NITRIFICATION IN A CHLORAMINATED DRINKING
WATER SUPPLY
by
HELEN WATTS
Thesis submitted in partial fulfilment for the degree of Master of Science at the
University of New South Wales.
Australian Water Technologies, EnSight Division, Sydney and
Department of Land and Water Conservation, Catchment Management and Community
Services Directorate, Sydney, New South Wales.
29 August 1996. "I hereby declare that this submission is my own work and that, to the best of my knowledge and believe, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgement is made in the text." SUMMARY
The investigation into nitrification within drinking water supply systems was initiated following difficulties encountered by Sydney Water with the maintenance of disinfection residuals within the chloraminated supplies. Preliminary water quality data and information from overseas studies indicated that the loss of disinfection residual may have been due to nitrification.
A three year water quality monitoring program of the Ryde delivery system and its bulkwater supply was undertaken to determine the occurrence and extent of nitrification throughout the system and over time. Alum dosing at Prospect Reservoir (the source for the Ryde Delivery system) took place for a two year period during the monitoring program. The water quality monitoring was supported by a limited survey of the nitrifying bacteria within biofilm and sediment sampled from the Ryde and other delivery systems. The other delivery systems were selected on the level of treatment and disinfection process.
Results of the water quality monitoring confirmed the occurrence of nitrification in the Ryde delivery system throughout the monitoring period April 1990 to April 1993. The introduction of alum treated water from Prospect Reservoir influenced the trend of the water quality variables monitored but did not eliminate nitrification. Autotrophic nitrifying bacteria were isolated from the Ryde delivery system as well as other systems that were supplied with chlorinated water.
The findings of this investigation are also consistent with the presence of nitrifying bacteria in the wider aquatic and terrestrial environment. In particular, the ability to survive in low nutrient environments.
11 The methods that have traditionally been used to study autotrophic nitrifying bacteria are not sensitive or accurate and can not be applied to study the bacteria in situ. Recent advances in the study of nitrifying bacteria and the use of model biofilm systems will more accurately determine the impact and control of nitrification on water quality within a drinking water system.
lll TABLE OF CONTENTS
SUMMARY ...... ii
LIST OF FIGURES ...... V
LIST OF TABLES ...... vi
LIST OF PLATES ...... vii
GENERAL INTRODUCTION ...... 1
CHAPTER 1: LITERATURE REVIEW ...... 3
1.1 NITRIFYING BACTERIA ...... 3 1.1.1 General Introduction...... 3 1.1. 2 Nitrification Activity in Drinking Water Systems ...... 5 1.1. 3 Methods Used for the Control Nitrification Activity...... I O 1.2 COLIFORM AFTERGROWTHS AND BIOFILMS IN DRINKING WATER SYSTEMS ...... 11 I. 2. I Coliforms and the Assessment of Water Quality ...... 12 1.2.2 Bacterial Aftergrowths ...... 13 1.2.3 Biofilms ...... _ ...... 15 1.2.4 Control of Bacterial Aftergrowth and Biofilms in Drinking Water Systems ...... 17 1.3 CHLORAMINES IN DRINKING WATER SYSTEMS ...... 19 1.3. 1 Introduction ...... 19 1.3.2 Chloramine Chemistry ...... 20 1.3.3 Relationship ofNitrite with Monochloramine...... 21 1.3.4 Use ofChloramines by Water Authorities ...... 22 1.4 STUDY AIMS ...... 23 CHAPTER 2: DETERMINATION OF NITRIFICATION WITHIN THE RYDE DELIVERY SYSTEM ...... 24
2.1 INTRODUCTION ...... 24 2. 1. 1 Sydney Water's Experience with Aftergrowths and the Introduction ofCh/oramines ...... 24 2.2 DESCRIPTION OF SYSTEM AND STUDY SITES ...... 27 2.2. 1 Supply of Water to Sydney...... 27 2.2.2 Description ofthe Ryde Distribution System ...... 29 2.3 METHODS ...... 31 2.3. 1 Sample Design ...... 32 2.3.2 Sample Collection ...... 35 2.3.3 Summary of Drinking Water Analysis Methods ...... 36 2.3.4 Analysis ofData ...... 39 2.4 RESULTS ...... 41 2. 4. 1 Changes in the concentration ofprimary variables at each site over time...... 41 2.4.2 Changes in Primary Variables with Distance from Prospect Reservoir...... 51 2.4.3 Strength ofrelationship between variables...... 52 2.5 DISCUSSION ...... 61 2.5. 1 Overview ...... 61 2. 5. 2 Dynamics ofNitrification within the Study System ...... 64 2. 5. 3 Nitrification and Water Quality Variables ...... 68 CHAPTER 3: ISOLATION AND CHARACTERISATION OF NITRIFYING BACTERIA ...... 71
3. 1 INTRODUCTION ...... 71 3 .2 METHODS ...... 72 3.2. 1 Sample Design and Collection...... 72 3.2.2 Culture Methods ...... 73 3.2.3 Detection ofActivity ...... 76
iv 3.2.4 General chemical analysis ...... 77 3.2.5 Estimation ofNumbers ...... 77 3. 2. 6 Isolation and Characterisation ofNitrifying Bacteria ...... 78 3.3 RESULTS ...... 80 3.3. I Detection ofNitrifying Activity in Sediment and Biofi/m ...... 80 3.3.2 Estimation ofNitrifying Bacterial Numbers ...... 80 3. 3. 3 Characterisation ofNitrifying Bacterial Isolates ...... 8 J 3.4 DISCUSSION ...... 89 CHAPTER 4: GENERAL DISCUSSION ...... 95
REFERENCES ...... 99
APPENDIX 1: SUMMARY STATISTICS FROM WATER QUALITY MONITORING ...... 109 Ax/./ Descriptive statistics for Upper Canal at Pipehead (HPR8) for quarters one to twelve...... /09 Ax I. 2 Descriptive statistics for Ryde Pumping Station (WPS5) for quarters one to twelve...... 113 Ax/.3 Descriptive statistics for Hermitage Reticulation Zone (ZN50) for quarters I to 12 ...... I 17 Ax 1.4 Descriptive statistics for Pymble Reticulation Zone (ZN97) for quarters I to 12 ...... 121 Ax I. 5 Descriptive statistics for Warringah Reticulation Zone (ZN 13 I) for quarters I to 12...... 12 5 Ax/.6 Descriptive statistics for Frenchs Forest Reticulation Zone (ZN283) for quarters I to 12 . .... 129 Ax I. 7 Descriptive statistics for Palm Beach Reticulation Zone (ZN/92) for quarters I to 12...... 133 Ax I. 8 Descriptive statistics for Hermitage Reservoir (R50) for quarters I to 12...... 13 7 Ax/.9 Descriptive statistics for Palm Beach Reservoir (R192) for quarters I to 12 ...... 141 Ax 1.10 Descriptive statistics for Frenchs Forest Reservoir (R283) for quarters 1 to 12...... 145 Ax 1.11 Descriptive statistics for Warringah Reservoir (Rl 31) for quarters 1 to 12...... 149 Axl.12 Descriptive statistics for Upper Canal at Prospect before ch/orammination (HPR3) for quarters I to 12 ...... 152 ACKNOWLEDGEMENTS ...... 156
LIST OF FIGURES
FIGURE 1: DIAGRAM OF SYDNEY'S WATER SUPPLY SYSTEM INCLUDING THE RYDE DELIVERY SYSTEM ...... 28
V LIST OF TABLES
TABLE 1: SUMMARY OF CASE STUDIES ...... 8 TABLE 3: SITE DESCRIPTION AND DISTANCE FROM CHLORAMINA TION POINT ON THE LOWER CANAL AT PROSPECT RESERVOIR ...... 34 TABLE 4: PROGRAMMED RESERVOIR CLEANINGS FOR STUDY SYSTEM ...... 35 TABLE 5: SUMMARY OF SAMPLING DATES AND DATA SET SUMMARY ...... 40 TABLE 6: CORRELATION MATRICES FOR ALL DATA POINTS (A) AND INDIVIDUAL SITES (B - I) ...... 58 TABLE 7: MEDIA FOR AMMONIA OXIDISING BACTERIA ...... 75 TABLE 8: MEDIA FOR NITRITE OXIDISING BACTERIA ...... 75 I TABLE 9: TRACE METAL SOLUTION ...... 76 TABLE 10:DETECTION OF NITRIFYING ACTIVITY WITHIN SAMPLES FROM R YDE DELIVERY SYSTEM ...... ••.•...... 82 TABLE 11 : DETECTION OF NITRIFYING ACTIVITY WITHIN SAMPLES FROM OTHER SYDNEY WATER DELIVERY SYSTEMS .•...... •..•.....•.....•...... •.•...... 83 TABLE 12: ESTIMATED NUMBERS OF NITRIFYING AND HETEROTROPHIC BACTERIA FROM SELECTED SAMPLES ...... 86 TABLE 13: MORPHOLOGY AND METABOLIC CHARACTERISTICS OF FIVE PURIFIED CUL TURES FROM RYDE DELIVERY SYSTEM ...... •..•...... 87
Vl LIST OF PLATES
PLATE l: AERIAL VIEW OF PROSPECT RESERVOIR ...... 31 PLATE 2: UPPER CANAL AT PIPEHEAD...... 31 PLATE 3: TRANSMISSION ELECTRON MICROGRAPHS OF THIN SECTIONS OF NITRIFYING BACTERIA ISOLA TED FROM THE RYDE DELIVERY SYSTEM ...... 88
vu GENERAL INTRODUCTION
Water authorities throughout Australia strive to deliver drinking water which is aesthetically acceptable and does not carry risk to the health of the consumer. Their ability to deliver acceptable drinking water is determined by comparing results of drinking water assessments with national or international guidelines for drinking water. No Australian standard currently exists for drinking water.
The health risk of drinking water has been determined, worldwide, by the use of indicator organisms such as coliform and faecal coliform bacteria. These indicator bacteria have traditionally been used to indicate recent faecal contamination by warm blooded animals, including humans. It has now been recognised by water authorities that the appearance of coliform bacteria in drinking water is not necessarily associated with recent faecal contamination as the coliforms can survive treatment and increase in numbers within a distribution system. Aftergrowth is the term commonly used to describe the increase in bacterial numbers either due to the recovery of injured cells following treatment or the subsequent entry of cells into the water phase. Entry into the water phase maybe due to a distribution malfunction such as a main break or from erosion or sloughing from biofilms attached to the surface of distribution mains. A problem facing water authorities is that the detection of a faecal contamination event may go unnoticed due to the masking of the incident by coliform aftergrowth which may result in a risk to public health. Additionally, the ability of a water authority to comply with drinking water guidelines will be greatly reduced.
Many Australian water authorities aim to maintain an effective disinfection residual throughout the distribution system as one mechanism to control coliform aftergrowth as well as providing a residual to combat a faecal contamination event. It is of concern for a water authority if non programmed deterioration of disinfection residual occurs in the distribution system.
1 Sydney Water (formerly named the Water Board of Sydney-Illawarra-Blue Mountains), Australia, converted part of its metropolitan distribution system from disinfection by chlorine to chloramine in February 1986, as one means to control coliform aftergrowth. In the following summer, the combined chlorine residuals for part of this system had dramatically reduced.
Turbidity, organic load and long detention times of water within a distribution system are examples of factors which may lead to reduced disinfection residuals, especially chlorine and chloramine. In chloraminated distribution systems the process of biological nitrification will also adversely affect the maintenance of effective chloramine residuals. Biological nitrification is a two stage process in which ammonia is first oxidised to nitrite then further to nitrate by different groups of bacteria, collectively known as nitrifying bacteria. These bacteria are found in most terrestrial and aquatic environments. Little research has been performed on nitrifying bacteria within distribution systems, beyond presumption that significant residual decay is due to nitrification. This prompted an investigation into the potential for nitrification within Sydney Water's distribution systems.
2 CHAPTER 1: LITERATURE REVIEW
1.1 NITRIFYING BACTERIA
1.1.1 General Introduction.
Biological nitrification is the term used to describe the activities of nitrifying bacteria, which oxidise ammonia to nitrite and nitrite to nitrate (Watson et al., 1981 ). Nitrification occurs in almost all natural habitats and is one of the key processes in the cycling of nitrogen (Kuenen and Robertson 1988).
Autotrophic and heterotrophic microbes are capable of the oxidation of nitrogen compounds, with the primary difference being the means by which energy is obtained. Nitrifying bacteria are chemolithotrophs and utilise the energy derived from nitrification to assimilate carbon dioxide. Chemolithotrophs obtain energy from the oxidation of inorganic electron donors. Most organisms that display this mode of nutrition are also autotrophic in that their carbon source is carbon dioxide (Atlas and Bartha, 1987; Hamilton, 1988). This type of growth distinguishes autotrophs from heterotrophic microbes which use organic molecules for energy and growth.
Heterotrophic nitrification does not appear to make a maJor contribution to the conversion of ammonia to nitrate. For example, Athrobacter will have a nitrification rate of 375 - 900 µgN/day/g of dry cells. This compares with a rate of 1 - 30 million µgN/day/g of dry cells for Nitrosomonas (Atlas and Bartha, 1987). Autotrophic nitrifiers, in many natural environments, represent only a small fraction of bacterial populations. This is explained by the slow rate of growth of the autotrophic nitrifying bacteria compared to that of heterotrophic bacteria (Hall, 1986). In natural environments, the growth rate of the nitrifying bacteria is controlled by substrate concentration, pH, temperature and oxygen tension. Even though optimal conditions of l-25mM of substrate, pH 7.5 - 8.0, and a temperature range of 25 to 30 have been
3 quoted, many environments with suboptimal conditions will support the nitrifiers growth (Watson et al., 1981).
Although the autotrophic nitrifiers are grouped together due to metabolic capabilities as the family Nitrobacteraceae, the ammonia-oxidising bacteria are a very diverse group of bacteria and only distantly related to nitrite-oxidising bacteria (Bock and Koops, 1992; Koops and Moller, 1992). All members of the family are Gram-negative and classically aerobic. Even though energy is obtained from both stages of oxidation less energy is obtained with the oxidation of nitrite and will occur at a faster rate. Classification within the family has been based on the range of cell morphologies the bacteria demonstrate and the arrangement of intracellular membranes (Prosser, 1989). Nitrosomonas and Nitrobacter appear to be the most studied genus of the ammonia- and nitrite-oxidising bacteria respectively.
Ammonia oxidation can be simply represented by the following transformation (Atlas and Bartha, 1987):
(~G = -66 kcal)
In aquatic environments ammonia is in equilibrium with the ammonium ion (NH4+) and, at a pH below 8, the ammonium ion form is greatly favoured. The oxidation to nitrite is multi-step, with the initial step producing hydroxylamine. This initial reaction is performed by membrane bound ammonia monooxygenase and uses oxygen supplied as molecular oxygen (Prosser, 1989). Prosser (1989) reports the ammonia monoxygenase uses ammonia as substrate rather than ammonium. The second step in the reaction obtains oxygen from a water molecule.
The oxidation of nitrite is single step and yields low amounts of energy (Atlas and Bartha, 1987):
(~G = -17 kcal)
4 Autotrophic nitrifiers are able to grow in a wide variety of habitats including soil, freshwater and marine, often with conditions outside those optimal for changing environmental conditions (Bock and Koops, 1992) In aquatic environments, the highest numbers of autotrophic nitrifiers are found at the sediment-water interface and the numbers of autotrophic nitrite oxidisers are often much higher than the numbers of autotrophic ammonia oxidisers (Bock and Koops, 1992). This observation could be a reflection of the metabolic versatility of some nitrite oxidisers which can use organic carbon compounds and gain energy from denitrification in the absence of oxygen (Bock and Koops, 1992).
1.1.2 Nitrification Activity in Drinking Water Systems.
The advantages of monochloramine as a disinfectant, have led many water suppliers to change to this form of disinfection (Wolfe et al., 1984). These advantages include the absence of trihalomethane formation (Symons et al., 1975) and the length of time the residual lasts within the distribution system (Wolfe et al., 1984). Some chloraminated systems appear to operate successfully for many years whereas others appear to be prone to biological nitrification which has undesirable effects on water quality (Wolfe et al., 1988, 1990; Cunliffe, 1991; Skadsen 1993).
In most natural environments, the concentration of ammonia is very low, and the two steps of the nitrification process appear to work together so that nitrite does not accumulate. In some circumstances, this can be exploited in treatment processes to improve the quality of drinking water by converting ammonia to nitrate (Rittman and Snoeyink, 1984) or as a first step in the removal of nutrients from wastewater.
However, if the first step of the nitrification process occurs more rapidly than the second step, that is, if nitrite is produced more quickly than it can be consumed, then nitrite can accumulate. When this occurs in chloraminated drinking water, concentrations of nitrite in the water phase increase. Even though concentrations of nitrite may remain lower
5 than guideline limits, it is the effect on chlorine residuals that is of most concern. Nitrite reacts very rapidly with free chlorine residuals and can lead to the degradation of combined chlorine residuals.
The chemical interaction between nitrite and combined chlorine is not well understood. Valentine ( 1985) reported that the decay of monochloramine proceeded at a more rapid rate in the presence of nitrite than was predicted according to simple hydrolysis. In contrast, Wolfe et al. (1988) suggested that the use of free ammonia by ammonia oxidising bacteria could shift the equilibrium of reaction so that monochloramine was hydrolysed.
Chloraminated distribution systems affected by nitrification are all characterised by a decrease in the concentration of total combined chlorine and a decrease in the concentration of ammonia, together with an increase in the concentration of nitrite. As a result of the decay of disinfectant residuals, microorganisms in the overall distribution system may proliferate and increases in bacterial numbers in the water phase maybe observed. A table summarising experiences with nitrification by various water authorities has been included to illustrate both the similarities and differences that can be experienced with nitrification in drinking water systems (Table 1) .
The involvement of nitrifying bacteria in biological nitrification in distribution systems is difficult to demonstrate conclusively because methods for counting and isolation of nitrifying bacteria are difficult and inaccurate (Underhill, 1990). Several of the investigators, reported in Table 1, have employed methods to isolate and count ammonia oxidising bacteria. Wolfe et al. ( 1988) and Cunliffe ( 1991) have isolated nitrifying bacteria from chloraminated supplies which showed typical evidence of nitrification. Wolfe et al. (1990) showed that nitrifying bacteria were present in greater concentrations in reservoir water which showed the characteristics of nitrification than in the influent supply which showed little evidence of nitrification. Skadsen (1993) used surrogate methods, such as an increase in heterotrophic bacteria and nitrite
6 concentrations in conjunction with a decrease in monochloramine concentrations, to confirm the occurrence of nitrification.
Nitrifying bacteria appear to be relatively resistant to monochloramine (Wolfe et al., 1990; Cunliffe, 1991 ). However, reports suggest that the results of inactivation studies are dependent on the test conditions. For instance, Wolfe et al. (1990) showed that ammonia-oxidising bacteria grown in tap water media were more resistant to monochloramine than bacteria grown in synthetic media. A comparison of these results with those of Cunliffe ( 1991) suggests that bacteria taken directly from the distribution system and tested in distribution water were much more resistant to monochloramine than laboratory cultured bacteria. The latter results would have the greatest relevance to conditions experienced in a distribution system. The observations in both of the above studies indicate that nitrifying bacteria can survive under conditions of high combined chlorine residual as the nitrifying bacteria were found in water containing combined chlorine residuals as high as 2mg/L.
Another factor which could enhance the resistance of nitrifying bacteria to disinfection in distribution systems is their survival and growth in biofilms and sediments. Nitrifying bacteria have been found in high numbers in fresh water sediments (Hall, 1986) and a study by Wolfe et al., ( 1990) showed greater numbers of nitrifying bacteria in samples of biofilm and sediment from a drinking water reservoir than were found in the water phase.
7 Table 1: Summary of Case Studies Observations Preceding Control Measures Employed Reference System Description Measured Variable Trend Event
Covered reservoir 1.J,total combined chlorine t HPC bacteria Break point chlorination Wolfe et al. (capacity 1970 ML) for .J, ammonia-N Increased retention t Cl2:NHrN ratio (1988). filtered, chloraminated 2tHPC bacteria3 time of water in Free chlorinate for period drinking water. t nitrite-N reservoir annually California, USA.
Covered reservoir .J, total combined chlorine No increase in HPC Break point chlorination Wolfe et al. (capacity 250 ML) for t HPC bacteria bacteria Free chlorinate for 1 month (1988) filtered, chloraminated t nitrite-N annually drinking water. t AOB4 California, USA.
Storage and distribution .J, total combined chlorine Not reported Flushing mains - not Negrin et al. system with filtered, t HPC bacteria or no change effective (1990) chloraminated drinking t nitrite-N Break point chlorination water. t AOB or no change Nitrification returned with California, USA. chloramination
8 Table 1: Summary of Case Studies continued Observations preceding Control Measures Employed System Description Measured Variable Trend event Reference
Several distribution .J,total combined chlorine Not reported t Chloramine doseage - Cunliffe ( 1991) systems: t HPC bacteria or no change unsuccessful filtered/unfiltered, tNOx 5 Periodic free chlorination chloraminated tAOB System operational changes South Australia, Australia.
Drinking water .J,monochloramine Change from sand Increased doseage rate of Skadsen (1993) distribution system: t HPC bacteria filters to GAC6 chloramines - not successful filtered, chloraminated t nitrite-N Excess ammonia dosed Flush mains - not successful Michigan, USA. by mistake Free chlorinate - nitrification returned with chloramination
Bulkwater mains: .J,total combined chlorine Not reported but No control at bulkwater stage Deal (1993) unfiltered chloraminated t nitrite-N -!-total combined of system. Rechlorination Sydney, Australia. chlorine under plants within reticulation investigation system.
1 Decrease in concentration 2 Increase in concentration 3 Heterotrophic Plate Count 4 Ammonia oxidising bacteria 5 nitrite-N + nitrate-N 6 Granulated activated carbon
9 1.1.3 Methods Used for the Control Nitrification Activity.
Efforts to control the occurrence of nitrification in water supplies have focused on monitoring to detect the onset of nitrification, and changes in the application of chloramination treatment. Wolfe et al. (1988) showed that increases in nitrite concentrations and heterotrophic plate counts were precursors of nitrification. Several other water authorities have also monitored these parameters in studies of nitrification (Cunliffe, 1991; Skadsen, 1993). Attempts have been made to control nitrification (Skadsen, 1993; Wolfe et al., 1988) by increasing the ratio of chlorine to ammonia and hence reduce the total amount of ammonia added to the water. Metropolitan Water District (MWD) of Southern California USA, alternate between free chlorination and chloramination while the Los Angeles District Water and Power (LADWP), who receive water from MWD, have chosen not to use chloramines within their system. A number of the case studies (Negrin et al., 1990; Skadsen, 1993) attempted flushing of reticulation mains in affected areas, but with only short term improvements in the control of nitrification.
Kirmeyer et al. (1993) noted in a survey of water authorities using chloramines within the United States that the authorities in colder climates did not appear to experience nitrification. Many of the water authorities who experienced nitrification and employed mechanisms for control also experienced re-occurrence of nitrification following further use of chloramines.
It is apparent from this brief presentation of case studies that no one single measure, apart from ceasing the use of chloramines, will control nitrification. Instead, the case study results suggest that a series of control measures may be required. This document does not attempt to report on control mechanisms for nitrification but rather focuses on determination of nitrification within a chloraminated supply. A review of experiences in the control of nitrification though, would provide some insight into the nitrifying bacteria's ability to survive unfavourable conditions.
10 1.2 COLIFORM AFTERGROWTHS AND BIOFILMS IN DRINKING WATER SYSTEMS.
A review of coliform aftergrowths and biofilms in drinking water systems is considered essential to the understanding of the development of nitrification within Sydney Water's chloraminated systems. Furthermore, the investigation of nitrification in the main metropolitan supply of Sydney Water illustrated the interrelationship between coliform aftergrowth, biofilms, disinfection methods, nitrification, loss of disinfection residual and re-emerging coliform aftergrowth.
11 1.2.1 Coliforms and the Assessment of Water Quality
A microbiological water quality indicator is used to determine the potential risk of a water related contamination episode, depending on the use of the water. (McNeill, 1985). For example microbiological indicators for recreational water may differ from those used for the assessment of drinking water quality. Traditionally, it is the coliform group of bacteria that have been used as indicators of faecal contamination in drinking water.
The coliform group consist of several genera of bacteria which are members of the family Enterobacteriaceae. The definition for Enterobacteriaceae relates to the method of detection and can be summarised as comprising of all aerobic and some facultative anaerobic, gram negative, non-spore forming bacteria that ferment lactose at 35°C (McNeill, 1985). Faecal coliforms, sometimes referred to as thermotolerant coliforms, are a subset of the coliform group and are defined by their ability to ferment lactose at
44.5°C. The National Health and Medical Research Council in conjunction with the Agricultural and Resource Management Council of Australia and New Zealand (ARMCANZ) consider faecal coliforms to be more indicative of recent faecal contamination than coliforms. This is because the coliform group of bacteria can also occur naturally in soil and vegetation and may be present in the absence of faecal contamination (NHMRC/ARMCANZ, 1993).
