can now create a base program for which the CR1000 can use to run the instrumentation. All wiring is assigned to specific ports on the CR1000 and all necessary instrumentation characteristics are defined.
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CRBasic Programming Editor:
With a simplistic base programming language file created by the Short Cut programming tool, the more robust and complex features of the CRBasic programming editor can be utilized. The following applications were performed within the CRBasic editor for the development of the infiltration trench program:
• Modification of the instrumentation units
• Addition and modification of the CR1000KD keyboard display unit
• Addition of numerical equations, such as the weir equation, overflow weir rating
curve, and time dependent running totals (i.e. 24 hour total rainfall).
• Addition of new program parameters, such as pressure transducer multiplier and
offset values and zero points.
• Implementation of output voltage pulses for control of water quality auto-
samplers.
Since CRBasic is a very advanced programming language, the best way to understand the applications performed above is to refer to the actual program shown in Appendix B. All the above bullets are covered within the actual program in Appendix B. User notes for each application are also displayed; these can be seen in italics. The program was developed with much help from the Campbell Scientific customer support. For example,
62 the development of the running total calculations was determined with the help of
Campbell Scientific support staff.
It should be noted that when using the CRBasic program, the F1 key is extremely useful.
By simply hitting the F1 key when the cursor is over any program variable, a convenient help screen window will appear with a short summary of how the variable functions within the program along with possible user inputs associated with the variable.
Loggernet:
Loggernet is a program used by Campbell Scientific to connect to the CR1000 data logger. Loggernet performs several applications for the CR1000 which include: downloading data, viewing data variables real time, entering calibration values, and uploading new .CR1 programming files. Loggernet can connect to the CR1000 directly on site or through the COM210 telephone modem in the water resourced lab located in
White Hall. The direct connection station is called “Lab_CR1000” and the modem connection is called “IT_CR1000.” The mainframe of the Loggernet program can be seen in Figure 4.7.
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Figure 4.7 Loggernet Mainframe Window
Upon opening the Loggernet program the stations will be listed on the left hand side with the connect/disconnect button directly below (see Figure 4.7). Once connected to the
CR1000, data can be collected from the data logger by clicking the “Custom” button under “Data Collection.” Once within the “Custom Data Collection” screen the user has several options as to the time frame and data variables (data table) for collection. The data table that contains the pressure transducer readings and rainfall data is called “RAW
DATA.” The RAW DATA data table and the TEMP table are the only data variables that are necessary for regular collection. Both data tables are collected weekly on
Mondays. It has not been determined how much data the CR1000 can hold without
“wrapping around.” The longest the data logger has gone without collecting data was two and a half weeks. The collected data is downloaded to the WREE infiltration trench
64 raw data folder located within the faculty drive. In order to view the data in excel, the user needs to open the file in excel and delineate the data by “comma.”
Loggernet also allows the user to view data variables real time. All data variables declared as “public” in the programming code are available for real time viewing. In order to view the real time data on the Loggernet mainframe screen (Figure 4.7) click on
“Numeric 1” under “Data Displays” in the lower middle half of the screen. By clicking the “add” button the list of publicly declared is displayed which can then be selected for input onto the screen. Several parameters are currently setup for viewing. A picture of the screen can be seen in Figure 4.8.
Figure 4.8 Data Display Screen Number 1 on Loggernet
The numeric data display screen is also useful for entering multiplier and offset values for each pressure transducer. On “Numeric 2” the multiplier value (M) and offset value (B) can be entered. It is of extreme importance that the B value be entered before the M
65 value. It is also important to enter the “Zero Pt” value for the inlet weir before entering the B and M values. To do so on either entry will otherwise result in false inflows occurring and the flow counter being wrong for the next storm event. The counter variable is discussed further in section 4.3.
Sending new .CR1 program files to the CR1000 is fairly straight forward. Simply click the “Send” button below the “Program” menu in the middle of the screen. Browse to the location of the new program and simply hit send. Before sending new programs make sure all the data is downloaded. All previous data is erased once a new program is sent.
4.3 Infiltration Trench Instrumentation and Setup – Quality
Section 4.3 will outline the instrumentation implemented for the collection of water quality sample volumes at the VUSP Infiltration Trench. The methods used for collecting inflow and overflow samples will be discussed in this section. Infiltration samples are also collected beneath the infiltration trench; however, since this data is not within the topic scope of this thesis, the sampling procedures for these samples will not be covered.
4.3.1 Inflow Sampling Procedures and Instrumentation
As previously alluded to in section 4.2.5 Data Logging and Retrieval, the primary reason for the changeover to Campbell Scientific data logging equipment was the ability to send
66 output signals based upon pressure transducer readings. In other words, Campbell
Scientific provides the ability to setup an automated sampler that would operate based upon the recorded weir measurements from the pressure transducer.
Before the implementation of automated samples, inflow samples were collected by grab sampling procedures in the inlet pre-treatment box. Often times the collection of these samples would occur after the storm. The person collecting the sample would take a grab sample from the surface of one of the compartments in the pre-treatment box that stored water in between storm events. This collection procedure would most likely impact water quality results since the pollutants would either settle out to the bottom of the bench or would pass through during the storm event, well before the actual time of sample collection. The results collected during the time of grab sample collection show that the inflow quality results were not reliable.
In order to collect samples during the storm event, a Sigma 900 Automated Sampler was installed in the trench cage. The sampler is triggered by one 12 volt 50 millisecond pulse sent from the Campbell Scientific CR1000. The SW-12 (switch twelve) control port on the CR1000 controls the pulses sent to the Sigma 900 for collection of samples. The SW-
12 control port has the ability to turn on and off for a duration of a user defined time span
(see program file Inflow Output Pulse Sent to Inflow Auto-Sampler in Appendix B).
The output pulses on the SW-12 port are controlled by the inlet weir and flow measurements. Since the CR1000 has the ability to calculate volumes using the v-notch
67 weir equation (equation 4.1) and the measurements from the inlet weir pressure transducer, output pulses can be sent to the Sigma 900 per every user defined flow volume increment. The inflow volume sampling increment was determined through the sampling methodology.
The inflow sampling methodology for this thesis was to capture a 400 to 500 milliliter flow weighted composite sample per every 0.25 inch of rainfall. In order to determine the sampling increment, the expected inflow volume for 0.25 inches of rain needed to be known. By observing previous storm events it was determined that on average 0.05 inches of abstractions occur for every 0.25 inch of rainfall. Approximately 340 ft3 (9600
L) of volume over the inlet weir equates to 0.20 inches of rainfall over the watershed.
However, this number can fluctuate depending upon conditions such as storm characteristics and instrumentation functionality. In order to get a sufficient composite sample, it was determined that the sampling increment should be one sample per every
35.314 ft3 (1000 L) over the inlet weir. On average, this would mean that nine-50 milliliter samples would be collected (which would produce one 450 milliliter composite sample) for every 0.25 inch of rainfall. The inflow Sigma 900 Automated Sampler is configured to take one sample per pulse and nine samples per bottle. The Sigma 900 is installed with 12 950 milliliter bottles with the capacity to handle 3.0 inches of rainfall
(12 x 0.25 inches = 3 inches). The sampling volume was calibrated at 75 milliliters per sample in order to ensure at least 500 milliliters for lab testing procedures; sometimes sampling volumes are less than 75 milliliters since calibrating for such a small volume is difficult on the Sigma 900. The samples tested in the laboratory for the purpose of this
68 thesis were 0.25 inches (the first bottle), 0.50 inches (the second bottle), 1.00 inches (the third and forth bottle mixed), and >1.00 inches (the remainder of the bottles mixed). The third and fourth bottles were mixed in order to expedite the laboratory testing procedures
When the automated sampler was first implemented the sampling rate was adjusted to better hone the relationship between rainfall, initial abstractions, and sampling rate. As mentioned above the initial abstractions can slightly fluctuate. In order to adjust the sampling increment, the number of samples per every 0.25 inch of rainfall can be adjusted. For example, if the inlet weir is seeing more than 0.05 inches in initial abstractions for a time period, the Sigma 900 can be adjusted to take eight samples per bottle instead of nine samples per bottle. At first the automated sampler functioned on six samples per bottle, however, as the rain events were plotted and the sampling points were observed it was determined that the sampling rate should be somewhere around eight or nine.
The inflow Sigma 900 uses vinyl intake sampling tubing of 15 feet in length. The tubing was pulled through the below ground conduit that connects the trench cage to the compartment behind the pretreatment bench. The end of intake sampling tubing is connected to a metal intake strainer. This strainer is fastened to the end of the 4 inch
PVC pipe that conveys water from the parking deck to the pretreatment bench. The end of the 4 inch PVC pipe is located in the first compartment of the pretreatment bench. A photo of the location of the inflow sampling point can be seen in Figure 4.9.
69
Figure 4.9 Location of the Inflow Sampling Point (Intake Strainer Location)
Since the tubing turns through a U shaped conduit, water accumulates in the conduit and freezes during cold temperatures. One storm during January of 2007 was missed because it was discovered that the intake tubing froze, which caused a blockage in the line.
The inflow automated sampler can also be turned on and off by the black box switch located alongside the CR1000 housing box. This is of great convenience for times when particular storms will not be sampled (i.e. snow storms/snow melt).
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4.3.2 Overflow Sampling Procedures and Instrumentation
The overflow sampling began two months after the start of automated inflow sampling.
At the onset a gutter hanger sampler was used to capture a sample of the overflow. The hanger was suspended from the end of the Palmer-Bowlus flume as pictured in Figure
4.10.
Figure 4.10 Overflow Hanger Sampler Located at the End of the Flume
Inside the cylinder is an approximately 1 liter volume sample bottle. The major disadvantage to using the gutter hanger sampler is that it only captures the very first
71 portion of overflow volume that enters the sample bottle. This can influence the results if external constituents from above the storm drain or from the adjacent wall enter the gutter sampler. Additionally, the concentrations can be higher if the initial concentrations of constituents are higher than the final concentrations of the overflow.
It was determined that the initial concentrations of the overflow were indeed higher at the beginning than at the end and there may have been some external pollutants entering from the above grate and nearby wall. In order to rectify this, another Sigma 900
Automated Sampler was installed for the overflow flume. The overflow Sigma 900 works in the same manner as the inlet Sigma 900. The CR1000 determines the volume through the overflow flume and for a user defined sampling increment sends out a voltage pulse to the overflow Sigma 900, which triggers a sample to be taken. In order to implement an automated sampler, a sampling reservoir needed to be constructed in order to submerge the intake strainer at the end of the vinyl sampling line. A picture of the sampling reservoir is shown in Figure 4.11
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Figure 4.11 Overflow Sampling Reservoir for Intake Strainer (Strainer Inside)
The reservoir hangs from the metal supports for the Palmer Bowlus flume. On the bottom of the reservoir is a rubber stopper which can be removed to let the water drain out between storm events.
An additional electrical part was also needed before the automated sampler could become fully operational. On the CR1000 there is only 1 SW-12 port for sending 12 volt pulses.
The control ports (C1, C2,…,C8) are only capable of sending 5 volt pulses, which are not strong enough to trigger Sigma 900 Automated Samplers. In order to increase the 5 volt pulse to 12 volts, a Crydom d1d07 solid state relay was needed (this can be seen in the middle of Figure 4.6). The Crydom solid state relay can increase the incoming voltage to
73 whatever is supplied to the source pin on the Crydom unit (pin 3). For a complete wiring detail of the Crydom solid state relay, consult the added pages at the beginning of the
CR1000 lab manual.
The sampling increment for the overflow automated sampler is not based upon rainfall such as the inflow sampling increment. The overflow sampling increment was determined based upon best judgment. At the minimum, a 300 milliliter sample is needed for when overflow occurs. In order to ensure a sufficient sample during the small storms, the sampling increment was set at one sample per every 20 ft3 (560 L, 150 gallons) through the overflow flume. The sample volume was set at 100 milliliters. One large three liter bottle was used to collect the sample. Multiple samples were not collected for analyzing the quality of the overflow; one large flow weighted composite sample was used for this thesis.
In Figure 4.11 it can be seen that the adjacent wall (where the water exits the overflow flume) is extremely close to the downstream exit end of the overflow flume. In order to protect the integrity of the overflow samples, a tarp was tied to the top of the storm grate to prevent “splash back” from the cement wall. A photograph of the tarp used is shown in Figure 4.12 (the tarp is upside down facing upward). The tarp also prevents anything from entering through the top.
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Figure 4.12 Overflow Tarp
Overflow water quality data from the gutter sampler hanger took place from November 2,
2006 to March 13, 2007. On March 14, 2007 the overflow automated sampler was installed and has been in use ever since.
4.4 Instrumentation Calibration
The following section will cover how and when each instrument at the VUSP infiltration trench is calibrated. The calibration methods of the rain gage, PS9800 Instrumentation
Northwest Pressure Transducers, and Sigma 900 Automated Samplers are covered within this section.
75 4.4.1 Rain Gage
Calibration of the rain gage takes place annually during the spring. The tipping bucket apparatus is located underneath the lid of the rain gage. The bucket tips are checked by manually tipping the bucket ten times. Each tip records 0.01 inches of rainfall, therefore the calibration should equal 0.10 inches of rainfall. If it is incorrect this could mean that the wiring to the data logger is damaged or the rain gage itself is damaged.
The bubble level sensor is located along side the tipping bucket apparatus at the bottom of the rain gage. The bubble level should be located within in the center of the level line.
The location of the bubble can be adjusted by turning the level screws on the outside base of the rain gage. An examination of the bubble level should be performed every month.
4.4.2 PS9800 Pressure Transducers
Calibration of the pressure transducers takes place twice a year; typically once in the spring and once in the fall. However, spot calibration may be needed if readings are erroneous.
All PS9800 pressure transducers use a mathematical linear relationship between pressure
(water depth) and voltage. The goal for each calibration is to determine the slope (m) and intercept (b) of the linear relationship. In order to calibrate the pressure transducers, set the m and b values to 1 and 0, respectively. Submerge each pressure transducer in
76 incremental water levels of known depth between anticipated measurement readings (i.e do not submerge overflow pressure transducer in water depths above 3 inches since this is outside of the anticipated measurement readings). Each time the pressure transducer is submerged at a known water level, record the reading displayed on the CR1000. (This value will oscillate high and low on the hundredth decimal place. Try as best to record a middle value between the low and high values.) Do this for at least five points between the upper and lower threshold values of the anticipated measurement readings for each pressure transducer. For example, for calibrating the inlet weir pressure transducer, submerge the transducer in 1, 3, 5, 8, 10, and 12 inches of water and record the respective readings for each water level. Plot the voltage (x-axis) versus the measured water level
(y-axis) and determine the slope as shown in Figure 4.13. Finally, enter the new m and b values on Numerical screen 2 of Loggernet.
3 2.5 2
1.5 1 y = 0.0926x - 37.278 R2 = 0.9905 0.5 0 Measured Water Depth (inches) 410 415 420 425 430 Voltage (mV)
Figure 4.13 Calibration Plot of the Overflow Pressure Transducer
77 4.4.3 Sigma 900 Automated Samplers
Sigma 900 automated samplers are calibrated on an “as needed” basis. When sample volumes are greater or lower than anticipated, calibration is required. In order to calibrate, hit the star button on the 900 and shift through the options until “Calibrate
Volume?” is reached. Hit “Yes”. It will ask you if you want “Timed Calibrate?”. Hit
“Yes”. Hold a graduated cylinder below the sample arm and start the pump. Hit “Stop” when the sample volume in the graduated cylinder is at a desired volume. Only “Timed
Calibrate” can be performed for both Sigma 900s at the infiltration trench. The “Auto
Calibrate” function is not an option due to the configuration of the sampling tube through the conduits below the pretreatment box.
4.5 Laboratory Testing and Procedures
All samples collected for this thesis were taken from the inflow and overflow sampling devices as described earlier in this chapter. Inflow samples were collected in 950 milliliter glass jars specially made to fit the base of the Sigma 900. Overflow samples were collected in high density polyethylene containers. All sample containers were washed in a 10% HCl acid solution before implementation as recommended by EPA guidelines. Upon collection, the samples were taken to Villanova University’s Civil and
Environmental Engineering Water Resources Laboratory for analysis. Analysis was typically completed within 24 hours after collection. The parameters tested were: total nitrogen, total phosphorous, orthophosphate, total suspended solids, total dissolved
78 solids, and dissolved metals (zinc, lead, and copper). Total suspended solids and total dissolved solids were tested more rigorously for this thesis. The following sections will describe in detail the laboratory procedures used to assess each water quality parameter.
The following sections are adapted from (Woodruff 2005) and the VUSP Quality
Assurance Quality Control Manual. All of the below water quality laboratory procedures were directly approved by the EPA. For more detailed descriptions of the below tests refer to the VUSP QA/QC Laboratory Manual.
4.5.1 Laboratory Methods - Nutrients
Samples were immediately analyzed for total phosphorous and total nitrogen concentration upon sample collection. The nutrient analysis was conducted utilizing a
Hach DR 4000 Spectophotometer unit and specific sampling kits provided by Hach for each nutrient. The instructions provided in the Hach user manual for each testing method were strictly followed. Spectrophotometry is the measurement of the light absorbance of a sample. This absorbance can be related to various chemical parameters through the use of experimental procedures. The spectrophotometer’s light source can be set to a wide range of wavelengths from the visible to the ultraviolet scale. The refractivity of the light through the sample determines the concentration of the pollutant within the sample vile.
Due to the measurement of light source in order to determine the existence of pollutants, extreme care must be taken to keep the vile clean and spotless upon measurement.
79
All values were verified by performing duplicate and spike samples upon randomly chosen samples. The tests were regarded as valid if the percent error for each sampling procedure was around 10%.
4.5.2 Laboratory Methods – Solids
Once the nutrient analysis was finished on each sample taken, the solids analysis was initiated. The first step was the total suspended solids (TSS) test. After the completion of the TSS test, 20 milliliters of sample was removed from the filtered sample for metals analysis. The remaining sample volume was then utilized for the total dissolved solids
(TDS) test.
The Standard Methods procedure 2540D was followed for TSS analysis. Predetermined volumes of sample were filtered through pre-washed 1.5 micron pore size filters. The filter papers were then transferred to pre-weighed tins and were dried at 105 degrees
Celsius according to procedures outlined in the standard methods. Once completely heated and desiccated the tins were reweighed and the resultant difference of weight per unit volume gave the total suspended solids in units of mg/L.
The Standard Methods (APHA, 1999) procedure 2540C was followed for TDS analysis.
A filter paper with a 1.5 micron pore size was utilized to filter out the suspended solids in the sample. The filtrate was then evaporated accordingly in pre-weighed and properly
80 prepared ceramic evaporating dishes. The sample volume for the TDS test was typically around 280 milliliters. The volume of the evaporation dishes for the TDS test is approximately 300 milliliters. The TDS test was typically completed within 72 hours of its initiation.