Over the past decade there has been concerns raised over the validity of coliforms as indicators in drinking water systems because the coliform bacteria are able to regrow outside their natural environment. Not withstanding the concerns, the NHMRC/ARMCANZ (1993) still use coliforms as indicators of drinking water quality due to the fact the group of bacteria are able to last longer in natural water systems than some pathogens and therefore indicate more remote incidents of faecal contamination. Coliform bacteria still remain useful indicators of water treatment efficiency and disinfection processes and current methods of detection are more accessible to
12 laboratories and provide, at present, results in quicker time than individual tests for pathogens.
1.2.2 Bacterial Aftergrowths
Once bacteria have entered a drinking water distribution system their numbers may remain static or decrease due to factors such as starvation, predation or exposure to an effective chlorine residual. In contrast to this scenario bacterial numbers may increase due to the availability of nutrients or protection from predation or exposure to disinfection residuals.
Regrowth and aftergrowth have both been used to describe either an increase in heterotrophic bacterial or coliform numbers (Le Chevallier, l 990a).The two terms have also been defined differently by different researchers (Brazos et al., 1985; Characklis, 1988). For the purpose of this review, aftergrowth will be the term used to describe the increase in bacterial numbers in a water distribution system after disinfection.
The extent of aftergrowth in a distribution system may be influenced by a wide variety of factors. Power and Nagy (1989) broadly grouped the factors as follows:
• availability of nutrients;
• microbial load;
• sedimentation and resuspension of particles;
• growth and detachment of biofilms;
• characteristics of the distribution system (for example age of mains and physical intensity);
• disinfection; and
• environmental considerations (for example temperature and prevailing weather conditions).
13 In general, aftergrowth in a drinking water distribution system is likely to occur when organic matter and sediment accumulate in pipes and/or, chlorine residuals are low, and/or water temperatures increase (Le Chevallier, 1990a).
Disinfection will be discussed in brief, as the choice of disinfectant and its application influence the amount of aftergrowth in a distribution system. If chloramine is the disinfectant of choice it may also lead to significant nitrification and further enhance aftergrowth. The interaction of a disinfectant with biofilms is of particular importance since most of the organisms within a distribution system are found attached to surfaces (Le Chevallier, 1990a). A number of studies have reported that attached organisms are more resistant to disinfection than planktonic organisms (Ridgeway and Olsen, 1982; Le Chevallier et al., 1984; Camper et al., 1986). The production of extracellular polymeric material by biofilm organisms is thought to afford protection against inhibition by chlorine residuals . Le Chevallier et al. (1987) reported that in a distribution system in New Jersey, coliforms were entering the water phase from biofilm growing in trunk mains. This proliferation and release of organisms from biofilm continued to occur despite the presence of 1.0 mg/L free chlorine in the water phase.
Results of a study comparing disinfection by free chlorine with that by chloramine indicated that combined chlorine residuals were more effective against biofilm bacteria than free chlorine. It was later proposed that the chloramines, which reacted with a narrower range of compounds than did free chlorine, were able to penetrate further into the biofilm (Le Chevallier 1990b)
The choice of chloramine as disinfectant may overcome the difficulties of persistence of disinfectant residual and the formation of undesirable by-products that may result from the use of chlorine. However, the formation of chloramine residuals requires the addition of ammonia to water and this source of nitrogen can become available for use by microorganisms.
14 1.2.3 Biofilms
Biofilms have been considered in more detail because of the relationship with nitrifying bacteria and aftergrowth. A biofilm can be simply described as a matrix of microorganisms and extracellular products attached to a surface (Marshall, 1984; Ridgeway and Olson, 1981) and that most bacteria in aquatic environments exist at solid-liquid interfaces (Le Chevallier, 1990a).
Biofilms in general, whether they are conceived as detrimental or beneficial, play an important role in everyday life. For example, biofilms in association with root cells, sewage treatment systems, fermentation industry or natural aquatic ecosystems are considered beneficial. Alternatively, biofilms associated with implants within the human body, heat exchange systems or watermains are considered detrimental (Characklis and Marshall, 1990).
A biofilm is a relatively complex system and consists of several interdependent compartments (Characklis and Marshall, 1990). The first of these is the substratum, or surface of attachment. In the biofilm lifecycle organic molecules are transported to the substratum where the surface becomes conditioned for attachment of cells transported from the bulk liquid phase to the conditioned surface. The bulk liquid, which immerses the biofilm and provides the medium for transport of cells and nutrients to and from the surface, can be considered as the second compartment. The third compartment is the matrix of microbial cells, water, inorganic material and extracellular polymeric substances which hold the biofilm together at the surface of attachment. The biofilm itself will experience a lifecycle of continued adhesion of cells, growth of adhered cells and detachment of cells from the biofilm (Characklis and Marshall, 1990).
The micro-environment of bacteria within a biofilm has several ecological considerations for researchers and a significant impact on a water authority's ability to meet drinking water guidelines or regulations. As most drinking water is low in
15 nutrients, the accumulation of microorganisms and nutrients at a solid-liquid interface creates a more favourable environment for sessile organism's growth and survival than an organism which is planktonic. Le Chevallier (1990a) considered the reasons for the more favourable environment for growth at the solid-liquid interface to be a higher rate of transport to the biofilm surface due to bulk-liquid flows. This would provide for the transport of nutrients to the biofilm surface. The extracellular polymeric substances produced by the biofilm provide a medium for attachment of cells and protection from disinfecting agents as well as the capture of nutrients.
Cells may become detached from a biofilm by continuous and uniform erosion of cells or by sloughing (Characklis and Marshall, 1990). Sloughing is the process whereby macroscopic patches ofbiofilm become detached from the biofilm structure (Characklis, 1988). Both of these processes can release particle-associated microbes into the water phase. The rates of detachment will be determined by the nature of the biofilm (for example thickness, species composition, nutrient status) as well as aspects of the bulk liquid phase such as shear stress or disinfectant residuals within a distribution system (Characklis, 1988).
A study conducted by Le Chevallier et al. (1987) on a New Jersey distribution system concluded that coliforms detected in the water phase arose from the biofilm in the trunk mains rather than breakthrough at treatment. The work of Inkster and Johnston (1993) and Strauch (1992) showed that Sydney Water experienced higher numbers of bacteria attached to sediments and biofilms than within the water phase. Power et al. (1989) found that coliforms and heterotrophic bacteria were two to three logs higher in sediments and biofilm than the drinking water tested within the same supply. These studies provide examples of microbial numbers in distribution system waters being outnumbered by those attached to distribution pipe surfaces.
The appearance of increased numbers of bacteria in the water phase may mask the presence of indicator organisms which may have entered the system as a result of a true breakdown in treatment processes or damage to the system (Le Chevallier, 1990a). The
16 problem of detecting a contamination event 1s exacerbated if indicator orgamsms colonise and grow in the biofilms.
1.2.4 Control of Bacterial Aftergrowth and Biofilms in Drinking Water Systems.
If the water treatment processes reduce the microbial load, the concentration of nutrients and the particulate matter of the water entering a distribution system with a high level of integrity, then the potential for aftergrowth in the distribution system should be reduced.
At present, it is not possible to prevent biofilms from forming in distribution systems but basic management techniques can be used to limit biofilm development and accumulation. These techniques include the use of treatment processes, including disinfection practices and system management programs. The quality of raw water and the level of treatment it undergoes will also affect the size and diversity of the microbial population within the distribution system.
Water containing very low concentrations of organic nutrients will support little aftergrowth (Rittman and Snoeyink, 1984). Studies by van der Kooij (1992) found that assimilable organic carbon decreased with distance from the treatment plant. It was suggested that water containing assimilable organic matter at concentrations less than 10 µg of carbon per litre did not promote the growth of microorganisms to a significant extent. Water of this quality was described as biologically stable.
In Australia, full treatment would ideally produce biologically stable water, but this is not always possible due to variations in the quality of raw water and the nature of treatment processes. For instance, treatments which include an oxidation step (to reduce the turbidity and inorganic material), may result in an increase in the amount of readily metabolised organic matter in the water (Schellart, 1986).
17 If this material is not removed before the water enters the distribution system, aftergrowth could be promoted. However, the same biological processes that result in aftergrowth can be exploited at the treatment plant to reduce the amounts of organic and inorganic nutrients in water, for instance by using fixed and fluidised bed filters (Rittman and Snoeyink, 1984 ).
The choice of disinfectant and its application will influence the potential for aftergrowth in the distribution system. The main disinfectant treatment used by water authorities since the early 1900's has been chlorine (Power and Nagy, 1989). Although chlorine is an effective bacteriocide under optimum conditions, its use in a distribution system will not necessarily control aftergrowth. A study of a water distribution system in New Jersey, USA by Le Chevallier et al. (1987) demonstrated that the maintenance of a free chlorine residual of 1mg/L was insufficient to eliminate the occurrence of aftergrowth of coliform bacteria. In another study, Olivieri et al. (1985) reported that coliform bacteria were detected in distribution water containing free chlorine residuals of up to 3 mg/L. Ridgway and Olson (1982) noted that in distribution water in California, USA, the presence or absence of chlorine in the water had no correlation with the number of microbes in a given sample.
Le Chevallier et al. (1988) tested the inactivation of attached and unattached bacteria by free chlorine and monochloramine. Bacteria growing as biofilms on the surface of granular activated carbon particles, metal coupons or glass slides were more resistant to free chlorine and monochloramine than were unattached organisms. An important observation made in this study was that monochloramine at 1mg/L had the same disinfection efficacy for biofilm organisms as free chlorine at the same concentration, even though monochloramine was considered a weaker oxidising agent. The authors interpreted these observations as indicating that monochloramine was better able to penetrate into the biofilm matrix and kill biofilm bacteria than was free chlorine. The authors noted that their observations were consistent with the experience of many water authorities which had changed the method of disinfection from chlorination to
18 chloramination and were able to effectively control the numbers of bacteria in their distribution water.
1.3 CHLORAMINES IN DRINKING WATER SYSTEMS
1.3.1 Introduction
An understanding of chloramination as a disinfection process and the complexities of chloramines within drinking water systems was considered integral to investigating the presence of nitrification within drinking water systems.
Disinfection is part of the drinking water treatment process and may be the only primary treatment (Power and Nagy, 1989) for some authorities. Ireland et al., (1983) reported that for many water supplies in Australia, disinfection has been the only form of treatment provided. Water entering Sydney's main metropolitan supply for example, has for many years received only coarse screening followed by disinfection and the addition of fluoride during normal operating conditions. Coincidently, this supply also has a history of coliform aftergrowth (Ireland et al., 1983).
Water authorities primarily choose a disinfection agent that is able to inactivate pathogens (Wolfe et al., 1984). An agent that also possesses residual characteristics provides an additional barrier, within a distribution system, to contamination resulting from aftergrowth. Le Chevallier (et al., 1984) reported that, even though the United States experienced a decrease in reported waterborne disease outbreaks between 1938 and 1955, the Centre for Disease Control cited an increase in outbreaks during the l 970's. The increase in reports may have been due to either improved reporting and detection methods and/or increased pressure on water treatment facilities due to poorer raw water quality. In 1978 for example, treatment deficiencies, such as inadequate filtration and breakdown in disinfection, resulted in 33% of waterborne disease outbreaks in community drinking water systems (Le Chevallier et al., 1984)
The United States has been using chlorine and chloramines as disinfection agents for drinking water since the beginning of this century (Kirmeyer et al., 1993). In 1907 the first procedure for preparing monochloramine by the combination of ammonia with
19 sodium hypochlorite was presented. By 1917 the Denver Union Water Company was useing pre-reacted chloramines to prevent bacterial aftergrowth problems (Wolfe et al., 1984). In 1938 it was determined that 16% out of the 2541 municipal water supplies in the USA used ammonia in the treatment phase. During World War II, with the inability to purchase ammonia, the percentage rapidly decreased (Wolfe et al., 1984). The use of chloramines in the United States has since increased in popularity. The interest in chloramines as a disinfection agent is likely to have increased due to stricter US federal regulations for trihalomethane levels in drinking water systems. Trihalomethanes are the product of a reaction between chlorine and certain organics.
1.3.2 Chloramine Chemistry
The dispersion of elemental chlorine in water results in rapid hydrolysis and formation of hypochlorous acid . The reaction is illustrated below:
The reaction to form hypochlorous acid will mostly go to completion in drinking water systems as it requires the pH of water to be above 3 (Wolfe et al., 1984). Hypochlorous acid will dissociate to form hypochlorite ion. Hypochlorous acid and hypochlorite ion are both strong oxidants and are collectively referred to as free available chlorine (Wolfe et al., 1984 ). The concentrations of each will depend on the pH of the aqueous environment:
HOCl <=> ocr + H+
The formation of inorganic chloramines are successive reactions in which the ammonia molecule becomes more chlorinated if the pH is low and the chlorine to nitrogen weight ratio is high. The reactions for chloramine formation can be illustrated by:
Monochloramine:
Dichloramine:
20 Nitrogen trichloride: HOCl + NHC12 <::> NC13 + H2O
Drinking water authorities will preferentially form monochloramine by adding excess ammonia in the range of Cl2:N of 5.5:1 by weight. Problems associated with taste and odour discourage the use of conditions favouring the formation of dichloramine or nitrogen trichloride (Wolfe et al., 1984).
There are two main reactions that result in the hydrolysis of monochloramines (Wolfe et al., 1984). The first one, illustrated below, is relatively slow but would result in the formation of hypochlorous acid and hence disinfection residuals would be maintained:
The second reaction leads to the formation of hydroxylamine and is extremely slow with little impact on water disinfection:
1.3.3 Relationship of Nitrite with Monochloramine.
Chloramines, as oxidants, would be expected to indirectly oxidise nitrite via a reaction involving the hydrolysis of monochloramine to hypochlorous acid, which would then oxidise nitrite. Valentine ( 1985) claims that a direct reaction between monochloramine and nitrite should not be excluded but the reaction is believed to be very slow. Valentine (1985) stated that, without a full investigation and understanding of the reaction kinetics, the potential reactions can not be disregarded especially under environmental conditions. This concern initiated laboratory controlled experiments to investigate the disappearance of monochloramine in the presence of nitrite. In these experiments, Valentine (1985) found that the rate of disappearance of monochloramine in the presence of nitrite was greater than the predicted rate by hydrolysis. The extrapolation
21 of these findings to the aqueous environment is questionable considering the complex nature of the reactions.
1.3.4 Use of Chloramines by Water Authorities
As described in 1.3 .1, chloramines have been used to treat drinking water systems since the beginning of this century and there are some obvious benefits and disadvantages to their use. Kirmeyer et al. (1993) summarised the advantages and disadvantages after conducting a survey of water utilities and relevant research from the United States. An important advantage found was that monochloramine residuals will persist in a drinking water system longer than chlorine thus providing an effective secondary disinfectant. Additionally, it was found that monochloramine has the ability to penetrate biofilms more successfully than chlorine and taste and odour formation is minimised. The other major advantage for water authorities in the USA is the tendency for trihalomethanes not to form when chloramines are used.
On the other hand, the disadvantages included adverse effects on specially filtered water such as those used for kidney dialysis machines and deleterious effects on elastomeric materials sometimes used in plumbing fixtures. Of most relevance to this investigation is that a number of water authorities surveyed by Kirmeyer (1993) experienced the onset of deleterious effects of nitrification.
While some water authorities have reported nitrification within their chloraminated drinking water systems, there are numerous examples of chloraminated drinking water systems which do not appear to experience nitrification (Kirmeyer, 1993). Of these, there were were authorities that had either filtered or unfiltered systems and some _experienced regrowth problems while others either did not or did not report it.
22 1.4 STUDY AIMS
Coupled with the knowledge that other water authorities had experienced the sudden loss of disinfectant residual due to nitrification and Sydney Water's own experience of such a loss within the Ryde Delivery System this investigation into biological nitrification was commissioned.
The aims of this investigation were to:
1. Confirm the presence of biological nitrification. 2. Determine the persistence of nitrification within the delivery system; and 3. Isolate and characterise nitrifying bacteria in both the water and biofilm phases.
23 CHAPTER 2: DETERMINATION OF NITRIFICATION WITHIN THE RYDE DELIVERY SYSTEM
2.1 INTRODUCTION
One of Sydney Water's corporate targets was to "achieve NHMRC drinking water quality guidelines as specified in the Operating Licence" (Water Board, 1994). Historically, the detection of large numbers of coliforms within Sydney Water's main metropolitan supply has prevented some supply areas from meeting the NH&MRC/ A WRC 1980 guidelines for drinking water (Cooper, 1987). The investigation of mechanisms to control coliform levels that exceed the guidelines, was therefore one of the issues addressed by a water quality improvement program within Sydney Water. The confirmation of nitrification activity and development of control mechanisms to prevent significant decay of chloramine residuals became one of the major investigations within the program. The implications for Sydney Water's maintenance of drinking water quality is that nitrite will impose a demand on chloramines and free chlorine will react preferentially with nitrite rather than with ammonia (Wolfe et al., 1988). Such reactions would result in an inability to maintain disinfectant residual and, hence, increase the potential for aftergrowth or a contamination event.
2.1.1 Sydney Water's Experience with Aftergrowths and the Introduction of Chloramines.
Aftergrowths of coliforms in Sydney's main metropolitan distribution system first came to prominence in 1976 (Ireland et al., 1983). Despite remedial measures, including main swabbing and temporary chlorination at strategic points, the problem persisted, generally throughout the main metropolitan supply, particularly the Ryde Delivery System. The dominant coliforms identified in the main metropolitan supply were Klebsiella oxytoca and Enterobacter species. Faecal coliforms were mostly absent in the reticulation water which suggested the coliform levels were not a result of a faecal
24 contamination event. Bacteriological monitoring of the raw water at the point of chlorination did not reveal coliform or faecal coliform bacteria under normal operating conditions, suggesting that the bacteria were multiplying in the system itself (Ireland et al. 1983).
Treatment of the main metropolitan supply's source water consisted of some fine screening, chlorination and the addition of fluoride. Sydney Water relied on draw-off of high quality source water primarily from Warragamba storage for distribution to the main supply system. The source water quality would deteriorate following heavy rain periods (Ireland et al., 1983 ).
The passage of water through Prospect Reservoir, a shallow open lake (50,000 ML), critically altered the quality of source water before distribution to the main supply systems because it experienced increased algal cell numbers. Ireland et al., (1983) claimed, on average, that Prospect Reservoir contributed a particulate load of approximately 500 tonnes/year to the distribution system. Almost half of this was fresh algal cells while the remainder was a mixture of organic and inorganic material.
In an effort to overcome aftergrowth problems in the distribution system, Sydney Water commenced chloramination of the main metropolitan supply at the South Prospect Reservoir offtake in February 1986. It was assumed the reason for the change in disinfection practice followed the lead by overseas water authorities, especially in the United States, involving a change from chlorine to chloramine usage. More recently, it has been reported (Thomas, 1990, Wolfe et al., 1994) that chloramine useage provided a residual that would penetrate further into the reticulation system even though the change to chloramine usage may have been prompted by more stringent disinfection by-product standards. Le Chevallier (1990a), in his review of coliform regrowth, reported that monochloramines are more successful than free chlorine at penetrating biofilm layers and inactivating attached organisms. In addition, he suggested that free chlorine was consumed before penetration of the biofilm layers because of its higher rate of reaction.
25 Chloramination of the two major trunk mains (2100 and 1800 mm diameter) at the South Prospect offtake was achieved by first injecting chlorine gas followed by the addition of 065 ammonia 300-400 m downstream. Chlorine and ammonia were dosed at the ratio of 2.5 - 3.0: 1 of chlorine to ammonia - N so as to minimise the formation of dichloramines and trichloramines (Duker, 1986).
Following the introduction of chloramination at South Prospect, increases in the total combined chlorine residuals were observed in the North and South Harbour systems. The extent and persistence of residuals throughout the supply system was also evident by the number of customer enquires related to the taste and odour of their drinking water. However, the benefits appeared to be short lived and, in the second half of 1986, total combined chlorine residuals at Ryde Pumping Station started to decline (Cooper, 1987).
Discussion with Dr. E. Means of the Metropolitan Water District of Southern California in 1987 ( Means, 1987) alerted Sydney Water to the possibility that the decrease in disinfection residual may have been due to the occurrence of biological nitrification within the distribution system. The Metropolitan Water District of Southern California had investigated biological nitrification episodes within their two finished water reservoirs (Wolfe et al. 1988, 1990).
Investigations into nitrification within chloraminated drinking water systems has highlighted key indicators of nitrification activity as (Cunliffe, 1991; Skadsen, 1993; Wolfe et al., 1988) a decrease in the concentration of total chlorine residual and ammoma with concurrent increases in the concentrations of nitrite and/or nitrate. Therefore the primary parameters for the monitoring of this activity were identified as total combined chlorine residual, ammonia-N, nitrite-N and oxidised nitrogen, which by subtraction nitrite-N would provide a value for nitrate-N.
Prior to 1988, monitoring of these primary parameters in the bulk water was limited, and in the reticulation system the only parameter, of the primary parameters for nitrification, monitored regularly was total chlorine residual. It was not possible
26 therefore to compare data for the primary variables before commencement of chloramination and during the study period.
2.2 DESCRIPTION OF SYSTEM AND STUDY SITES
2.2.1 Supply of Water to Sydney
Sydney Water delivers water to a population of approximately 3.7 million people. Its area of drinking water operation is approximately 13,000 square kilometres, extending from the Hawkesbury River in the north to Gerroa in the south and from the coastline of Sydney to Mt Victoria in the west (refer Figure 1). Some of Sydney Water's catchment areas extend beyond the operational boundaries.
Water is pumped or delivered by gravity through approximately 20,500 kilometres of mains and 260 service reservoirs. The water is sourced from four main catchments - the Upper Nepean, the Warragamba, the Shoalhaven and the Woronora - with minor supplies drawn from the Hawkesbury River and tributaries. The main metropolitan system draws water from the Warragamba and Upper Nepean catchments. Water can also be supplemented from the Shoalhaven catchment. The water from the Warragamba catchment is impounded by Warragamba Dam to form Lake Burragorang. Supply for Sydney is delivered by gravity from the Warragamba to Prospect Reservoir and would account for at least 80% of water supplied to the main metropolitan system. The remainder is delivered from the Upper Nepean catchment (consisting of four dams) by a series of natural river channels and diversion weirs on two of the rivers. Tunnels, aqueducts and open canals (collectively known as the Upper Canal) then deliver the water to Prospect Reservoir (Sydney Water, 1995).
At the time of this investigation, Prospect Reservoir (Plate 1) was the major distribution reservoir for the main metropolitan supply system. All water leaving Prospect was, under normal operating conditions, treated by screening, disinfection and fluoridation. Water was then distributed via major pumping stations to the distribution system.
27 Figure 1: Diagram of Sydney's Water Supply System including the Ryde Delivery system.
~
~( •Pr\s p~ct ·~ T SCALE 0 5 10
KILOMETRES
SCALE o , a
KILOM[ l R[ S
KEY
D Chlo romination Plant
• Ch lor inat ion Plant
Prospect Reservoir
N 00 2.2.2 Description of the Ryde Distribution System
The Ryde Distribution System was chosen for a field investigation because initial pilot sampling by Sydney Water indicated a loss of combined chlorine residual in the presence of detectable levels of nitrite. The Ryde Distribution System studied was defined as (a) the bulkwater system which carried water from Prospect Reservoir to Ryde Pumping Station, and (b) the Ryde Delivery system which distributes water from Ryde Pumping Station to the metropolitan customers north of Sydney Harbour (Figure 1).
The bulkwater is chloraminated shortly after leaving Prospect Reservoir and is carried, via two trunk mains (2100 and 1800 mm diameter) and an open canal, to Pipehead (Plate 2) where a degree of mixing in the distribution chamber occurs before distribution to Ryde Pumping Station. The degree of mixing depends on the demand for water in the southern or northern suburbs of metropolitan Sydney. At times of peak flow, the retention time of water between Prospect and Ryde Pumping Station can be less than 24 hours.
The Ryde Delivery System receives water from Ryde Pumping Station via a series of trunk mains, service reservoirs and smaller reticulation mains within defined service reservoir supply zones. The time taken for a plug of water to arrive at a customer's tap will depend upon the distance from the source, the operation of the service reservoir and consumer demand. The service reservoirs in this system vary in capacity from 0.05 to 77 megalitres (Sydney Water, 1995) and will also vary in operation from flow through to a balance reservoir. Balance reservoirs assist in the maintenance of water pressure and provide additional supply when water demand increases. The mains within the reticulation system vary in age, size, construction material (for example cement lined in situ or cast iron) and whether the main is defined as dead end or circulating. The delivery system investigated was typical of other systems within Sydney Water's main metropolitan supply.
29 Due to a history of coliform aftergrowth within the Ryde Delivery System, Sydney Water had commissioned several relocatable chlorination units, positioned at the outlets of several service reservoirs (Duchatel, 1989). These chlorination units were designed to dose chlorine gas at a maximum dose of lmg/L.
During the study period, normal remedial maintenance was carried out within the distribution system. These activities included routine mains flushing, service reservoir cleaning and dewatering of trunk mains for maintenance (Duchatel, 1989). Also during the study period non-programmed remedial maintenance such as main breaks and remedial chlorination of reservoirs or reticulation mains would have occurred.
Specific sites monitored within the study system are described within the section on methods.
30 Plate 1: Aerial view of Prospect Reservoir, which was considered as the source for the purpose of this investigation
Plate 2: Sampling site for the Lower Canal at Pipehead. This site was located downstream of the chloramination point on the canal at Propsect and u/s of the distribution chamber and bulkwater mains.
31 2.3 METHODS
Some reported investigations of nitrification within chloraminated drinking water supplies have primarily considered the fate of significant chemical variables in the water phase over time (Wolfe et al., 1988; Cunliffe, 1991; Skadsen, 1993). The variables monitored included total chlorine residual, ammonium, nitrite and oxidised nitrogen. In order to determine whether biological nitrification was occurring within this study, a similar approach was adopted.
This chapter presents the results of the chemical monitoring which are then supported by bacteriological isolation and identification as described in Chapter 3.
2.3.1 Sample Design
The statistically rigorous sampling program involved monitoring water quality with respect to nitrification activity throughout the study system. For the purpose of this investigation this was from from Prospect Reservoir to the furthest supply zone within the Ryde Delivery System. Strategic sites were selected throughout the study system to enable consideration to the changes in nitrification activity from the source.
The three year monitoring program was designed for sampling all year. This accounted for the effect of seasonal variation and reduced the possible bias from activities associated with the management of the overall system, which could influence water quality.
For quality control purposes triplicate samples, were taken at Ryde Pumping station each sampling occasion.
32 Sampling Sites
The sites were chosen along the length of the distribution system and could be easily divided into the three categories of bulkwater, service reservoirs and supply zones. The bulkwater system represented Prospect Reservoir to Ryde Pumping Station. The sites within this part of the system were a mixture of major trunk mains (site WPS5) and an open canal (site HPR8 and HPR3). One of the sites within the canal was at Prospect Reservoir, before treatment by disinfection (HPR3)and for the purposes of this investigation was considered representative of the raw water.
Service reservoirs were included because of the impact they can have on water quality in general and on combined chlorine residual in particular. For example, some service reservoirs are operated so that they have a long detention time and little mixing of water within the storage can occur, hence a negative impact on total chlorine residual. The service reservoirs monitored were those associated with the supply zones within this investigation. Five supply zones were chosen for this investigation and were selected on the basis of increasing distance from Prospect Reservoir. Three reticulation sites were chosen within each supply zone. Each of the three reticulation sites were of varying distance from the supply zone's service reservoir. The only exception was the Palm Beach supply zone which only had one site due to the small size of the zone. The sampling points within the supply zones were the front garden taps of households.
From August 1990 to June 1992, the water entering Prospect Reservoir from Warragamba Dam was alum dosed due to poorer quality raw water. This period of time overlapped with the monitoring period of the study system, and was not a planned treatment procedure when the sampling program was designed.
33 Table 2: Site Description and Distance from Chloramination Point on the Lower Canal at Prospect Reservoir.
SITE CODE SITE DESCRIPTION DISTANCE FROM TREATMENT@ PROSPECT (km)
1 HPR3 Canal: Lower Canal ex Prospect, before -0.3 chloramination 1HPR8 Canal: Lower Canal ex Prospect, at Pipe Head, 7.5 after chloramination WPS5 Trunk main: Ryde Pumping Station 20.5 "'ROSO Reservoir: Hermitage 22.0 Zone 50 Reticulation: 8 Woodbine Cr, Ryde 27.3 (2.4)'3 Zone 50 Reticulation: 23 Beach St, Tennyson 27.3 (4.3) Zone 50 Reticulation: 8 Alfred St, Woolwich 27.3 (9.3) -'R097 Reservoir: Pymble No. 1 30.6 3R098 E& W Reservoir: Pymble No. 2 (East & West half) 30.6 Zone 97 Reticulation: 37 Narelle Ave, Gordon 37.8 (2.3) Zone 97 Reticulation: 2 Carona Ave Gladesville 37.8 (6.9) Zone 97 Reticulation: 12 Portview Rd, St Leonards 37.8 (12.3) R283 Reservoir: Frenchs Forest 38.9 Zone 283 Reticulation: 22 Salemo St, Forestville 42.2 (2.4) Zone 283 Reticulation: 27 Milham Cr, Forestville 42.2 (3.2) Zone 283 Reticulation: 142 Killamey Dr, Killamey 42.2 (4.2) Heights
"'R131 E&W Reservoir: W arringah (East & West half) 41.4 Zone 131 Reticulation: 2 Ryan Pl, Brookvale 53.1 (3.1) Zone 131 Reticulation: 12 Bolwarra St, Elanora Heights 53.1 (11.7) Zone 131 Reticulation: 11 George St, Avalon 53.1 (20.3) Rl92 Reservoir: Palm Beach Elevated 65.5 ( Zone 192 Reticulation: 120 Pacific Rd, Palm Beach 66.5 (1.0) l •Site not sampled from 19 February 1992 to 20 August 1992 due to Occupational Health and Safety requirements.
34 2Site not sampled from 19 June 1991 to 3 October 1991 due to reservoir maintenance. 3Site not sampled from 10 February 1992 to 24 April 1993 due to Occupational Health and Safety requirements. 13The distance is the average for the sites within the zone from chloramination at Prospect, while the figure in brackets is the distance from the zone supply service reservoir.
Reservoir Cleaning and Rechlorination Plant Commissioning
Three of the reservoirs monitored had rechlorination plants on the outlet main of the service reservoir. The outlet of Warringah Reservoir had a rechlorination plant operating from December 1988, while Hermitage Reservoir's plant was commissioned in December 1990. Each of the service reservoirs monitored for the study were emptied for programmed cleaning during the three years of monitoring (Table 4).
Table 3: Programmed Reservoir Cleanings for Study System.
Reservoir 1990 1991 1992 ROSO August June - October R097, R098 E & W August April R283 July Rl31 E August Rl31 W July R192 July
2.3.2 Sample Collection
The taps from which samples were collected within trunk mains, service reservoirs or front garden taps were flushed for 2 to 3 minutes before the sample was collected. This was to ensure the sample collected was representative of the water within the main or reservoir. Samples for bacteriological analyses were collected in autoclaved sterile
35 glass 250 ml bottles which had a dechlorinating agent added prior to sterilisation. Samples for chemical analyses were collected in acid washed plastic bottles while samples for pH were collected in untreated plastic bottles. Samples which were analysed at the laboratory were stored on ice or in a refrigerator ( <4°C) and analysed the same day, or within 24 hours of sample collection.
Samples were collected on the same day each week within the first five months of monitoring. A review of this data revealed the potential for sample bias and therefore the program was modified. The revised program was changed to a three week sampling cycle where-by each supply zone was sampled once within the cycle. That is, all reticulation sites within the one supply zone were sampled once per week. The exception was again the Palm Beach supply zone which only had one reticulation site and was therefore sampled each week within the cycle. All bulkwater and service reservoir sites were sampled once within the three week cycle. The cycle was referred to as a block and sampling occurred over two days, randomly chosen within each week.
2.3.3 Summary of Drinking Water Analysis Methods
Laboratory analyses were conducted for pH, ammoniacal-nitrogen (referred to as ammonium-N), oxidised nitrogen (NOx-N), nitrite-nitrogen (nitrite-N), total coliform bacteria and heterotrophic plate counts at 3 7°C and 20°C. The concentration of nitrate nitrogen (nitrate-N) in the samples collected was determined by difference, from the concentrations of NOx-N and nitrite-N.
Field determinations of temperature and total combined chlorine were conducted on site. Total combined chlorine was determined by amperometric titration or DPD colorimetric method. A summary of methods employed follows.
36 Total chlorine residual
Total chlorine residual was determined in the field at the time the sample was taken. One of two methods were employed, either amperometric titration or DPD colorimetric method.
Amperometric Titration Method
A Wallace and Tiernan titrator was used to determine total chlorine residual in the field. The method was based on the manufacturer's user manual and APHA (1989) 4500-Cl D.
DPD Colorimetric Method
This method was performed using a HACH DRl00 colorimeter and a method developed from the manufacturer's manual (ih ed., 1989) and APHA ( 1989) 4500-Cl G.
pH
The pH of drinking water samples was measured in the laboratory usmg the electrometric method of calibrated probes within the range 0 to 14 pH units, according to APHA (1989) 4500-H+B.
Ammonium-nitrogen
Concentrations of ammonium-nitrogen (ammonium-N) were measured in the laboratory on a SKALAR autoanalyser using the method based on the automated phenate method described in APHA (1989) 4500-NH3 H. Modifications to the method involved the use of sodium dichloroisocyanurate and sodium salicylate as set out in the SKALAR Autoanalyser instruction manual. The method though could not be used to distinguish
37 between ammonium and free ammonia as the method measures not only ammonium-N and ammonia-N but also monochloramine-N and dichloramine-N.
Nitrite-nitrogen
The concentration of nitrite-nitrogen (nitrite-N) was determined in the laboratory by colorimetric method using sulfanilamide/a.-naphthylamine reaction as described in
APHA (1989) 4500-NO2-.
Oxidised-nitrogen
Concentrations of oxidised-nitrogen (NOx-N) were determined on a SKALAR Autoanalyser using the automated cadmium reduction method of APHA (1989) 4500- NO3 - F.
Nitrate-nitrogen
NOx-N consists of nitrite-N and nitrate-N. Hence the concentration of nitrate-N was estimated as the difference between measured concentrations ofNOx-N and nitrite-N.
Total coliform bacteria
Total coliform bacteria numbers were determined in the laboratory by membrane filtration, based on APHA (1983) 9222 B and the Department of the Environment (1983). Plates were incubated for 18 to 20 hours at 37°C.
38 Heterotrophic plate count
Heterotrophic bacteria numbers were determined by the pour plate based on APHA (1989) 9215 B, using R2A agar (Difeo). Two pour plates were prepared for each sample with one incubated at 37°C for 48 hours and the other at 20°C for 7 days.
2.3.4 Analysis of Data
In order to determine the occurrence of nitrification and its extent within the study system, the analysis of field data focused primarily on the variables most likely to indicate nitrification trends. These variables included total combined chlorine, ammonium-N, nitrite-N and nitrate-N.
All statistics and graphics were performed usmg Microsoft@ Excel Version 4 for Windows™ Series. Descriptive statistics performed included mean, standard error, median, standard deviation, variance, minimum, maximum, count and 95% confidence limit for each of the primary variables at each site for each quarter. Descriptive statistics are tabled in Appendix 2.
The assessment of data from the three year sampling program to determine changes in variables over time and distance required each year to be divided into quarters closely resembling water temperature seasons. The statistical treatment of the data set is summarised in Table 4.
39 Table 4: Summary of Sampling Dates and Data Set Summary
BLOCKS DATES QUARTER 1-3 12-Apr-90 to 7-Jun-90 Ql 4-7 14-Jun-90 to 5-Sep-90 Q2 8-11 l l-Sep-90 to 29-Nov-90 Q3 12-15 11-Dec-90 to 7-Mar-91 Q4 16-19 13-Mar-91 to 30 May-91 Q5 20-23 5-Jun-91 to 22-Aug-91 Q6 24-28 28-Aug-91 to 5-Dec-91 Q7 29-32 l 1-Dec-91 to 5-Mar-92 Q8 33-36 1 l-Mar-92 to 28-May-92 Q9 37-40 3-Jun-92 to 20-Aug-92 QIO 41-45 26-Aug-92 to 3-Dec-92 Qll 46-48 9-Dec-92 to l 8-Feb-93 Ql2
Line graphs were plotted for variables over time with discontinuous lines representative of non sample collection periods. This method of presentation was chosen because it was the potential relationship or trend between variables over time that required illustration. Columnar graphs were used to illustrate the fluctuation of variable concentration with distance.
With the exception of temperature, correlation matrices for all variables and sites were determined to assess the strength of the relationship between variables. Preliminary assessments indicated weak correlation factors for temperature which lead to the elimination of the variable in subsequent assessments. Variables that were included in the matrices were total combined chlorine, ammonium-N, nitrite-N, nitrate-N, coliforms, heterotrophic plate counts at 3 7 and 20°C and pH. Distance was included for the analysis of all data. The following labels were adopted to describe ranges of the correlation coefficient (Rowntree, 1981 ):
0.0 to 0.2 very weak 0 .4 to O. 7 moderate 0.7 to 0.9 strong 0.2 to 0.4 weak 0.9 to 1.0 very strong
40 2.4 RESULTS
Descriptive statistics for the field data are presented in Appendix 4. Graphical presentation of the field data analyses for site over time are presented in Figures 2-13. Graphical presentation for distance from the source are in Figures 14-25.
2.4.1 Changes in the concentration of primary variables at each site over time.
Figure 2: Upper Canal at Prospect before Chloramination (HPR3).
As would be expected for this site negligible concentrations of total combined chlorine residual were detected. Nitrite concentrations were also negligible with ammonia concentrations of some significance. Nitrate experienced the highest concentration of all the variables.
The site does not show trends indicative of nitrification over time.
Figure 3: Upper Canal at Pipehead after Chloramination (HPR8).
The Canal was closed for maintenance between March and August 1992 ( corresponding with the end of quarter 8 and the beginning of quarter 11 ), thus eliminating the need for sampling.
From the data, total chlorine and arnmonium-N remained relatively stable over time and were of higher concentrations than the other two variables. Nitrite-N fluctuated between quarter 1 and 6 before remaining relatively stable, but was generally negligible over the sampling period. Nitrate-N was relatively stable except for a small decline between quarter 4 and 7. The trend for nitrate-N is comparable to the data presented for the Lower Canal before chloramination (Figure 2).
41 Based on trends typical for nitrification activity, there is no evidence for nitrification at this site.
Figure 4: Ryde Pumping Station (WPS5).
The middle period of sampling (quarters 3 to 10) experienced negligble concentration of nitrite-N compared to the periods prior to quarters 3 and after quarter 10. The commencement of alum dosing at Prospect Reservoir coincided with quarter 3 and dosing ceased at the end of quarter 10.
Trends characteristic for nitrification are more evident after quarter 10 when increasing nitrite concentrations are concurrent with decreasing total chlorine and ammonium-N concentrations.
Figure 5 and Figure 6: Hermitage Reservoir (ROSO) and Supply Zone (Zone 50) respectively.
Both the service reservoir and supply zone exhibited comparable trends for total chlorine concentrations over the monitoring period. Ammonium-N fluctuated between quarter 1 and 4 then, declined between quarter 10 and 12 in a similar fashion to total chlorine. The ammonium-N concentrations for the supply zone have a similar trend to the reservoir after quarter 7 but differ between quarter 1 and 6, with an increasing trend evident.
Notably, nitrite-N concentrations for the reservoir and supply zone showed different levels of activity between quarters 4 and 10. The reservoir had minimal concentrations, in contrast to the supply zone which had obvious fluctuations in concentrations. In general, after quarter 10 both the reservoir and supply zone showed an increasing trend in mean nitrite-N concentrations.
42 On the whole, nitrate-N concentrations followed a similar trend for both reservoir and the supply zone. The overall trend was also comparable to the bulkwater sites. Mean concentrations for nitrate-N were higher within the zone than the reservoir.
Total chlorine, ammonium-N and nitrite-N concentrations within the reservoir after quarter 10 are comparable to trends experienced at Ryde Pumping Station (WPS5) for the same period (Figure 4). Hermitage zone has a similar trend but experienced a decrease in nitrite-N mean concentration in the last quarter of sampling. As for Ryde Pumping Station, there appears to be a relationship between the timing of the commencement and cessation of alum dosing with noticeable changes in the mean concentration of primary variables, particularly nitrite-N.
Figure 7: Pymble Zone (Zone 97).
Pymble Reservoir (R097) was not included in the graphical presentation due to the incompleteness of the data set following issues related to Occupational Health and Safety (OH&S) which preventing further sampling.
Within the Pymble supply zone, total chlorine and ammonium-N followed similar patterns, whereas the pattern for nitrite-N mean concentration was comparable to that experienced in Hermitage zone (Figure 6). The potential influence of alum dosing on nitrite-N concentrations is still apparent within the supply system. The overall pattern for nitrate-N was comparable to the bulkwater sites (Figures 2-4) and the Hermitage supply zone, indicating that nitrate concentrations were influenced by the source water.
As was the case for Hermitage supply zone, trends in the Pymble supply zone that are typical to nitrification are more evident in the final period of sampling (quarters 10 to 12 inclusive) for total chlorine, ammonia-N and nitrite-N. However, the relationship of variables is not fully reflected in quarters 1 to 5. Nitrite-N and ammonia-N show opposing trends but total chlorine acts independently to the fluctuating levels of nitrite-N.
43 Figure 8 and 9: Frenchs Forest Reservoir (R283) and Supply Zone (Zone 283) respectively.
The relationship between mean concentrations for the reservoir and zone are comparable to Hermitage zone (Figure 6) and reservoir (Figure 5). Evidence for detectable nitrification activity is more pronounced after quarter 10 again indicating the potential influence that alum dosing had on the source water.
Figure 10 and 11: Warringah Reservoir (Rl31E&W) and Supply Zone (Zone 131) respectively.
Graphical presentation of the data for Warringah reservoir was included even though data was incomplete due to OH&S requirements for sample collection. Both Warringah and Hermitage (ROSO) Reservoirs had rechlorination plants on the outlet mams, therefore giving rise to the potential to impact on nitrifying activity.
As for Hermitage and Frenchs Forest (Figures 5-9), the relationship between the reservoir and the supply zone is comparable. Evidence for detectable nitrification activity is again more pronounced after quarter 10.
Figure 12 and 13: Palm Beach Reservoir (Rl92) and Supply Zone (Zone 192) respectively.
The overall trends for the Palm Beach reservoir and supply zone differ from the previously described sites in the study system. Nitrate-N mean concentrations are clearly higher than the other variables and the trend for nitrate-N, which was clearly evident throughout the study system, was still evident for both the reservoir and zone. The difference between the trends for reservoirs and zones that was evident in Figures 5-11 was not evident for the Palm Beach. This was probably due to the small size of the supply zone and the small reservoir which was sometimes bypassed when the zone was being supplied.
44 Interestingly, nitrite-N was always present in the Palm Beach reservoir particularly between quarters 4 and 10. This was in contrast to what was experienced in the other reservoirs (Figures 5, 8 and 10) where concentrations of 0.01 mg(N)/L were detected. This may be attributed to the distance between Palm Beach reservoir and the source water which could have resulted in a reduced influence from the source water. This is somewhat evident for mean nitrate-N concentrations which did not show as similar a trend to the previous sites.
45 Figure 2: Mean Levels for Upper canal at Prospect (HPR3) - before chloramlnatlon
Alum on Alum off
...... ,..,.,." ...... _...... , .,,.,,...... ,. - / ~ __/ 0.1 ~
./ .11 ----- _/ ? I ------• ~ l O.Q1 2 3 4 5 6 7 8 9 10 11 12 Cluartera (Apr '90 · Feb '93]
Figure 3: Mean Levels for Lower canal @ Pipe Head (HPRB) Alum off
...... ······················---·
0.1
0.01 2 3 4 5 6 7 8 9 10 11 12 Quartera (Apr '90 • Feb '93]
Figure 4: Mean Levels for Ryde Pumping Station (WPSS)
AJum on Alum off
------Total N02 0.10
..3 NH3 ? I II• .! 0.01 2 3 4 5 6 7 8 9 10 11 12 Quartera (Apr '90 · Feb '119) Where legend Is total combined chlorine; nltrite-N; nltrate--N; ammonlum-N respecth \
46 Figure 5: Mean Levels for Hermitage Reservoir (RSO)
Numon Alum off 1.00
- ~ - , ~ < ._' ~ --- .. ./ / - - --~ __.,,,,, ' Y-. / --- ~ "ii ~ / ) 0 .10 IV/4 I ,
, ~ 3 / \\- ---, / 'f. \ I .. ./ I IV / l• \ 0.01 . - - -./ 2 3 4 5 6 7 8 9 10 11 12 Quarters (Apr 'VO · Feb '93)
Figure 6: Mean Levels for Hermitage Zone (Zone 50)
AJum on Alum off
0.10
J r' I II• !: 0.01 2 3 4 5 6 7 8 9 10 11 12 Quarters (Apr 'VO · Feb '93)
Figure 7: Mean Levels for Pymble Zone (Zone 97)
i=:l ~
2 3 4 5 6 7 8 9 10 11 12 Quarters (Apr 'VO - Feb '93) Where legend Is total combined chlorine; nitrite-N; nltrate--N, ammonlum-N respectf\ '
47 Figure 8: Mean Levels for Frenchs Forest Reservoir (R283)
Alum on Alum off 1.00
A
- ~ ---- r----..... ~ =-- ' ,..------....._ ~ / \ ,/ _... ~ v-- -.-- "" "'-- - 0.10 ~ / - ~ ...... f : \-- ~ - '"'··=--·-*·\ -- I t I I -~ --~ i J \ I \ Ii .! . ~ . J \ 0.01 . ------. ~ 2 3 4 5 6 7 8 9 10 11 12 Quarter-a (Apr '90 - Feb '93)
Figure 9: Mean Levels for Frenches Forest Zone (Zone 283)
Alum on Alum off 1.00
~ Total iij • u .. - N02 g 0.10 N03 ..J..... eC'I • NH3
0.0 1 2 3 4 5 6 7 8 9 10 11 12 Quarters (ARr '90 • Feb '93) Where legend is total combined chlorine; nijrije-N; nijrate-N; ammonium-N respective~
48 Figure 10: Mean Levels for Warrlngah Res (R131East & West)
AJum on Alum off
_... / - y tr--..__;_ / ~ 0.1 I/ \ /
/ ii ,J i" :s -- I\ • ll ~ .! 0.01 . - . . 2 3 4 5 6 7 8 9 10 11 12 Querterw (Apr '90 to Mar '92)
Figure 11: Mean Levels for Warringah Zone (Zone131)
AJum on Alum off
Total
- 4- N02 0 .10 ----+---- N03 ii NH3 i" :s • l 0.01 2 3 4 5 6 7 8 9 10 11 12 Quarterw (Apr '90 · Feb '93) Where ~nd is total combined chlorine; nitrite-N; nltrate-N;ammonium-N respectN
49 Figure 12: Mean Levels for Palm Beach Reservoir (R192)
Alum on AJum off 1.00 - ,-. ------._ _._ - -- 0.10 ~ - l A • ,.,.____..."""""'/ ------...... ,..
. ,., - -- .,.. / ,/ ··\-- ~ - '- '- ' - ,/ \ , v K' ~ r1---- ~ // \ ; -.,..._ " / I \ I \ 0.01 A l - - V- 2 3 4 5 6 7 8 9 10 11 12 Quarte,. (Apr '110 - Feb '93)
Figure 13: Mean Levels for Palm Beach Zone tzone 192) 1.00
-----Total
0.10 ----+- N03
3 --NH3 ~ I II• .!
0 .01 2 3 4 5 6 7 8 9 10 11 12 Quarte,. (Apr '110 - Feb '93) Where legend Is total combined chlorine: nltrite-N: nltrat~N: ammonlum-N respectt-.
50 2.4.2 Changes in Primary Variables with Distance from Prospect Reservoir.
The graphs have been plotted for the sites from the Upper Canal at Pipehead (7.5 km from Prospect Lake) to Palm Beach . Only the reticulation zones, showing average distance from Prospect Lake, and bulk water sites have been plotted. Service reservoirs have been excluded.
Quarters 1 and 2 (Figures 14 and 15)
Similar trends for changes in the variables tested with distance were evident in the first two quarters. Total chlorine and ammonium-N decrease with distance and nitrate-N had higher mean concentrations within the reticulation system. Nitrite-N generally decreases with increasing distance from Ryde Pumping Station.
Quarters 3 to 9 (Figures 16 to 22)
Total chlorine and ammonium-N have comparable similar trends to one another, with there being a general decrease in the concentration of the two variables with distance. The increase in total chlorine within the Warringah zone (53 km's) is more evident within these graphs and can be explained by the influence of the rechlorination plant on the outlet to the reservoir. It is interesting that Hermitage zone, which had a rechlorination plant commissioned in quarter 4, did not show a similar increase in the reticulation system. Quarters 1 and 2 (Figures 14 and 15 respectively), experienced mean concentrations of total chlorine and ammonia-N below 0.1 mg/L in the reticulation system. For quarters 3 to 9 on the other hand, the mean concentrations for the same variables were mostly higher than 0.1 mg/L for the reticulation system. The exception was the Palm Beach zone (Zone 192) located at the extremity of the supply system. As was evident in graphs 4 to 11, the dosing of alum for quarters 3 to 9 at Prospect Reservoir may have produced favourable conditions for the maintenance of both total chlorine and ammonium-N.
51 For quarters 3 to 6 (Figures 16 to 19) nitrite-N decreases with distance within the bulkwater system whereas it is more stable within the bulkwater over quarters 7 to 9 (Figures 20 to 22). Generally nitrite-N then increases with distance within the reticulation system. Nitrate-N is not as stable within the reticulation system and mean concentrations are generally lower when compared to quarters 1 and 2 (Figures 14 and 15).
Quarters JO to 12 (Figures 23 to 25)
Very similar trends to the first two quarters of the sampling regime (Figures 14 and 15) were established during the last three quarters. Total chlorine and ammonium-N concentrations decreased with distance which may have coincided with the cessation of alum dosing at Prospect Reservoir, thus producing less favourable conditions for maintaining the two primary variables.