4.5.3 Laboratory Methods – Dissolved Metals
As described previously, approximately 20 milliliters of sample were taken from the total sample volume following the TSS filtration. Each sample was placed in a 50 mL HDPE container. As per EPA Method 7010, all metals sample containers were washed with 1:1 nitric acid (HNO3). Metals samples were preserved with 70% HNO3 and analyzed on a
Perkin-Elmer 2380 Atomic Absorption Spectophotometer with its Graphite Furnace equipment. An auto-sampler unit was used in conjunction with the Graphite Furnace equipment to perform the sample analysis. A number of standard concentrations were run in conjunction with each sample set run in order to calculate the concentration of metals in solution using the absorbance values determined by the unit for that individual run. This calibration procedure was conducted for each group of samples analyzed.
4.5.4 Laboratory Methods – Low Trace Ions
The analytical determination of ions in solution, especially at traces levels, can be accomplished by the use of High Pressure Liquid Chromatography (HPLC) and ion
81 exchange resins to separate the ion mixture with suitable detection of the ions as they exit the ion exchange columns.
The samples were analyzed for the parameters listed in Table 3.1. Also listed in the table are the test methods.
Table 3.1. Low Trace Ions Tested Using HPLC
Parameter Test Method Chlorides (Cl-) MODIFIED EPA METHOD 300.1
- Nitrites (NO2 -N) MODIFIED EPA METHOD 300.1
- Nitrates (NO3 -N) MODIFIED EPA METHOD 300.1 - Phosphate (PO4 -P or PO4) MODIFIED EPA METHOD 300.1
4.6 Water Quality Data Analysis
This section outlines the procedures performed in order to answer the research objectives outlined in section 1.3. The sampling procedures and water quality data were gathered with respect to the inflow sampling procedures outlined in section 4.3.1. All of the pollutants outlined in section 4.5 were used in this analysis.
4.6.1 Determination of First Flush Phenomenon
Is there a first flush phenomenon at the VUSP Infiltration Trench? What quantity of runoff depth is necessary to capture the first flush?
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Concentration based theory/empirical first flush theory (EPA definition) in conjunction with a statistical Student’s t-test were implemented on the water quality data to determine the existence of a first flush phenomenon.
The concentration over the storm duration was plotted over the hydrograph of the storm event in order to observe the behavior of the pollutant distribution. The EPA definition of event mean concentration was also determined for each storm event. The approximate location during the storm event when the partial event mean concentration dipped below the event mean concentration was determined.
The exact definition of when the first flush takes place was determined based upon the concentration and t-test results. If a pollutant was determined to have a decreasing pollutant concentration, the pollutant would be said to exhibit a first flush when the
PEMC is greater than the EMC. For verification purposes a statistical Student’s T-test was performed. If a pollutant was proven statistically to be different to a 90 percent certainty using Student’s t-test, the pollutant would be said to exhibit the “first flush” phenomenon.
The following section outlines the theory and applicability of Student’s t-test for first flush analysis.
83 4.6.1.1 Student’s T-Test
Student’s t-test is a statistical test that compares the population means of two independent, normally distributed, small sample sizes (less than 30). The t-test is defined as the number of standard errors by which the two sample means are separated:
_ _ Difference between two means y - y t = = 1 2 (equation 4.3) Standard error of difference s.e.difference
Special cases exist for the t-test depending upon whether or not it is statistically shown that the population variances are equal. To test the equality of variances from two independent normal populations, the F-test is implemented.
2 S1 F = 2 (equation 4.4) S2
The calculated F statistic is compared to the F acceptance region at a user defined probability error. The F acceptance region limits are determined using a table of F-values for a two tailed distribution and the degree of freedom for each sample population. The degree of freedom for each sample is defined as:
V1 = n1 – 1 (equation 4.5)
V2 = n2 – 1 (equation 4.6)
84 Where: V = the degree of freedom and n = the sample size. In order to determine the upper limit of the acceptance region, the F table is used for (V1, V2) at a user defined probability α. The lower limit is determined using the following equation and the F table:
1 Lower limit = (equation 4.7) F(V2 , V1)
If the calculated F value for the compared samples is within the F acceptance region, it can be concluded that the variances are equal. If the calculated F value is outside of the F acceptance region, the variances are not equal.
T-test case 1 (for equal variance and equal or unequal sample populations):
When the sample population variances were shown to be statistically equal and the number of samples in each population also equal, the following t-test was performed:
_ _ (y - y ) t = 1 2 (equation 4.8)
2 1 1 Sp ( + ) n1 n2
2 2 (n1 - 1)S1 + (n2 - 1)S2 Sp = (equation 4.9) n1 + n2 - 2
V = n1 + n2 – 2 = 2(n – 1) (equation 4.10)
85 T-test case 2 (for unequal variance and equal sample populations n1 = n2):
When the sample population variances were shown to be statistically unequal and the number of samples in each population equal, the following t-test was performed:
_ _ (y - y ) t = 1 2 (equation 4.11) 1 ( S 2 + S 2 ) n 1 2
V = n1 + n2 – 2 = 2(n – 1) (equation 4.12)
T-test case 3 (for unequal variance and unequal sample populations n1 ≠ n2):
When the sample populations were shown to be statistically unequal and the number of samples in each population also unequal, the following t-test was performed:
_ _ (y - y ) t = 1 2 (equation 4.13) S 2 S 2 1 + 2 n1 n2
(S 2/n + S 2/n )2 V = 1 1 2 2 (equation 4.14) (S 2/n )2 (S 2/n )2 1 1 + 2 2 (n1 - 1) (n2 - 1)
86 Often the calculated V value is not an integer. When this occurs, the value of V is rounded down.
The calculated t value from one of the above cases is then compared to the statistically known t value at a user defined probability level found in a t distribution table. If the calculated t value lies within ± known t value range from the table, than the two sample means are statistically equal. If the calculated t value is outside of the t value range from the table, than the two sample means are statistically unequal.
4.6.2 First Flush Capture Analysis
What portion of the first flush is the VUSP Infiltration Trench capturing? What potential impact would this capture have on the pollutant transport within the trench?
In order to determine how pollutants are typically distributed into the infiltration trench, cumulative pollutant mass rainfall depth curves were developed for each pollutant during the observed storm events. The average curve was determined using the linear regression technique previously described in section 3.3.3. In order to obtain an actual value from the dimensionless pollutant mass rainfall depth curves, total pollutant mass total rainfall depth curves were developed in order to provide an understanding of the quantity of pollutants being distributed during each respective storm event. Using both of these curves the portion of the inflow pollutants being captured by the VUSP Infiltration
Trench was determined.
87
Based upon the inherent dimensions/design volume capture of the trench, the theoretical pollutant removal efficiency was determined for each pollutant. The theoretical pollutant removal efficiency for each constituent was determined for all rainfall events (suspected snowfall events were omitted) observed since the construction of the VUSP Infiltration
Trench. The pollutant capture was determined under the assumption that 100% of the incoming pollutants were captured during the rising limb. The rising limb is defined as the period of the storm event in which the trench begins to fill to when the trench begins to overflow (or recede).
The amount of pollutant being captured during times of overflow from the trench was determined by comparing the mean of the last three inflow samples (0.5, 1.0, and greater than 1.0 inch samples) to the mean overflow samples for each pollutant. Only overflow samples from the automated sampler were considered in this analysis. Since the infiltration trench overflows around 0.20 inches of rainfall, it is necessary to consider the concentration of the last three observed inflow samples when comparing to the single composite overflow sample. If it is determined that the mean overflow sample concentration is less than the mean inflow concentration of the last three samples, it can be concluded that the bed is still capturing pollutants during times of overflow. If the overflow concentration is higher than the inflow concentration, it can be then considered that the capture efficiency is minimal.
88 Using this known theoretical pollutant removal efficiency, design recommendations for the VUSP Infiltration Trench were made in order to achieve the pollutant removal target levels set forth by the 2007 Pennsylvania BMP Manual.
The total mass of pollutants captured during each storm event was also determined to provide an understanding of the theoretical loading of pollutants within the trench.
4.6.3 The Influence of Storm Characteristics
Do rainfall characteristics play a role in the possible first flush phenomenon at the
VUSP Infiltration Trench?
Each individual storm observed for pollutants was investigated to see if storm characteristics played a role in the transport of pollutants. The storm characteristics investigated include the major parameters included in the empirical Gupta Saul equation
(equation 3.1). These parameters include: duration of rainfall, maximum intensity, and antecedent dry time. The parameters were logarithmically regressed against pollutant concentration and pollutant mass for each individual sample during the storm event, and the total storm event. The coefficient of determination was used to determine the correlation (or lack thereof) between storm characteristics and pollutant transport.
Chapter 5 - Results
89 5.1 Introduction
The results of this thesis are presented in the order of the questions presented in section
4.6. The pollutant concentrations, along with the statistical t-test analysis of the means, were used to determine the existence of the first flush. The pollutant transport characteristics of each pollutant were determined using mass volume curves. Using these curves, along with the concentration values, the pollutant capture efficiency for 2006 was determined at the VUSP Infiltration Trench. Finally, the storm characteristics of each storm event were investigated to see if any significant correlation exists between the
Gupta Saul equation (equation 3.1) and the first flush at the VUSP Infiltration Trench.
5.2 Determination of First Flush
The number of storm events observed for the first flush for each pollutant is shown in
Table 5.1
Table 5.1. Number of Events Observed for First Flush
90 Pollutant Number of Observed Storm Events Suspended Solids 20 Dissolved Solids 20 Total Nitrogen 10 Total Phosphorus 9 Nitrite 6 Nitrate 6 Phosphate 6 Chloride 6 Dissolved Metals 9 (Cu, Pb, Cd, Cr)
The number of storm events corresponding to the depth in rainfall for each of these observed events is listed in Table 5.2 for each pollutant.
Table 5.2. Number of Storm Events Observed Depending upon Rainfall Depth
0.5 Inch 1.0 Inch >1.0 Inch Pollutant Rainfall Rainfall Rainfall Total Suspended Solids 3 9 8 Total Dissolved Solids 3 9 8 Total Nitrogen 2 4 4 Total Phosphorus 2 4 3 Nitrite 1 4 1 Nitrate 1 4 1 Phosphate 1 4 1 Dissolved Metals 1 4 4 (Cu, Pb, Cd, Cr)
The concentrations of each of these pollutants were plotted on one single graph to gain a general understanding of the “spread” and general trend of the pollutant over the duration of the storm event. These pollutant graphs are shown in Figures 5.1 through 5.12 in the
91 order of the pollutants listed in Table 5.1. The mean of each data set was also plotted on graph to obtain the “average” storm event. The event mean concentration for all storm events was also calculated and plotted on each graph. The raw data used to produce
Figures 5.1 through 5.12 can be obtained in Appendix C. Three outliers were omitted from the plots below (4/25/07 Storm Event for TSS, 3/15/07 for TDS, 9/14/06 for TP, and 11/2/06 for Cu, Pb, and Cr). The data from these storm events for these pollutants significantly skewed the data.
100
80 Sample Data Points Mean of Samples TSS EMC 60
40
20 TSS Concentration (mg/L) Concentration TSS
0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.1 Observed Total Suspended Solids Concentrations during Storm Events
92 160
Sample Points )
L Mean of Samples 120 TDS EMC
80
40 DS Concentration (mg/ Concentration DS T
0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.2 Observed Total Dissolved Solids Concentrations during Storm Events
16 Sample Data Points Mean of Samples
12 TN EMC
8
4 TN Concentration (mg/L) Concentration TN
0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.3 Observed Total Nitrogen Concentrations during Storm Events
93
1.00 ) L 0.80 Sample Data Points Mean of Samples 0.60 Total Phosphorus EMC
0.40 TP Concentration (mg/ Concentration TP 0.20
0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.4 Observed Total Phosphorus Concentrations during Storm Events 0.20
0.16 Sample Points Mean of Samples Nitrite EMC 0.12
0.08
0.04 Nitrite Concentration (mg/L) Concentration Nitrite
0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.5 Observed Nitrite Concentrations during Storm Events
94
0.80 Sample Points Mean of Samples Nitriate EMC 0.60
0.40
0.20 Nitrate Concentration (mg/L)
0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.6 Observed Nitrate Concentrations during Storm Events 0.12
Sample Points Mean of Samples Phosphate EMC 0.08
0.04 Phosphate (mg/L) Concentration
0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.7 Observed Phosphate Concentrations during Storm Events
95
5.00 ) L 4.00 Sample Points Mean of Samples Chloride EMC 3.00
2.00
1.00 Chloride Concentration (mg/ Concentration Chloride
0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.8 Chloride Concentrations during Storm Events ) L
30.00 Sample Data Points Mean of Samples Copper EMC
20.00
10.00
Dissolved Copper Concentration (ug/ 0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.9 Observed Dissolved Copper Concentrations during Storm Events
96
8.00 ) L Sample Data Points Mean of Samples 6.00 Lead EMC
4.00 ead (ug/ Concentration L 2.00 Dissolved Dissolved 0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.10 Observed Dissolved Lead Concentrations during Storm Events 1.60
Sample Data Points Mean of Samples 1.20 Cadmium EMC
0.80
0.40
Dissolved Cadmium Concentration (ug/L) Concentration Cadmium Dissolved 0.00 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.11 Observed Dissolved Cadmium Concentrations during Storm Events
97
)
L 25.00
20.00
15.00 Sample Data Points Mean of Samples Chromium EMC 10.00
5.00
0.00 Dissolved Chromium Concentration (ug/ Concentration Chromium Dissolved 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Cumulative Rainfall During Storm (inches)
Figure 5.12 Observed Dissolved Chromium Concentrations during Storm Events
For the pollutants that exhibited a decreasing mean concentration though the storm event a statistical Student’s T test was performed to determine if the mean samples statistically differ from each other. The pollutants that exhibited a decreasing mean concentration were: total suspended solids, total dissolved solids, total nitrogen, nitrate, chloride, copper, and cadmium. Even though total nitrogen and dissolved cadmium’s mean concentration increase slightly upwards, statistical analysis was performed on these pollutants to see if a difference exists earlier in the mean storm event.
98 The results of the preliminary F test to determine the proper case t-test (described previously in section 4.6.1.2) are shown in table 5.3. The full detailed F test results are shown in Appendix D.
Table 5.3 F-Test Comparison Results (Respective T-Test Used)
Pollutant 0.25 to 0.5 0.25 to 1.0 0.25 to >1.0 0.5 to 1.0 0.5 to >1.0 1.0 to >1.0 TSS case 2 case 3 case 3 case 3 case 3 case 1 TDS case 1 case 1 case 2 case 3 case 1 case 1 TN case 1 case 3 case 1 case 3 case 1 case 3 Nitrate case 1 case 1 n/a case 3 n/a n/a Phosphate case 1 case 1 n/a case 1 n/a n/a Chloride case 1 case 1 n/a case 1 n/a n/a Copper case 2 case 3 case 3 case 1 case 1 case 3 Cadmium case 2 case 1 case 1 case 1 case 1 case 1
Using the respective t-test from Table 5.3, the percent certainty of the hypothesis that the samples in each comparison are different was determined for each decreasing pollutant.
These percent certainties are shown in Table 5.4; calculated t values that were off of the t-test test critical value table were listed as “similar” (i.e. no statistical difference exists between the two compared mean samples). The full statistical numbers calculated for the t-test can be obtained in Appendix E.
99
Table 5.4 T-Test Results for Decreasing Mean Pollutants (Above 90% in Bold)
Pollutant 0.25 to 0.5 0.25 to 1.0 0.25 to >1.0 0.5 to 1.0 0.5 to >1.0 1.0 to >1.0 TSS 98% 99% 99% 91% 97% similar TDS 84% 94% 99% similar 81% similar TN similar similar similar similar similar similar Nitrate 80% 80% n/a similar n/a n/a Phosphate similar similar n/a similar n/a n/a Chloride similar 80% n/a 80% n/a n/a Copper 89% 92% 96% similar 81% 80% Cadmium similar 90% similar similar similar similar
From the above results it can most likely be deduced that the first flush at the VUSP
Infiltration Trench is around 1.0 inch of rainfall for decreasing mean pollutants.
5.3 Pollutant Transport Characteristics
The transport of pollutants to the VUSP Infiltration Trench over the duration of the storm event was investigated through the formation of cumulative pollutant mass/cumulative rainfall plots. These plots can be seen in Figures 5.13 through 5.23 for each respective pollutant. Each line represents a different storm observed. A line that is located above the black dashed bisector is colored blue and these represent storms where the pollutant load is transported earlier in the storm. The red lines represent pollutant load that is transported later in the storm. The magenta lines represent a delayed pollutant release.
And the green line is the average storm event developed from the mean of the regression b values from all the storm events. The data used to formulate these plots can be seen in
Appendix F.
100 1.0
0.8
0.6 SS Load T
0.4 Cumulative
0.2 Average b value = 0.6070 Total Suspended Solids
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.13 Cumulative Mass/Rainfall Plot for Total Suspended Solids
1.0
0.8
0.6
0.4 Cumulative TDS Load
0.2 Average b value = 0.7895 Total Dissolved Solids
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.14 Cumulative Mass/Rainfall Plot for Total Suspended Solids
101 1.0
oad 0.8 L
0.6
0.4
Average b = 0.7934 0.2 Cumulative Total Nitrogen Nitrogen Total Cumulative Total Nitrogen
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.15 Cumulative Mass/Rainfall Plot for Total Nitrogen
1.0 oad
L 0.8
0.6
0.4
Average b = 1.4403 0.2
Cumulative Total Phosphorus Total Phosphorus
0.0
0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.16 Cumulative Mass/Rainfall Plot for Total Phosphorus
102 1.0
0.8
0.6
0.4 Cumulative Nitrite Load Nitrite Cumulative 0.2 Average b = 1.4943 Nitrite
0.0
0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.17 Cumulative Mass/Rainfall Plot for Nitrite
1.0
0.8
0.6
0.4 Cumulative Nitrate Load Nitrate Cumulative 0.2 Average b = 0.4841 Nitrate
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.18 Cumulative Mass/Rainfall Plot for Nitrate
103 1.0
0.8 oad L
0.6
0.4
0.2 Average b = 1.1685 Cumulative Phosphate Phosphate Cumulative Phosphate
0.0
0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.19 Cumulative Mass/Rainfall Plot for Phosphate
1.0
0.8 oad L
0.6
0.4
Cumulative Chloride Chloride Cumulative 0.2 Average b = 0.7439 Chloride
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.20 Cumulative Mass/Rainfall Plot for Chloride
104 1.0 oad
L 0.8
0.6
0.4
0.2 Average b = 0.6376
Cumulative Dissolved Copper Copper Dissolved Cumulative Dissolved Copper
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.21 Cumulative Mass/Rainfall Plot for Dissolved Copper
1.0
0.8
0.6
0.4
0.2 Average b = 0.6436 Dissolved Cadmium Cumulative DissolvedCadmium Load
0.0
0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.22 Cumulative Mass/Rainfall Plot for Dissolved Cadmium
105 1.0 oad L 0.8
0.6
0.4
0.2 Average b = 1.3026 Dissolved Chromium Cumulative Dissolved Chromium
0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.23 Cumulative Mass/Rainfall Plot for Dissolved Chromium
Two storms were measured more discretely for total suspended solids and total dissolved solids for verification purposes of the averaging and mixing method performed for the other storm events. The April 14, 2007 storm event (5.82 inches of rainfall) and April 25,
2007 storm event (1.84 inches) was observed for this verification process. Instead of mixing the 0.75 inch bottle with the 1.0 inch bottle, and all of the >1.0 inch bottles, each individual bottle was measured discretely for TSS and TDS. Since the inflow automated sampler is only capable of sampling up to 3.0 inch rainfall events (quantity of 12 – 0.25 inch bottles in the automated sampler), the whole storm event was not observed on April
14. Figures 5.24 through 5.27 are a comparison of the Cumulative Mass/Rainfall Plots if the bottles were measured independently or averaged (i.e. mixed).