2.4.3 Strength of relationship between variables.
Moderate to very strong correlation factors between variable parameters have been highlighted in Table 6. Figures in bold show moderate to very strong positive or negative relationships. Correlation matrices for individual sites were also determined because of the influence on correlation factors that arose from combining all sites. It is interesting to note the differences between (a) all sites combined versus individual sites and (b) service reservoirs and their respective supply zones. As would be expected, the strength of the correlation factor and the variable change depending on whether all data is included or if the analysis was site specific.
The correlation matrix for all sites combined (Table 6a) is further support to what was generally evident within the graphs. That is, the positive relationship between total chlorine and ammonium-N and the negative relationship between total chlorine with distance. Only a weak relationship with nitrite-N and total chlorine was evident over all
52 sites. Ammonium-N experienced a negative correlation (moderate) with both nitrite-N and nitrate-N.
The correlation factors for total chlorine and nitrite showed no consistency as was illustrated within the table. The direction is primarily negative, as would be expected, except for the sites where the mean concentrations for either of the variables would be almost negligible and the correlation factor close to zero.
The differences that occur between the water quality of service reservoirs and their respective supply zone is evident for both Hermitage (Table 6c and d) and Frenchs Forest (Table 6e and f).The strength of the correlation factors for the primary variables vary and in some circumstances a moderate relationship with secondary variables was established.
53 Figure 14: Mean Levels of Varlable with Distance for Q1 (1990)
1.00 0.90 0.80 0.70 0.60 0 .50 3 .. 0.40 ? 0.30 0.20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (km)
Figure 15: Mean Levels of Varlable with Distance for Q2 (1990)
1.00 0.90 0.80 0.70 0.60 0.50 3 .. 0.40 ? 0.30 0.20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (km)
Figure 16: Mean Levels of Variable with Distance for Q3 (1990)
1.00 0.90 0.80 0 .70 • Total 0.60 . N02 0.50 . N03 J 0.40 ? • NH3 0.30 0 .20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (km) Where legend Is total combined chlorine; nttrite--N; nttrate--N; ammonium-N respecill,
54 Figure 17: Mean Levels of Varlable with Distance for Q4 (1990)
1.00 0.90 0.80 0.70 0.60 0 .50 j ,, 0.40 0.30 0.20 0.10 0.00 7 .5 20.5 27 38 42 53 66 Distance (km)
Figure 18: Mean Levels of Variable with Distance for as (1991)
1.00 ...... ------~--- 0.90 0.80 0.70 ...1 0.60 ~ 0.50 e 0.40 0.30 0.20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (km)
Figure 19: Mean Levels of Variable with Distance for Q6 (1991)
1.04 mg/L 1.00 0.90 0.80 0.70 . Total 0 .60 . N02 0.50 3 . N03 ., 0.40 ,, . NH3 0.30 0 .20 0.10 0.00 7.5 20.5 27 38 42 53 66 Dlotance (km) Where legend is total combined chlorine; nitrite-N; nltrate-N; ammonlum-N respetti',
55 Figure 20: Mean Levels of Variable with Distance for Q7 (1991)
1.00 0.90 0.80 0.70 0.60 0.50 3 0.40 ..? 0.30 0.20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (mg/l)
Figure 21: Mean Levels of Variable with Distance for Q8 (1991)
1.00 ...... 0.90 0.80 0.70 0.60 0.50 J 0.40 ? 0.30 0.20 0.10 0.00 7.5 20.5 27 38 42 53 66 Distance (km)
Figure 22: Mean Levels of Variable with Distance for Q9 (1992)
0.9 0.8 0.7 . Total 0 .6 . N02 0.5 . N03 3 .. 0.4 ? . NH3 0.3 ············•··· .... 0.2 0.1 (Not sampled) 0 7.5 20.5 27 38 42 53 66 Distance (km) Where legend is total comb'ned chlorine; nhrtte--N; nitrate-N; ammonium•N res~
56 Figure 23: Mean Levels of Variable with Distance for Q10 (1992)
0.9 0.8 0 .7 0.6 0.5 3 'f. 0.4 0.3 0.2 0.1 (Not sampled) 0 7.5 20.5 27 38 42 53 66 Dlatance (lun)
Figure 24: Mean Levels of Variable with Distance for Q11 (1992)
1.00 ......
0.90 0 .80 0.70 0.60 0.50 l 0.40 0 .30 0 .20 0 .10 0 .00 7 .5 20.5 27 38 42 53 66 Dlatance (km)
Figure 25: Mean Levels of Variable with Distance for Q12 (1992)
1.00 0.90 0 .80 0.70 • Total 0.60 . N02 0.50 3 . N03 ., 0 .40 r" • NH3 0.30 0.20 0.10 0 .00 7.5 20.5 27 38 42 53 66 Dlatance (km) Where legend ls total combined chlorine; nltrite-N; nltrate-N: ammonium-N respedlv
57 Table 5: Correlation Matrices for all Data Points (a) and Individual Sites (b - i). (a) Correlation Matrix for all Data
Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Distance Total 1.00 NO2 -0.36 1.00 NO3 -0.50 0.14 1.00 NH3 0.82 -0.45 -0.50 1.00 Coli -0.07 0.03 0.08 -0.09 1.00 HPC37 -0.18 0.02 0.08 -0.19 0.75 1.00 HPC20 -0.29 0.19 0.24 -0.32 0.04 0.18 1.00 pH -0.13 0.26 0.09 -0.19 0.01 0.00 0.06 1.00 Distance -0.51 0.24 0.55 -0.43 0.04 -0.02 0.13 0.15 1.00
(b) Correlation Matrix for Ryde Pumping Station (WPS5) Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Total 1.00 NO2 -0.67 1.00 NO3 -0.08 0.17 1.00 NH3 0.60 -0.50 0.00 1.00 Coli 0.04 -0.06 0.11 0.02 1.00 HPC37 0.00 0.01 0.01 0.05 -0.01 1.00 HPC20 -0.31 0.34 0.18 -0.15 -0.01 0.06 1.00 pH -0.18 0.35 -0.03 -0.09 -0.05 0.05 0.05 1.00
(c) Correlation Matrix for Hermitage Reservoir (R50) Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Total 1.00 NO2 -0.72 1.00 NO3 0.07 0.20 1.00 NH3 0.74 -0.81 -0.16 1.00 Coli -0.13 0.10 0.10 -0.14 1.00 HPC37 -0.32 0.28 -0.06 -0.28 0.04 1.00 HPC20 -0.42 0.48 0.04 -0.47 -0.01 0.54 1.00 pH -0.10 0.20 0.06 -0.18 0.15 0.01 -0.01 1.00
( d) Correlation Matrix for Hermitage Zone (Zone 50) Total NO2 NO3 NH3 Co/i HPC 37 HPC 20 pH Total 1.00 NO2 -0.22 1.00 NO3 -0.40 -0.17 1.00 NH3 0.76 -0.33 -0.47 1.00 Coli -0.16 0.16 0.12 -0.17 1.00 HPC37 -0.14 0.00 0.06 -0.09 0.15 1.00 HPC20 -0.36 0.06 0.26 -0.35 0.15 0.10 1.00 pH 0.09 0.50 -0.19 -0.11 0.08 -0.08 0.09 1.00
58 (e) Correlation Matrix for Frenchs Forest Reservoir (R283) Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Total 1.00 NO2 -0.40 1.00 NO3 -0.47 0.31 1.00 NH3 0.51 -0.69 -0.52 1.00 Coli -0.09 0.09 0.07 -0.14 1.00 HPC37 -0.16 0.15 0.17 -0.26 0.93 1.00 HPC20 -0.33 0.43 0.30 -0.51 0.01 0.11 1.00 pH 0.05 0.33 0.18 -0.28 -0.08 -0.07 -0.03 1.00
(f) Correlation Matrix for Frenchs Forest Zone (Zone 283) Total NO2 NO3 NH3 Co/i HPC 37 HPC 20 pH Total 1.00 NO2 -0.02 1.00 NO3 -0.42 -0.21 1.00 NH3 0.69 -0.27 -0.34 1.00 Coli -0.14 -0.07 0.21 -0.14 1.00 HPC37 -0.26 0.02 0.34 -0.27 0.50 1.00 HPC20 -0.30 -0.05 0.38 -0.32 -0.01 0.24 1.00 pH 0.01 0.50 -0.05 -0.30 0.15 0.16 0.15 1.00
(g) Correlation Matrix for Palm Beach Reservoir (Rl92) Total NO2 NO3 NH3 Co/i HPC 37 HPC 20 pH Total 1.00 NO2 0.08 1.00 NO3 -0.13 -0.47 1.00 NH3 0.24 -0.13 -0.15 1.00 Coli -0.09 -0.10 0.14 -0.14 1.00 HPC37 -0.17 -0.13 0.27 -0.28 0.05 1.00 HPC20 -0.11 -0.31 0.36 -0.34 0.01 0.17 1.00 pH 0.00 0.43 -0.02 -0.43 0.06 0.14 0.00 1.00
(h) Correlation Matrix for Palm Beach Zone (Zone 192) Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Total 1.00 NO2 0.10 1.00 NO3 -0.16 -0.49 1.00 NH3 0.27 -0.02 -0.21 1.00 Coli -0.10 -0.14 0.29 -0.23 1.00 HPC37 -0.17 -0.27 0.39 -0.35 0.51 1.00 HPC20 -0.18 -0.32 0.33 -0.38 0.24 0.32 1.00 pH -0.13 0.28 0.11 -0.51 0.19 0.21 0.03 1.00
59 (i) Correlation Matrix for Upper Canal at Pipehead (HPR8) Total NO2 NO3 NH3 Coli HPC 37 HPC 20 pH Total l.00 NO2 -0.18 1.00 NO3 -0.06 -0.04 l.00 NH3 0.07 -0.22 -0.13 l.00 Coli -0.18 0.03 0.13 -0.07 l.00 HPC37 -0.08 -0.04 0.04 -0.02 0.05 l.00 HPC20 -0.08 0.00 0.05 -0.03 0.00 0.76 l.00 pH 0.04 0.09 -0.16 0.22 -0.15 0.03 0.13 1.00
60 2.5 DISCUSSION
2.5.1 Overview
The Ryde Delivery System is a typical dynamic water distribution system with programmed management practices such as reservoir and main cleanings, secondary chlorination and planned changes in water movement. There are also many unaccountable factors including changes in water movement due to main breaks, leaking mains and general system malfunctions that may go unnoticed for periods of time. The monitoring program was not designed to take into consideration the fluctuations and variations that are known to occur in a distribution system but their influences on outcomes have been considered during interpretation of the results.
Nitrification within a drinking water system has been defined as the loss of combined chlorine residual and ammonium concentrations with concurrent increases in nitrate and nitrite (Cunliffe, 1991). Nitrification was evident throughout the study system for the duration monitored, from April 1990 to April 1993. This included a period of alum dosing at Prospect Reservoir from August 1990 to June 1992. The trends typical for nitrification are evident within the graphs (Figures 2 to 25) and the correlation matrices presented in Table 6. Total chlorine and ammonium levels decreased through the system while nitrite and nitrate levels increased. This general trend is comparable to reported occurrences of biological nitrification from Australia and overseas.
The distance from the point of disinfection is a factor that may influence total chlorine concentration and is also likely to link with other "causes", for example turbidity, to result in a decrease of total chlorine concentration. The strong evidence for nitrification as the major cause exists by the fact that total chlorine and ammonia-N decreased in the presence of nitrite-N and nitrate-N over the three year sampling period. Even though nitrification is believed to be the major influence on the reduction of total chlorine concentration, there is no doubt that a complex "cause and effect" relationship exists with total chlorine residual within a chloraminated drinking water system. The scope of
61 this study though, was not to investigate all the factors that may be detrimental to total chlorine residual but to determine the presence of nitrification within the study system.
A summary of several case studies of nitrification in drinking water systems were presented in the literature review (Table 1). These case studies provide valuable comparisons to the experiences of Sydney Water.
Wolfe et al. (1988, 1990) reported the loss of combined chlorine residual and ammonium concentrations in a covered water storage reservoir that received filtered chloraminated water. An increase in the concentration of nitrite was detected over time and the concentration of nitrifying bacteria in the water column was also observed in the storage reservoirs. A similar experience was reported by Skadsen (1993) for a water distribution system in Michigan, USA. Reported experience of nitrification within drinking water systems in Australia have also been made by Thomas (1990) and Cunliffe ( 1991 ), who both provided accounts on South Australian water supplies.
There are some notable differences between Sydney Water's monitored experiences with nitrification and the cases reported above. Sydney Water experienced nitrifying activity in all sections of the study system (bulkwater, service reservoirs and reticulation) over the monitoring period. This is evident by the trend experienced throughout the study system. During this time Sydney Water did not take direct steps to reduce nitrifying activity. Examples of two management practices which may have had some indirect benefits to control nitrification are discussed. Alum dosing at Prospect Reservoir was commenced due to a deterioration in water quality entering Prospect Reservoir and continued until June 1992 because water quality entering the distribution system hindered bacterial aftergrowth. The reduction in bacterial aftergrowth enabled compliance to drinking water quality guidelines, for microbiological parameters, within the Ryde supply system. Additionally the commissioning of secondary chlorination plants was directly associated with strategies ~o control coliform aftergrowth.
62 In the case studies referred to, authorities introduced direct control mechanisms once activity increased to the extent that water quality significantly deteriorated. The cases reported discrete episodes of nitrification rather than the detection of continuous nitrification, as was Sydney Water's experience. These discrete episodes may have been due to the introduced mechanisms to control nitrification which reduced detection for a period of time, before nitrification was again evident. This particularly appears to be the experience of the Michigan system (Skadsen, 1993). The Metropolitan Water District of Southern California and Los Angeles Department of Water and Power appear to have recently overcome nitrification by reverting to chlorination for several months of the year (Negrin et al., 1990; Wolfe et al., 1990).
The Metropolitan Water District (MWD) of Southern California (Wolfe et al., 1988) experienced nitrification within a covered storage reservoir, which could be considered similar to Sydney Water's bulkwater system ie. after treatment and before distribution within a reticulation system. The experience of Sydney Water was that nitrifying activity first became evident towards the end of the bulkwater system (Ryde Pumping Station) and, even though nitrite levels were detected at low levels within that part of the bulkwater system before and after alum dosing, total combined chlorine conditions could still be maintained. MWD's experience with nitrification was an inability to maintain total combined chlorine residual leaving the reservoirs. An obvious explanation for the difference between the bulkwater systems of the two authorities' would be system design and operation, primarily related to longer retention time within the covered reservoir compared to major distribution mains.
The study of nitrification in South Australia by Cunliffe (1991) provided an example of nitrite accumulation at the one site over time. One chloraminated site monitored over approximately a two year period experienced nitrite accumulation with the onset of increased activity. The nitrite concentrations were not sustained as nitrate concentrations increased. The reported does not indicate whether the appearance of two separate steps within the nitrification process were also evident over distance.
63 The experience within Sydney Water's study system was the apparent distinct two stages to the process that were evident over distance, particularly in the absence of alum treatment, ie. ammonium oxidation predominated early in the system and nitrite oxidation predominated at the extremities.
2.5.2 Dynamics of Nitrification within the Study System.
While nitrification was evident in the study system throughout the monitoring period from April 1990 to April 1993, the actual dynamics of activity changed. This is illustrated by the three stages of results in Figures 2 to 25. In quarters 1 and 2 (stage 1) the accumulation of nitrite and nitrate in the bulkwater system between Pipehead and Ryde Pumping Station suggests that both steps of the nitrification process were occurring. The general trend with distance from Ryde Pumping station was a continued accumulation followed by a decline of nitrite, as the nitrate concentration increased.
It is likely that ammonium oxidation predominated during stage 1 because of the accumulation of nitrite. As nitrite rarely accumulates in natural aerobic environments, the question as to why it should do so in chloraminated drinking water arises.
The greater number of ammonium oxidisers than nitrite oxidisers, described in Chapter 3, may be a logical explanation as to why nitrite accumulation occurred yet, Belser (1979) reported that the presence of a high nitrifying population does not mean high activity. Low nitrate concentrations may occur in an active population as a result of processes such as denitrification. Environmental factors that are detrimental to nitrite oxidation include oxygen availability and pH (Belser, 1979). Nitrite oxidisers in soil and sediments may be out competed by ammonium oxidisers for oxygen. This proposition may also apply to nitrifiers in biofilms. The pH within a biofilm microenvironment is likely to experience a different pH to the water phase. However, this investigation did not have the advantage of microelectrodes to measure pH gradients or oxygen status within the biofilm.
64 If the pH within the biofilm was higher than the water phase then free ammonia, which ·inhibits nitrite oxidation may have been present. Also, the presence of nitrous acid would inhibit nitrite oxidation (Belser, 1979). Jones and Hood (1980) found that the inhibitory effects of ammonium and nitrite on nitrification were dependent on the concentration of ammonia and nitrous acid which is a function of pH. Similarly a report by Anthonisen et al. (1976) indicated that concentrations of the two substrates for nitrification, ammonia and nitrite, were related to inhibition of nitrification and pH determined the degree of inhibition.
Studies on nitrite accumulation or inhibition of nitrification could be performed on model biofilm systems. Such a system is the RotoTorque™ 1125 designed by the Centre for Biofilm Engineering in Montana, USA. The advantages for using such a biofilm system include the ability to modify the rate of flow to suit the required application, the ability to completely mix the bulk fluid to eliminate concentration gradients and the ease by which substrates or inhibitors can be added to the bulk fluid (Eager, 1995a). Most importantly, the RotoTorque™ system has been designed to simulate processes involved in the development of biofilms and are particularly appropriate for modelling processes in distribution systems.
Between quarters 3 and 10 (stage 2) nitrite was virtually absent from the bulkwater system. Nitrate was detected, but it did not increase through the bulkwater system. The nitrate detected may have been more a result of nitrate concentrations in the source water as source water trends were similar to what was observed within the system during this phase of monitoring. To a lesser extent, nitrifying activity within the bulkwater system may have also been occurring but at a reduced level of activity due to the improved water quality conditions of the source water as a result of alum dosing at Prospect Reservoir. During this period of monitoring, the rate of nitrite oxidation was comparable with ammonium oxidation. However, from the data collected it is not possible to ascertain if the latter was occurring. A way to determine activity would be to use pilot scale investigations which mimic sections of a distribution system.
65 Due to the complexities of a drinking water system environment it would be expected that a number of interrelated physical, chemical and biological reactions are occurring to reflect the net result observed. At the microscale the relationship of ammonification and denitrification as well as heterotrophic nitrification would be worth investigating as it appears to have received little attention especially within biofilms even though some evidence exists. Belser (1979), for example, reported that nitrite produced from nitrate reduction in an anaerobic environment may be available for nitrite oxidisers after diffusion into an aerobic environment.
During stage 2 of sampling, the nitrite concentrations increased towards the furthest supply zones of the distribution system, and nitrate remained relatively stable before it increased at the furthest zone - Palm Beach. It appeared as though ammonium oxidation activity had shifted further away from the point of treatment when compared to the first stage of sampling.
During the last two quarters of the sampling (stage 3), the pattern of activity rapidly adjusted to that seen in the first phase of monitoring. That is, nitrification occurred throughout the study system with ammonium oxidation predominating in the first half of the system replaced by a predominance of nitrate oxidation towards the end of the system.
The marked changes in nitrification dynamics that have been discussed in the paragraphs above correspond with the commencement of alum dosing at Prospect Reservoir and the subsequent cessation of alum dosing. This would suggest some link between alum dosing and the nitrifying activity measured. The potential influence of alum dosing on nitrifying activity is pursued further in the discussion that follows.
The deterioration in the quality of source water, entering Prospect reservoir from Warragamba Dam, brought about the need to introduce alum dosing at the inlet of Warragamba pipelines to Prospect Reservoir. Alum treatment would have affected a wide variety of water quality variables, which could have directly or indirectly impacted
66 on the concentrations of the nitrification variables monitored. The potential impact of alum treatment on nitrification is still worth considering because of the linkages suggested within the observed data. It is important to note the nitrification monitoring program was designed before alum treatment commenced or was considered. Therefore, it would be difficult to draw statistically rigorous conclusions from the observations made as the design of the nitrification program had very different objectives.
One of the possible explanations for the change in the dynamics of nitrification was that alum treatment is likely to have improved the ability for total combined chlorine residuals to persist further in the distribution system due to the decrease in factors, for example turbidity, which may have placed a demand on chlorine residuals. The persistence in residual could have then contributed to a decline in detectable nitrifying activity. It is possible that total chlorine residuals may have reached, and then maintained, an effective threshold against the microbial activities primarily on the surface of the biofilm but also within the water phase.
Supporting evidence has been found by researchers (Eager, 1995a) who have reported on tests conducted on model biofilm systems that were established to study the effect of filtered water on nitrification for Sydney Water. Model biofilm systems have been used widely to study a variety of biofilm processes (Wimpenny, 1988). The model system used in the investigation at Sydney Water was that of the RotoTorque™ system described earlier in this Chapter.
During tests performed by Eager ( 1995b) it was observed that increasing the concentration of monochloramine had no inhibitory effect on nitrifying activity, until the disinfectant dose reached 2 mg/L. It must be remembered that the investigation was carried out under controlled laboratory conditions and dosing regimes for the Ryde Delivery System during the study period were not greater than 1 mg/L of disinfectant. Le Chevallier et al. ( 1990) made similar observations in model experiments on the effect of monochloramine on biofilms growing on pipes. In a report of a nitrification episode within a chloraminated drinking water system (Skadsen, 1993) the
67 concentration of disinfectant residual and the numbers of heterotrophic plate count bacteria were considered to show a threshold relationship at sample sites with evidence of nitrification. It should be noted that the work conducted in Michigan by Skadsen (1993) used heterotrophic plate counts as a surrogate for isolating nitrifying bacteria and therefore no information on nitrifying bacteria is available.
2.5.3 Nitrification and Water Quality Variables
The correlation matrices presented in Table 6 provide some interesting insights into the complexities of nitrification within drinking water systems by the fact that no consistently strong correlations existed between the variables monitored. It is also notable for future monitoring of nitrification that not all variables monitored in this study need to be included in the monitoring program. In fact, total chlorine residuals, amrnonium-N, nitrite-N, nitrate-N need only be monitored and can be referred to as primary variables of nitrification.
Most reports on nitrification in chloraminated drinking water systems show that an accumulation of nitrite in the water occurs concurrently with an accelerated loss of total chlorine residuals. It is known that nitrite reacts rapidly with hypochlorite and that nitrite could increase the rate of hydrolysis of monochloramine. Recent investigations (Hao et al., 1994; Margerum et al., 1994) show that nitrite can react directly with monochloramine, leading to its decay and the production of nitrate. Correlations for the study system between total chlorine and nitrite showed in general that concentrations changed inversely to each other but the relationship was not always strong. The intervention of uncontrolled factors, including the potential effect of alum treatment and operation of the rechlorination plants, means that the interpretation of this statistical assessment needs to be considered with caution.
The use of verbal descriptions in correlations is not encouraged by Rowntree (1981). Rowntree's concerns are related to the context in which the results are interpreted and
68 that correlation does not necessarily imply cause. Meaning that, one variable does not somehow causes or determines another variable. When considering the level of interpretation on the correlation matrices it should be remembered that even though the descriptions have been used as a guide in this investigation, the matrices are just further support to trends evident in the graphs, demonstrating the change in relationships between variables as water moves through the system.
There are several limitations to the interpretation of the results of the water quality variables in isolation with the nitrification process in this investigation.For example, one of the limitations was the absence of flow data. This investigation was unable to consider flow as a parameter due to the absence of flow meters within the study system and project constraints on their installation. The determination of flow would have assisted with the interpretation of water movement through the system and the potential for nitrification under different flow regimes.
Other limitations to interpretation, within this study, include the need to assess the decay of total chlorine residuals in the absence of nitrification and the inability to determine the effect of other causes of decay, such as nutrient, particulates, temperature, distance and flow. An understanding of the role of decay factors would also assist in the development of control mechanisms. Further investigations using model biofilm systems, similar to those described by Eager (1995a), could progress the establishment of models which could be verified for a distribution system.
Despite the limitations identified above, it could be expected that the presence of nitrite and nitrate in drinking water contributed to the decay of the combined total chlorine residuals. Further investigations into nitrification by Sydney Water, using simple bench experiments, found that nitrite added to chloraminated water from the Ryde delivery system accelerated the decay of total combined chlorine residuals (Eager, 1995b). These results are not definitive, but supported field observations of the coexistence of total combined chlorine residual with nitrite. This would suggest the reaction between monochloramine and nitrite is not rapid.
69 Investigators have found nitrifying bacterial numbers are higher in sediments or biofilms than in the aqueous phase (Hall, 1986; Wolfe et al., 1990). The environmental conditions at the solid-liquid interface are different to the conditions in the aqueous phase and bacteria associated with biofilms may exhibit physiological processes and metabolic rates different to their free-living counterparts (Fletcher, 1984). Therefore the concentrations of the primary variables determined in the study system's water phase does not provide an insight into nutrient fluxes at the micro scale within a biofilm containing nitrifying bacteria.
This investigation did not attempt to surrogate primary variable concentrations as a determinant for processes that may be occurring within a biofilm. In this case, primary variable concentrations in the water phase were measured as a means of determining the presence and persistence of nitrification. The following chapter discusses the isolation and characterisation of nitrifying bacteria from sediment and biofilm.
70 CHAPTER 3: ISOLATION AND CHARACTERISATION OF NITRIFYING BACTERIA
3.1 INTRODUCTION
Studies of the microbiology of nitrification in chloraminated drinking water systems have focused on autotrophic nitifiers, particularly ammonia oxidising bacteria, as these bacteria are most clearly implicated in nitrification. Wolfe et al. ( 1990) looked for ammonia-oxidising bacteria in sediments and biofilms as well as the water phase. Their results showed that the numbers of ammonia-oxidising bacteria attached to surfaces were greater than planktonic numbers. This observation concurs with Kuenen and Robertson ( 1988) who considered that in aquatic environments the numbers of nitrifying bacteria attached to surfaces would be much greater than the numbers suspended within the water phase.
The study of autotrophic nitrifying bacteria in drinking water systems presents many difficulties with the isolation, purification and culturing of the bacteria. The numbers of nitrifiers in water samples may bear little resemblance to their activity in-situ. Statistical analysis of data from a field investigation in South Australia found there was no overall correlation between numbers of ammonia-oxidising bacteria and concentration of ammonium, oxidised nitrogen or nitrite in the water phase (Thomas, 1990).
Nitrification in chloraminated drinking water systems 1s often characterised by measuring changes in the concentration of oxidised nitrogen compounds in the water phase. The complex microbial interactions affecting these changes makes it difficult to determine the contribution of other microbial groups, such as heterotrophic nitrifiers and denitrifiers, to the overall outcome measured. It was not the purpose of this investigation to study all of the potential groups of bacteria which may be responsible for the changes in oxidised nitrogen measured in the water phase. The investigation aimed to demonstrate if autotrophic nitrifying bacteria were present in the chosen study system as supporting evidence for the chemical parameters determined and discussed in Chapter 2. It was also of interest to determine whether nitrifying bacteria were present in other
71 Sydney Water distribution systems of different source waters and varying levels of treatment.
3.2 METHODS
3.2.1 Sample Design and Collection.
Sediment or biofilm samples were obtained from different parts of the study system from April 1991 to July 1993. Generally, sites were selected to ensure different stages of the distribution system were sampled, including major trunk mains, service reservoirs and reticulation mains. The three service reservoirs chosen for sediment samples within the study system were reservoirs monitored as part of the field investigation of the water phase (see Chapter 2).
Between April 1992 and August of the same year other Sydney Water distribution systems were sampled to determine the presence of nitrifying bacteria in other systems. The systems chosen were classified as receiving either filtered or non filtered water that was either chlorinated or chloraminated. The types of samples obtained from either the study system or the other systems depended on the availal?ility of access to the site. The samples could be categorised as either sediments from the floors of service reservoirs, biofilm scraped from the inside of trunk mains or a slurry of sediment, biofilm and main water obtained from the flushing or swabbing of a reticulation main.
All samples could only be obtained when maintenance works allowed access to mains or service reservoirs. Programmed maintenance normally occurred in the winter months when water demand was less and interruptions to supply could be afforded. This meant that the majority of sampling occurred during the cooler months. Overall, most sampling could be described as opportunistic rather than systematic. For this reason, some supplies were represented by many samples compared fewer samples in other supplies.
72 Samples were collected aseptically in sterile 250 ml screw capped bottles, containing sodium thiosulphate as the dechlorinating agent, added prior to sterilisation. Samples were kept between 2 and 6°C until analysed, which was normally within 24 hours of collection.
Samples were analysed for ammonium-N, nitrite-N, oxidised-N, iron and orgamc matter. Bacteriological analyses conducted on the samples included tests to detect nitrifying activity, estimation of nitrifying and heterotrophic bacterial numbers and isolation and characterisation of autotrophic nitrifying bacteria.
3.2.2 Culture Methods
For the purpose of the detection of nitrifying activity, enrichment, isolation and culture of autotrophic nitrifying bacteria, the same media was used. The media recipes are described in Tables 7, 8 and 9, but basically contained the following components:
• a mineral salts solution containing the major cations and anions found in drinking water, as well as selected trace elements;
• a buffering agent, either particulate CaCo3 or a synthetic buffer suitable for biological work; and
• ammonium (as ammonium sulphate - (NH4) 2SO4) or nitrite (as sodium nitrite -
NaNo2) as a substrate for the growth of ammonia-oxidisers and nitrite-oxidisers, respectively.
The ammonium in (NH4) 2SO4 added to the aqueous medium at a pH>7 will form an equilibrium between ammonium (NH4+) and ammonia (NH3), which is the substrate for autotrophic ammonia oxidising bacteria. Liquid culture was used as autotrophic nitrifying bacteria were not grown successfully on solid media when attempted within the laboratory.
73 Initially, media were made in accordance with the recipes published in the literature (Watson et al., 1981). Later, the media formulations were modified to better suit the needs of the nitrifiers studied (Bock and Koops, 1992; Koops and Moller, 1992), it is acknowledged however that the composition of the modified media may have been selective (Belser, 1979).
Nitrifying cultures were grown in 50 ml volumes in 250 ml flasks or in 25 ml volumes in 100 ml flasks. Incubation was normally 20°C ± 1°C in the dark, or when additional aeration was required flasks were incubated in the dark on a rotary shaking table (150- 200 rpm) at room temperature (20°C to 23°C). During the growth of ammonia oxidising bacteria the pH tended to decrease due to the production of acid. To counteract this the pH of these cultures was adjusted by the addition of sterile lN NaOH or 5% (w/v)
K 2C03•
The cultures were tested regularly for the presence of heterotrophic contaminants by inoculating R2A agar (Difeo, USA) with a few drops of the culture. Alternatively, 0.5 ml volumes of culture were inoculated into 4.5 ml volumes of four different liquid media (Watson et al., 1981). The four liquid media included 0.25 strength nutrient broth (Oxoid, UK), 0.25 strength AC broth (Difeo), 0.25 strength tryptone soya broth (Oxoid) and 0.25 strength fluid thyoglycollate medium (Difeo). Inoculated media were incubated for three weeks at 20°C and, if no growth was detected, the original culture was said to have contained only autotrophic bacteria.
74 Table 6: Media for ammonia oxidising bacteria
Ingredient Medium 1 Medium2 Deionised water (ml/ 1000 1000
CaC03 (g)3 7 NaCl (mg/ 2000 584 KCl (ml) 74 MgS04. 7H20 (mg) 50 49 CaC12.2H20 (mg) 20 47 KH2PO (mg) 54 K2HP04 (mg) 500
KHC03 (mg) 20 FeS04.7H20 50 Trace metal soln. (ml) 1 Phenol red (mg) 10 10 (NH4)2S04 as required as required HEPES (mM)4 20
Table 7: Media for nitrite oxidising bacteria
Ingredient Medium 11 Medium35 Deionised water (ml)6 1000 1000
CaC03 (g)6 7000 3 NaCl (mg)6 2000 500 MgS04. 7H20 (mg) 50 49 CaC12.2H20 (mg) 20 KH2PO (mg) 150 150 FeS04.7H20 0.15 (NH4)6Mo70 24.4H20 (mg) 0.05 Trace metal soln. (ml) 1 NaN02 as required as required HEPES (mM) 20 1 Adapted from Watson et al. (1981) 2 Adapted from Koops et al. (1991) 3 Ingredients were autoclaved together. All other components were added aseptically from sterile solutions 4 HEPES: N-2-hydroxyethylpiperazine-N-2-ethanesulfonic acid 5 Adapted from Bock et al. 1990
75 Table 8: Trace Metal Solution 1
Ingredient 0.1 M HCl (ml) 1000 MnSO4.2H2O (mg) 44.6 H38O4 (mg) 49.4 ZnSO4.7H2O (mg) 43.1 (NH4)6Mo7O24.4H2O (mg) 55.0 FeSO4• 7H2O (mg) 173 CuSO4.5H2O (mg) 25.0 1 Adapted from Koops et al.( 1991) and Bock et al. ( 1990)
3.2.3 Detection of Activity.
Aqueous suspensions of sediment or biofilm (1 - 10% w/v) were inoculated into ammonia or nitrite media and incubated as described in 3.2.2 Culture Methods. The activity of ammonia oxidising bacteria was indicated by an accumulation of nitrite in the culture fluid, accompanied by a decrease in the concentration of ammonium. The activity of nitrite oxidising bacteria was indicated by an accumulation of nitrite and a decrease in the concentration of nitrite in the culture fluid. In cultures containing both types of nitrifying bacteria, a decrease in the concentration of ammonium was accompanied by an accumulation of nitrate, without the detection of nitrite.
The activity was determined by chemical analysis using spot tests, which provided a presence or absence indicator of substrates and products in either the inoculated cultures or uninoculated media. The spot tests could also be conducted more routinely. Nessler's reagent was used for the spot test to determine the presence of ammonia or ammonium in the test media. The reagent was made according to APHA (1989) 4500-NH3 C. The rapid detection of nitrite in the test media was performed by the addition of one drop of reagent containing 8g of sulfanilic acid per 5N acetic acid to one drop of solution containing 5g of N,N dimethyl-cx-naphthylamine per litre of 5N acetic acid. The test reagent was then mixed with 0.1-0.25 ml of test media or culture and any colour change was noted after 2 to 2.5 minutes.
76 The presence of nitrate in the liquid cultures or uninoculated media could be determined rapidly by the spot test, providing there was no nitrite present . The test was performed by first adding the test reagent for the nitrite spot test and, if negative, a pinch of zinc dust was added. Any nitrate present would then be reduced to nitrite which would then react with the previously added test reagents.
Quantitative determinations of ammonium-N, nitrite-N and nitrate-N in the culture media or uninoculated media was performed using the methods previously described for the quantification in water samples (see Chapter 2). The cultures were first centrifuged or filtered before analysis to remove particulate material.
3.2.4 General chemical analysis.
The general chemical analyses of sediment or biofilm samples were performed by A WT EnSight's analytical laboratory's procedure SC007/0l. The determination of dry weight in samples required the drying of a measured volume of sample to a constant weight at 105°C. Organic matter in samples of dried sediment were then determined by measuring the loss of sample weight upon ignition at 550°C. Organic matter was then quoted as a percentage of dry weight of solids.
3.2.5 Estimation of Numbers
The numbers of nitrifying bacteria in samples were estimated using a Most Probable Number (MPN) Technique adapted from APHA (1989) 9221 method for coliforms. This method is based on the growth of bacteria in artificial media and is known to be inaccurate for nitrifying bacteria, underestimating natural populations by 1 or more orders of magnitude (Belser, 1979; Underhill, 1990). At the time of this study, more reliable methods for determining nitrifying bacterial numbers were not available. In view of the limitations of the method, the estimates of numbers should be interpreted only as an indication of the potential magnitude of populations.
77 Serial 10-fold dilutions of sediment or biofilm or slurry were made in a buffered diluent. The diluent was made from mains water collected at Ryde Pumping Station. The diluent was dechlorinated, filtered (0.45µm pore size, Millipore) and autoclaved. A sterile solution of buffer (5mM HEPES) was added to give a final pH of 7.5-8.0. Aliquots of appropriate dilutions were inoculated into replicate volumes of ammonia and nitrite media. Incubation was for 6 to 8 weeks in the dark at 20°C. The presence of ammonium, nitrite and nitrate were performed using the spot tests described in the previous section, following the incubation period. MPN calculations were performed using a FORTRAN program devised by D. J. Best, CSIRO, IAPP Biometrics Unit, Food Research Laboratory, North Ryde, NSW.
Heterotrophic bacteria numbers were determined using the spread plate method adapted from APHA (1989) 9215C, but using R2A agar. Colonies were counted following the incubation of plates at 20°C ± 1°C for 21 days.
3.2.6 Isolation and Characterisation of Nitrifying Bacteria
Several stages of enrichment and purification of cultures were implemented to determine the presence of autotrophic nitrifying bacteria. Slow growth of the autotrophic nitrifying bacteria make them very difficult to isolate directly from the original sample by standard laboratory culture methods, that use selective media.
Enrichment of the culture was followed by purification. This step required repeated subculture and serial dilution (Watson et al., 1981), followed by testing for heterotrophic contamination. Presumptive evidence for the presence of autotrophic nitrifying bacteria was considered when the accumulation of nitrite and or nitrate was detected (Prosser, 1989) and heterotrophic contaminants were absent.
Identification of the autotrophic nitrifiers was by examination of the internal membrane structure of the cells using transmission electron microscopy (TEM). This technique
78 was used because many of the autotrophic bacteria possess extensive internal membrane structures in arrangements which are characteristic for each genus (Bock and Koops, 1992; Koops and Moller, 1992). The transmission electron microscopy was performed at the University of NSW and cell preparation performed at AWT EnSight. The method for the preparation of cells was provided by the university's Electron Microscopy Unit.
Cells were harvested by centrifugation, resuspended in 25% (w/v) glutaraldehyde and incubated at 4°C for 4 hours. After this primary fixation, cells were washed by centrifugation and resuspension three times in 0. lM sodium cacodylate buffer to remove the glutaraldehyde. Cells were then stored overnight at 4°C.
A second fixation was carried out by resuspending the cells in 2% (w/v) osmium tetroxide and incubating at 4°C for 4 hours. Cells were then washed in sodium cacodylate buffer and stored as a pellet at 4°C. Cell pellets were mixed with molten, cooled agar and when the mixture was set, the agar was cut into cubes approximately 1 mm 3• Further processing of the sample, before electron microscopy, was performed by the Electron Microscopy Unit, University of NSW, using the method described by Glauert (1965) and Hayat and Giaquinta (1970).
79 3.3 RESULTS
3.3.1 Detection of Nitrifying Activity in Sediment and Biofilm.
The results of nitrifying activity within samples of sediment and biofilm for the study system (Ryde Delivery System) are provided in Table 10. Ammonia- and nitrite oxidising activity were detected in all samples except Palm Beach Reservoir. For the sample taken from Palm Beach Reservoir only nitrite-oxidising activity was detected.
The results for the detection of nitrifying activity within the other systems sampled are presented in Table 11. Nitrifying activity was detected in all of the samples except Bringelly Reservoir outlet main (Table I la). In the two reservoirs within the Upper Avon supply (Table llb) ammonia-oxidising activity was detected while nitrite oxidising activity was not. The remaining samples had evidence of both ammonia- and nitrite-oxidising activity.
3.3.2 Estimation of Nitrifying Bacterial Numbers.
Eight sediment and biofilm samples which possessed both ammonia and nitrite oxidising activity were selected to estimate numbers of nitrifying and heterotrophic bacteria. Results presented in Table 12 show that heterotrophic bacteria dominated the nitrifying bacteria by one to eight orders of magnitude. This variation in magnitude appeared to be site specific and greatest for sites that were not chloraminated, as indicated within Table llc. Heterotrophic bacterial counts ranged from 106 to 109 cfu/g DW solids while nitrifying bacteria fluctuated from 10 1 to 106 MPN/g DW solids. The ammonia-oxidising bacteria generally dominated the nitrite-oxidising bacteria by one to two and, in one case by five orders of magnitude. Unfortunately, the method of repeated
80 subculture and dilution to separate heterotrophic bacteria from autotrophs is likely to have favoured faster growing strains.
For the small population tested, there appeared to be no relationship with system sampled or with percentage organic content. Interestingly, ammonia-oxidisers were present at 103 to 106 bacteria per g whether the reservoir was in a chloraminated or chlorinated system. This would indicate that the ammonia-oxidisers are ubiquitous in nature and sustainable concentrations of ammonia are available in chlorinated supplies. The estimates of nitrifying bacteria in the study system were in the higher range of counts and showed less fluctuation between ammonia- and nitrite-oxidisers.
3.3.3 Characterisation of Nitrifying Bacterial Isolates
The method employed to separate autotrophic nitrifiers from heterotrophic bacteria (repeated subculture and dilution) did not provide information on what species of ammonia- or nitrite-oxidiser was present in each culture. Transmission electron microscopy was used to examine the internal membrane structures within the cells of three isolates. The presence and arrangement of the internal membranes is characteristic for different types of autotrophic nitrifiers (Bock and Ko~ps, 1992; Koops and Moller, 1992).
The metabolism and morphology of some of the isolates are described in Table 13. Transmission electron micrographs (Plate 3a-c) demonstrate parallel layers of intra cellular membranes in all three isolates of nitrifying bacteria isolated from the Ryde Delivery system. The peripheral arrangement of the layers suggests that the ammonia oxidising isolate may belong to the genus Nitrosomonas and the nitrite-oxidising isolates may belong to the genus Nitrobacter.
81 Table 9: Detection of nitrifying activity within samples from Ryde Delivery system.
Nitrifying Sampling Site Source of Water Sample Type Activity1 Date AO NO 4-Aug-91 Hunters Hill Prospect via Ryde Main FI ushing + + Reticulation Main P.S. 24-Jul-91 Frenchs Forest Prospect via Ryde Sediment from + + Reservoir (R283) P.S. res. floor 16-Aug-91 Pymble Reservoir Prospect via Ryde Sediment from + + (R97, R98) P.S. res. floor 15-Jul-92 Ryde Pumping Prospect Biofilm from + + Station (WPS5) Reservoir manifold 18-Aug-92 Pymble- Prospect via Ryde Biofilm from + + W arringah Main P.S. mam @ Bantry Bay Rd 28-Nov-92 Ryde Suction Prospect Biofilm from + + Main @ Rosehill Reservoir main 7-May-93 Palm Beach Prospect via Ryde Sediment from - + Reservoir (Rl92) P.S. res. floor
I .. Act1v1ty 1s descnbed by the followmg: AO= ammonia oxidation NO= nitrite oxidation +=activity detected - = activity not detected.
82 Table 10: Detection of nitrifying activity within samples from other Sydney Water delivery systems. TlOa: Source - Warragamba catchment. Nitrifying Activity
Sampling Site System Treatment1 Type of Sample AO NO Date
29-Apr-92 W arragamba Pipeline at Warragamba Pipeline Cl2 Biofilm + + MamreRd 21-Jul-92 Ashfield Reservoir (R3) Prospect Reservoir via u Sediment + + Potts Hill (Sth Harbour ) NH2Cl
24-Jul-92 Potts Hill Reservoir No. l Prospect Reservoir via u Sediment + + Potts Hill (Sth Harbour ) NH2Cl
24-Jul-92 Wiley Park Reservoir (Rl 9) Prospect Reservoir via u Sediment + + Potts Hill (Sth Harbour ) NH2Cl
24-Jul-92 Mt Misery Reservoir (Rl 79) South Prospect Reservoir u Sediment + + Cl2 30-Jul-92 Sutherland Reservoir (Rl 75) Prospect Reservoir via u Sediment + + Potts Hill ( or Woronora NH2Cl ( or Cl2) Dam)
12-Aug-92 Bringelly Reservoir Outlet Orchard Hills Water F Sediment - - Main (from R209 & R304) Treatment Works (WTW) Cl2
2-Jul-93 Pressure Tunnel at Shaft 1 Prospect Reservoir via u Biofilm + + Potts Hill (Sth Harbour ) NH2Cl U = unfiltered, F = Filtered, NH2Cl = chloramination, Cl2 = chlorinated.
83
NO NO
-
-
+ +
84 84
Activity Activity
+ +
+ +
+ + +
+ +
+ + +
Nitrifying Nitrifying
AO AO
Sample Sample
of of
Sediment Sediment
Sediment Sediment
Sediment Sediment
Sediment Sediment
Sediment Sediment
Type Type
2 2
2 2
Cl
Cl
Cl Cl
Cl Cl
Cl Cl
2 2
2 2
2
2
2
F F
u u
u u
u u
u u
Cl
Cl
NH
NH
NH
Summer: Summer:
Summer: Summer:
Treatment Treatment
Winter: Winter:
Winter: Winter:
Treatment Treatment
Pass Pass
s s
System System
Water Water
Avon Avon
Avon Avon
Canal Canal
Works(WTW) Works(WTW)
Upper Upper
Broughton' Broughton'
Upper Upper
Upper Upper
Nepean Nepean
7) 7)
(R306) (R306)
(R309) (R309)
(R34 (R34
Catchment Catchment
Reservoir Reservoir
Site Site
Reservoir Reservoir
Reservoir Reservoir
Reservoir Reservoir
South South
Nepean Nepean
Reservoir Reservoir
No.2 No.2
Upper Upper
Dapto Dapto
Berkeley Berkeley
Thirlmere Thirlmere
(R301) (R301)
(R412) (R412)
Appin Appin
Narellan Narellan
-
Source Source
Date Date
Sampling Sampling
21-Jul-92 21-Jul-92
23-Jul-92 23-Jul-92
22-Jul-92 22-Jul-92
29-Jul-92 29-Jul-92
10-Aug-92 10-Aug-92 TlOb: TlOb:
NO NO
+ +
+ + + +
+ +
85 85
Activity Activity
+ +
+ +
+ +
+ +
Nitrifying Nitrifying
AO AO
Sample Sample
of of
Sediment Sediment
Sediment Sediment Sediment Sediment
Sediment Sediment
Type Type
Cl) Cl)
2
2 2
2 2
2 2
NH
u u
u u
u u u u
Cl
Cl
Cl
(or (or
2 2
Treatment Treatment
Cl
via via
or or
Main Main
Dam Dam Dam-
Dam-Penshurst Dam-Penshurst
Dam-Penshurst Dam-Penshurst
System System
Reservoir Reservoir
Hill Hill
Potts Potts Woronora Woronora
Prospect Prospect
Woronora Woronora
Helensburg Helensburg Main Main
Woronora Woronora Woronora Woronora
Main Main
75) 75)
(Rl (Rl
(R268) (R268)
(R268) (R268)
Site Site
Reservoir Reservoir
Reservoir Reservoir
Catchment Catchment
Reservoir Reservoir
Reservoir Reservoir
Sutherland Sutherland
(R348) (R348)
Helensburg Helensburg Menai Menai
Menai Menai
Woronora Woronora
Source-
Date Date
30-Jul-92 30-Jul-92
5-Aug-92 5-Aug-92
Sampling Sampling
4-Aug-92 4-Aug-92 4-Aug-92 4-Aug-92 TlOc: TlOc: Table 11: Estimated numbers of nitrifying and heterotrophic bacteria from selected samples.
1 Site Estimated Number of bacteria per g (DW ) solids Organic Ammonia Nitrite Heterotrophic Matter(% oxidisers oxidisers bacteria DW1 solids) Pymble-Warringah
Main @ Bantry Bay 2 X 105 2 X 104 4 X 106 6 Rd Ryde Suction Main
@Rosehill 4 X 106 <5 X 106 2 X 107 41 Ashfield Reservoir
(R3) 2 X 105 9 X 104 5 X 108 16 Sutherland Reservoir
(Rl 75) <1 X 105 6 X 102 2 X 107 21 Narellan South
Reservoir (R301) 1 X 104 2 X 105 6 X 107 12 Dapto Reservoir
(R347) 4 X 106 <1 X 104 2 X 109 35 Menai Reservoir
(R268) 2 X 103 2 X 10 1 2 X 108 15 Helensburg
Reservoir (R348) 3 X 106 6 X 10 1 2 X 109 15 I DW = Dry Weight
86 Table 12: Morphology and metabolic characteristics of five purified cultures from Ryde Delivery System. Site Morphology Metabolism Internal Membrane Possible Genus Structure' Hunters Hill rod AO parallel, peripheral Nitrosomonas (Zn50) Hunters Hill rod NO parallel, peripheral Nitrobacter (Zn50) Frenchs Forest rod NO parallel, peripheral Nitrobacter Reservoir (R283) Ryde PS curved rod AO ND'" Nitrosolobus" (WPSS) Pymble rod AO ND Reservoir (R98) I Detennmed by transm1ss1on electron microscopy 2 ND = not determined 3 Burrell and Blackall (1994)
87 Expected page number is not in the original print copy. Plate 3: Transmission Electron Micrographs of thin sections of nitrifying bacteria isolated from the Ryde Delivery System. A: ammonia-oxidising isolate from Hunters Hill (Zn50), magnification x 50,000. B: nitrite-oxidising isolate from Hunters Hill (Zn50), magnification x 80,000 C: nitrite-oxiding isolate from Frenchs Forest Reservoir (R283), magnification x 80,000.
A
B
...
-v·
·<.-.. ··.< 89 C
··i .
• .
. ., ,. ' .
90 3.4 DISCUSSION
The results of this study detected nitrifying activity throughout the Ryde Delivery system and both ammonia- and nitrite-oxidising bacteria could also be isolated and identified from the same system. Activity was also detected from sediments of other Sydney Water delivery systems that received different levels of treatment. This indicated that both ammonia- and nitrite-oxidation was occurring or had occurred within other drinking water systems. The ability of the nitrifying isolates to grow in media without added organic nutrients, and the production of nitrite or nitrate at the expense of ammonia or nitrite, respectively, strongly suggests that the isolates were autotrophic nitrifying bacteria (Prosser, 1989).