106 1.0 Discrete Samples Mixed Samples 0.8
0.6
0.4
Cumulative TSS Load Discrete Samples b = 0.6543 0.2 Mixed Samples b = 0.8265
0.0 0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.24 Comparison between Averaged and Discrete Samples (TSS) April 14
1.0 Discrete Samples Mixed Samples 0.8
0.6
0.4
Cumulative TSS Load Discrete Samples b = 0.9069 0.2 Mixed Samples b = 1.1301
0.0 0.00.20.40.60.81.0 Cumulative Rainfall Depth
Figure 5.25 Comparison between Averaged and Discrete Samples (TSS) April 25
107 1.0 Discrete Samples Mixed Samples 0.8
0.6
0.4
Cumulative TDS Load Cumulative Discrete Samples b = 0.7129 0.2 Mixed Samples b = 0.7360
0.0 0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.26 Comparison between Averaged and Discrete Samples (TDS) April 14
1.0 Discrete Samples Mixed Samples 0.8
0.6
0.4
Cumulative TDS Load Cumulative Discrete Samples b = 0.7757 0.2 Mixed Samples b = 0.8269
0.0 0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.27 Comparison between Averaged and Discrete Samples (TDS) April 25
108
As seen from Figures 5.24 through 5.27 there are some differences in the shape of the curves depending on the sampling procedure. The implications of Figures 5.24 through
5.27 are discussed in more depth in Chapter 6.
5.4 Theoretical Pollutant Removal Efficiency
As previously stated, for this thesis it is assumed that the pollutant capture period at the
VUSP Infiltration Trench occurs from when runoff is filling the trench bed to overflow conditions. A typical month at the VUSP Infiltration Trench with regards to bed water depth and rainfall is shown in Figure 5.28.
Figure 5.28 Typical Water Level Readings within VUSP Infiltration Trench
109
Individual rain events for this thesis are separated by 24 hours of dry time. Each individual rain event was analyzed the rainfall and bed depth in order to calculate the total rainfall (V) and capture rainfall (v). The total rainfall is simply the sum of the rainfall within each event. The rainfall capture is defined as either the total rainfall (i.e. the infiltration trench does not overflow) or the total rainfall until the bed overflows (i.e. the sum of the rainfall until the bed depth reaches 5.21 feet). Second filling periods (i.e. bed depths that drop below 5.21 feet and rise above 5.21 once again) were not considered part of the capture rainfall. Typically these second filling periods are very small in terms of size and rainfall amounts in comparison to the first large filling period at the onset of the storm event. The calculated rainfall capture amounts are shown in Figure 5.29.
The rainfall events when overflow takes place are shown in Figure 5.30. Only observing overflow events provides assessment of the infiltration trench when rainfall events exceed the trench’s maximum design capabilities (i.e. how much rainfall is captured before overflow).
110 1
0.8
0.6 all Capture (Inches) all Capture f
ain 0.4 R
0.2 Storm Event Storm Event
0
5 5 5 6 7 /0 /0 /0 /06 /0 /0 5/04 3 7 1 /7 5 7/7/04 1 /1 /2 2 4/7/06 1 /1 0/ 1 6 1/ 7/22 1 3 1 1 Figure 5.29 Depth of Rainfall Captured at the VUSP Infiltration Trench (All Rainfall Events)
0.8
0.6 OVERFLOW EVENTS ONLY
all Capture (Inches) all Capture 0.4 f
0.2 Storm Event Rain
0
4 6 6 6 7 /0 /05 /05 /0 /0 /0 0 7/06 /1 0 5 /7 /4/ /2 4 /1 /25 2 4 7/12 1 1 3 8/ 11 1 1
Figure 5.30 Depth of Rainfall Captured at the VUSP Infiltration Trench (Overflow Events Only)
111
From Figures 5.29 and 5.30 the rainfall capture fluctuates depending upon season. Also of note, since the first flush depth was determined to be 1.0 inches of rainfall, the above plots can also be considered a percentage of the first flush captured by the VUSP
Infiltration Trench.
With the capture rainfall (v) and the total rainfall (V) calculated, the dimensionless rainfall parameter (v/V) was calculated for each rain event. This number was subsequently used for determining the position of each pollutant on the respective mass/rainfall curve. The corresponding dimensionless mass number on the y-axis provides the overall removal efficiency for each pollutant for each separate storm event.
In order to transform the dimensionless mass values into actual numbers the total pollutant mass for each storm event is needed. In order to determine the total mass value for each pollutant, the total mass and total rainfall for each storm event observed for water quality data collection was plotted. The line was produced under the assumptions that rainfall depth and pollutant transport follow a linear relationship though the origin.
The mathematical line produced was used to determine the total mass captured and the total mass captured by the VUSP Infiltration Trench. The total rainfall depth and total pollutant mass plots for each respective pollutant are shown in Figures 5.31 through 5.37.
112 3000
oad (grams) oad 2000 L
y = 342.97x R2 = 0.7639 1000 Total Suspended Solids
0 0.0 2.0 4.0 6.0 Rainfall Depth (inches)
Figure 5.31 Total Rainfall Depth and Total Suspended Solids Load 8000 ams)
r y = 1091.5x 6000 R2 = 0.0959 oad (g oad L
4000
2000 Total Dissolved Solids
0 0.0 2.0 4.0 6.0 Rainfall Depth (inches)
Figure 5.32 Total Rainfall Depth and Total Dissolved Solids Load
113 400
300 ams) r oad (g oad L 200 y = 62.049x R2 = -0.0943 ogen r
100 Total Nit
0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Rainfall Depth (inches)
Figure 5.33 Total Rainfall Depth and Total Nitrogen Load
30 ams) r 20 oad (g oad L us us r y = 8.4518x R2 = 0.4237 10 Total Phospho
0 0.00.51.01.52.02.53.0 Rainfall Depth (inches)
Figure 5.34 Total Rainfall Depth and Total Phosphorus Load
114 12
Phosphate Nitrite 8 Nitrate ams) r
y = 1.8991x y = 2.2827x R2 = 0.6717 R2 = 0.6455 4 Total Load (g y = 1.4662x R2 = -2.7343
0 0.0 1.0 2.0 3.0 Rainfall Depth (inches)
Figure 5.35 Total Rainfall Depth and Total Ion Load
120
ams) 80 r y = 32.448x R2 = -1.7316 oad (g L ide r 40 Chlo
0 0.0 1.0 2.0 3.0 Rainfall Depth (inches)
Figure 5.36 Total Rainfall Depth and Total Chloride Load
115
0.4 y = 0.1531x R2 = -0.4563 ams) r 0.3 oad (g oad L
0.2 Dissolved Copper Dissolved Chromium Dissolved Cadmium
0.1 y = 0.0283x R2 = -0.853 y = 0.0093x Total Dissolved Metals R2 = 0.1953 0 0.0 1.0 2.0 3.0 Rainfall Depth (inches)
Figure 5.37 Total Rainfall Depth and Total Dissolved Metals Load
The mass capture values for each pollutant somewhat follow the same trend as the rainfall capture plot in Figures 5.29 and 5.30. The pollutant mass captured for total suspended solids is shown in Figures 5.38 and 5.39.
116 500
400
300
200
100 Total SuspendedSolids Captured (grams)
0
4 4 5 6 6 7 7 /04 /0 /0 /0 /05 /05 /06 /0 /0 /06 /0 /0 /7 8 /6 6 /1 0 /4 6 5 /7 2 6 7 2 /1 7 /1 2 /2 1 /2 9/1 1 2 5 8/2 1 2 5/1 11
Figure 5.38 Mass of Total Suspended Solids Captured (All Events)
500
400 OVERFLOW EVENTS ONLY
300
200
100 Total Suspended Solids Captured (grams) Captured Solids Total Suspended
0
4 5 5 6 6 7 /0 /06 /06 /07 2 4 3/0 7/0 /3 7/0 /1 6/0 /1 2 2 1 2 2 1 2 7 1/ 3/ 9/ 6/ 10/5/0 5/ 1
Figure 5.39 Mass of Total Suspended Solids Captured (Overflow Events Only)
117
Summation of the points in Figure 5.38 provides the theoretical total pollutant mass captured for total suspended solids. The same approach was applied for the remainder of the pollutants. The rainfall captured by the Infiltration Trench compared to the observed rainfall is shown in Figure 5.40.
The theoretical pollutant efficiency can be determined by dividing the sum of the pollutant captured by the sum of the pollutant transported. Both the captured and transported results are shown in Figure 5.40 and 5.41 for each respective pollutant.
Figure 5.40 provides all of the results in SI gram units and Figure 5.41 provides the results in English pound units (excluding dissolved metals due to the small quantities).
Figure 5.40 Rainfall Captured during Trench Fill Time and Total Observed Rainfall
118
Figure 5.41 Theoretical Pollutants Captured (Blue) by and Transported (Red) to the VUSP Infiltration Trench (Gram Units)
119
Figure 5.42 Theoretical Pollutants Captured by and Transported to the VUSP Infiltration Trench (Pound Units)
120 The total theoretical pollutant capture efficiency for each pollutant is listed in Table 5.5.
Table 5.5. Total Theoretical Pollutant Capture Efficiencies (%) for Pollutants at the VUSP Infiltration Trench
Total Suspended Solids Total Dissolved Solids Total Nitrogen Total Phosphorus 36 28 28 15
Nitrite Nitrate Phosphate Chloride 15 43 19 30
Dissolved Copper Dissolved Cadmium Dissolved Chromium 35 34 17
The total pollutant capture efficiency does not provide a sense of the yearly performance of the Infiltration Trench. The yearly average of the v/V was plotted on the average mass/rainfall distribution plots to provide an understanding of the evolution of the trench capture efficiency for each pollutant. The yearly dimensionless depth analysis is show in
Figure 5.42.
121
2004 (0.24) 1.0 2007 (0.15) 2005 (0.27) 0.8 2006 (0.20) oad L Total Suspended Solids 0.6 Cd, Cu Total Dissolved Solids NO2, TP, NO3 Total Nitrogen PO4, Cr Total Phosphorus TN 0.4 Nitrite Cl Nitrate TSS Phosphate TDS Chloride Cumulative Pollutant 0.2 Dissolved Copper Dissolved Cadmium Dissolved Chromium 0.0
0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Rainfall Depth
Figure 5.43 Average Transport Curves with the Evolution of the Dimensionless Rainfall Parameter
122
5.5 Influence of Rainfall Characteristics
The rainfall characteristics for each storm event were investigated in order to verify the
Gupta Saul empirical methodology (equation 3.1) for determining the first flush load.
Each separate storm event hydrograph was broken down to determine the rainfall characteristics during each sample region of the composite inflow samples. An example of a storm event hydrograph broken down into the sample regions is shown in Figure
5.43. All storm events broken down by sample region can be seen in Appendix G. Total suspended and total dissolved solids were also plotted on the graph to give a general understanding of the importance of rainfall characteristics. In the plot each differently colored region represents each sample. Each diamond represents a point in which the automated sampler took a sample. The dashed horizontal lines represent the EMC of the respective pollutant. Each red rain mark represents a 0.25 inch of rainfall within the storm event.
The influence of rainfall characteristics were analyzed using single and multiple logarithmic regression in Microsoft Excel. Logarithmic regression was chosen over strait linear regression due to the mathematical exponential definition of the first flush equation established by Gupta Saul. Antecedent dry time (hrs) and maximum 1 hour rainfall intensity (in/hr) were analyzed singularly and regressed against both pollutant concentration and pollutant mass for each collection period. A combination of antecedent dry time and maximum rainfall intensity was also regressed. Finally, both parameters along with time duration for each sample period were simultaneously
123 regressed together according to the Gupta Saul equation. The fit of each regression analysis was analyzed using the coefficient of determination (r2). Coefficient of determination values range from zero to one. A value of 1 indicates a perfect correlation in the analysis. At the other extreme, if the coefficient of determination is 0, the analysis indicates no correlation between the regressed parameters.
0.00
0.40 0.04 all (in) f Total Rainfall = 2.92 inches 0.08
Antecedent Dry Time = 173.0 hours Rain Total Inflow Volume = 121500 L Autosampler = 6 Samples per Bottle 60
0.30 s)
f 40 0.25 Inch Sample 0.50 Inch Sample 1.00 Inch Sample > 1.0 Inch Sample 20 Sampling Points 0.20 TSS Concentration
TSS EMC 0 TDS Concentration (mg/L) TDS Concentration 16 TDS EMC low at Weir Inlet Box (c Box Inlet Weir lowat f 12 In 0.10
8
4
0.00 0 TSS Concentration (mg/L)
9 29 5 5:49 9: 06 1:39 06 06 /06 17:19 /06 21: 7 /28/ /27 0/28/ 0/28/ 1 1 10 10 10/2
Figure 5.44 October 27, 2006 Storm Event and Respective Sampling Locations
124
The regression analysis was only performed on total suspended solids and total dissolved solids due to insufficient sample data in the remaining pollutants. Tables 5.6 through 5.9 show the coefficient of determination values for the regression analysis performed on
0.25, 0.5, 1.0, and greater 1.0 inch samples, respectively. Table 5.10 is depicts the results from regression analysis for the total storm for each separate event.
Table 5.6. Coefficient of Determination Values for Regression Analysis between 0.25 inch TSS and TDS samples with Various Storm Characteristics
Max 1 hr Intensity, 0.25 Inch Max 1 hr Ant Dry Max 1 hr Intensity, Ant Dry Time, Sample Intensity Time (hrs) Ant Dry Time Rainfall Duration Total Suspended Solids (mg/L) 0.00 0.00 0.01 0.05 Total Suspended Solids (grams) 0.00 0.00 0.01 0.04 Total Dissolved Solids (mg/L) 0.06 0.06 0.17 0.21 Total Dissolved Solids (grams) 0.00 0.01 0.01 0.05
Table 5.7. Coefficient of Determination Values for Regression Analysis between 0.50 inch TSS and TDS samples with Various Storm Characteristics
Max 1 hr Intensity, 0.50 Inch Max 1 hr Ant Dry Max 1 hr Intensity, Ant Dry Time, Sample Intensity Time (hrs) Ant Dry Time Rainfall Duration Total Suspended Solids (mg/L) 0.00 0.02 0.03 0.05 Total Suspended Solids (grams) 0.01 0.10 0.10 0.13 Total Dissolved Solids (mg/L) 0.06 0.07 0.10 0.14 Total Dissolved Solids (grams) 0.06 0.13 0.15 0.17
125
Table 5.8. Coefficient of Determination Values for Regression Analysis between 1.00 inch TSS and TDS samples with Various Storm Characteristics
Max 1 hr Intensity, 1.00 Inch Max 1 hr Ant Dry Max 1 hr Intensity, Ant Dry Time, Sample Intensity Time (hrs) Ant Dry Time Rainfall Duration Total Suspended Solids (mg/L) 0.11 0.00 0.11 0.13 Total Suspended Solids (grams) 0.22 0.00 0.22 0.30 Total Dissolved Solids (mg/L) 0.06 0.00 0.07 0.07 Total Dissolved Solids (grams) 0.01 0.00 0.01 0.03
Table 5.9. Coefficient of Determination Values for Regression Analysis between Greater than 1.00 inch TSS and TDS samples with Various Storm Characteristics
Max 1 hr Intensity, >1.00 Inch Max 1 hr Ant Dry Max 1 hr Intensity, Ant Dry Time, Sample Intensity Time (hrs) Ant Dry Time Rainfall Duration Total Suspended Solids (mg/L) 0.67 0.10 0.30 0.33 Total Suspended Solids (grams) 0.43 0.08 0.75 0.75 Total Dissolved Solids (mg/L) 0.54 0.02 0.07 0.46 Total Dissolved Solids (grams) 0.24 0.10 0.56 0.72
126
Table 5.10. Coefficient of Determination Values for Regression Analysis between Total Storm Event TSS and TDS samples with Various Storm Characteristics
Max 1 hr Intensity, Total Storm Max 1 hr Ant Dry Max 1 hr Intensity, Ant Dry Time, Event Intensity Time (hrs) Ant Dry Time Rainfall Duration Total Suspended Solids Storm EMC (mg/L) 0.15 0.03 0.18 0.22 Total Suspended Solids Total Mass (grams) 0.32 0.03 0.34 0.34 Total Dissolved Solids Storm EMC (mg/L) 0.13 0.05 0.18 0.18 Total Dissolved Solids Total Mass (grams) 0.20 0.00 0.20 0.22
From the results presented in Tables 5.6 through 5.9 it can be seen that the regression analysis has a better agreement at the end of the storm event than at the beginning. The coefficient of determination values steadily becomes a better fit as the storm samples progress from the beginning to the end of the storm event. The greater than 1.0 inch sample shows to have the best correlation between pollutant distribution and storm characteristics. It can also be seen from Table 5.10 that the lumping of the storm samples as one single entity according to the Gupta Saul equation produced poor regression trend results.
127 Chapter 6 – Discussion
6.1 Introduction
This chapter discusses and dissects the results presented in Chapter 5 in a more in depth and provocative manner. The following sections in this chapter provide more insight into the first flush determination, the theoretical VUSP Infiltration Trench pollutant capture efficiency, the design implications for the future of infiltration trench BMP construction, and the influence of storm characteristics upon pollutant distribution.
6.2 First Flush Determination
The following sections discuss the first flush phenomena using concentration theory, statistical t-test verification, and mass transport theory. Each pollutant investigated within this thesis has been evaluated using each of the above theories.