An extensive study of drinking water distribution systems in South Australia (Thomas, 1990; Cunliffe, 1991) receiving different source water and different levels of treatment found that autotrophic ammonia-oxidising bacteria were detected in both chlorinated and chloraminated supplies receiving filtered or unfiltered water. The authors concluded that nitrifying bacteria were ubiquitous in chloraminated systems and the frequency of detection was greater in water of lower quality and increased distance from the point of disinfection. It was also reported by the authors that the frequency of detection of nitrifying bacteria decreased as chlorine residuals increased, but that nitrifiers were still detected in more than 50% of samples containing 2-3 mg/L total chlorine residual and in 20% of samples containing 5 mg/L or more.
The results of the South Australian study need to be carefully considered in light of the difficulty associated with laboratory methods for culturing nitrifiers and the inaccuracy of the most probable number technique. The South Australian study appears to have monitored only the water phase for nitrifying bacteria and the detection of either nitrite or nitrate during enumeration was considered as evidence of nitrifying bacteria. Never the less, the South Australian observations, in conjunction with the monitored Sydney Water delivery systems, show that nitrifying bacteria were ubiquitous in drinking water
89 distribution systems and were able to avoid the effects of lower substrate. Jones and Morita (1985) reported that cell numbers of a marine isolated Nitrosomonas neither increased or decreased during 25 weeks in an ammonium free environment. Cell size also remained constant. The results of the study suggest that the bacterium was well adapted to surviving long periods in substrate deprivation conditions. These results could also apply to low nutrient conditions that would be experienced in non chloraminated water supply systems or in the study system during the period of alum dosing.
In samples of biofilm and sediment taken from the Sydney Water distribution systems, heterotrophic bacteria outnumbered the autotrophic nitrifying bacteria. This is consistent with the findings from studies of natural environments (Hall, 1986). However, but in some samples, the ammonia-oxidisers were detected in larger numbers than the nitrite oxidisers. This is contrary to what has been found in other environments (Bock and Koops, 1992) but provides some support to this study's finding of nitrite accumulation, discussed in Chapter 2. This discussion pointed out that the pH within the biofilm may have been the primary factor influencing nitrite accumulation. The observations of Morrill and Dawson (1967) provide further support with the findings that, depending on the pH of soil, the numbers of Nitrosomonas were larger than Nitrobacter.
It is difficult to relate the number of nitrifying bacteria to the gross characteristics of the sediment, such as the percentage dry weight of organic matter or heterotrophic bacterial numbers (Table 12). Similarly numbers did not appear to be related to the type of treatment of the source water (Table 11 ). There are several reasons for the difficulty in concluding too much from the sediment results. Firstly, the time consuming method of repeated subculture restricted the number of sediment samples that were analysed. Secondly, the most probable number method used to count the autotrophic nitrifiers is also known to be inaccurate by underestimating natural populations (Belser, 1979; Underwood, 1990), thereby limiting the conclusive comments that can be made.
90 The presence of nitrifying bacteria in sediments and biofilm, in conjunction with the trends monitored within the water phase (Chapter 2), implies that a resident population of the bacteria exist in the distribution systems. The findings of nitrifying bacteria within sediments and biofilms are consistent with Kuenen and Robertson ( 1988) and Wolfe et al. (1990). Other investigations of nitrifying bacteria in aqueous environments (Cooke and White, 1987; Jones and Hood, 1980; Rudd et al., 1988) have isolated a greater number of nitrifiers from the sediment-water interface while other studies appear to have successfully isolated from the water phase (Cuncliffe, 1991; Jones and Morita, 1985). This raises the question as to the efficiency of oxidation of planktonic cells and the method of transport of cells through an aqueous system.
The cells immobilised in sediment or biofilm have the opportunity to metabolise ammonium or nitrite in continuous flow. Firstly, the effective oxidation of these substrates in the aqueous phase is doubtful because there is likely to be minimal contact between low numbers of cells and low substrate concentrations. Secondly, the slow growth rate of the chemolithotrophs would lead to ineffective oxidation of low substrate concentrations for the planktonic cells. To confirm that the immobilised cells are the main source of nitrification activity studies could be performed on model biofilm systems such as the RotoTorque™ system described in Chapter 2. This could be achieved by reducing the residence time of cells in the aqueous phase to a point where their metabolism is negligible, while measuring for change in nitrification substrates and products.
The model of coliform aftergrowth in drinking water distribution systems as a result of re-entry to the water phase by sloughing or resuspension (Le Chevallier et al., 1987), could also be applied to nitrifying bacteria in drinking water systems. Nitrifying bacteria could be transported downstream after resuspension from biofilms or sediments to where favourable conditions for nitrifying activity within biofilms are present. Also, sloughing implies that cells are attached to fragments of biofilm, hence the nitrifying bacteria would still be in a more favourable position for metabolic oxidation or survival in non-favourable conditions than planktonic cells.
91 In the laboratory, cultures of autotrophic nitrifiers are successfully stored for months at low temperatures and without the addition of nutrients (Bock and Koops, 1992). This indicates that autotrophic nitrifiers have the ability to withstand non favourable conditions and resume activity when the environment becomes more favourable. Jones and Morita (1985) showed an initial lag phase when starved Nitrosomonas isolates were exposed to increasing concentrations of ammonium. The lag before activity that was evident maybe associated with enzyme-substrate kinetics or the physiological state of cells. The lag is not unexpected though if the starved cells survive because of an ability to decrease endogenous metabolism and conserve cellular sources of carbon (Jones and Morita, 1985).
The ability of nitrifying bacteria to survive periods of substrate deprivation and then resume activity during more favourable conditions may have been important in the occurrence of nitrification episodes reported in the literature. For example following the winter months (Wolfe et al., 1988; Skadsen, 1993; Deal, 1993) or after remedial chlorination of a system (Negrin et al., 1990). In addition, the residence of nitrifying bacteria in chlorinated supplies, such as Woronora Dam supply, provides further evidence to the ability of nitrifying bacteria to survive with limited nutrients. The limited nutrients may arise from atmospheric ammonia with the cells exhibiting ammonium scavenging capabilities (Jones and Morita, 1985) or from ammonification which is the mineralisation of indigenous organic nitrogen (Belser, 1979).
The enrichment and isolation methods employed, whilst providing confirmation that nitrifying bacteria were present, gave no measure of activity, spatial arrangement within the biofilm or spatial relationship between ammonia- verse nitrite oxidisers. The study of the ecology of nitrifiers has been limited by the lack of quick and reliable method for counting nitrifying populations. This is the case with the use of the MPN technique which can lead to underestimations of population numbers due to long incubation times, depending on the medium used (Belser, 1979). The media and incubation time may also
92 lead to selectivity of ammonia-oxidisers over nitrite-oxidisers or even genuses within each of the groups (Belser, 1979).
The potential for selectivity is evident from the results of this study. The purified cultures from the Ryde Delivery system were confirmed by transmission electron microscopy (Table 13 and Plate 4) to be of the genus Nitrosomonas and Nitrobacter. These two members of the family Nitrobactereace have been the most commonly studied, possibly because the previously methods employed favoured the selection of the two bacteria. The fourth isolate identified (Table 13) was identified by gene sequencing (Burrell and Blackall, 1994) as Nitrosolobus. The development and application of gene probes to study the ecology of nitrifiers in biofilms is a recent advancement in microbial ecology (Ashbolt, 1994; Burrell and Blackall, 1994; McCraig, in press).
The application of specific fluorescent antibody techniques, while offering improvements over MPN technique in terms of precision and the amount of time to get a result, have major disadvantages in terms of specificity for different strains and the need to use cultured isolates (Belser, 1979; Underwood, 1990). As previously mentioned, gene probes provide a recent advancement to the study of nitrifying bacteria in-situ. The application of gene probe techniques to the study of nitrifiers in biofilms within drinking water systems would provide numerous advantages (Hioms et al., 1992). These advantages would include an easier more rapid differentiation of ammonia-oxidisers, nitrite-oxidisers and heterotrophic bacteria without the need for sub-culturing and the study of nitrifiers in biofilms without disrupting the biofilm structure.
An extremely powerful study of the spatial and temporal arrangement of nitrifiers within biofilms would combine a number of recent advancements of methods used in microbial ecology. For example, the combination of specific gene probes and fluorescent dyes can be used to differentiate metabolic activities in conjunction with
93 confocal laser scanning microscopy. Confocal laser scanning microscopy provides for three dimensional non-invasive imaging of the biofilm's internal structure. Thus the microscope can be used in conjunction with a variety of techniques, to enhance the scope of investigations, including m1crosensors to study oxygen gradient (Lewandowski, 1993) and model biofilm systems (Eager, 1995a).
94 CHAPTER 4: GENERAL DISCUSSION
Biological nitrification occurred in the Ryde Delivery System during the study period April 1990 to April 1993 and was confirmed firstly by determining the existence of typical trends of "indicator" variables. That is, a decrease in total chlorine residual and ammonium concentration in the presence of an increase in nitrite and nitrate concentrations. During this time nitrification was evident throughout the study system even though the dynamics of nitrification changed during the monitoring period. The change in dynamics was complimentary to a period of alum dosing at the inlet to Prospect Reservoir. Following cessation of alum dosing, the trend in nitrification was similar to the trends detected before alum dosing commenced.
Secondly, sediment and biofilm samples from the study system were analysed for the presence of ammonia and nitrite oxidising activity and further purified for identification of autotrophic nitrifying bacteria. The presence of autotrophic nitrifying bacteria within the study system in conjunction with the water quality monitoring data of the indicator variables provided evidence that biological nitrification occurred through the study system.
In addition, nitrifying activity and the presence of nitrifying bacteria was confirmed for other Sydney Water delivery systems. The purpose for monitoring the additional delivery systems was to determine the prevalence of nitrifying bacteria in drinking water systems especially under varying nutrient conditions that would result from the method of disinfection and level of treatment. The systems were selected on the basis of different levels of treatment (filtration verse no filtration) and disinfectant (chloramine verse chloramine ). Activity for either ammonia- and nitrite-oxidising bacteria were detected in all but one site, which received filtered chlorinated water. Numbers of nitrifying bacteria, that were determined for a sample of sites from other delivery systems, indicated that both ammonia- and nitrite-oxidising bacterial numbers were comparable to those of the study system.
95 The study of nitrifying bacteria has, in the past, relied on culture-based methods. These methods are not well suited to the study of the occurrence, activities and significance of nitrification in natural habitats because the bacteria are difficult to isolate in the laboratory, and their slow growth rates and low growth yields hamper the study of their physiology and ecology (Prosser, 1986).
The limited amount of information on the diversity of nitrifiers in field samples presents problems for isolation. This is because the growth conditions chosen for the detection of one type of ammonia-oxidiser or nitrite-oxidiser may not favour the growth of other types (Belser, 1979). Further, culture-based method for counting nitrifiers are inaccurate and estimates of numbers of nitrifiers in field samples may have little relevance to activities in-situ (Belser and Mays, 1982).
In environmental samples, autotrophic bacteria may be outnumbered 100-fold (or even 1000-fold) by heterotrophic bacteria which differs from coliform bacteria detection. It is not possible to use selective media to promote the growth of nitrifiers while inhibiting the growth of heterotrophs. Instead, the isolation procedure relies on a series of enrichment cultures devoid of organic nutrients but supplied with ammonia or nitrite. Even so, it may be difficult to separate autotrophs from heterotrophs as the latter may survive on very low concentrations of organic nutrients, even on the waste products of the nitrifiers. The whole process of achieving pure cultures of autotrophic nitrifying bacteria may take many months. The interpretation of results maybe hindered as the relationship between autotrophic and heterotrophic nitrification is not well understood, and assessment of the relative contribution of the two groups to overall nitrification is difficult to make. The cultural methods, presented in Chapter 3, do not provide any information on spatial arrangements or temporal relationships and population numbers do not necessarily correlate with activity levels.
This study concentrated on detecting activity or isolating nitrifying bacteria from sediment or biofilm samples rather than water samples. Methods available at the time
96 prevented nitrifying bacteria being detected or counted in biofilms without destroying the structure of the biofilm. Thus, information on the relationship of nitrifiers with other microbes within the biofilm was lost.
In light of the above considerations, limitations are placedon the amount of information which can be gained from environmental samples using culture based methods presently available. New methods, currently being advanced, present powerful tools for the investigation of ecological processes within biofilms. This is particularly the case for studying the processes either in-situ or with very little disruption to the natural formation or structure of the biofilm. For drinking water biofilms and their impact on water quality the relevance is pertinent because few investigations had previously focussed on biofilms. A discussion on recent advances of methods to study the ecology of nitrifying bacteria was provided in Chapter 3. The combined application of such methods would progress the advancement of knowledge of spatial relationships of nitrifying bacteria, heterotrophic nitrifiers and denitrifiers in aquatic systems and the role of plank.tonic verse sessile nitrifiers. A greater understanding of the inhibitory effects, of pH, oxygen, temperature and substrate concentrations, particularly on biofilms would be enhanced by model biofilm systems in combination with methods such as micro sensors and confocal laser scanning microscopy.
The cost of infrastructure and remedial maintenance in the water industry places a greater need on the improvement of knowledge and understanding of the ecology and dynamics of drinking water systems. Furthermore, the approach needs to be multidisciplined to enable best management practise. With respect to future investigations of nitrification within chloraminated drinking water systems greater consideration needs to be given to system hydraulics to assist with the assessment of the interactions between nitrifying bacteria, biofilms and chloramine molecules. For example, perhaps the role the chloramine molecule may play in nitrification could be ascertained.
97 The questions that have been raised in this study provide an example of the application of micro, pilot and "real life" scale investigations. The "real-life" scale provides the broader picture that enable hypotheses to be proposed and further defined at the microscale. Pilot scale investigations, such as model biofilm systems designed to mimic a distribution system, enable the testing of the hypotheses to provide a platform on which control mechanisms can be developed and adapted for application in a real system. The application of all three scales would enhance future investigations of nitrification within chloraminated drinking water systems.
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108 APPENDIX 1: SUMMARY STATISTICS FROM WATER QUALITY MONITORING
Axl.1 Descriptive statistics for Upper Canal at Pipehead (HPR8) for quarters one to twelve.
Descriptive Statistics for HPR8 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.81 Mean 0.03 Mean 0.19 Mean 0.40 Std Error 0.03 Std Error 0.01 Std Error 0.03 Std Error 0.04 Median 0.81 Median 0.02 Median 0.18 Median 0.45 Std Dev 0.09 Std Dev 0.03 Std Dev 0.08 Std Dev 0.11 Variance 0.01 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.72 Minimum 0.00 Minimum 0.10 Minimum 0.17 Maximum 0.99 Maximum 0.09 Maximum 0.39 Maximum 0.49 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.06 Con (95%) 0.02 Con (95%) 0.06 Con (95%) 0.07
Descriptive Statistics for HPR8 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.88 Mean 0.01 Mean 0.19 Mean 0.41 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.04 Median 0.88 Median 0.00 Median 0.20 Median 0.42 Std Dev 0.05 Std Dev 0.01 Std Dev 0.04 Std Dev 0.13 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.02 Minimum 0.79 Minimum 0.00 Minimum 0.09 Minimum 0.18 Maximum 0.98 Maximum 0.02 Maximum 0.23 Maximum 0.69 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.07
109 Descriptive Statistics for HPR8 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.82 Mean 0.01 Mean 0.20 Mean 0.37 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.80 Median 0.01 Median 0.20 Median 0.35 Std Dev 0.07 Std Dev 0.00 Std Dev 0.01 Std Dev 0.03 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.72 Minimum 0.0 l Minimum 0.18 Minimum 0.34 Maximum 0.94 Maximum 0.01 Maximum 0.23 Maximum 0.43 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.04 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.02
Descriptive Statistics for HPR8 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.87 Mean 0.01 Mean 0.18 Mean 0.36 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.88 Median 0.01 Median 0.20 Median 0.35 Std Dev 0.11 Std Dev 0.00 Std Dev 0.03 Std Dev 0.04 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.70 Minimum 0.01 Minimum 0.13 Minimum 0.27 Maximum 1.04 Maximum 0.01 Maximum 0.21 Maximum 0.44 Count 11.00 Count 11.00 Count 11.00 Count 11.00 Con (95%) 0.06 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.03
Descriptive Statistics for HPR8 Q5 1991 TOTAL NO2 NO3 NH3 Mean 0.95 Mean 0.02 Mean 0.l0Mean 0.32 Std Error 0.06 Std Error 0.01 Std Error 0.0 l Std Error 0.02 Median 1.00 Median 0.01 Median 0.09 Median 0.33 Std Dev 0.19 Std Dev 0.02 Std Dev 0.03 Std Dev 0.07 Variance 0.04 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.45 Minimum 0.01 Minimum 0.07 Minimum 0.13 Maximum 1.16 Maximum 0.09 Maximum 0.20 Maximum 0.44 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.01 Con (95%) 0.02 Con 0.04 (95%)
110 Descriptive Statistics for HPR8 Q6 1991 TOTAL NO2 NO3 NH3 Mean 1.04 Mean 0.01 Mean 0.12 Mean 0.45 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.04 Median 1.02 Median 0.01 Median 0.11 Median 0.40 Std Dev 0.08 Std Dev 0.00 Std Dev 0.04 Std Dev 0.13 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.02 Minimum 0.92 Minimum 0.01 Minimum 0.08 Minimum 0.38 Maximum 1.23 Maximum 0.01 Maximum 0.20 Maximum 0.82 Count 11.00 Count 11.00 Count 11.00 Count l 1.00 Con (95%) 0.05 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.08
Descriptive Statistics for HPR8 Q7 1991 TOTAL NO2 NO3 NH3 Mean 1.01 Mean 0.01 Mean 0.30 Mean 0.35 Std Error 0.04 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 1.00 Median 0.01 Median 0.31 Median 0.37 Std Dev 0.14 Std Dev 0.00 Std Dev 0.04 Std Dev 0.05 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.80 Minimum 0.0 l Minimum 0.22 Minimum 0.19 Maximum 1.20 Maximum 0.01 Maximum 0.35 Maximum 0.41 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.07 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.03
Descriptive Statistics for HPR8 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.93 Mean 0.01 Mean 0.35 Mean 0.34 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.93 Median 0.01 Median 0.36 Median 0.34 Std Dev 0.11 Std Dev 0.00 Std Dev 0.02 Std Dev 0.04 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.76 Minimum 0.01 Minimum 0.32 Minimum 0.27 Maximum 1.10 Maximum 0.01 Maximum 0.37 Maximum 0.41 Count 10.00 Count 10.00 Count 10.00 Count 10.00 Con (95%) 0.07 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.03
111 Descriptive Statistics for HPR8 Q 11 1992 TOTAL NO2 NO3 NH3 Mean 0.92 Mean 0.01 Mean 0.28 Mean 0.39 Std Error 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.92 Median 0.01 Median 0.28 Median 0.39 Std Dev 0.09 Std Dev 0.00 Std Dev 0.01 Std Dev 0.03 Variance 0.01 Variance 0.00 Variance 0. 00 Variance 0.00 Minimum 0. 72 Minimum 0.0 I Minimum 0.24 Minimum 0.34 Maximum 1.10 Maximum 0.0 I Maximum 0.30 Maximum 0.46 Count 14.00 Count 14.00 Count 14.00 Count 14.00 Con (95%) 0.05 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.02
Descriptive Statistics for HPR8 Q12 1992 TOTAL NO2 NO3 NH3 Mean 0.77 Mean 0.01 Mean 0.28 Mean 0.41 Std Error 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.03 Median 0.76 Median 0.01 Median 0.28 Median 0.39 Std Dev 0.10 Std Dev 0.00 Std Dev 0.01 Std Dev 0.07 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.66 Minimum 0.0 I Minimum 0.27 Minimum 0.35 Maximum 0.91 Maximum 0.01 Maximum 0.29 Maximum 0.58 Sum 6.19 Sum 0.08 Sum 2.22 Sum 3.28 Count 8.00 Count 8.00 Count 8.00 Count 8.00 Con (95%) 0.07 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.05
112 Axl.2 Descriptive statistics for Ryde Pumping Station (WPS5) for quarters one to twelve.
Descriptive Statistics for WPS5 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.51 Mean 0.14 Mean 0.26 Mean 0.24 Std Error 0.03 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.57 Median 0.14 Median 0.24 Median 0.22 Std Dev 0.17 Std Dev 0.05 Std Dev 0.10 Std Dev 0.06 Variance 0.03 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.07 Minimum 0.04 Minimum 0.12 Minimum 0.15 Maximum 0.69 Maximum 0.25 Maximum 0.53 Maximum 0.36 Count 28.00 Count 28.00 Count 28.00 Count 28.00 Con (95%) 0.06 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.02
Descriptive Statistics for WPS5 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.67 Mean 0.11 Mean 0.25 Mean 0.30 Std Error 0.01 Std Error 0.01 Std Error 0.01 Std Error 0.02 Median 0.68 Median 0.11 Median 0.24 Median 0.27 Std Dev 0.07 Std Dev 0.05 Std Dev 0.05 Std Dev 0.11 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.52 Minimum 0.02 Minimum 0.18 Minimum 0.17 Maximum 0.79 Maximum 0.19 Maximum 0.38 Maximum 0.62 Count 40.00 Count 40.00 Count 40.00 Count 40.00 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.03
Descriptive Statistics for WPS5 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.77 Mean 0.00 Mean 0.22 Mean 0.37 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.78 Median 0.00 Median 0.21 Median 0.37 Std Dev 0.10 Std Dev 0.01 Std Dev 0.02 Std Dev 0.04 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.50 Minimum 0.00 Minimum 0.18 Minimum 0.29 Maximum 0.96 Maximum 0.04 Maximum 0.28 Maximum 0.46 Count 57.00 Count 57.00 Count 57.00 Count 57.00 Con (95%) 0.03 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01
113 Descriptive Statistics for WPS5 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.84 Mean 0.00 Mean 0.19 Mean 0.34 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.82 Median 0.00 Median 0.20 Median 0.35 Std Dev 0.08 Std Dev 0.00 Std Dev 0.03 Std Dev 0.05 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.68 Minimum 0.00 Minimum 0.13 Minimum 0.22 Maximum 1.03 Maximum 0.01 Maximum 0.22 Maximum 0.45 Count 60.00 Count 60.00 Count 60.00 Count 60.00 Con (95%) 0.02 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.01
Descriptive Statistics for WPS5 Q5 1991 TOTAL NO2 NO3 NH3 Mean 0.89 Mean 0.00 Mean 0.10 Mean 0.33 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.00 Median 0.92 Median 0.00 Median 0.10 Median 0.33 Std Dev 0.16 Std Dev 0.00 Std Dev 0.02 Std Dev 0.04 Variance 0.03 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.00 Minimum 0.00 Minimum 0.08 Minimum 0.25 Maximum 1.15 Maximum 0.01 Maximum 0.14 Maximum 0.42 Count 60.00 Count 60.00 Count 60.00 Count 60.00 Con (95%) 0.04 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01
Descriptive Statistics for WPS5 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.92 Mean 0.00 Mean 0.14 Mean 0.39 Std Error 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.92 Median 0.00 Median 0.12 Median 0.40 Std Dev 0.08 Std Dev 0.00 Std Dev 0.05 Std Dev 0.04 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.75 Minimum 0.00 Minimum 0.09 Minimum 0.32 Maximum 1.15 Maximum 0.01 Maximum 0.25 Maximum 0.52 Count 60.00 Count 60.00 Count 60.00 Count 60.00 Con (95%) 0.02 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.01
114 Descriptive Statistics for WPS5 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.88 Mean 0.01 Mean 0.3 l Mean 0.35 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.88 Median 0.01 Median 0.32 Median 0.35 Std Dev 0.1 l Std Dev 0.00 Std Dev 0.04 Std Dev 0.07 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.60 Minimum 0.01 Minimum 0.23 Minimum 0.21 Maximum 1.15 Maximum 0.02 Maximum 0.36 Maximum 0.82 Count 75.00 Count 75.00 Count 75.00 Count 75.00 Con (95%) 0.02 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.01
Descriptive Statistics for WPS5 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.86 Mean 0.01 Mean 0.35 Mean 0.34 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.89 Median 0.01 Median 0.35 Median 0.33 Std Dev 0.15 Std Dev 0.00 Std Dev 0.03 Std Dev 0.09 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.21 Minimum 0.01 Minimum 0.23 Minimum 0.02 Maximum 1.05 Maximum 0.03 Maximum 0.38 Maximum 0.47 Count 54.00 Count 54.00 Count 54.00 Count 54.00 Con (95%) 0.04 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.02
Descriptive Statistics for WPS5 Q9 1992 TOTAL NO2 NO3 NH3 Mean 1.01 Mean 0.01 Mean 0.36 Mean 0.39 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 1.00 Median 0.01 Median 0.37 Median 0.40 Std Dev 0.12 Std Dev 0.00 Std Dev 0.04 Std Dev 0.06 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.77 Minimum 0.0 l Minimum 0.22 Minimum 0.19 Maximum 1.20 Maximum 0.02 Maximum 0.41 Maximum 0.50 Count 60.00 Count 60.00 Count 60.00 Count 60.00 Con (95%) 0.03 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.02
115 Descriptive Statistics for WPS5 Q 10 1992 TOTAL NO2 NO3 NH3 Mean 1.00 Mean 0.01 Mean 0.29 Mean 0.43 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 1.00 Median 0.01 Median 0.28 Median 0.42 Std Dev 0.11 Std Dev 0.00Std Dev 0.03 Std Dev 0.07 Variance 0.01 Variance 0.00Variance 0.00 Variance 0.01 Minimum 0.85 Minimum 0.01 Minimum 0.25 Minimum 0.25 Maximum 1.20 Maximum 0.01 Maximum 0.35 Maximum 0.62 Count 57.00 Count 57.00Count 57.00 Count 57.00 Con (95%) 0.03 Con (95%) 0.00Con 0.01 Con (95%) 0.02 (95%)
Descriptive Statistics for WPS5 Ql 1 1992 TOTAL NO2 NO3 NH3 Mean 0.79 Mean 0.02 Mean 0.28 Mean 0.37 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.80 Median 0.01 Median 0.28 Median 0.37 Std Dev 0.10 Std Dev 0.02 Std Dev 0.02 Std Dev 0.04 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.60 Minimum 0.01 Minimum 0.25 Minimum 0.16 Maximum 0.95 Maximum 0.07 Maximum 0.35 Maximum 0.43 Count 67.00 Count 67.00 Count 67.00 Count 67.00 Con (95%) 0.02 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01
Descriptive Statistics for WPS5 Q12 1992 TOTAL NO2 NO3 NH3 Mean 0.53 Mean 0.11 Mean 0.33 Mean 0.24 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.53 Median 0.11 Median 0.33 Median 0.23 Std Dev 0.08 Std Dev 0.03 Std Dev 0.03 Std Dev 0.06 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.39 Minimum 0.05 Minimum 0.25 Minimum 0.16 Maximum 0.69 Maximum 0.18 Maximum 0.40 Maximum 0.40 Count 40.00 Count 40.00 Count 40.00 Count 40.00 Con (95%) 0.03 Con (95%) 0.01 Con (95%) 0.01 Con (95%) 0.02