6.2.1 Pollutants with Decreasing Concentrations
First flush concentration theory is centered around the principle that the pollutant concentration is highest at the onset of the storm event and eventually decreases to a constant base level throughout the remainder of the storm event. The difficulty in this theory is pinpointing the highest level concentration and also pinpointing the subsequent temporal span in the proceeding drop in concentration to the base line concentration.
128 Determining “how high?” and “how quickly?” will answer if a first flush exists and when a first flush is over. In order to answer these two questions most accurately, as many discrete sampling points as possible throughout the storm event should be collected.
Through a series of several small samples throughout the storm event, this thesis successfully accomplished this critical aspect in first flush concentration theory. It helped ensure, to the best practical purposes, that all of the high concentration points within a storm event were observed.
The pollutants that most consistently exhibited a first flush concentration theory were: total suspended solids, total dissolved solids, dissolved copper, and dissolved cadmium.
These pollutants most often had an initial high concentration with respect to the final concentration. The rest of the pollutants (total phosphorus, phosphate, nitrite, dissolved lead, and dissolved chromium) did not consistently exhibit a first flush concentration theory. However, it should be mentioned that dissolved lead only had four observed concentrations out of twenty eight possible samples, and it is therefore hypothesized that this pollutant is present in very limited quantities at the VUSP Infiltration Trench.
The event mean concentrations (EMC) for all the pollutants were determined by calculating the sum of the total load of all the storm events divided by the sum of the total volume of all the storm events. The EPA defines the point where the individual sample
(partial event mean concentration) is less than the EMC is the point where the first flush ends. The mean of each sample was also calculated in order to gain a general understanding of a typical storm event. The point where the mean sample storm event
129 crosses below the EMC is when the average storm event first flush phenomena is over.
Since by definition the calculation of the EMC is always between two data points it can be misunderstood for first flush determination. It is therefore critical that the EMC EPA first flush definition be only applied to pollutants that have decreasing concentrations throughout the storm event. In the case of total phosphorus, phosphate, and nitrite these pollutants fluctuate above and below the EMC throughout the storm event. One possible explanation for this fluctuation is the characteristics of the contributing drainage area.
Since the VUSP Infiltration Trench drainage area consists entirely of a parking garage in an urban setting it can be hypothesized that phosphorus and its derivatives are already at base level concentration levels due to the lack of phosphorus sources. Nitrite levels may also be assumed under the same conditions; however this is probably not due to the fact that nitrogen based pollutants are limited at the infiltration trench, but due to the fact that this specific nitrogen derivative is in limited quantities in comparison to total nitrogen and nitrate.
Dissolved metals are at even lower concentrations than phosphorus, phosphate, and nitrite (ppb compared to ppm) but it can be hypothesized that these pollutants have relatively lower baseline concentrations when compared to the initial high concentration.
The gap between the initial high concentration and the baseline concentration is possibly why a first flush is observed in dissolved copper and cadmium and not in phosphorus derivatives even though phosphorus loading rates in terms of mass are much greater. In other words, the first flush determination is inherently dependent upon the ability to pinpoint the difference between the high point concentration and the baseline
130 concentration. If no first flush is observed it may be due to the fact that the drainage area under investigation just does not have an adequate source for the particular pollutant and is consistently at base level conditions.
For the pollutants with decreasing concentrations, the concentration first flush theory showed that the partial event mean concentration is lower than the event mean concentration between 0.5 inch and 1.0 inch of rainfall. Therefore meaning that the first flush is somewhere between 0.5 inch and 1.0 inch of rainfall.
6.2.2 Statistical T-test Verification
In order to further verify the EMC first flush results of when the first flush occurs, a statistical Student’s T-test was implemented for the pollutants with decreasing concentrations.
The results from the test indicate that a statistical difference is present in all pollutants with decreasing concentrations except total nitrogen at 1.0 inch of rainfall (0.25 to 1.0 comparison). The pollutants with the strongest statistical difference are the solids. In other words, the pollutants that are most likely to exhibit a first flush at the VUSP
Infiltration Trench at 1.0 inch are total suspended solids (99% certainty) and total dissolved solids (94% certainty). Dissolved copper (92% certainty), dissolved cadmium
(90% certainty), nitrate and chloride (80% certainty, respectively) all show a statistically significant likelihood for a first flush to occur at 1.0 inch of rainfall. It was shown that
131 Student’s statistical t-test for the comparison for the difference of means was successful in verifying the results from the EMC first flush concentration results.
Also of great importance from the statistical t-test results is the comparison of the later samples (1.0 inch to >1.0 inch comparison). All of the downward decreasing pollutants showed a statistical similarity between these sample populations. The similarity of these samples indicates an achievement of the base level concentrations for the pollutants in question. The base level achievement is further evidence that the first flush phenomena occurs at 1.0 inches of rainfall at the VUSP Infiltration Trench.
6.2.3 Mass/Rainfall Dimensionless Distribution Curves
The primary advantages to constructing dimensionless mass/rainfall distribution curves are the ability for comparative analysis of storm events through normalizing of the data and the ability to mathematically define the shape of the curves. The plots also allow for several curves to be compared on one single plot even though the size of the storm events and concentrations of the pollutant may widely vary. The mathematical analysis allows for the construction of an average distribution curve through calculation of the b coefficient (equation 3.2). This average pollutant distribution curve can then be utilized for any size storm event, which can be very useful knowledge for designing pollutant capture devices, such as stormwater best management practices.
132 The average distribution curves for each pollutant in this thesis were determined through linear regression of each individual storm event. The coefficient derived from this regression is often referred to as the “b coefficient”. The b coefficient allows for a mathematical description (mathematical power curve) of the distribution of each storm event. By averaging the b coefficients of each individual storm event, the average storm distribution for any given storm event can be obtained. Even though individual storm events may vary over and above this average curve, this gives the best understanding of the pollutant distribution without actual data.
As previously stated in section 3.3.3, the b coefficient is inversely proportional to the strength of the pollutant first flush phenomenon. Pollutants with b coefficients less than
1 are above the 45 degree bisector and are transported earlier in the storm event (as shown in Figure 3.2). The pollutants that had curves above the 45 degree bisector were: total suspended solids, total dissolved solids, total nitrogen, nitrate, chloride, dissolved copper, and dissolved cadmium. These are the same pollutants with downward decreasing pollutants as previously listed in section 5.2. The b coefficients for these pollutants vary anywhere between 0.4841 (nitrate) to 0.7934 (total nitrogen). The pollutants that showed the strongest first flush phenomena listed in ranking order according to their respective average b coefficient are: nitrate, total suspended solids, dissolved copper, dissolved cadmium, chloride, total dissolved solids, and total nitrogen.
These results agree fairly well with the t-test results in Table 5.4. The pollutants with the most confidence in statistical difference at 1.0 inches of rain in order of rank are: total suspended solids, total dissolved solids, dissolved copper, dissolved cadmium, chloride,
133 nitrate, and total nitrogen. The only pollutants out of place with regards to rank are nitrate and total dissolved solids. Overall the statistical t-test did a good job verifying which pollutants showed the strongest first flush phenomena.
An important factor in the shape of the curve is the number of sampling points during the storm event. A critical assumption for this thesis is that the pollutant concentration over
1.0 inch of rainfall only has slight variation in comparison to the first 1.0 inch of rainfall.
In order to verify this assumption and verify the shape of the curves, a discrete sample curve was compared to a mixed sample curve for two large storm events for total suspended solids and total dissolved solids. The results from these storm events showed that there is more variation at the end of the event for total suspended solids than expected. As shown in Figure 5.24 and 5.25 (section 5.2) the total suspended solids discrete curves can fluctuate even over 1.0 inch of rainfall, which can subsequently affect the b coefficient. The percent error on the b coefficient was 26% and 25%, respectively for the two storm events. The total dissolved solids matched fairly well with percent errors of 3% and 7%, respectively. The results show in both cases that mixing the over
1.0 inch samples causes the first flush effect to be dampened when compared to the discrete samples (i.e. the mixed samples have higher b coefficients).
The primary disadvantage to dimensionless curves is that they do not reveal quantity of pollutant without some base knowledge of the total mass transported for a given storm event. In order to extract an actual pollutant mass from any point along the average distribution curve, the dimensionless rainfall and total pollutant mass transported during
134 the storm event in question must be known. The dimensionless rainfall (i.e. volume) is often a fairly easy number to determine since it is simply the ratio of the capture volume divided by the total volume of the storm event. Determining the total mass transported throughout the storm event, however, is often very challenging without sufficient data collection.
6.3 Theoretical Pollutant Capture Efficiency
The following sections discuss the results of the theoretical pollutant capture of the
VUSP Infiltration Trench presented in section 4.4.
The following underlying assumptions in determining the theoretical pollutant capture at the VUSP Infiltration Trench are:
1.) The capture of pollutants occurs from the beginning of the rainfall up until the trench first begins to overflow.
2.) The pollutants are transported according to the average cumulative mass/rainfall plots for each respective pollutant for all storm events.
3.) The total mass of pollutants transported is dependent upon the total rainfall of the respective storm event.
The first assumption is also known as the rainfall capture. The pollutant capture period is assumed to occur during the initial filling of the trench only. This assumption is based
135 upon the idea that once the trench begins to overflow, the majority of the pollutants bypass the bed and exit through the overflow pipe or surface pavers. There may be small amounts of pollutants that enter during overflow that become captured within bed material but it is assumed that this value is minimal compared to the mass captured during the time of filling. The composite overflow samples help verify this assumption.
Table 6.1 shows a comparison between the overflow composite samples and the average of the inflow samples when overflow is known to take place (the numerical mean of the
0.5 inch, 1.0 inch, and greater than 1.0 inch rainfall samples).
Table 6.1 Comparison of Average Composite Overflow Sample to Average Inflow Samples During Overflow
Average 0.5, 1.0, and >1.0 Average Overflow Sample (mg/L) (mg/L) TSS 6.5 5.21 TDS 35.07 39.74 TN 1.92 1.63 TP 0.18 0.21 Cl 1.28 4.61 PO4 0.04 0.00 NO2 0.05 0.36 NO3 0.14 0.41 Cu 4.05 5.80 Cd 0.20 0.31 Cr 1.88 0.00
From Table 6.1 it can be seen that most of the pollutants have a higher overflow sample indicating that the bed material is not capturing pollutants. Total suspended solids, total nitrogen, phosphate, and dissolved chromium overflow samples did show greater overflow values indicating that perhaps these pollutants are being captured during
136 overflow.
The amount of rainfall captured for each storm event is shown in Figure 5.29. The amount of rainfall captured when overflow is known to occur is shown in Figure 5.30.
Figure 5.30 provides an insight into the evolution of the VUSP Infiltration Trench rainfall capture when the design limitations are exceeded. It can be seen from Figure 5.30 that the minimum rainfall capture has held fairly constant baseline rainfall capture volume of approximately 0.19 inches of rainfall since inception of the VUSP Infiltration Trench.
The spring seasonal peaks can most likely be attributed fluctuations in infiltration rates of the surrounding soil. For further insight regarding the evolution of the infiltration rates of the VUSP Infiltration Trench refer to Emerson (2007.)
The dimensionless rainfall parameter is determined using the rainfall capture for each event. The dimensionless rainfall parameter is determined by dividing the rainfall capture by the total rainfall for each respective event. This number can then be used in conjunction with the average cumulative mass/rainfall curves (Figures 5.13 through 5.23) to determine the percentage of pollutant mass transported for each respective pollutant during the capture period. From Figure 5.13 through Figure 5.23, the average curve falls within a wide spread of the single observed events. However, the average curve provides the best interpretation of how the pollutant transport of previous unobserved events would have behaved. The average curves for total suspended solids, total dissolved solids, nitrate, chloride, dissolved copper, and dissolved cadmium provide the best interpretation of previous storm events since the spread of the single event data points are
137 not as drastic.
The assumption that the total mass of pollutants transported is dependent upon the total rainfall of the respective storm event is necessary in order to convert the dimensionless mass parameter into a discrete mass value. Using the dimensionless rainfall parameter determined from the first assumption, the respective dimensionless pollutant mass parameter can be determined from the average transport curve in Figure 5.13 through
Figure 5.23. Quantifying the amount of pollutant mass captured from the dimensionless pollutant mass parameter is impossible without a total mass value. The simplest way to determine the total mass for a respective storm event is to develop a linear relationship between total rainfall for the storm event and total pollutant mass. The relationship between total rainfall and total pollutant mass for each pollutant for all observed storm events can be seen in Figure 5.31 through Figure 5.37. The spread in Figures 5.31 through 5.37 can be attributed to factors such as antecedent dry time and rainfall intensity, as previously shown in the Gupta Saul first flush equation (equation 3.1.) Although the total rainfall and total mass linear relationship may not be the best possible solution to determining the total mass distributed for rainfall events, it does provide a good general understanding of the order of magnitude for each pollutant. For example, from Figure
5.41 the total mass captured for total suspended solids and total dissolved solids is in the order of magnitude of tens of thousands of grams, while the dissolved metals captured is in the order of single digit grams.
Figure 5.40, Figure 5.41 and Table 5.5 provide a good understanding of the VUSP
138 Infiltration Trench performance both in terms of quantity and quality. It is important to note that the results presented do not include infiltration during times of trench overflow.
As expected, the pollutants with the lowest b values in the development of the average transport curve were the pollutants with the best theoretical pollutant capture efficiency.
Often it is considered that total suspended solids (sediments) govern the performance and
“lifespan” of infiltration trenches (Siriwardene 2007, Behnke 1969, Berend 1967). The loading of solids within an infiltration trench has been shown to result in the clogging of the surrounding soil/trench boundary interface thus reducing infiltration capacities (i.e. performance). The theoretical total suspended solids loading of the VUSP Infiltration
Trench was determined to be approximately 17,000 grams between July 2005 and May
2007. The typical density of total suspended solids from surface water runoff is approximately 4 grams per cm3 (Chapra 1997). Assuming this density value, the deposited solids within the trench would equal 4250 cm3 over 2.88 years, resulting in a loading rate of 1,475 cm3 per year. The total effective storage volume of the VUSP
Infiltration Trench is 180 ft3 (5.1 m3). This results in a lifespan of approximately 3,500 years before the trench would completely fill with sediments.
6.4 The Importance of Drainage to Footprint Area Loading Ratio
Without question the most critical design aspect of the VUSP Infiltration Trench is the area loading ratio of the contributing drainage area to the top area of the infiltration trench. The VUSP Infiltration Trench area loading ratio is 158:1 (20,400 ft2 to 130 ft2).
139 The 2007 Pennsylvania Stormwater Best Practices Manual recommends a 5:1 area loading ratio.
The intention of the VUSP Infiltration Trench is to test the limits of infiltration trench design and expedite the "life span" process within a heavily monitored research setting.
With that in mind, the area loading ratio is a critical aspect and should be taken into account when evaluating the results from this thesis.
Suppose the loading ratio of the VUSP Infiltration Trench was designed according to the
Pennsylvania State recommended standard 5:1 area loading ratio for a infiltration trench.
This would increase the top area of the trench to 4,080 ft2 (or decrease the drainage area to 650 ft2). If the VUSP Infiltration Trench implemented this area loading ratio, the results of this thesis would be drastically different since the dimensionless rainfall parameter is strictly dependent upon the area loading ratio. If the overflow design depth were held constant at 5.2 feet, the decrease in area loading ratio to 5:1 would result in an increase in effective storage size from 0.19 inches to 3.2 inches over the contributing drainage area. Only five recorded rainfall events between July 2005 and May 2007 exceeded 3.2 inches of rainfall. If assuming all rainfall above 3.2 inches within each separate rain event is overflow, the total rainfall overflow within this time span would be equal to 7.65 inches. This would result in an approximate 93% rainfall capture (a dimensionless rainfall parameter equal to 0.93). Table 6.2 shows the theoretical percent captures for a 5:1 loading ratio for each pollutant using the average transport curves presented in Figures 5.13 through Figure 5.23.
140
Table 6.2. Total Theoretical Pollutant Capture Efficiencies for a 5:1 Loading Ratio Infiltration Trench (Overflow Depth = 5.2 feet)
Total Suspended Solids Total Dissolved Solids Total Nitrogen Total Phosphorus 96 94 94 90
Nitrite Nitrate Phosphate Chloride 90 97 92 95
Dissolved Copper Dissolved Cadmium Dissolved Chromium 95 95 91
From Table 6.2 it is shown that the capture efficiencies drastically increase as the loading ratio is decreased. It is also important to note that the spread of the capture efficiency for the pollutants is not as great for a 5:1 design trench. This is due to the average transport curves being more divergent earlier in the storm event. From Figure 5.42 it can be seen that the value of the dimensionless rainfall parameter is more of a factor for highly loaded infiltration trenches as opposed to trenches with small loading ratios.
Since the percent capture is significantly larger for a 5:1 loading ratio, the total suspended solids deposition within the trench would also increase. A 96% capture rate would result in 16,400 grams (36 pounds) of total suspended solids deposited per year. Using the same density assumption as previously, this would result in a loading rate of approximately 4,100 cm3 per year of total suspended solids deposited within the trench.
Assuming the contributing drainage area remains constant, the trench storage size would increase to approximately 21,200 ft3 (600 m3) for a 5:1 loading ratio. The resulting lifespan would equal approximately 140,000 years for a 5:1 loading ratio infiltration
141 trench with an overflow depth equal to 5.2 feet.
6.5 Influence of Storm Characteristics on the First Flush
The results yielded from the regression analysis of total suspended solids and total dissolved solids with storm characteristics were found to be most revealing with respect to the time frame of the storm event. The regression results steadily improved with respect to the start time of the storm event. The results for early in the storm event (0.25 inch sample) were shown to have to no correlation between pollutant transport and storm characteristics. In comparison to the latter stages of the storm event (>1.0 inch sample), the correlation was found to be much more reliable.
This disparity between the regression results can most likely be attributed to the complexity and unpredictability of the first flush. Storm characteristics such as antecedent dry time, intensity, and rainfall duration may be significant to pollutant transport; however, these variables may be less crucial to first flush transport when in comparison to anthropogenic factors. Some examples of these anthropogenic factors would be motor vehicle traffic volume and overall “wear and tear” on the contributing area in between storm events. However, determining these factors is very difficult in between storm events and much further research regarding this hypothesis is needed.
The storm characteristics regression correlation at the end of the storm event also indicates that once the first flush is purged, predicting pollutant transport is much more
142 reliable using regression techniques from storm characteristics. Incidentally, these regression results further verify that the first flush is present at the VUSP Infiltration
Trench for the first 1.0 inch of rainfall.
The results from the regression analysis also shows that lumping storm events into one conglomerate pollutant distribution over the entire storm event is not reliable methodology for predicting pollutant transportation based upon storm characteristics at the VUSP Infiltration Trench. It is hypothesized from the regression results, that there are separate sets of factors which govern the transport of pollutants for early and latter stages of a storm event at the VUSP Infiltration Trench (i.e first flush pollutants and non- first flush pollutants). Furthermore, it is also hypothesized that the first flush pollutants are largely driven by anthropogenic factors, while the non–first flush pollutants are driven largely by storm characteristics. This distinction between these two sets of pollutants may explain why the regression analysis fit better for the latter part of the storm event.
143
Chapter 7 – Conclusions
The VUSP Infiltration Trench was determined to exhibit a first flush phenomenon for total suspended solids, total dissolved solids, dissolved copper, and dissolved cadmium.
The first flush was determined to last until 1.0 inches of rainfall and was verified using
Student’s statistical t-test. Total phosphorus, total nitrogen, nitrite, nitrate, phosphate, chloride, and dissolved chromium were observed not to exhibit a first flush. Dissolved lead was observed to be in limited quantities and was rarely present in the runoff samples.
The observed transport data was averaged for each individual pollutant in order to determine the theoretical capture efficiency of the VUSP Infiltration Trench from July
2004 to May 2007. The pollutants that exhibited stronger average transport curves produced higher theoretical capture efficiencies. The theoretical capture efficiencies ranged from 43% (nitrate) to 15% (total phosphorus).
It was determined that the capture efficiency for each pollutant is strongly dependent upon the storage volume of the infiltration trench. Had the VUSP Infiltration Trench been designed according to the 2006 Pennsylvania Stormwater Best Practices Manual recommended guideline area loading ratio of 5:1, the theoretical capture efficiency for every pollutant would have been above 90%.
It was also shown that the term “lifespan” for the VUSP Infiltration Trench may not be
144 applicable. Even for a heavily overloaded infiltration trench such as the one at Villanova
University (158:1 area loading ratio), the calculated “lifespan” for the filling of sediments is in the magnitude of thousands of years. In conclusion, if it is assumed that pore clogging sediments accumulate on the bottom of the trench, it is anticipated that infiltration through the side walls of the VUSP Infiltration Trench should be serviceable for many lifetimes before it becomes completely filled with suspended solids thus rendering it useless.
Storm characteristics (maximum 1 hour intensity, antecedent dry time, and rainfall duration) were shown to have a greater influence at the end of the storm event for total suspended solids and total dissolved solids. Predicting first flush loads at the VUSP
Infiltration Trench based upon storm characteristics yielded poor results from the regression analysis. The disparity between the beginning and end storm event regression analysis is attributed to the complex multiple factors that can effect first flush loads, namely from anthropogenic sources.
Upon completion of this research it is concluded that the VUSP Infiltration Trench exhibits a first flush and it plays a significant role in which pollutants are captured at a better efficiency. It is also concluded that designing a 5:1 area loaded infiltration trench with a 100% impervious parking deck drainage area around the first flush is unnecessary due to the lack of divergence between average pollutant transport curves in the latter portion of storm events.
145
Chapter 8 – Recommendations for Future Research
Upon completion of this research several interesting questions have arose as to the function of the VUSP Infiltration Trench with regard to the relationship between pollutant capture and trench performance.
One recommendation is to investigate the pollutant capture more thoroughly. This involves two areas of emphasis: (1) physically investigating the constituents at the bottom of the trench and (2) simultaneously sampling the water within the trench bed and the overflow during storm events. The first point of emphasis would exactly determine how much solids have accumulated at the bottom of the trench and if this amount is in the vicinity of the loading results presented in this research. The second point of emphasis is to determine how well the trench bed material captures pollutants during times of overflow. If a significant portion of pollutants are indeed captured during overflow, the loading and capture efficiency values presented in this thesis would need adjustment.
Another recommendation is to further investigate the seasonal variation of the pollutants and to gather more data points in order to better describe the average transport curves.
There is never enough data with regards to water quality investigations.
It would also be interesting to investigate pretreatment technologies such as clean out traps at the bottom of the parking deck downspouts and comparing the capture
146 efficiencies to the results found in this thesis. How much of an impact would a pretreatment device have upon the loading of pollutants within the trench?
One final recommendation would be to resize the parking deck conveyance system in order to achieve a 5:1 loading ratio. Having actual data from a 5:1 loaded infiltration trench and comparing to the results found in this thesis would be of great value for verifying recommended guidelines.
147
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150 Appendix A: CAD Drawings of VUSP Infiltration Trench
PLAN VIEW
151
CROSS SECTIONAL VIEW
152 Appendix B: CRBasic TRENCH[23].CR1 Programming File (User Notes in Italics)
'CR1000 'VUSP Infiltration Trench
'This statement should keep the public variables (calibration coefs. in memory after power down.) PreserveVariables
'State calibration coefficients. Public WEIR_M,WEIR_B,WELL_M,WELL_B,OVER_M,OVER_B
'Declare Data Variables Public Batt_Volt,Rain,WEIR,WELL,OVER,Well_Temp
'Declare 24 hour total rainfall calculation variables Public Rain24HrTot Dim Rain24HrSum,RainOldest24,RainNewest24,RainTrim24
'Declare 48 hour total rainfall calculation variables Public Rain48HrTot Dim Rain48HrSum,Rain_Oldest48,Rain_Newest48,Rain_Trim48
'Declare inflow variables Public inflow_vol, zero_pt, Inflow24HrTot, countinflowvol, weir_ht Dim Inflow24HrSum,InflowOldest24,InflowNewest24,InflowTrim24
'Declare overflow weir variables Public overflow_vol, Overflow24HrTot, countovervol, flume_ht Dim Overflow24HrSum, OverflowOldest24, OverflowNewest24, OverflowTrim24
'Declare units for data record variables
Units Batt_Volt=Volts Units Rain=in Units WEIR=in Units WELL=ft Units OVER=in Units Well_Temp=Deg F Units inflow_vol=cu ft Units overflow_vol=cu ft
'Define Data Tables
‘Create Raw Data Table used for data collection DataTable(RAWDATA,True,-1) DataInterval(0,1,Min,10) Totalize(1,Rain,FP2,False) Average(1,WEIR,FP2,False) Average(1,WELL,FP2,False) Average(1,OVER,FP2,False) EndTable
'Create the battery table DataTable(BATT,True,-1) DataInterval(0,1440,Min,10) Minimum(1,Batt_Volt,FP2,False,False) EndTable
'Create the well temperature table DataTable(TEMP,True,-1)
153 DataInterval(0,15,Min,10) Average(1,Well_Temp,FP2,False) EndTable
'Create the inflow table DataTable(INFLOW,True,-1) DataInterval(0,1,Min,10) Totalize(1,inflow_vol,FP2,False) EndTable
'Create the overflow table DataTable(OVERFLOW,True,-1) DataInterval(0,1,Min,10) Totalize(1,overflow_vol,FP2,False) EndTable
'Define Custom Menu and Keyboard Display Configuration
DisplayMenu("Infiltration Trench",-2)
SubMenu("Current Data") DisplayValue("24hr Rain",Rain24HrTot) DisplayValue("48hr Rain",Rain48HrTot) DisplayValue("Weir (in)",WEIR) DisplayValue("Well (ft)",WELL) DisplayValue("Over (in)",OVER) DisplayValue("Well Temp",Well_Temp) DisplayValue("Batt Volt",Batt_Volt) DisplayValue("24hr Inflow",Inflow24HrTot) DisplayValue("24hr Overflow", Overflow24HrTot) DisplayValue("Inflow Counter",countinflowvol) DisplayValue("Overflow Counter",countovervol) EndSubMenu
SubMenu("Calibration") SubMenu("Weir Probe") MenuItem("Weir [m]",WEIR_M) MenuItem("Weir [b]",WEIR_B) EndSubMenu SubMenu("Well Probe") MenuItem("Well [m]",WELL_M) MenuItem("Well [b]",WELL_B) EndSubMenu SubMenu("Overflow Probe") MenuItem("Over [m]",OVER_M) MenuItem("Over [b]",OVER_B) EndSubMenu EndSubMenu
SubMenu("Zero Points") SubMenu("Weir Probe") MenuItem("Weir Zero Pt",zero_pt) EndSubMenu EndSubMenu
EndMenu
'Main Program BeginProg
'The flow proportional sampling is dependent on the 5 sec scan rate Scan(5,Sec,1,0)
154 'Default Datalogger Battery Voltage measurement Batt_Volt: Battery(Batt_Volt) 'Generic Tipping Bucket Rain Gauge measurement Rain_in: PulseCount(Rain,1,1,2,0,0.01,0) 'Generic Differential Voltage measurements WEIR: VoltDiff(WEIR,1,mV2500,1,True,0,_60Hz,WEIR_M,WEIR_B) 'Generic Differential Voltage measurements WELL: VoltDiff(WELL,1,mV2500,2,True,0,_60Hz,WELL_M,WELL_B) 'Generic Differential Voltage measurements OVER: VoltDiff(OVER,1,mV2500,3,True,0,_60Hz,OVER_M,OVER_B) '107 Temperature Probe measurement set for degrees Fahrenheit Therm107(Well_Temp,1,7,1,10000,_60Hz,1.8,32.0) 'changed to 10,000 'Call Data Tables and Store Data CallTable(RAWDATA) CallTable(BATT) CallTable(TEMP) CallTable(INFLOW) CallTable(OVERFLOW)
'Calculation of the previous 24hr rainfall
RainTrim24=RainTrim24+Rain 'The "Trim" variable simply makes the 24hr total a realtime total, instead of waiting (1min) for the data table to update.
If IfTime(0,1,Min) Then 'Run the following when the time is on the minute 'Acquire oldest value from data table to remove from total. RainOldest24=RAWDATA.Rain_Tot(1,1441) : If RainOldest24=NaN Then RainOldest24=0 'Acquire newest value from data table to add to total. RainNewest24=RAWDATA.Rain_Tot(1,1) : If RainNewest24=NaN Then RainNewest24=0 'Calculate the running sum of rainfall values Rain24HrSum=Rain24HrSum+RainNewest24-RainOldest24 'reset the trim variable bc if this statement is run,the sum is up to date RainTrim24=0
EndIf Rain24HrTot=Rain24HrSum+RainTrim24
'Calculation of the previous 48hr rainfall (same process as above)
Rain_Trim48=Rain_Trim48+Rain If IfTime(0,1,Min) Then Rain_Oldest48=RAWDATA.Rain_Tot(1,2881) : If Rain_Oldest48=NaN Then Rain_Oldest48=0 Rain_Newest48=RAWDATA.Rain_Tot(1,1) : If Rain_Newest48=NaN Then Rain_Newest48=0 Rain48HrSum=Rain48HrSum+Rain_Newest48-Rain_Oldest48 Rain_Trim48=0 EndIf Rain48HrTot=Rain48HrSum+Rain_Trim48
'Calculation of the inflow volume in cubfic feet weir_ht=WEIR-zero_pt If weir_ht>0 Then inflow_vol=(8/15)*64.34^(.5)*.58*(tan(1.5708/2))*(weir_ht/12)^(5/2)*5 'the weir flow equation is multiplied by 5 seconds to calculate the volume of 'flow (cu ft) that has passed between scans, so that this can be accounted for 'with the countvol variable. EndIf
'Calculation of the previous 24hr inflow (same as rainfall)
155 InflowTrim24=InflowTrim24+inflow_vol If IfTime(0,1,Min) Then InflowOldest24=INFLOW.inflow_vol_tot(1,1441) : If InflowOldest24=NaN Then InflowOldest24=0 InflowNewest24=INFLOW.inflow_vol_tot(1,1) : If InflowNewest24=NaN Then InflowNewest24=0 Inflow24HrSum=Inflow24HrSum+InflowNewest24-InflowOldest24 InflowTrim24=0 EndIf Inflow24HrTot=Inflow24HrSum+InflowTrim24
Inflow Output Pulse Sent to Inflow Auto-Sampler
If Inflow24HrTot<=.1 then countinflowvol = 35.314 'If the cumulative flow over the past 24 hours is less than 0.1 cu ft this signifies 'a seperate rain event. This step ensures that countvol is reset to 35.314 'between storm events. Because there will be left over volume in countvol '(i.e. flow stops at 500 L), this also sets countvol at 35.314 for when the program 'is first run. EndIf
'Send output pulse to autosampler for every 35.314 cu ft (1000 L). 'countinflowvol is a dummy counter variable to ensure that the program will take a sample 'every 1000 L. If there is inflow, the inflow will be subtracted from countinflowvol 'until countinflowvol is <=0 (as shown in the below step)
If inflow_vol>0 then countinflowvol = countinflowvol - inflow_vol If countinflowvol<= 0 then SW12(1) 'When the counter variable rolls over, this step sends out Delay(0,50,msec) 'a 50 millisecond 12V pulse to the autosampler, which is SW12(0) 'programmed to take one sample per pulse. countinflowvol = 35.314+countinflowvol 'This step resets countinflowvol in the above equation EndIf EndIf 'The pulse is sent out of the SW12 port to Pin C on the autosampler, and a ground 'from the CR1000 is connected to Pin B.
'Calculation of the overflow volume in gallons
flume_ht=OVER-1 'the height of the water above the sump should be used to calculate the flow, sump ht = 1 inch If flume_ht>0 Then overflow_vol= (0.0146*flume_ht^4 - 0.2406*flume_ht^3 + 7.6794*flume_ht^2 + 1.8336*flume_ht - 0.0192)*(1/60)*5 'the overlfow volume is being calculated using a rating curve spefically designed for the 'overflow flume. The units used to develop this curve are in (inches and gallons per minute). 'In order to change the gallons per minute to gallons the rating curve units must be changed to 'gallons per second (1/60) and multipied by the time increment (5 seconds) EndIf
'Calculation of the previous 24hr overflow (same as rainfall)
OverflowTrim24=OverflowTrim24+overflow_vol If IfTime(0,1,Min) Then OverflowOldest24=OVERFLOW.overflow_vol_tot(1,1441) : If OverflowOldest24=NaN Then OverflowOldest24=0 OverflowNewest24=OVERFLOW.overflow_vol_tot(1,1) : If OverflowNewest24=NaN Then OverflowNewest24=0 Overflow24HrSum=Overflow24HrSum+OverflowNewest24-OverflowOldest24 OverflowTrim24=0 EndIf Overflow24HrTot=Overflow24HrSum+OverflowTrim24
156
Overflow Output Pulse to Auto-Sampler
If Overflow24HrTot<=.1 then countovervol = 150 'If the cumulative flow over the past 24 hours is less .1 gallons this signifies 'a seperate rain event. This step ensures that countvol is reset to 150 'between storm events. Because there will be left over volume in countvol '(i.e. flow stops at 5 gallons) This also sets countvol at 150 for when the program 'is first run.
EndIf
'Send output pulse to autosampler for every 150 gallons of volume over the overflow weir. 'countovervol is a dummy counter variable to ensure that the program will take a sample 'every 150 gallons. If there is inflow, the inflow will be subtracted from countvol 'until countvol is <=0 (as shown in the below step)
If overflow_vol>0 then countovervol = countovervol - overflow_vol If countovervol<= 0 then PortSet(1,1) 'When the counter variable rolls over, this step sends out Delay(0,500,msec) 'a 50 millisecond +5V pulse to the autosampler, which is PortSet(1,0) 'programmed to take one sample per pulse. countovervol = 150+countovervol 'This step resets countovervol in the above equation EndIf EndIf 'The pulse is sent out of the C1 control port to Pin B on the autosampler, and a ground 'from the CR1000 is connected to Pin C.