116 Axl.3 Descriptive statistics for Hermitage Reticulation Zone (ZN50) for quarters 1 to 12.
Descriptive Statistics for ZN 50 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.04 Mean 0.55 Mean 0.03 Std Error 0.01 Std Error 0.01 Std Error 0.03 Std Error 0.01 Median 0.04 Median 0.04 Median 0.52 Median 0.03 Std Dev 0.04 Std Dev 0.03 Std Dev 0.09 Std Dev 0.02 Variance 0.11 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.41 Minimum 0.00 Maximum 0.15 Maximum 0.10 Maximum 0.70 Maximum 0.04 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.06 Con (95%) 0.01
Descriptive Statistics for ZN 50 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.11 Mean 0.06 Mean 0.56 Mean 0.03 Std Error 0.02 Std Error 0.02 Std Error 0.02 Std Error 0.01 Median 0.08 Median 0.05 Median 0.56 Median 0.01 Std Dev 0.07 Std Dev 0.05 Std Dev 0.08 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.45 Minimum 0.00 Maximum 0.23 Maximum 0.19 Maximum 0.70 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.04 Con (95%) 0.03 Con (95%) 0.05 Con (95%) 0.02
Descriptive Statistics for ZN 50 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.20 Mean 0.01 Mean 0.41 Mean 0.16 Std Error 0.04 Std Error 0.00 Std Error 0.05 Std Error 0.04 Median 0.22 Median 0.01 Median 0.38 Median 0.17 Std Dev 0.14 Std Dev 0.01 Std Dev 0.17 Std Dev 0.14 Variance 0.02 Variance 0.00 Variance 0.03 Variance 0.02 Minimum 0.04 Minimum 0.00 Minimum 0.07 Minimum 0.00 Maximum 0.42 Maximum 0.03 Maximum 0.61 Maximum 0.44 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.08 Con (95%) 0.01 Con (95%) 0.09 Con (95%) 0.08
117 Descriptive Statistics for ZN 50 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.22 Mean 0.01 Mean 0.38 Mean 0.16 Std Error 0.05 Std Error 0.00 Std Error 0.03 Std Error 0.02 Median 0.14 Median 0.01 Median 0.39 Median 0.16 Std Dev 0.18 Std Dev 0.01 Std Dev 0.10 Std Dev 0.07 Variance 0.03 Variance 0.00 Variance 0.0 l Variance 0.01 Minimum 0.02 Minimum 0.00 Minimum 0.20 Minimum 0.05 Maximum 0.50 Maximum 0.02 Maximum 0.54 Maximum 0.25 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.10 Con (95%) 0.00 Con (95%) 0.06 Con (95%) 0.04
Descriptive Statistics for ZN 50 Q5 1991
TOTAL NO2 NO3 NH3 Mean 0.33 Mean 0.06 Mean 0.26 Mean 0.12 Std Error 0.06 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.33 Median 0.07 Median 0.24 Median 0.13 Std Dev 0.22 Std Dev 0.02 Std Dev 0.07 Std Dev 0.06 Variance 0.05 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.02 Minimum 0.02 Minimum 0.18 Minimum 0.03 Maximum 0.80 Maximum 0.10 Maximum 0.38 Maximum 0.20 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.12 Con (95%) 0.01 Con (95%) 0.04 Con (95%) 0.03
Descriptive Statistics for ZN 50 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.44 Mean 0.06 Mean 0.27 Mean 0.18 Std Error 0.07 Std Error 0.0 l Std Error 0.03 Std Error 0.03 Median 0.43 Median 0.06 Median 0.24 Median 0.18 Std Dev 0.25 Std Dev 0.04 Std Dev 0.10 Std Dev 0.09 Variance 0.06 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.14 Minimum 0.00 Minimum 0.17 Minimum 0.04 Maximum 0.87 Maximum 0.12 Maximum 0.44 Maximum 0.33 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.14 Con (95%) 0.02 Con (95%) 0.05 Con (95%) 0.05
118 Descriptive Statistics for ZN 50 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.52 Mean 0.03 Mean 0.41 Mean 0.23 Std Error 0.03 Std Error 0.0 l Std Error 0.02 Std Error 0.02 Median 0.53 Median 0.03 Median 0.39 Median 0.24 Std Dev 0.13 Std Dev 0.02 Std Dev 0.07 Std Dev 0.08 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.25 Minimum 0.00 Minimum 0.30 Minimum 0.11 Maximum 0.70 Maximum 0.08 Maximum 0.52 Maximum 0.44 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.07 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.04
Descriptive Statistics for ZN 50 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.54 Mean 0.02 Mean 0.47 Mean 0.24 Std Error 0.05 Std Error 0.00 Std Error 0.02 Std Error 0.03 Median 0.57 Median 0.02 Median 0.46 Median 0.22 Std Dev 0.19 Std Dev 0.02 Std Dev 0.06 Std Dev 0.10 Variance 0.03 Variance 0. 00 Variance 0.00 Variance 0.01 Minimum 0.18 Minimum 0.00 Minimum 0.41 Minimum 0.10 Maximum 0.80 Maximum 0.05 Maximum 0.57 Maximum 0.47 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.01 Con (95%) 0.04 Con (95%) 0.06
Descriptive Statistics for ZN 50 Q9 1992 TOTAL NO2 NO3 NH3 Mean 0.58 Mean 0.01 Mean 0.50 Mean 0.24 Std Error 0.06 Std Error 0.00 Std Error 0.02 Std Error 0.03 Median 0.59 Median 0.01 Median 0.49 Median 0.26 · Std Dev 0.19StdDev 0.01 Std Dev 0.06 Std Dev 0.09 Variance 0.04 Variance 0. 00 Variance 0.00 Variance 0.01 Minimum 0.31 Minimum 0.00 Minimum 0.44 Minimum 0.01 Maximum 0.89 Maximum 0.04 Maximum 0.60 Maximum 0.37 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.05
119 Descriptive Statistics for ZN 50 Q 10 1992 TOTAL NO2 NO3 NH3 Mean 0.61 Mean 0.03 Mean 0.37 Mean 0.30 Std Error 0.05 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.60 Median 0.02 Median 0.34 Median 0.30 Std Dev 0.17 Std Dev 0.04 Std Dev 0.07 Std Dev 0.08 Variance 0.03 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.38 Minimum 0.00 Minimum 0.30 Minimum 0.17 Maximum 0.88 Maximum 0.13 Maximum 0.48 Maximum 0.43 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.10 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.04
Descriptive Statistics for ZN 50 Ql 1 1992 TOTAL NO2 NO3 NH3 Mean 0.34 Mean 0.16 Mean 0.38 Mean 0.13 Std Error 0.04 Std Error 0.02 Std Error 0.02 Std Error 0.02 Median 0.40 Median 0.16 Median 0.35 Median 0.10 Std Dev 0.17 Std Dev 0.07 Std Dev 0.08 Std Dev 0.10 Variance 0.03 Variance 0.01 Variance 0.01 Variance 0.01 Minimum 0.06 Minimum 0.05 Minimum 0.32 Minimum 0.01 Maximum 0.60 Maximum 0.30 Maximum 0.62 Maximum 0.33 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.09 Con (95%) 0.04 Con (95%) 0.04 Con (95%) 0.05
Descriptive Statistics for ZN 50 Q12 1992 TOTAL NO2 NO3 NH3 Mean 0.14 Mean 0.11 Mean 0.51 Mean 0.03 Std Error 0.04 Std Error 0.04 Std Error 0.03 Std Error 0.01 Median 0.09 Median 0.04 Median 0.51 Median 0.02 Std Dev 0.11 Std Dev 0.13 Std Dev 0.10 Std Dev 0.03 Variance 0.01 Variance 0.02 Variance 0.01 Variance 0.00 Minimum 0.04 Minimum 0.01 Minimum 0.37 Minimum 0.00 Maximum 0.36 Maximum 0.37 Maximum 0.67 Maximum 0.08 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.07 Con (95%) 0.09 Con (95%) 0.07 Con (95%) 0.02
120 Ax 1.4 Descriptive statistics for Pymble Reticulation Zone (ZN97) for quarters 1 to 12.
Descriptive Statistics for ZN 97 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.07 Mean 0.52 Mean 0.02 Std Error 0.02 Std Error 0.01 Std Error 0.03 Std Error 0.01 Median 0.05 Median 0.08 Median 0.52 Median 0.02 Std Dev 0.06 Std Dev 0.04 Std Dev 0.08 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.03 Minimum 0.01 Minimum 0.41 Minimum 0.00 Maximum 0.20 Maximum 0.12 Maximum 0.65 Maximum 0.08 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.04 Con (95%) 0.02 Con (95%) 0.05 Con (95%) 0.02
Descriptive Statistics for ZN 97 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.08 Mean 0.06 Mean 0.52 Mean 0.02 Std Error 0.01 Std Error 0.0 l Std Error 0.02 Std Error 0.01 Median 0.08 Median 0.05 Median 0.50 Median 0.01 Std Dev 0.03 Std Dev 0.04 Std Dev 0.08 Std Dev 0.03 Variance 0.00 Variance 0. 00 Variance 0.01 Variance 0.00 Minimum 0.04 Minimum 0.01 Minimum 0.42 Minimum 0.00 Maximum 0.14 Maximum 0.12 Maximum 0. 72 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.05 Con (95%) 0.02
Descriptive Statistics for ZN 97 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.20 Mean 0.02 Mean 0.43 Mean 0.12 Std Error 0.04 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.16 Median 0.01 Median 0.42 Median 0.15 Std Dev 0.16 Std Dev 0.02 Std Dev 0.06 Std Dev 0.07 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.05 Minimum 0.00 Minimum 0.36 Minimum 0.01 Maximum 0.62 Maximum 0.05 Maximum 0.55 Maximum 0.20 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.09 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.04
121 Descriptive Statistics for ZN 97 Q4 1990 TOTAL NO2 NO3 NHJ Mean 0.22 Mean 0.01 Mean 0.34 Mean 0.18 Std Error 0.03 Std Error 0.00 Std Error 0.02 Std Error 0.01 Median 0.22 Median 0.01 Median 0.35 Median 0.18 Std Dev 0.12 Std Dev 0.00 Std Dev 0.06 Std Dev 0.03 Variance 0.01 Variance 0. 00 Variance 0.00 Variance 0.00 Minimum 0.05 Minimum 0.01 Minimum 0.25 Minimum 0.12 Maximum 0.40 Maximum 0.02 Maximum 0.43 Maximum 0.23 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.07 Con (95%) 0.00 Con (95%) 0.03 Con (95%) 0.02
Descriptive Statistics for ZN 97 Q5 1991 TOTAL NO2 NO3 NH3 Mean 0.28 Mean 0.06 Mean 0.27 Mean 0.12 Std Error 0.03 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.30 Median 0.06 Median 0.26 Median 0.12 Std Dev 0.09 Std Dev 0.04 Std Dev 0.05 Std Dev 0.05 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.09 Minimum 0.00 Minimum 0.20 Minimum 0.04 Maximum 0.40 Maximum 0.14 Maximum 0.35 Maximum 0.20 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.05 Con (95%) 0.02 Con (95%) 0.03 Con (95%) 0.03
Descriptive Statistics for ZN 97 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.45 Mean 0.03 Mean 0.29 Mean 0.19 Std Error 0.07 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.54 Median 0.03 Median 0.31 Median 0.22 Std Dev 0.24 Std Dev 0.02 Std Dev 0.08 Std Dev 0.09 Variance 0.06 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.06 Minimum 0.00 Minimum 0.18 Minimum 0.05 Maximum 0. 78 Maximum 0.09 Maximum 0.39 Maximum 0.30 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.14 Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.05
122 Descriptive Statistics for ZN 97 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.47 Mean 0.03 Mean 0.41 Mean 0.24 Std Error 0.03 Std Error 0.00 Std Error 0.02 Std Error 0.02 Median 0.49 Median 0.03 Median 0.41 Median 0.21 Std Dev 0.14 Std Dev 0.01 Std Dev 0.06 Std Dev 0.10 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.25 Minimum 0.01 Minimum 0.29 Minimum 0.15 Maximum 0.67 Maximum 0.06 Maximum 0.55 Maximum 0.56 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.07 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.05
Descriptive Statistics for ZN 97 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.37 Mean 0.02 Mean 0.50 Mean 0.21 Std Error 0.06 Std Error 0.00 Std Error 0.02 Std Error 0.02 Median 0.41 Median 0.02 Median 0.47 Median 0.22 Std Dev 0.21 Std Dev 0.01 Std Dev 0.07 Std Dev 0.07 Variance 0.05 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.06 Minimum 0.00 Minimum 0.38 Minimum 0.11 Maximum 0.73 Maximum 0.04 Maximum 0.63 Maximum 0.34 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.12 Con (95%) 0.01 Con (95%) 0.04 Con (95%) 0.04
Descriptive Statistics for ZN 97 Q9 1992 TOTAL NO2 NO3 NH3 Mean 0.39 Mean 0.01 Mean 0.53 Mean 0.20 Std Error 0.06 Std Error 0.00 Std Error 0.02 Std Error 0.02 Median 0.43 Median 0.01 Median 0.50 Median 0.18 Std Dev 0.20 Std Dev 0.01 Std Dev 0.08 Std Dev 0.07 Variance 0.04 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.08 Minimum 0.00 Minimum 0.42 Minimum 0.13 Maximum 0.71 Maximum 0.02 Maximum 0. 70 Maximum 0.33 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.00 Con (95%) 0.05 Con (95%) 0.04
123 Descriptive Statistics for ZN 97 Q 10 1992 TOTAL NO2 NO3 NH3 Mean 0.56 Mean 0.02 Mean 0.40 Mean 0.30 Std Error 0.06 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.62 Median 0.02 Median 0.36 Median 0.29 Std Dev 0.19 Std Dev 0.02 Std Dev 0.08 Std Dev 0.07 Variance 0.04 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.28 Minimum 0.00 Minimum 0.31 Minimum 0.16 Maximum 0. 78 Maximum 0.06 Maximum 0.55 Maximum 0.41 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.1 I Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.04
Descriptive Statistics for ZN 97 Q 11 1992 TOTAL NO2 NO3 NH3 Mean 0.30 Mean 0.09 Mean 0.46 Mean 0.07 Std Error 0.07 Std Error 0.02 Std Error 0.02 Std Error 0.02 Median 0.15 Median 0.07 Median 0.46 Median 0.05 Std Dev 0.25 Std Dev 0.06 Std Dev 0.08 Std Dev 0.07 Variance 0.06 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.00 Minimum 0.02 Minimum 0.33 Minimum 0.00 Maximum 0.70 Maximum 0.19 Maximum 0.59 Maximum 0.22 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.13 Con (95%) 0.03 Con (95%) 0.04 Con (95%) 0.04
Descriptive Statistics for ZN 97 Q 12 1992 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.07 Mean 0.58 Mean 0.01 Std Error 0.02 Std Error 0.02 Std Error 0.02 Std Error 0.00 Median 0.07 Median 0.06 Median 0.59 Median 0.01 Std Dev 0.07 Std Dev 0.07 Std Dev 0.06 Std Dev 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.00 Minimum 0.00 Minimum 0.43 Minimum 0.00 Maximum 0.21 Maximum 0.21 Maximum 0.65 Maximum 0.03 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.05 Con (95%) 0.05 Con (95%) 0.04 Con (95%) 0.01
124 Ax 1.5 Descriptive statistics for Warringah Reticulation Zone (ZN131) for quarters 1 to 12.
Descriptive Statistics for ZN 131 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.08 Mean 0.03 Mean 0.60 Mean 0.01 Std Error 0.0 l Std Error 0.01 Std Error 0.02 Std Error 0.00 Median 0.08 Median 0.01 Median 0.60 Median 0.01 Std Dev 0.04 Std Dev 0.04 Std Dev 0.06 Std Dev 0.01 Variance 0. 00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.02 Minimum 0.00 Minimum 0.50 Minimum 0.00 Maximum 0.17 Maximum 0.11 Maximum 0.72 Maximum 0.04 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.03 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.01
Descriptive Statistics for ZN 131 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.10 Mean 0.02 Mean 0.53 Mean 0.02 Std Error 0.02 Std Error 0.01 Std Error 0.04 Std Error 0.01 Median 0.09 Median 0.01 Median 0.53 Median 0.01 Std Dev 0.06 Std Dev 0.03 Std Dev 0.13 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.02 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.26 Minimum 0.00 Maximum 0.23 Maximum 0.11 Maximum 0.73 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.02 Con (95%) 0.07 Con (95%) 0.02
Descriptive Statistics for ZN 131 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.22 Mean 0.06 Mean 0.37 Mean 0.14 Std Error 0.05 Std Error 0.01 Std Error 0.03 Std Error 0.02 Median 0.16 Median 0.05 Median 0.36 Median 0.14 Std Dev 0.19 Std Dev 0.04 Std Dev 0.09 Std Dev 0.08 Variance 0.03 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.05 Minimum 0.01 Minimum 0.22 Minimum 0.01 Maximum 0.60 Maximum 0.13 Maximum 0.51 Maximum 0.27 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.02 Con (95%) 0.05 Con (95%) 0.05
125 Descriptive Statistics for ZN 131 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.24 Mean 0.03 Mean 0.29 Mean 0.21 Std Error 0.04 Std Error 0.0 l Std Error 0.01 Std Error 0.01 Median 0.23 Median 0.02 Median 0.29 Median 0.21 Std Dev 0.15 Std Dev 0.02 Std Dev 0.05 Std Dev 0.04 Variance 0. 02 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.21 Minimum 0.16 Maximum 0.55 Maximum 0.09 Maximum 0.38 Maximum 0.30 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.09 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.02
Descriptive Statistics for ZN 131 Q5 1991 TOTAL NO2 NO3 NH3 Mean 0.34 Mean 0.06 Mean 0.23 Mean 0.14 Std Error 0.08 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.33 Median 0.04 Median 0.23 Median 0.15 Std Dev 0.27 Std Dev 0.03 Std Dev 0.06 Std Dev 0.06 Variance 0.07 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.04 Minimum 0.02 Minimum 0.15 Minimum 0.04 Maximum 0.86 Maximum 0.11 Maximum 0.31 Maximum 0.22 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.15 Con (95%) 0.02 Con (95%) 0.03 Con (95%) 0.03
Descriptive Statistics for ZN 131 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.45 Mean 0.07 Mean 0.25 Mean 0.19 Std Error 0.06 Std Error 0.02 Std Error 0.02 Std Error 0.02 Median 0.51 Median 0.06 Median 0.25 Median 0.21 Std Dev 0.19 Std Dev 0.06 Std Dev 0.07 Std Dev 0.07 Variance 0.04 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.20 Minimum 0.02 Minimum 0.16 Minimum 0.08 Maximum 0.71 Maximum 0.24 Maximum 0.35 Maximum 0.31 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.11 Con (95%) 0.03 Con (95%) 0.04 Con (95%) 0.04
126 Descriptive Statistics for ZN 131 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.38 Mean 0.05 Mean 0.40 Mean 0.22 Std Error 0.05 Std Error 0.00 Std Error 0.02 Std Error 0.03 Median 0.46 Median 0.05 Median 0.40 Median 0.22 Std Dev 0.18 Std Dev 0.01 Std Dev 0.07 Std Dev 0.10 Variance 0.03 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.08 Minimum 0.03 Minimum 0.30 Minimum 0.10 Maximum 0.61 Maximum 0.07 Maximum 0.53 Maximum 0.52 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.09 Con (95%) 0.01 Con (95%) 0.04 Con (95%) 0.05
Descriptive Statistics for ZN 131 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.51 Mean 0.02 Mean 0.45 Mean 0.22 Std Error 0.07 Std Error 0.00 Std Error 0.02 Std Error 0.02 Median 0.53 Median 0.02 Median 0.43 Median 0.24 Std Dev 0.23 Std Dev 0.01 Std Dev 0.06 Std Dev 0.07 Variance 0.05 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.05 Minimum 0.00 Minimum 0.36 Minimum 0.12 Maximum 0.78 Maximum 0.04 Maximum 0.55 Maximum 0.33 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.13 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.04
Descriptive Statistics for ZN 131 Q9 1992 TOTAL NO2 NO3 NH3 Mean 0.66 Mean 0.02 Mean 0.45 Mean 0.27 Std Error 0.04 Std Error 0.00 Std Error 0.03 Std Error 0.02 Median 0.70 Median 0.02 Median 0.45 Median 0.28 Std Dev 0.15 Std Dev 0.01 Std Dev 0.09 Std Dev 0.07 Variance 0.02 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.34 Minimum 0.00 Minimum 0.21 Minimum 0.09 Maximum 0.84 Maximum 0.03 Maximum 0.58 Maximum 0.36 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.08 Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.04
127 Descriptive Statistics for ZN 131 Q 10 1992 TOTAL NO2 NO3 NH3 Mean 0.54 Mean 0.03 Mean 0.38 Mean 0.30 Std Error 0.05 Std Error 0.01 Std Error 0.01 Std Error 0.02 Median 0.58 Median 0.03 Median 0.37 Median 0.30 Std Dev 0.18 Std Dev 0.02 Std Dev 0.04 Std Dev 0.08 Variance 0.03 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.24 Minimum 0.01 Minimum 0.34 Minimum 0.20 Maximum 0.74 Maximum 0.07 Maximum 0.46 Maximum 0.49 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.10 Con (95%) 0.01 Con (95%) 0.02 Con (95%) 0.04
Descriptive Statistics for ZN 131 Q 11 1992 TOTAL NO2 NO3 NH3 Mean 0.18 Mean 0.14 Mean 0.44 Mean 0.06 Std Error 0.05 Std Error 0.02 Std Error 0.03 Std Error 0.02 Median 0.08 Median 0.13 Median 0.40 Median 0.02 Std Dev 0.21 Std Dev 0.08 Std Dev 0.12 Std Dev 0.07 Variance 0.04 Variance 0.01 Variance 0.01 Variance 0.00 Minimum 0.00 Minimum 0.00 Minimum 0.32 Minimum 0.00 Maximum 0.70 Maximum 0.28 Maximum 0.67 Maximum 0.21 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.10 Con (95%) 0.04 Con (95%) 0.06 Con (95%) 0.04
Descriptive Statistics for ZN 131 Q 12 1992 TOTAL NO2 NO3 NH3 Mean 0.03 Mean 0.07 Mean 0.60 Mean 0.01 Std Error 0.00 Std Error 0.03 Std Error 0.03 Std Error 0.00 Median 0.04 Median 0.06 Median 0.60 Median 0.00 Std Dev 0.01 Std Dev 0.08 Std Dev 0.09 Std Dev 0.01 Variance 0.00 Variance 0.01 Variance 0.01 Variance 0.00 Minimum 0.02 Minimum 0.00 Minimum 0.45 Minimum 0.00 Maximum 0.05 Maximum 0.24 Maximum 0.73 Maximum 0.02 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.06 Con (95%) 0.00