NextScan EndPr
157
Appendix C: Observed Water Quality Data * = outlier
Total Suspended Solids EMC = 7.37 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 10/11/2006 10/17/2006 10/19/2006 10/27/2006 11/2/2006 11/7/2006 11/12/2006 IT IN-0.25 29.10 53.30 7.60 10.52 14.40 14.22 11.80 10.00 13.80 IT IN-0.5 19.20 6.25 6.70 3.42 9.14 4.40 8.30 4.00 4.48 IT IN-1.0 4.00 6.30 1.33 8.57 7.30 7.00 2.67 IT IN->1.0 4.39 4.66 3.00 1.67
11/16/2006 11/22/2006 1/5/2007 1/7/2007 3/15/2007 3/23/2007 4/4/2007 4/12/2007 4/14/2007 4/25/2007* 5/16/2007 Mean 20.60 7.00 12.60 7.60 62.30 92.30 3.67 34.33 21.30 94.70* 35.70 24.32 12.50 4.60 6.90 3.00 12.30 39.10 1.67 5.33 23.30 183.60* 12.00 9.82 4.30 2.80 0.70 1.60 9.00 6.80 5.33 15.50 69.30* 9.00 5.76 2.00 0.80 10.70 68.30* 3.89
Total Dissolved Solids EMC = 34.00 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 10/11/2006 10/17/2006 10/19/2006 10/27/2006 11/2/2006 11/7/2006 11/12/2006 IT IN-0.25 102.70 10.70 6.07 65.90 65.50 54.67 68.46 57.50 52.36 IT IN-0.5 89.60 43.20 87.50 49.20 60.00 38.99 54.28 51.76 43.45 IT IN-1.0 93.90 91.80 39.60 55.70 23.30 42.50 45.00 IT IN->1.0 46.50 14.30 45.00 29.30
11/16/2006 11/22/2006 1/5/2007 1/7/2007 3/15/2007* 3/23/2007 4/4/2007 4/12/2007 4/14/2007 4/25/2007 5/16/2007 Mean 98.90 30.60 24.30 26.70 626* 68.46 25.71 50.00 43.00 136.00 65.70 55.11 27.30 44.00 4.60 2.60 143* 54.28 22.86 32.14 19.60 45.00 56.40 41.30 14.00 48.80 8.90 9.50 90.3* 1.00 1.00 19.00 40.00 49.60 34.86 27.30 21.70 13.30 34.90 29.04
158
Total Nitrogen EMC = 1.92 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006 11/7/2006 11/16/2006 4/4/2007 4/12/2007 5/16/2007 Mean IT IN-0.25 1.50 0.40 8.90 10.80 0.00 0.30 2.60 0.25 4.20 3.30 3.23 IT IN-0.5 1.90 0.70 9.60 0.40 0.40 3.40 1.60 2.75 0.50 1.70 2.30 IT IN-1.0 0.01 0.00 13.10 0.80 3.80 0.60 1.70 1.50 2.69 IT IN->1.0 0.00 2.90 0.20 0.00 0.78
Total Phosphorus EMC = 0.18 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006 11/7/2006 4/4/2007 4/12/2007 5/16/2007 Mean IT IN-0.25 0.30 0.53 0.09 0.71 0.00 0.17 0.03 0.07 0.00 0.20 IT IN-0.5 3.07 0.25 0.13 0.12 0.45 0.26 0.09 0.07 0.16 0.19 IT IN-1.0 0.33 0.06 0.83 0.10 0.08 0.05 0.15 0.21 IT IN->1.0 0.00 0.14 0.12 0.13
Nitrite EMC = 0.04 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 11/7/2006 4/4/2006 4/12/2006 5/16/2006 Mean IT IN-0.25 0.03 0.03 0.07 0.01 0.00 0.00 0.02 IT IN-0.5 0.02 0.02 0.19 0.08 0.11 0.02 0.07 IT IN-1.0 0.03 0.00 0.00 0.10 0.00 0.03 IT IN->1.0 0.05 0.05
Nitrate EMC = 0.13 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 11/7/2006 4/4/2006 4/12/2006 5/16/2006 Mean IT IN-0.25 0.05 0.32 0.18 0.21 0.44 0.66 0.31 IT IN-0.5 0.05 0.15 0.00 0.06 0.07 0.54 0.14 IT IN-1.0 0.05 0.00 0.10 0.00 0.51 0.13 IT IN->1.0 0.00 0.00
159
Phosphate EMC = 0.01 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 11/7/2006 4/4/2006 4/12/2006 5/16/2006 Mean IT IN-0.25 0.04 0.00 0.08 0.11 0.00 0.00 0.04 IT IN-0.5 0.04 0.00 0.06 0.00 0.00 0.05 0.03 IT IN-1.0 0.04 0.00 0.00 0.00 0.00 0.01 IT IN->1.0 0.09*
Chloride EMC = 1.33 mg/L All Below Values in mg/L 9/14/2006 10/5/2006 11/7/2006 4/4/2006 4/12/2006 5/16/2006 Mean IT IN-0.25 1.63 1.40 0.40 2.67 4.82 4.53 2.57 IT IN-0.5 4.41 2.37 0.59 2.09 1.37 3.05 2.31 IT IN-1.0 0.96 0.00 2.22 1.05 2.31 1.31 IT IN->1.0 0.21 0.21
Dissolved Copper EMC = 4.59 ug/L All Below Values in ug/L Sample 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006* 11/7/2006 4/4/2007 4/11/2007 5/16/2007 Mean IT-IN 0.25 6.57 9.31 9.64 31.03 352* 7.80 5.30 6.60 8.80 10.63 IT-IN 0.5 2.67 9.31 3.03 5.38 9.5* 3.80 5.20 3.10 7.00 4.94 IT-IN 1.0 5.55 4.25 2.42 3.60 4.40 4.40 6.20 4.40 IT-IN >1.0 4.37 2.42 1.65 2.81
Dissolved Lead EMC = 0.15 ug/L All Below Values in ug/L Sample 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006* 11/7/2006 4/4/2007 4/11/2007 5/16/2007 Mean IT-IN 0.25 0.00 0.00 6.57 0.00 256.05* 0.00 0.00 0.00 0.00 0.82 IT-IN 0.5 0.00 5.14 0.00 5.04 0.00* 0.00 0.00 0.00 0.00 1.27 IT-IN 1.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 IT-IN >1.0 0.00 0.00 0.00 0.00
160
Dissolved Cadmium EMC = 0.26 ug/L All Below Values in ug/L Sample 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006 11/7/2006 4/4/2007 4/11/2007 5/16/2007 Mean IT-IN 0.25 0.33 0.44 0.72 0.26 1.15 0.46 0.00 1.56 0.00 0.55 IT-IN 0.5 0.22 0.51 0.00 0.41 0.42 0.23 0.00 0.82 0.00 0.29 IT-IN 1.0 0.00 0.00 0.15 0.00 0.00 0.83 0.00 0.14 IT-IN >1.0 0.24 0.00 0.27 0.17
Dissolved Chromium EMC = 2.73 ug/L All Below Values in ug/L Sample 9/14/2006 10/5/2006 10/11/2006 10/17/2006 11/2/2006* 11/7/2006 4/4/2007 4/11/2007 5/16/2007 Mean IT-IN 0.25 1.22 10.66 4.76 0.00 63.46* 0.00 0.00 2.66 3.44 2.84 IT-IN 0.5 0.92 23.66 3.24 0.00 3.11* 0.00 0.00 0.00 3.31 3.89 IT-IN 1.0 1.83 2.59 3.23 0.00 0.00 0.00 2.48 1.45 IT-IN >1.0 0.91 0.00 0.00 0.30
161 Appendix D: Detailed F-Test Results for Pollutants Exhibiting Downward Mean Concentrations
Total Suspended Solids IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 IT-IN 0.25 IT-IN >1.0 Mean 24.32315789 9.820526316 Mean 24.32315789 5.7625 Mean 24.32315789 4.2775 Variance 525.8518006 81.77470526 Variance 525.8518006 14.35327333 Variance 525.8518006 10.64722143 Observations 19 19 Observations 19 16 Observations 19 8 df 18 18 df 18 15 df 18 7 F 6.4304946 F 36.63636777 F 49.38864136 P(F<=f) one-tail 0.000126032 P(F<=f) one-tail 3.14198E-09 P(F<=f) one-tail 1.17133E-05 F Critical one-tail 2.595592232 F Critical one-tail 2.791908293 F Critical one-tail 4.500773205 UNEQUAL F> F Critical UNEQUAL UNEQUAL
IT-IN 0.5 IT-IN 1.0 IT-IN 0.5 IT-IN >1.0 Mean 9.820526316 5.7625 Mean 9.820526316 3.888571429 Variance 81.77470526 14.35327333 Variance 81.77470526 11.00994762 Observations 19 16 Observations 19 7 df 18 15 df 18 6 F 5.697286143 F 7.427347349 P(F<=f) one-tail 0.00069869 P(F<=f) one-tail 0.010082487 F Critical one-tail 2.791908293 F Critical one-tail 5.202134547 UNEQUAL UNEQUAL
IT-IN 1.0 IT-IN >1.0 Mean 5.546666667 3.888571429 Variance 14.57992381 11.00994762 Observations 15 7 df 14 6 F 1.324250061 P(F<=f) one-tail 0.383918968 F Critical one-tail 5.296811496 EQUAL
162 Total Dissolved Solids IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 IT-IN 0.25 IT-IN >1.0 Mean 55.10894737 41.30394737 Mean 55.10894737 34.8588235 Mean 55.10894737 29.0375 Variance 1059.827421 566.4618127 Variance 1059.827421 805.185074 Variance 1059.827421 159.4141071 Observations 19 19 Observations 19 17 Observations 19 8 df 18 18 df 18 16 df 18 7 F 1.870960049 F 1.316253189 F 6.648266205 P(F<=f) one-tail 0.096757846 P(F<=f) one-tail 0.29277098 P(F<=f) one-tail 0.008169939 F Critical one-tail 2.595592232 F Critical one-tail 2.717003314 F Critical one-tail 4.500773205 EQUAL EQUAL UNEQUAL
IT-IN 0.5 IT-IN 1.0 IT-IN 0.5 IT-IN >1.0 Mean 41.30394737 34.85882353 Mean 41.30394737 29.0375 Variance 566.4618127 805.1850735 Variance 566.4618127 159.414107 Observations 19 17 Observations 19 8 df 18 16 df 18 7 F 0.703517528 F 3.553398271 P(F<=f) one-tail 0.234722271 P(F<=f) one-tail 0.046934553 F Critical one-tail 0.378737474 F Critical one-tail 4.500773205 UNEQUAL EQUAL
IT-IN 1.0 IT-IN >1.0 Mean 41.30394737 29.0375 Variance 566.4618127 159.4141071 Observations 19 8 df 18 7 F 3.553398271 P(F<=f) one-tail 0.046934553 F Critical one-tail 4.500773205 EQUAL
163 Total Nitrogen IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 IT-IN 0.25 IT-IN >1.0 Mean 3.225 2.295 Mean 3.225 2.68875 Mean 3.225 0.775 Variance 14.41069444 7.636916667 Variance 14.41069444 19.19358393 Variance 14.41069444 2.015833333 Observations 10 10 Observations 10 8 Observations 10 4 df 9 9 df 9 7 df 9 3 F 1.886978093 F 0.7508079 F 7.148752928 P(F<=f) one-tail 0.179038593 P(F<=f) one-tail 0.336678347 P(F<=f) one-tail 0.066416226 F Critical one-tail 3.178893105 F Critical one-tail 0.303697901 F Critical one-tail 8.812299555 EQUAL UNEQUAL EQUAL
IT-IN 0.5 IT-IN 1.0 IT-IN 0.5 IT-IN >1.0 Mean 2.295 2.68875 Mean 2.295 0.775 Variance 7.636916667 19.19358393 Variance 7.636916667 2.015833333 Observations 10 8 Observations 10 4 df 9 7 df 9 3 F 0.397889039 F 3.788466308 P(F<=f) one-tail 0.099255396 P(F<=f) one-tail 0.150289018 F Critical one-tail 0.303697901 F Critical one-tail 8.812299555 UNEQUAL EQUAL
IT-IN 1.0 IT-IN >1.0 Mean 2.68875 0.775 Variance 19.19358393 2.015833333 Observations 8 4 df 7 3 F 9.521414103 P(F<=f) one-tail 0.045496814 F Critical one-tail 8.886742956 UNEQUAL
164 Nitrate IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 Mean 0.309966667 0.144216667 Mean 0.309966667 0.13048 Variance 0.046554519 0.039689634 Variance 0.046554519 0.045553252 Observations 6 6 Observations 6 5 df 5 5 df 5 4 F 1.172964181 F 1.021980136 P(F<=f) one-tail 0.432648731 P(F<=f) one-tail 0.505698612 F Critical one-tail 5.050329058 F Critical one-tail 6.256056502 EQUAL EQUAL
IT-IN 0.5 IT-IN 1.0 Mean 0.144216667 0.13048 Variance 0.039689634 0.045553252 Observations 6 5 df 5 4 F 0.871279918 P(F<=f) one-tail 0.431061839 F Critical one-tail 0.192597783 UNEQUAL
165 Chloride IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 Mean 2.574016667 2.31375 Mean 2.574016667 1.3081 Variance 3.175043706 1.770934831 Variance 3.175043706 0.93231083 Observations 6 6 Observations 6 5 df 5 5 df 5 4 F 1.792863097 F 3.40556347 P(F<=f) one-tail 0.268619338 P(F<=f) one-tail 0.129299091 F Critical one-tail 5.050329058 F Critical one-tail 6.256056502 EQUAL EQUAL
IT-IN 0.5 IT-IN 1.0 Mean 2.31375 1.3081 Variance 1.770934831 0.93231083 Observations 6 5 df 5 4 F 1.899511165 P(F<=f) one-tail 0.276964927 F Critical one-tail 6.256056502 EQUAL
166 Dissolved Copper IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 IT-IN 0.25 IT-IN >1.0 Mean 10.63025 4.936375 Mean 10.63025 4.403571429 Mean 10.63025 2.813666667 Variance 70.18017621 5.297710839 Variance 70.18017621 1.523361286 Variance 70.18017621 1.965240333 Observations 8 8 Observations 8 7 Observations 8 3 df 7 7 df 7 6 df 7 2 F 13.2472644 F 46.06929221 F 35.71073472 P(F<=f) one-tail 0.001483345 P(F<=f) one-tail 8.50027E-05 P(F<=f) one-tail 0.027505983 F Critical one-tail 3.78704354 F Critical one-tail 4.206658488 F Critical one-tail 19.35321754 UNEQUAL UNEQUAL UNEQUAL
IT-IN 0.5 IT-IN 1.0 IT-IN 0.5 IT-IN >1.0 Mean 4.936375 4.403571429 Mean 4.936375 2.813666667 Variance 5.297710839 1.523361286 Variance 5.297710839 1.965240333 Observations 8 7 Observations 8 3 df 7 6 df 7 2 F 3.47764571 F 2.695706347 P(F<=f) one-tail 0.075009339 P(F<=f) one-tail 0.297133815 F Critical one-tail 4.206658488 F Critical one-tail 19.35321754 EQUAL EQUAL
IT-IN 1.0 IT-IN >1.0 Mean 4.403571429 2.813666667 Variance 1.523361286 1.965240333 Observations 7 3 df 6 2 F 0.775152667 P(F<=f) one-tail 0.341956686 F Critical one-tail 0.194429484 UNEQUAL
167 Dissolved Cadmium IT-IN 0.25 IT-IN 0.5 IT-IN 0.25 IT-IN 1.0 IT-IN 0.25 IT-IN >1.0 Mean 0.545111111 0.288777778 Mean 0.545111111 0.140571429 Mean 0.545111111 0.169666667 Variance 0.270656111 0.077192194 Variance 0.270656111 0.095941286 Variance 0.270656111 0.021772333 Observations 9 9 Observations 9 7 Observations 9 3 df 8 8 df 8 6 df 8 2 F 3.506262687 F 2.821059871 F 12.43119453 P(F<=f) one-tail 0.047503088 P(F<=f) one-tail 0.11157694 P(F<=f) one-tail 0.076555516 F Critical one-tail 3.438101233 F Critical one-tail 4.146804162 F Critical one-tail 19.3709929 UNEQUAL EQUAL EQUAL
IT-IN 0.5 IT-IN 1.0 IT-IN 0.5 IT-IN >1.0 Mean 0.288777778 0.140571429 Mean 0.288777778 0.169666667 Variance 0.077192194 0.095941286 Variance 0.077192194 0.021772333 Observations 9 7 Observations 9 3 df 8 6 df 8 2 F 0.804577444 F 3.545425897 P(F<=f) one-tail 0.377213359 P(F<=f) one-tail 0.238567194 F Critical one-tail 0.279284337 F Critical one-tail 19.3709929 EQUAL EQUAL
IT-IN 1.0 IT-IN >1.0 Mean 0.140571429 0.169666667 Variance 0.095941286 0.021772333 Observations 7 3 df 6 2 F 4.406568843 P(F<=f) one-tail 0.196485826 F Critical one-tail 19.32953402 EQUAL
168 Appendix E: T-Test Results for Downward Decreasing Pollutants
Total Suspended Solids IT-IN 0.25 IT-IN 0.5 Mean 24.32315789 9.820526316 Variance 525.8518006 81.77470526 Observations 19 19 df 23 t Stat 2.564515095 P(T<=t) one-tail 0.008665012 t Critical one-tail 1.713871517 P(T<=t) two-tail 0.017330024 t Critical two-tail 2.068657599 STATISTICALLY DIFFERENT AT 95% t Stat = t Critical at 98.2% STATISTICALLY DIFFERENT AT 98% CERTAINTY Total Suspended Solids IT-IN 0.25 IT-IN 1.0 Mean 24.32315789 5.7625 Variance 525.8518006 14.35327333 Observations 19 16 df 19 t Stat 3.472255868 P(T<=t) one-tail 0.00127547 t Critical one-tail 1.729132792 P(T<=t) two-tail 0.00255094 t Critical two-tail 2.09302405 STATISTICALLY DIFFERENT AT 95% t Stat = t Critical at 99.76% STATISTICALLY DIFFERENT AT 99% CERTAINTY Total Suspended Solids IT-IN 0.25 IT-IN >1.0 Mean 24.32315789 3.888571429 Variance 525.8518006 11.00994762 Observations 19 7 df 20 t Stat 3.778404156 P(T<=t) one-tail 0.000590263 t Critical one-tail 1.724718218 P(T<=t) two-tail 0.001180526 t Critical two-tail 2.085963441 STATISTICALLY DIFFERENT AT 95% t Stat = t Critical at 99% STATISTICALLY DIFFERENT AT 99% CERTAINTY
169 Total Suspended Solids IT-IN 0.5 IT-IN 1.0 Mean 9.820526316 5.7625 Variance 81.77470526 14.35327333 Observations 19 16 df 25 t Stat 1.779389218 P(T<=t) one-tail 0.043668901 t Critical one-tail 1.708140745 P(T<=t) two-tail 0.087337803 t Critical two-tail 2.059538536 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 91.01% STATISTICALLY DIFFERENT AT 91% CERTAINTY Total Suspended Solids IT-IN 0.5 IT-IN >1.0 Mean 9.820526316 3.888571429 Variance 81.77470526 11.00994762 Observations 19 7 df 24 t Stat 2.446966744 P(T<=t) one-tail 0.011052881 t Critical one-tail 1.710882067 P(T<=t) two-tail 0.022105763 t Critical two-tail 2.063898547 STATISTICALLY DIFFERENT AT 95% t Stat = t Critical at 97.87% STATISTICALLY DIFFERENT AT 97% CERTAINTY Total Suspended Solids IT-IN 1.0 IT-IN >1.0 Mean 5.7625 3.888571429 Variance 14.35327333 11.00994762 Observations 16 7 Pooled Variance 13.39803741 df 21 t Stat 1.12973747 P(T<=t) one-tail 0.135663217 t Critical one-tail 1.720742871 P(T<=t) two-tail 0.271326434 t Critical two-tail 2.079613837 STATICALLY SIMILAR AT 95% AND ALL CERTAINTIES
170 Total Dissolved Solids IT-IN 0.25 IT-IN 0.5 Mean 55.10894737 41.30394737 Variance 1059.827421 566.4618127 Observations 19 19 Pooled Variance 813.1446169 df 36 t Stat 1.492156304 P(T<=t) one-tail 0.072185222 t Critical one-tail 1.688297694 P(T<=t) two-tail 0.144370443 t Critical two-tail 2.028093987 STATISTICALLY SIMILAR AT 95% t Stat = t Critical 84.8% STATISTICALLY DIFFERENT AT 84% CERTAINTY Total Dissolved Solids IT-IN 0.25 IT-IN 1.0 Mean 55.10894737 34.85882353 Variance 1059.827421 805.1850735 Observations 19 17 Pooled Variance 939.9957281 df 34 t Stat 1.978402965 P(T<=t) one-tail 0.028015642 t Critical one-tail 1.690924198 P(T<=t) two-tail 0.056031284 t Critical two-tail 2.032244498 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 94.20% STATISTICALLY DIFFERENT AT 94% CERTAINTY Total Dissolved Solids IT-IN 0.25 IT-IN >1.0 Mean 55.10894737 29.0375 Variance 1059.827421 159.4141071 Observations 19 8 df 25 t Stat 2.996378566 P(T<=t) one-tail 0.003045468 t Critical one-tail 1.708140745 P(T<=t) two-tail 0.006090936 t Critical two-tail 2.059538536 STATISTICALLY DIFFERENT AT 95% CERTAINTY t Stat = t Critical at 99.2% STATISTICALLY DIFFERENT AT 99% CERTAINTY
171
Total Dissolved Solids IT-IN 0.5 IT-IN 1.0 Mean 41.30394737 34.85882353 Variance 566.4618127 805.1850735 Observations 19 17 df 31 t Stat 0.733644407 P(T<=t) one-tail 0.234338995 t Critical one-tail 1.695518742 P(T<=t) two-tail 0.46867799 t Critical two-tail 2.039513438 STATISTICALLY SIMILAR AT 95% AND ALL TABLE VALUES Total Dissolved Solids IT-IN 0.5 IT-IN >1.0 Mean 41.30394737 29.0375 Variance 566.4618127 159.4141071 Observations 19 8 Pooled Variance 452.4884552 df 25 t Stat 1.36821693 P(T<=t) one-tail 0.091708224 t Critical one-tail 1.708140745 P(T<=t) two-tail 0.183416448 t Critical two-tail 2.059538536 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 81.3% STATISTICALLY DIFFERENT AT 81% CERTAINTY Total Dissolved Solids IT-IN 1.0 IT-IN >1.0 Mean 34.85882353 29.0375 Variance 805.1850735 159.4141071 Observations 17 8 Pooled Variance 608.6460838 df 23 t Stat 0.550349746 P(T<=t) one-tail 0.293692675 t Critical one-tail 1.713871517 P(T<=t) two-tail 0.58738535 t Critical two-tail 2.068657599 STATISTICALLY SIMILAR AT 95% AND ALL TABLE VALUES
172
Total Nitrogen IT-IN 0.25 IT-IN >1.0 Mean 3.225 2.295 Variance 14.41069444 7.636916667 Observations 10 10 Pooled Variance 11.02380556 df 18 t Stat 0.626328507 P(T<=t) one-tail 0.269480983 t Critical one-tail 1.734063592 P(T<=t) two-tail 0.538961966 t Critical two-tail 2.100922037 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Total Nitrogen IT-IN 0.25 IT-IN 1.0 Mean 3.225 2.68875 Variance 14.41069444 19.19358393 Observations 10 8 df 14 t Stat 0.273644403 P(T<=t) one-tail 0.394176814 t Critical one-tail 1.761310115 P(T<=t) two-tail 0.788353628 t Critical two-tail 2.144786681 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Total Nitrogen IT-IN 0.25 IT-IN >1.0 Mean 3.225 0.775 Variance 14.41069444 2.015833333 Observations 10 4 Pooled Variance 11.31197917 df 12 t Stat 1.231296837 P(T<=t) one-tail 0.120899078 t Critical one-tail 1.782287548 P(T<=t) two-tail 0.241798156 t Critical two-tail 2.178812827 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES
173
Total Nitrogen IT-IN 0.5 IT-IN 1.0 Mean 2.295 2.68875 Variance 7.636916667 19.19358393 Observations 10 8 df 11 t Stat -0.221400474 P(T<=t) one-tail 0.414418473 t Critical one-tail 1.795884814 P(T<=t) two-tail 0.828836947 t Critical two-tail 2.200985159 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Total Nitrogen IT-IN 0.5 IT-IN >1.0 Mean 2.295 0.775 Variance 7.636916667 2.015833333 Observations 10 4 Pooled Variance 6.231645833 df 12 t Stat 1.029219923 P(T<=t) one-tail 0.161837754 t Critical one-tail 1.782287548 P(T<=t) two-tail 0.323675508 t Critical two-tail 2.178812827 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Total Nitrogen IT-IN 1.0 IT-IN >1.0 Mean 2.68875 0.775 Variance 19.19358393 2.015833333 Observations 8 4 df 9 t Stat 1.123181633 P(T<=t) one-tail 0.145213594 t Critical one-tail 1.833112923 P(T<=t) two-tail 0.290427189 t Critical two-tail 2.262157158 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES
174
Nitrate IT-IN 0.25 IT-IN 0.5 Mean 0.309966667 0.144216667 Variance 0.046554519 0.039689634 Observations 6 6 Pooled Variance 0.043122076 df 10 t Stat 1.382497432 P(T<=t) one-tail 0.09845627 t Critical one-tail 1.812461102 P(T<=t) two-tail 0.19691254 t Critical two-tail 2.228138842 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 80.2% STATISTICALLY DIFFERENT AT 80% Nitrate IT-IN 0.25 IT-IN 1.0 Mean 0.309966667 0.13048 Variance 0.046554519 0.045553252 Observations 6 5 Pooled Variance 0.046109511 df 9 t Stat 1.383881343 P(T<=t) one-tail 0.100391596 t Critical one-tail 1.833112923 P(T<=t) two-tail 0.200783193 t Critical two-tail 2.262157158 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 80.0% STATISTICALLY DIFFERENT AT 80% Nitrate IT-IN 0.5 IT-IN 1.0 Mean 0.144216667 0.13048 Variance 0.039689634 0.045553252 Observations 6 5 df 8 t Stat 0.109541301 P(T<=t) one-tail 0.457735554 t Critical one-tail 1.859548033 P(T<=t) two-tail 0.915471108 t Critical two-tail 2.306004133 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES
175 Chloride IT-IN 0.25 IT-IN 0.5 Mean 2.574016667 2.31375 Variance 3.175043706 1.770934831 Observations 6 6 Pooled Variance 2.472989268 df 10 t Stat 0.286660637 P(T<=t) one-tail 0.390111819 t Critical one-tail 1.812461102 P(T<=t) two-tail 0.780223639 t Critical two-tail 2.228138842 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Chloride IT-IN 0.25 IT-IN 1.0 Mean 2.574016667 1.3081 Variance 3.175043706 0.93231083 Observations 6 5 Pooled Variance 2.178273539 df 9 t Stat 1.416488962 P(T<=t) one-tail 0.095150306 t Critical one-tail 1.833112923 P(T<=t) two-tail 0.190300612 t Critical two-tail 2.262157158 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 80.74% STATISTICALLY DIFFERENT AT 80% CERTAINTY Chloride IT-IN 0.5 IT-IN 1.0 Mean 2.31375 1.3081 Variance 1.770934831 0.93231083 Observations 6 5 Pooled Variance 1.398213053 df 9 t Stat 1.404508807 P(T<=t) one-tail 0.096862912 t Critical one-tail 1.833112923 P(T<=t) two-tail 0.193725824 t Critical two-tail 2.262157158 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 80.47% STATISTICALLY DIFFERENT AT 80% CERTAINTY
176
Dissolved Copper IT-IN 0.25 IT-IN 0.25 Mean 10.63025 4.936375 Variance 70.18017621 5.297710839 Observations 8 8 df 8 t Stat 1.853715388 P(T<=t) one-tail 0.050449799 t Critical one-tail 1.859548033 P(T<=t) two-tail 0.100899597 t Critical two-tail 2.306004133 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 89.86% STATISTICALLY DIFFERENT AT 89% CERTAINTY Dissolved Copper IT-IN 0.25 IT-IN 1.0 Mean 10.63025 4.403571429 Variance 70.18017621 1.523361286 Observations 8 7 df 7 t Stat 2.076696819 P(T<=t) one-tail 0.038228547 t Critical one-tail 1.894578604 P(T<=t) two-tail 0.076457094 t Critical two-tail 2.364624251 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 92.21% STATISTICALLY DIFFERENT AT 92% CERTAINTY Dissolved Copper IT-IN 0.25 IT-IN >1.0 Mean 10.63025 2.813666667 Variance 70.18017621 1.965240333 Observations 8 3 df 8 t Stat 2.545753439 P(T<=t) one-tail 0.017200647 t Critical one-tail 1.859548033 P(T<=t) two-tail 0.034401293 t Critical two-tail 2.306004133 STATISTICALLY DIFFERENT AT 95% t Stat = t Critical at 96.21% STATISTICALLY DIFFERENT AT 96% CERTAINTY
177 Dissolved Copper IT-IN 0.5 IT-IN 1.0 Mean 4.936375 4.403571429 Variance 5.297710839 1.523361286 Observations 8 7 Pooled Variance 3.555703353 df 13 t Stat 0.54594985 P(T<=t) one-tail 0.29717291 t Critical one-tail 1.770933383 P(T<=t) two-tail 0.59434582 t Critical two-tail 2.160368652 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Dissolved Copper IT-IN 0.5 IT-IN >1.0 Mean 4.936375 2.813666667 Variance 5.297710839 1.965240333 Observations 8 3 Pooled Variance 4.557161838 df 9 t Stat 1.468765211 P(T<=t) one-tail 0.087980374 t Critical one-tail 1.833112923 P(T<=t) two-tail 0.175960748 t Critical two-tail 2.262157158 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 81.90% STATISTICALLY DIFFERENT AT 81% CERTAINTY Dissolved Copper IT-IN 1.0 IT-IN >1.0 Mean 4.403571429 2.813666667 Variance 1.523361286 1.965240333 Observations 7 3 df 3 t Stat 1.70191489 P(T<=t) one-tail 0.093662933 t Critical one-tail 2.353363435 P(T<=t) two-tail 0.187325866 t Critical two-tail 3.182446305 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 80.89% STATISTICALLY DIFFERENT AT 80% CERTAINTY
178 Dissolved Cadmium IT-IN 0.25 IT-IN 0.5 Mean 0.545111111 0.288777778 Variance 0.270656111 0.077192194 Observations 9 9 df 12 t Stat 1.303861296 P(T<=t) one-tail 0.108369401 t Critical one-tail 1.782287548 P(T<=t) two-tail 0.216738803 t Critical two-tail 2.178812827 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Dissolved Cadmium IT-IN 0.25 IT-IN 1.0 Mean 0.545111111 0.140571429 Variance 0.270656111 0.095941286 Observations 9 7 Pooled Variance 0.195778329 df 14 t Stat 1.814216466 P(T<=t) one-tail 0.045563908 t Critical one-tail 1.761310115 P(T<=t) two-tail 0.091127817 t Critical two-tail 2.144786681 STATISTICALLY SIMILAR AT 95% t Stat = t Critical at 90.69% STATISTICALLY DIFFERENT AT 90% CERTAINTY Dissolved Cadmium IT-IN 0.25 IT-IN >1.0 Mean 0.545111111 0.169666667 Variance 0.270656111 0.021772333 Observations 9 3 Pooled Variance 0.220879356 df 10 t Stat 1.198282945 P(T<=t) one-tail 0.129217261 t Critical one-tail 1.812461102 P(T<=t) two-tail 0.258434523 t Critical two-tail 2.228138842 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES
179
Dissolved Cadmium IT-IN 0.5 IT-IN >1.0 Mean 0.288777778 0.140571429 Variance 0.077192194 0.095941286 Observations 9 7 Pooled Variance 0.085227519 df 14 t Stat 1.007365722 P(T<=t) one-tail 0.165426814 t Critical one-tail 1.761310115 P(T<=t) two-tail 0.330853629 t Critical two-tail 2.144786681 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Dissolved Cadmium IT-IN 0.5 IT-IN >1.0 Mean 0.288777778 0.169666667 Variance 0.077192194 0.021772333 Observations 9 3 Pooled Variance 0.066108222 df 10 t Stat 0.694889574 P(T<=t) one-tail 0.251475139 t Critical one-tail 1.812461102 P(T<=t) two-tail 0.502950278 t Critical two-tail 2.228138842 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES Dissolved Cadmium IT-IN 1.0 IT-IN >1.0 Mean 0.140571429 0.169666667 Variance 0.095941286 0.021772333 Observations 7 3 Pooled Variance 0.077399048 df 8 t Stat -0.151552727 P(T<=t) one-tail 0.441645905 t Critical one-tail 1.859548033 P(T<=t) two-tail 0.88329181 t Critical two-tail 2.306004133 STATISTICALLY SIMILAR AT 95% AND ALL CERTAINTIES
180 Appendix F: Calculated Cumulative Dimensionless Rainfall and Pollutant Data Used to Compile Mass/Rainfall Plots
Storm 9/14/2006 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu Cum Cd Cum Cr 0 0 0 0 0 0 0 0 0 0 0 0 0.1754386 0.4547652 0.18319631 0.4361451 0.05089059 0.178925 0.1688543 0.16623534 0.1688543 0.2285988 0.3236771 0.1615888 0.3450292 0.75481647 0.34302484 0.9885955 0.83206107 0.2982084 0.3377086 0.61598864 0.3377086 0.3214997 0.5414415 0.2827804 0.5847953 0.87983783 0.67802257 0.9944108 1 0.6560584 0.6754171 0.8117996 0.6754171 0.7077169 0.5414415 0.7675467 1 1 1 1 1 1 1 1 1 1 1 1 b = 0.3909 b = 0.9666 b = 0.3280 b = 1.2089 b = 1.0133 b = 1.0027 b = 0.8429 b = 1.0027 b = 0.8904 b = 0.6619 b = 1.4910 Storm 10/5/2006 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu Cum Cd Cum Cr 0 0 0 0 0 0 0 0 0 0 0 0 0.4035088 0.94707977 0.34200906 0.5452823 0.81647703 0.7589091 0.8174146 0.55350068 0.6772676 0.6772676 0.6430732 0.485982 1 1 1 1 1 1 1 1 1 1 1 1 b = 0.0599 b = 1.1822 b = 0.6682 b = 0.2234 b = 0.3040 b = .2221 b = 0.6517 b = 0.4294 b = 0.4294 b = 0.4865 b = 0.7951 Storm 10/11/2006 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum Cu Cum Cr 0 0 0 0 0 0 0 0.3186813 0.26559017 0.22521536 0.4810811 0.148432 0.4314363 0.3427327 0.4945055 0.49972887 0.45417254 1 0.57723555 0.5671585 0.5759032 1 1 1 1 1 1 1 b = 1.1114 b = 1.2533 b = 0.4639 b = 1.4240 b = 0.7544 b = 0.8844 Storm 10/17/2006 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum Cu Cum Cd Cum Cr 0 0 0 0 0 0 0 0 0.1811024 0.4278875 0.23629497 0.2530731 0.25868266 0.6796257 0.2656903 0.0001549 0.3070866 0.56699161 0.41270943 0.2624461 0.30240367 0.797471 0.6842295 0.0003098 0.5590551 0.67518369 0.69669367 0.8763826 0.90721101 0.9035318 0.9981339 0.99971822 1 1 1 1 1 1 1 1 b = 0.5049 b = 0.7997 b = 0.8894 b = 0.8124 b = 0.2120 b = 0.5820 b = 5.2732
181
Storm 10/19/2006 Cum Rain Cum TSS Cum TDS 0 0 0 0.3157895 0.39791753 0.3153477 0.5394737 0.65048464 0.60421582 1 1 1 b = 0.7766 b = 0.9600 Storm 10/27/2006 Cum Rain Cum TSS Cum TDS 0 0 0 0.0784983 0.1661929 0.18189009 0.1569966 0.21768929 0.31157868 0.2696246 0.38856368 0.46661944 1 1 1 b = 0.7425 b = 0.6449 Storm 11/2/2006 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum Cu Cum Cd Cum Cr 0 0 0 0 0 0 0 0 0.5365854 0.61970404 0.59110663 0.0278567 0.09639747 0.9769954 0.7589559 0.95894862 1 1 1 1 1 1 1 1 b = 0.7687 b = 0.8446 b = 5.7518 b = 3.7577 b = 0.0374 b = 0.4430 b = 0.0673 Storm 11/7/2206 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu Cum Cd 0 0 0 0 0 0 0 0 0 0 0 0.0817844 0.16254118 0.08248422 0.039815 0.08625436 0.0855043 0.9275618 0.11985802 0.0694865 0.2094658 0.1245002 0.1524164 0.22755765 0.15673436 0.4910511 0.21817279 0.309953 0.9326723 0.29828645 0.1208574 0.3115133 0.1859348 0.267658 0.4551153 0.27866754 0.7033975 0.31964851 0.309953 0.9428934 0.29828645 0.1208574 0.5048663 0.1859348 1 1 1 1 1 1 1 1 1 1 1 b = 0.7251 b = 0.9890 b = 0.8552 b = 0.9097 b = 0.8579 b = 0.0344 b = 0.7953 b = 1.1639 b = 0.6071 b = 0.9181
182
Storm 11/12/2006 Cum Rain Cum TSS Cum TDS 0 0 0 0.2166667 0.51031462 0.21297438 0.3916667 0.67598197 0.38970733 0.775 0.87345154 0.75578247 1 1 1 b = 0.4358 b = 1.0114 Storm 11/16/2006 Cum Rain Cum TSS Cum TDS Cum TN 0 0 0 0 0.1136364 0.39980239 0.34302466 0.220339 0.4393939 0.64240092 0.43771195 0.3559322 0.7727273 0.80930872 0.53482713 1 1 1 1 1 b = 0.4408 b = 0.5789 b = 0.7584 Storm 11/22/2006 Cum Rain Cum TSS Cum TDS 0 0 0 0.3904762 0.491612 0.25374869 0.6095238 0.81467131 0.61861608 1 1 1 b = 0.6811 b = 1.3524 Storm 1/5/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.4385965 0.62974437 0.68721009 0.8070175 0.97460438 0.81729925 1 1 1 b = 0.5331 b =0.4859
183 Storm 1/7/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.1846154 0.4983316 0.304602 0.3461538 0.69504145 0.33426362 0.6384615 0.90486528 0.5510216 1 1 1 b = 0.3845 b = 0.8223 Storm 3/15/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.2531646 0.73611358 0.71981224 0.5822785 0.88144579 0.88424219 1 1 1 b = 0.2244 b = 0.2377 Storm 3/23/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.4444444 0.47807996 0.44831364 1 1 1 b = 0.9100 b = .9893 Storm 4/4/2007 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu 0 0 0 0 0 0 0 0 0 0 0.2674419 0.21644388 0.51135462 0.0621128 0.25233645 0.1460101 0.5627819 0.3121986 0.9974516 0.2941802 0.5348837 0.31493469 0.96602465 0.7453542 0.58878505 0.9827988 0.6784432 0.55657877 0.9983926 0.5828098 1 1 1 1 1 1 1 1 1 1 b = 1.2865 b = 0.4253 b = 1.8062 b = 1.0078 b = 1.196 b = 0.4697 b = 0.8926 b = 0.0021 b = 0.9158
184
Storm 4/12/2007 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu Cum Cd Cum Cr 0 0 0 0 0 0 0 0 0 0 0 0 0.2717391 0.67813204 0.59384466 0.5123779 0.28823766 0.0032254 0.8354457 0.57683497 0.2464813 0.3519765 0.3814465 0.99885 0.5543478 0.78341733 0.97556801 0.5733753 0.57647532 0.3564068 0.9613451 0.74074338 0.4929626 0.5172988 0.5814733 0.99923 1 1 1 1 1 1 1 1 1 1 1 1 b = 0.3178 b = 0.3391 b = 0.5863 b = 0.9512 b = 3.5602 b = 0.1259 b = 1.0960 b = 0.4370 b = 0.8552 b = 0.7702 b =0.001 Storm 4/15/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.0461538 0.06804456 0.10701628 0.0803419 0.14132331 0.1559617 0.1623932 0.23867937 0.25053423 1 1 1 b = 0.8265 b = 0.763 Storm 4/25/2007 Cum Rain Cum TSS Cum TDS 0 0 0 0.1413043 0.07743784 0.20236499 0.1956522 0.22767815 0.26932399 0.3586957 0.34110822 0.38836222 1 1 1 b = 1.1301 b = 0.8269 Storm 5/16/2007 Cum Rain Cum TSS Cum TDS Cum TN Cum TP Cum NO2 Cum NO3 Cum Cl Cum PO4 Cum Cu Cum Cr 0 0 0 0 0 0 0 0 0 0 0 0.34375 0.49213821 0.25366864 0.3730347 0.01802984 0.0500896 0.2616044 0.3355374 0.0018672 0.2673763 0.2529355 0.390625 0.65756281 0.47142985 0.5652041 0.30650726 0.8715583 0.4784141 0.56164353 0.9952121 0.480062 0.496672 1 1 1 1 1 1 1 1 1 1 1 b = 0.5688 b = 1.0730 b = 0.7853 b = 2.6680 b = 1.6435 b = 1.0499 b = 0.8441 b = 3.3174 b = 1.0368 b = 1.0503
185 Appendix G: Sample Plots of Storm Events
9/14/2006 10/5/2006
0.00 0.00
0.16 0.06 0.01 all (in) 0.02 all (in) f f 0.02 Total Rainfall = 1.71 inches 0.04 Rain Rain Antecedent Dry Time = 55.5 hours Total Inflow Volume = 42400 L 120 50 Autosampler = 6 Samples per Bottle
40 0.12 Total Rainfall = 0.57 inches
s) s) Antecedent Dry Time = 130.2 hours f 0.25 Inch Sample 80 f Total Inflow Volume = 15000 L 30 0.50 Inch Sample 0.04 Autosampler = 6 Samples per Bottle 1.00 Inch Sample 20 > 1.0 Inch Sample 40 Sampling Points 0.25 Inch Sample 10 0.08 TSS Concentration 0.50 Inch Sample TSS EMC Sampling Points TDS Concentration (mg/L) TDS Concentration (mg/L) TDS Concentration 0 TSS Concentration 0 TDS EMC 30 TSS EMC 60 TDS Concentration 0.02 low at Weir Inlet Box (c Box Inlet Weir low at (c Box Inlet Weir low at TDS EMC f f
In 20 In 0.04 40
10 20
0.00 0 0.00 0 TSS Concentration (mg/L) Concentration TSS TSS Concentration (mg/L) Concentration TSS
9 9 9 9 9 9 1 3 59 2 :09 :1 :39 :4 5 : :19 :49 : 1 9 3: 2:29 9:09 3: 15:29 23 12 22 6 6: 10 1 23:19 6 1 6 0 6 /06 2:59 /06 7 06 /06 /06 8:09 / 06 /06 /06 7:39 4 /0 / /0 6/06 7 4/06 1 4 15 15 5/06 5 6/06 14 / / / 1 9/1 9/14 /14 /1 9 9 10/ 10/6 0/ 10 10/7 9 9/14/06 9 9/1 9/ 10/ 10/6/0 1 10/6/ 10/6/06
186
10/11/2006 10/17/2006
0.00 0.00
0.30 0.04 0.16 0.02 0.08 ainfall (in) ainfall (in) R Total Rainfall = 0.91 inches R Antecedent Dry Time = 104.4 hours 100 80 Total Inflow Volume = 34000 L Total Rainfall = 1.27 inches Autosampler = 6 Samples per Bottle Antecedent Dry Time = 130.5 hours 80 0.12 Total Inflow Volume = 46000 L Autosampler = 6 Samples per Bottle 60 0.25 Inch Sample 60 0.20 0.50 Inch Sample 40 1.00 Inch Sample Sampling Points 40 0.25 Inch Sample 0.50 Inch Sample TSS Concentration 20 20 1.00 Inch Sample TSS EMC 0.08 TDS Concentration > 1.0 Inch Sample TDS Concentration (mg/L) TDS Concentration (mg/L) TDS EMC 0 Sampling Points 0 8 TSS Concentration 12 TSS EMC 0.10 TDS Concentration 6 TDS EMC Inflow at Weir Inlet Box (cfs) Inflow at Weir Inlet Box (cfs) 8 0.04
4
4 2
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L)
4 4 4 4 4 4 2 04 :14 : 4 :5 :0 :1 3:5 9: 3 2 22 6 1 2 06 2 6 / 06 /06 1 /06 18: 7/0 /0 / 1 7 0/12 /17/06 18 0/11 0/11 1 10/ 0/17 0 0/1 1 1 10/11/06 1 1 1
187
10/19/2006 10/27/2006
0.00 0.00
0.16 0.02 0.40 0.04
0.04 Total Rainfall = 2.92 inches 0.08 ainfall (in) ainfall (in) R R Antecedent Dry Time = 173.0 hours Total Rainfall = 0.76 inches Total Inflow Volume = 121500 L Antecedent Dry Time = 41.2 hours 80 Autosampler = 6 Samples per Bottle 60 Total Inflow Volume = 27600 L Autosampler = 6 Samples per Bottle
0.12 60 0.30 40 0.25 Inch Sample 0.25 Inch Sample 40 0.50 Inch Sample 0.50 Inch Sample 1.00 Inch Sample 1.00 Inch Sample > 1.0 Inch Sample 20 20 Sampling Points Sampling Points 0.08 0.20 TSS Concentration TSS Concentration TDS Concentration (mg/L) TSS EMC 0 (mg/L) TDS Concentration TSS EMC 0 TDS Concentration 16 TDS Concentration 16 TDS EMC TDS EMC
12 12 Inflow at Weir Inlet Box (cfs) Inflow at Weir Inlet Box (cfs) 0.04 0.10
8 8
4 4
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L)
4 4 4 9 59 24 4 5 :19 :39 9: 4:0 1:2 21: 6 1:34 6 5: 6 1 17 2 6 5:49 6 9: 6 0 0 0 6 6 6 0 0 / 0 /06 1 / /0 0 /0 8 8 2 /20/ /2 /20/ /2 / 0 20/ 0/19 10 10 1 0/ 0/27 10 10/28/ 10 1 1 10/27/0 1
188
11/2/2006 11/7/2006
0.00 0.00
0.08 0.30 all (in) all (in) f 0.02 0.04 f Rain Rain Total Rainfall = 0.41 inches Total Rainfall = 2.69 inches Antecedent Dry Time = 92.5 hours 80 Antecedent Dry Time = 134.0 hours 60 Total Inflow Volume = 14500 L Total Inflow Volume = 121400 L Autosampler = 7 Samples per Bottle Autosampler = 6 Samples per Bottle
0.06 60 s) s) f f 40 0.20 0.25 Inch Sample 0.25 Inch Sample 40 0.50 Inch Sample 0.50 Inch Sample 1.00 Inch Sample Sampling Points 20 > 1.0 Inch Sample TSS Concentration 20 Sampling Points 0.04 TSS EMC TSS Concentration
TDS Concentration TDS (mg/L) Concentration 0 (mg/L) TDS Concentration TSS EMC 0 TDS EMC 12 TDS Concentration 10 TDS EMC 0.10 low at Weir Inlet Box (c Box Inlet Weir low at
low at Weir Inlet Box (c Box Inlet Weir low at 8 f f In In 8 0.02 6
4 4
2
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L) 4 4 4 4 4 49 :0 :14 :34 :0 : :04 2 2 5:59 8 2 10 6 1 6 3:5 6 6 14: 6 23 0 /06 /06 8 8/06 6:24 /06 0 /0 /2/0 /2/ /2/0 /2/06 1/ 8 /8/06 18:54 8 1 1 11/ 1 11 1 11 1 11/7 11/ 11/8/ 11 11/
189
11/12/2006 11/16/2006
0.00 0.00
0.25 0.02 0.60 0.08
0.04 Total Rainfall = 1.32 inches 0.25 Inch Sample 0.16 ainfall (in) ainfall (in) Antecedent Dry Time = 52.0 hours 0.50 Inch Sample R Total Rainfall = 1.21 inches R Total Inflow Volume = 71300 L Antecedent Dry Time = 83.4 hours Autosampler = 8 Samples per Bottle 1.00 Inch Sample Total Inflow Volume = 48400 L 60 100 > 1.0 Inch Sample 0.20 Autosampler = 8 Samples per Bottle Sampling Points 80 TSS Concentration 40 TSS EMC 60 0.25 Inch Sample 0.40 TDS Concentration 0.50 Inch Sample TDS EMC 0.15 40 1.00 Inch Sample 20 > 1.0 Inch Sample 20 Sampling Points TSS Concentration TDS Concentration (mg/L) 0 TDS Concentration (mg/L) 0 TSS EMC 0.10 16 25 TDS Concentration TDS EMC 0.20 20 12 Inflow at Weir Inlet Box (cfs) Inflow at Weir Inlet Box (cfs) 15 0.05 8 10
4 5
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L)
9 9 9 9 4 :4 :09 29 0 :24 :04 1:1 9:39 8 2:1 6: 9:44 1 15:2 1 1 1 13: 14:44 16 18 1 6 6 6 6 6 6 6 6 21:24 /06 3:59 0 0 0 /0 /06 23 3 / /0 /06 /0 2/ 2 13/06 16/06 /12/06 1 /12 1/ 16 /16 1/1 1 1/ 11/1 1 1/13 1/ 1 1 1 11 1 11/13/0 11/16 11/ 11 11/16/0 1 11/16/
190
11/22/2006 1/5/2007
0.00 0.00
0.16 0.02 0.25 all (in) 0.04 f 0.04 ainfall (in) R Rain 50 25 Total Rainfall = 0.57 inches 0.20 Antecedent Dry Time = 56.3 hours 40 Total Inflow Volume = 21400 L 0.12 20 Total Rainfall = 1.05 inches Autosampler = 8 Samples per Bottle s)
Antecedent Dry Time = 139.3 hours 30 f Total Inflow Volume = 27600 L 15 Autosampler = 8 Samples per Bottle 20 0.25 Inch Sample 0.15 10
0.50 Inch Sample 10 0.25 Inch Sample 0.08 1.00 Inch Sample 5 0.50 Inch Sample Sampling Points TDS Concentration (mg/L) 0
1.00 Inch Sample (mg/L) Concentration TDS TSS Concentration 0 Sampling Points TSS EMC 8 0.10 16 TDS Concentration TSS Concentration TDS EMC TSS EMC low at Weir Inlet Box (c Box Inlet Weir at low
6 f TDS Concentration 12 Inflow at Weir Inlet Box (cfs) 0.04 In TDS EMC 4 0.05 8
2 4
0.00 0 TSS Concentration (mg/L) 0.00 0 9 9 9 9 TSS Concentration (mg/L) 59 0 4 :39 : : 9 9:3 3: 2:49 19 :29 18 2 1 : 06 7 2:5 7:09 1 1 06 06 11:19 06 15:2 7 8:49 1 2 7 /06 14:29 3/ / / 0 7 1 7 7 2 2/06 3 3/06 / 0 /0 6/0 /22/ 2 1/2 /2 /23/06 2 / 11/23/06 2 1 1 1/2 1/5 /5/ 5 1 11/2 11 11/ 11/23 1 1 11 1 1/5/0 1/
191
1/7/2007 3/15/2007
0.00 0.00
0.16 0.20
0.02 all (in) all (in) f 0.02 f
0.04 Rain Total Rainfall = 0.57 inches Rain Antecedent Dry Time = 39.1 hours 30 800 Total Inflow Volume = 21400 L Autosampler = 8 Samples per Bottle 0.16
0.12 600 s)
s) Total Rainfall = 0.79 inches f f 20 Antecedent Dry Time = 112.1 hours Total Inflow Volume = 24900 L Autosampler = 8 Samples per Bottle 400 0.25 Inch Sample 0.12 10 0.50 Inch Sample 200 1.00 Inch Sample 0.08 Sampling Points TDS Concentration (mg/L) TSS Concentration 0 (mg/L) Concentration TDS 0.25 Inch Sample 0 TSS EMC 8 0.08 0.50 Inch Sample 80 TDS Concentration 1.00 Inch Sample Sampling Points low at Weir Inlet Box (c Box Inlet Weir low at low at Weir Inlet Box (c Box Inlet Weir at low TDS EMC f f 6 TSS Concentration 60 In In 0.04 TSS EMC 0.04 TDS Concentration 4 40 TDS EMC
2 20
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L) 9 9 4 4 49 0 :3 1: 9:0 3:14 7 2 7 7 3:24 /07 1:5 7 1 /0 07 2 6 /0 5/ 1 16/07 6: 8/07 11:44 / /7 1/8 1/8/0 / 1 3/ 3 1 1/7/07 1 3/15/07 17:39 3/
192
3/22/2007 4/4/2007
0.00 0.00
0.16 0.20 0.02 all (in)
0.02 all (in) f Total Rainfall = 0.45 inches f Antecedent Dry Time = 73.6 hours Total Rainfall = 0.89 inches 0.04 Total Inflow Volume = 20200 L 0.04 Antecedent Dry Time = 52.5 hours Rain Autosampler = 8 Samples per Bottle Rain Total Inflow Volume = 33300 L 80 Autosampler = 8 Samples per Bottle 30 0.16
0.12 60 s) s) f f 20 0.25 Inch Sample 0.25 Inch Sample 0.50 Inch Sample 40 0.50 Inch Sample Sampling Points 0.12 1.00 Inch Sample TSS Concentration 10 Sampling Points TSS EMC 20 TSS Concentration 0.08 TDS Concentration TSS EMC TDS Concentration (mg/L)
TDS EMC (mg/L) TDS Concentration 0 TDS Concentration 0 12 0.08 TDS EMC 8 low at Weir Inlet Box (c Box Inlet Weir low at low at Weir Inlet Box (c Box Inlet Weir low at f f 6 In 8 In 0.04
0.04 4
4 2
0.00 0 TSS Concentration (mg/L) 0.00 0 TSS ConcentrationTSS (mg/L) 4 4 4 24 44 5 4 4 4 :0 :3 : :24 5 9 4 :44 : 7 3: 5 7:2 9:04 9 0 7 15: 07 7 7 /07 7 0 3 0 /07 /07 /07 8:14 / 24/ /23/ 2 /24/07 0:14/ 4 4 4 4/0 /22/07 23:143 3/ 23/ /23/07 20:043 3 4/ 4/4/07 6:3 4/ 4/ 4/4 4/ 3/22/07 1 3 3/23/07 11 3/ 3 4/4/07 10:44
193
4/12/2007 5/16/2007
0.00 0.00
1.00 0.04 3.00 all (in)
0.02 all (in) f f Total Rainfall = 0.93 inches 0.08 0.04
Antecedent Dry Time = 176.9 hours Rain Rain Total Inflow Volume = 36500 L Total Rainfall = 0.65 inches Autosampler = 8 Samples per Bottle 50 Antecedent Dry Time = 85.9 hours 80 0.80 Total Inflow Volume = 38000 L Autosampler = 8 Samples per Bottle 40 60 s) s) f 0.25 Inch Sample f 30 0.50 Inch Sample 2.00 40 1.00 Inch Sample 0.25 Inch Sample 0.60 20 Sampling Points 0.50 Inch Sample TSS Concentration 1.00 Inch Sample 20 10 TSS EMC Sampling Points TDS Concentration TSS Concentration TDS Concentration (mg/L) TDS Concentration TDS Concentration (mg/L) Concentration TDS TDS EMC 0 TSS EMC 0 0.40 40 TDS Concentration 40 TDS EMC 1.00 low at Weir Inlet Box (c Box Inlet Weir low at low at Weir Inlet Box (c Box Inlet Weir low at f f 30 30 In In
0.20 20 20
10 10
0.00 0 0.00 0 TSS Concentration (mg/L) TSS Concentration (mg/L) Concentration TSS 0 0 9 20 0 2 34 3 : : : : :44 :49 5 6 6:41 1 1 7 2 7 4 7 1 7 17: 0 07 0:29 /07 6 /07 8 /07 14:41 /07 0 2/0 2/0 2 2 6 6 6/07 1 1 1 /1 1 /16/ /1 16/ 4/12/ 4/ 4/ 4/1 4/ 5 5/ 5 5 5/
194 4/14/2007 (Discrete/Mixed Comparison)
0.00
0.30 0.04 Total Rainfall = 5.82 inches
Rainfall (in) Rainfall Antecedent Dry Time = 50.8 hours 50 Total Inflow Volume = 212500 L Autosampler = 9 Samples per Bottle 40 0.25 Inch Sample 30 0.50 Inch Sample
0.20 0.75 Inch Sample 20 1.00 Inch Sample 1.25 Inch Sample 10 1.50 Inch Sample 1.50 Inch Sample
0 TDS Concentration (mg/L) 2.00 Inch Sample
25 2.25 Inch Sample 2.50 Inch Sample Sampling Points 20 0.10 TSS Conc (Mixed) TSS Conc (Discrete) InflowWeirat Inlet Box (cfs) 15 TSS EMC (Mixed) TSS EMC (Discrete) 10 TDS Conc (Mixed) TDS Conc (Discrete) 5 TDS EMC (Mixed) TDS EMC (Discrete) TSS Concentration (mg/L) 0.00 0
4 4 4 9 :4 :49 :5 :59 :0 0 0 5:0 7 7 2 0 0 /07 11:19 /15/07 5 4/15/ 4/15/ 4 4/15/07 7 4/15/07 9:14/1 4/14/07 2 4/14/07 22 4
195 4/25/2007 (Discrete/Mixed Comparison)
0.00
3.00 0.08
0.16 Rainfall (in) Rainfall
160 Total Rainfall = 1.84 inches Antecedent Dry Time = 152.1 hours Total Inflow Volume = 126200 L 120 Autosampler = 8 Samples per Bottle s) f 80 2.00 1st Sample 2nd Sample
40 3rd Sample 4th Sample 5th Sample 0 6th Sample 200 (mg/L) TDS Concentration 7th Sample Sampling Points 160 TSS Conc (Mixed) 1.00 TSS Conc (Discrete) low at Weir Inlet Box (c Box Inlet Weir low at f 120 TSS EMC (Mixed) In TSS EMC (Discrete) TDS Conc (Mixed) 80 TDS Conc (Discrete) TDS EMC (Mixed) 40 TDS EMC (Discrete) TSS Concentration (mg/L) 0.00 0
9 9 9 :0 :5 0 1:49 2:3 7 /07 0 7/07 7 2 /27/07 0 2 4/ 4 4/ 4/27/
196