128 Axl.6 Descriptive statistics for Frenchs Forest Reticulation Zone (ZN283) for quarters 1 to 12.
Descriptive Statistics for ZN 283 Q 1 1990 TOTAL NO2 NO3 NH3 Mean 0.05 Mean 0.04 Mean 0.58 Mean 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.02 Std Error 0.00 Median 0.04 Median 0.04 Median 0.58 Median 0.01 Std Dev 0.01 Std Dev 0.04 Std Dev 0.06 Std Dev 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.03 Minimum 0.00 Minimum 0.48 Minimum 0.00 Maximum 0.06 Maximum 0.13 Maximum 0.69 Maximum 0.03 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.01 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.01
Descriptive Statistics for ZN 283 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.06 Mean 0.04 Mean 0.56 Mean 0.03 Std Error 0.01 Std Error 0.01 Std Error 0.03 Std Error 0.01 Median 0.06 Median 0.03 Median 0.60 Median 0.01 Std Dev 0.02 Std Dev 0.03 Std Dev 0.09 Std Dev 0.03 Variance 0.00 Variance 0. 00 Variance 0.01 Variance 0.00 Minimum 0.03 Minimum 0.00 Minimum 0.42 Minimum 0.00 Maximum 0.09 Maximum 0.11 Maximum 0.67 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.01 Con (95%) 0.02 Con (95%) 0.05 Con (95%) 0.02
Descriptive Statistics for ZN 283 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.11 Mean 0.01 Mean 0.50 Mean 0.07 Std Error 0.04 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.07 Median 0.01 Median 0.50 Median 0.08 Std Dev 0.13 Std Dev 0.01 Std Dev 0.05 Std Dev 0.05 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.03 Minimum 0.00 Minimum 0.38 Minimum 0.01 Maximum 0.51 Maximum 0.02 Maximum 0.57 Maximum 0.14 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.07 Con (95%) 0.00 Con (95%) 0.03 Con (95%) 0.03
129 Descriptive Statistics for ZN 283 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.11 Mean 0.01 Mean 0.45 Mean 0.11 Std Error 0.02 Std Error 0.00 Std Error 0.03 Std Error 0.02 Median 0.10 Median 0.01 Median 0.46 Median 0.10 Std Dev 0.06 Std Dev 0.01 Std Dev 0.10 Std Dev 0.06 Variance 0.00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.02 Minimum 0.00 Minimum 0.30 Minimum 0.04 Maximum 0.21 Maximum 0.02 Maximum 0.59 Maximum 0.22 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.00 Con (95%) 0.05 Con (95%) 0.03
Descriptive Statistics for ZN 283 Q5 1991 TOTAL NO2 NO3 NH3 Mean 0.16 Mean 0.04 Mean 0.32 Mean 0.10 Std Error 0.03 Std Error 0.01 Std Error 0.01 Std Error 0.02 Median 0.13 Median 0.04 Median 0.32 Median 0.08 Std Dev 0.09 Std Dev 0.02 Std Dev 0.05 Std Dev 0.05 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.22 Minimum 0.03 Maximum 0.33 Maximum 0.07 Maximum 0.40 Maximum 0.20 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.05 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.03
Descriptive Statistics for ZN 283 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.34 Mean 0.05 Mean 0.30 Mean 0.16 Std Error 0.05 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.31 Median 0.05 Median 0.28 Median 0.16 Std Dev 0.18 Std Dev 0.02 Std Dev 0.08 Std Dev 0.08 Variance 0.03 Variance 0.00 Variance 0.01 Variance 0.01 Minimum 0.11 Minimum 0.03 Minimum 0.21 Minimum 0.04 Maximum 0.62 Maximum 0.08 Maximum 0.45 Maximum 0.26 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.10 Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.04
130 Descriptive Statistics for ZN 283 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.45 Mean 0.05 Mean 0.40 Mean 0.21 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.02 Median 0.45 Median 0.04 Median 0.40 Median 0.20 Std Dev 0.09 Std Dev 0.02 Std Dev 0.06 Std Dev 0.09 Variance 0.01 Variance 0.00 Variance 0. 00 Variance 0.01 Minimum 0.28 Minimum 0.02 Minimum 0.31 Minimum 0.10 Maximum 0.58 Maximum 0.08 Maximum 0.48 Maximum 0.50 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.05 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.04
Descriptive Statistics for ZN 283 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.30 Mean 0.05 Mean 0.51 Mean 0.22 Std Error 0.03 Std Error 0.01 Std Error 0.02 Std Error 0.04 Median 0.27 Median 0.04 Median 0.49 Median 0.20 Std Dev 0.12 Std Dev 0.04 Std Dev 0.07 Std Dev 0.14 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.02 Minimum 0.18 Minimum 0.00 Minimum 0.42 Minimum 0.07 Maximum 0.60 Maximum 0.14 Maximum 0.62 Maximum 0.58 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.07 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.08
Descriptive Statistics for ZN 283 Q9 1992 TOTAL NO2 NO3 NH3 Mean 0.34 Mean 0.02 Mean 0.48 Mean 0.23 Std Error 0.05 Std Error 0.00 Std Error 0.03 Std Error 0.02 Median 0.38 Median 0.02 Median 0.50 Median 0.25 Std Dev 0.18 Std Dev 0.01 Std Dev 0.10 Std Dev 0.06 Variance 0.03 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.04 Minimum 0.00 Minimum 0.19 Minimum 0.12 Maximum 0.58 Maximum 0.05 Maximum 0.58 Maximum 0.30 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.10 Con (95%) 0.01 Con (95%) 0.06 Con (95%) 0.03
131 Descriptive Statistics for ZN 283 Q 10 1992 TOTAL NO2 NO3 NH3 Mean 0.47 Mean 0.05 Mean 0.40 Mean 0.27 Std Error 0.04 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.47 Median 0.03 Median 0.38 Median 0.26 Std Dev 0.15 Std Dev 0.04 Std Dev 0.06 Std Dev 0.08 Variance 0.02 Variance 0.00 Variance 0.00 Variance 0.01 Minimum 0.30 Minimum 0.01 Minimum 0.33 Minimum 0.14 Maximum 0.75 Maximum 0.14 Maximum 0.50 Maximum 0.37 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.08 Con (95%) 0.02 Con (95%) 0.03 Con (95%) 0.05
Descriptive Statistics for ZN 283 Ql 1 1992 TOTAL NO2 NO3 NH3 Mean 0.21 Mean 0.20 Mean 0.39 Mean 0.06 Std Error 0.03 Std Error 0.02 Std Error 0.02 Std Error 0.01 Median 0.25 Median 0.20 Median 0.37 Median 0.05 Std Dev 0.10 Std Dev 0.07 Std Dev 0.07 Std Dev 0.06 Variance 0.01 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.06 Minimum 0.07 Minimum 0.31 Minimum 0.00 Maximum 0.40 Maximum 0.29 Maximum 0.59 Maximum 0.19 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.05 Con (95%) 0.03 Con (95%) 0.04 Con (95%) 0.03
Descriptive Statistics for ZN 283 Q 12 1992 TOTAL NO2 NO3 NH3 Mean 0.04 Mean 0.05 Mean 0.61 Mean 0.01 Std Error 0.0 l Std Error 0.02 Std Error 0.02 Std Error 0.00 Median 0.04 Median 0.05 Median 0.62 Median 0.01 Std Dev 0.02 Std Dev 0.06 Std Dev 0.06 Std Dev 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.02 Minimum 0.00 Minimum 0.50 Minimum 0.00 Maximum 0.10 Maximum 0.17 Maximum 0.68 Maximum 0.03 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.04 Con (95%) 0.01
132 Ax 1.7 Descriptive statistics for Palm Beach Reticulation Zone (ZN192) for quarters 1 to 12.
Descriptive Statistics for ZN192 QI 1990 TOTAL NO2 NO3 NH3 Mean 0.07 Mean 0.04 Mean 0.57 Mean 0.02 Std Error 0.02 Std Error 0.02 Std Error 0.03 Std Error 0.00 Median 0.05 Median 0.02 Median 0.61 Median 0.01 Std Dev 0.05 Std Dev 0.06 Std Dev 0.10 Std Dev 0.01 Variance 0. 00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.03 Minimum 0.01 Minimum 0.33 Minimum 0.01 Maximum 0.18 Maximum 0.19 Maximum 0.63 Maximum 0.03 Count 9.00 Coun' 9.00 Count 9.00 Count 9.00 Con (95%) 0.03 Con (95%) 0.04 Con (95%) 0.06 Con (95%) 0.01
Descriptive Statistics for ZNI 92 Q2 1990 TOTAL NO2 NO3 NH3 Mean 0.04 Mean 0.01 Mean 0.63 Mean 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.02 Std Error 0.01 Median 0.04 Median 0.01 Median 0.61 Median 0.01 Std Dev 0.02 Std Dev 0.00 Std Dev 0.08 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.03 Minimum 0.01 Minimum 0.51 Minimum 0.01 Maximum 0.08 Maximum 0.02 Maximum 0. 77 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.01 Con (95%) 0.00 Con (95%) 0.04 Con (95%) 0.02
Descriptive Statistics for ZN 192 Q3 1990 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.04 Mean 0.46 Mean 0.07 Std Error 0.02 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.06 Median 0.03 Median 0.45 Median 0.06 Std Dev 0.08 Std Dev 0.03 Std Dev 0.06 Std Dev 0.06 Variance 0.01 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.01 Minimum 0.01 Minimum 0.40 Minimum 0.01 Maximum 0.25 Maximum 0.11 Maximum 0.62 Maximum 0.15 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.04 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.03
133 Descriptive Statistics for ZNl 92 Q4 1990 TOTAL NO2 NO3 NH3 Mean 0.06 Mean 0.03 Mean 0.40 Mean 0.13 Std Error 0.02 Std Error 0.00 Std Error 0.02 Std Error 0.01 Median 0.04 Median 0.02 Median 0.39 Median 0.13 Std Dev 0.05 Std Dev 0.01 Std Dev 0.06 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.01 Minimum 0.01 Minimum 0.27 Minimum 0.08 Maximum 0.18 Maximum 0.06 Maximum 0.49 Maximum 0.17 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.01 Con (95%) 0.03 Con (95%) 0.02
Descriptive Statistics for ZNl 92 QS 1991 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.09 Mean 0.32 Mean 0.07 Std Error 0.02 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.07 Median 0.10 Median 0.31 Median 0.06 Std Dev 0.06 Std Dev 0.03 Std Dev 0.05 Std Dev 0.04 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.02 Minimum 0.01 Minimum 0.26 Minimum 0.02 Maximum 0.18 Maximum 0.11 Maximum 0.47 Maximum 0.12 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.02 Con (95%) 0.03 Con (95%) 0.02
Descriptive Statistics for ZNl 92 Q6 1991 TOTAL NO2 NO3 NH3 Mean 0.09 Mean 0.09 Mean 0.34 Mean 0.06 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.09 Median 0.11 Median 0.36 Median 0.04 Std Dev 0.04 Std Dev 0.03 Std Dev 0.07 Std Dev 0.05 Variance 0. 00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.02 Minimum 0.02 Minimum 0.14 Minimum 0.01 Maximum 0.17 Maximum 0.13 Maximum 0.43 Maximum 0.16 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.03
134 Descriptive Statistics for ZN192 Q7 1991 TOTAL NO2 NO3 NH3 Mean 0.11 Mean 0.08 Mean 0.45 Mean 0.12 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.10 Median 0.08 Median 0.44 Median 0.12 Std Dev 0.04 Std Dev 0.04 Std Dev 0.06 Std Dev 0.03 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.08 Minimum 0.03 Minimum 0.37 Minimum 0.07 Maximum 0.18 Maximum 0.15 Maximum 0.54 Maximum 0.16 Count 15.00 Count 15.00 Count 15.00 Count 15.00 Con (95%) 0.02 Con (95%) 0.02 Con (95%) 0.03 Con (95%) 0.01
Descriptive Statistics for ZNl 92 Q8 1991 TOTAL NO2 NO3 NH3 Mean 0.12 Mean 0.05 Mean 0.54 Mean 0.12 Std Error 0.07 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.06 Median 0.04 Median 0.56 Median 0.12 Std Dev 0.23 Std Dev 0.04 Std Dev 0.06 Std Dev 0.04 Variance 0.05 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.01 Minimum 0.02 Minimum 0.37 Minimum 0.04 Maximum 0.80 Maximum 0.15 Maximum 0.60 Maximum 0.19 Count 11.00 Count 11.00 Count 11.00 Count 11.00 Con (95%) 0.13 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.02
Descriptive Statistics for ZNl 92 Q9 1992 TOTAL NO2 NO3 NH3 Mean 0.12 Mean 0.03 Mean 0.59 Mean 0.17 Std Error 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.10 Median 0.03 Median 0.59 Median 0.17 Std Dev 0.04 Std Dev 0.01 Std Dev 0.03 Std Dev 0.02 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.08 Minimum 0.01 Minimum 0.54 Minimum 0.13 Maximum 0.20 Maximum 0.05 Maximum 0.63 Maximum 0.20 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.02 Con (95%) 0.01 Con (95%) 0.02 Con (95%) 0.01
135 Descriptive Statistics for ZN192 QIO 1992 TOTAL NO2 NO3 NH3 Mean 0.10 Mean 0.07 Mean 0.50 Mean 0.16 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.08 Median 0.06 Median 0.50 Median 0.16 Std Dev 0.05 Std Dev 0.04 Std Dev 0.07 Std Dev 0.05 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.01 Minimum 0.01 Minimum 0.38 Minimum 0.09 Maximum 0.20 Maximum 0.15 Maximum 0.59 Maximum 0.24 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.03 Con (95%) 0.03 Con (95%) 0.04 Con (95%) 0.03
Descriptive Statistics for ZNl 92 QI 1 1992 TOTAL NO2 NO3 NH3 Mean 0.04 Mean 0.10 Mean 0.56 Mean 0.02 Std Error 0.0 l Std Error 0.02 Std Error 0.03 Std Error 0.01 Median 0.02 Median 0.10 Median 0.56 Median 0.01 Std Dev 0.04 Std Dev 0.07 Std Dev 0.10 Std Dev 0.04 Variance 0.00 Variance 0.00 Variance 0.01 Variance 0.00 Minimum 0.01 Minimum 0.01 Minimum 0.34 Minimum 0.01 Maximum 0.15 Maximum 0.20 Maximum 0.69 Maximum 0.16 Count 13.00 Count 13.00 Count 13.00 Count 13.00 Con (95%) 0.02 Con (95%) 0.04 Con (95%) 0.05 Con (95%) 0.02
Descriptive Statistics for ZN192 Ql2 1992 TOTAL NO2 NO3 NH3 Mean 0.05 Mean 0.01 Mean 0.69 Mean 0.01 Std Error 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.00 Median 0.05 Median 0.01 Median 0.68 Median 0.01 Std Dev 0.02 Std Dev 0.00 Std Dev 0.04 Std Dev 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Variance 0.00 Minimum 0.02 Minimum 0.01 Minimum 0.65 Minimum 0.01 Maximum 0.08 Maximum 0.01 Maximum 0.77 Maximum 0.02 Count 8.00 Count 8.00 Count 8.00 Count 8.00 Con (95%) 0.02 Con (95%) 0.00 Con (95%) 0.03 Con (95%) 0.00
136 Axl.8 Descriptive statistics for Hermitage Reservoir (RSO) for quarters 1 to 12.
Descriptive Statistics for R50 Q 1 1990
Total NO2 NO3 NH3 Mean 0.12 Mean 0.28 Mean 0.31 Mean 0.04 Std Error 0.02 Std Error 0.01 Std Error 0.03 Std Error 0.01 Median 0.10 Median 0.30 Median 0.28 Median 0.04 Std Dev 0.04 Std Dev 0.04 Std Dev 0.09 Std Dev 0.03 Minimum 0.08 Minimum 0.19 Minimum 0.27 Minimum 0.01 Maximum 0.20 Maximum 0.31 Maximum 0.54 Maximum 0.10 Count 8.00 Count 8.00 Count 8.00 Count 8.00
Descriptive Statistics for R50 Q2 1990
Total NO2 NO3 NH3 Mean 0.22 Mean 0.23 Mean 0.32 Mean 0.14 Std Error 0.03 Std Error 0.03 Std Error 0.02 Std Error 0.02 Median 0.18 Median 0.21 Median 0.30 Median 0.15 Std Dev 0.09 Std Dev 0.12 Std Dev 0.07 Std Dev 0.08 Minimum 0.13 Minimum 0.07 Minimum 0.23 Minimum 0.00 Maximum 0.48 Maximum 0.50 Maximum 0.50 Maximum 0.24 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for R50 Q3 1990
Total NO2 NO3 NH3 Mean 0.44 Mean 0.01 Mean 0.24 Mean 0.32 Std Error 0.04 Std Error 0.01 Std Error 0.01 Std Error 0.01 Median 0.44 Median 0.00 Median 0.24 Median 0.32 Std Dev 0.15 Std Dev 0.02 Std Dev 0.02 Std Dev 0.03 Minimum 0.18 Minimum 0.00 Minimum 0.22 Minimum 0.28 Maximum 0.70 Maximum 0.06 Maximum 0.29 Maximum 0.41 Count 12.00 Count 12.00 Count 12.00 Count 12.00
137 Descriptive Statistics for R50 Q4 1990
Total NO2 NO3 NH3 Mean 0.38 Mean 0.00 Mean 0.21 Mean 0.32 Std Error 0.04 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.34 Median 0.00 Median 0.22 Median 0.32 Std Dev 0.15 StdDev 0.00 Std Dev 0.03 Std Dev 0.02 Minimum 0.18 Minimum 0.00 Minimum 0.16 Minimum 0.28 Maximum 0.65 Maximum 0.01 Maximum 0.26 Maximum 0.36 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for R50 Q5 1991
Total NO2 NO3 NH3 Mean 0.52 Mean 0.00 Mean 0.12 Mean 0.36 Std Error 0.04 Std Error 0.00 Std Error 0.01 Std Error 0.04 Median 0.53 Median 0.00 Median 0.12 Median 0.32 Std Dev 0.12 Std Dev 0.00 Std Dev 0.02 Std Dev 0.16 Minimum 0.32 Minimum 0.00 Minimum 0.10 Minimum 0.28 Maximum 0.71 Maximum 0.01 Maximum 0.17 Maximum 0.85 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for R50 Q6 1991
Total NO2 NO3 NH3 Mean 0.60 Mean 0.00 Mean 0.12 Mean 0.34 Std Error 0.01 Std Error 0.00 Std Error 0.00 Std Error 0.02 Median 0.60 Median 0.00 Median 0.12 Median 0.34 Std Dev 0.02 Std Dev 0.00 Std Dev 0.00 Std Dev 0.02 Minimum 0.58 Minimum 0.00 Minimum 0.12 Minimum 0.32 Maximum 0.61 Maximum 0.00 Maximum 0.12 Maximum 0.35 Count 2.00 Count 2.00 Count 2.00 Count 2.00
138 Descriptive Statistics for R50 Q7 1991
Total NO2 NO3 NH3 Mean 0.69 Mean 0.00 Mean 0.34 Mean 0.34 Std Error 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.68 Median 0.00 Median 0.35 Median 0.33 Std Dev 0.04 Std Dev 0.00 Std Dev 0.02 Std Dev 0.02 Minimum 0.63 Minimum 0.00 Minimum 0.31 Minimum 0.33 Maximum 0.79 Maximum 0.00 Maximum 0.36 Maximum 0.38 Count 9.00 Count 9.00 Count 9.00 Count 9.00
Descriptive Statistics for R50 Q8 1991
Total NO2 NO3 NH3 Mean 0.70 Mean 0.00 Mean 0.36 Mean 0.34 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.68 Median 0.00 Median 0.36 Median 0.34 Std Dev 0.08 Std Dev 0.00 Std Dev 0.02 Std Dev 0.04 Minimum 0.59 Minimum 0.00 Minimum 0.31 Minimum 0.26 Maximum 0.81 Maximum 0.00 Maximum 0.39 Maximum 0.43 Count 11.00 Count l 1.00 Count 11.00 Count 11.00
Descriptive Statistics for RSO Q9 1992
Total NO2 NO3 NH3 Mean 0.74 Mean 0.00 Mean 0.38 Mean 0.40 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.02 Median 0.75 Median 0.00 Median 0.38 Median 0.41 Std Dev 0.07 Std Dev 0.00 Std Dev 0.03 Std Dev 0.06 Minimum 0.61 Minimum 0.00 Minimum 0.30 Minimum 0.26 Maximum 0.85 Maximum 0.00 Maximum 0.41 Maximum 0.47 Count 12.00 Count 12.00 Count 12.00 Count 12.00
139 Descriptive Statistics for R50 Q 10 1992
Total NO2 NO3 NH3 Mean 0.80 Mean 0.00 Mean 0.30 Mean 0.41 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.02 Median 0.80 Median 0.00 Median 0.30 Median 0.39 Std Dev 0.11 Std Dev 0.00 Std Dev 0.03 Std Dev 0.05 Minimum 0.60 Minimum 0.00 Minimum 0.27 Minimum 0.36 Maximum 0.97 Maximum 0.01 Maximum 0.36 Maximum 0.50 Count 11.00 Count 11.00 Count 11.00 Count 11.00
Descriptive Statistics for R50 Ql 1 1992
Total NO2 NO3 NH3 Mean 0.54 Mean 0.03 Mean 0.31 Mean 0.33 Std Error 0.05 Std Error 0.01 Std Error 0.01 Std Error 0.03 Median 0.55 Median 0.01 Median 0.31 Median 0.36 Std Dev 0.19 Std Dev 0.04 Std Dev 0.02 Std Dev 0.10 Minimum 0.08 Minimum 0.00 Minimum 0.27 Minimum 0.00 Maximum 0.90 Maximum 0.14 Maximum 0.35 Maximum 0.39 Count 14.00 Count 14.00 Count 14.00 Count 14.00
Descriptive Statistics for R50 Q12 1992
Total NO2 NO3 NH3 Mean 0.23 Mean 0.14 Mean 0.37 Mean 0.15 Std Error 0.03 Std Error 0.01 Std Error 0.01 Std Error 0.01 Median 0.21 Median 0.14 Median 0.37 Median 0.16 Std Dev 0.10 Std Dev 0.03 Std Dev 0.03 Std Dev 0.03 Minimum 0.13 Minimum 0.11 Minimum 0.34 Minimum 0.08 Maximum 0.40 Maximum 0.19 Maximum 0.43 Maximum 0.19 Count 9.00 Count 9.00 Count 9.00 Count 9.00
140 Axl.9 Descriptive statistics for Palm Beach Reservoir (R192) for quarters 1 to 12.
Descriptive Statistics for Rl 92 QI 1990
Total NO2 NO3 NH3 Mean 0.07 Mean 0.04 Mean 0.56 Mean 0.01 Std Error 0.03 Std Error 0.01 Std Error 0.04 Std Error 0.00 Median 0.05 Median 0.02 Median 0.60 Median 0.01 Std Dev 0.08 Std Dev 0.04 Std Dev 0.11 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.33 Minimum 0.00 Maximum 0.28 Maximum 0.09 Maximum 0.63 Maximum 0.03 Count 9.00 Count 9.00 Count 9.00 Count 9.00
Descriptive Statistics for Rl 92 Q2 1990
Total NO2 NO3 NH3 Mean 0.04 Mean 0.01 Mean 0.66 Mean 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.04 Std Error 0.01 Median 0.04 Median 0.01 Median 0.64 Median 0.01 Std Dev 0.01 Std Dev 0.01 Std Dev 0.14 Std Dev 0.03 Minimum 0.02 Minimum 0.00 Minimum 0.53 Minimum 0.00 Maximum 0.05 Maximum 0.02 Maximum 1.07 Maximum 0.09 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for Rl 92 Q3 1990
Total NO2 NO3 NH3 Mean 0.06 Mean 0.06 Mean 0.44 Mean 0.08 Std Error 0.0 l Std Error 0.02 Std Error 0.02 Std Error 0.02 Median 0.05 Median 0.06 Median 0.45 Median 0.08 Std Dev 0.04 Std Dev 0.05 Std Dev 0.06 Std Dev 0.05 Minimum 0.02 Minimum 0.00 Minimum 0.33 Minimum 0.01 Maximum 0.16 Maximum 0.19 Maximum 0.55 Maximum 0.15 Count 12.00 Count 12.00 Count 12.00 Count 12.00
141 Descriptive Statistics for Rl 92 Q4 1990
Total NO2 NO3 NH3 Mean 0.06 Mean 0.03 Mean 0.36 Mean 0.15 Std Error 0.01 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.08 Median 0.03 Median 0.35 Median 0.15 Std Dev 0.04 Std Dev 0.01 Std Dev 0.05 Std Dev 0.02 Minimum 0.00 Minimum 0.02 Minimum 0.28 Minimum 0.10 Maximum 0.10 Maximum 0.05 Maximum 0.45 Maximum 0.18 Count l 1.00 Count l 1.00 Count 11.00 Count l 1.00
Descriptive Statistics for Rl 92 Q5 1991
Total NO2 NO3 Nill Mean 0.10 Mean 0.08 Mean 0.31 Mean 0.07 Std Error 0.02 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.09 Median 0.10 Median 0.30 Median 0.06 Std Dev 0.05 Std Dev 0.03 Std Dev 0.05 Std Dev 0.03 Minimum 0.04 Minimum 0.00 Minimum 0.25 Minimum 0.03 Maximum 0.23 Maximum 0.12 Maximum 0.46 Maximum 0.11 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for Rl 92 Q6 1991
Total NO2 NO3 Nill Mean 0.11 Mean 0.12 Mean 0.32 Mean 0.07 Std Error 0.01 Std Error 0.01 Std Error 0.01 Std Error 0.01 Median 0.10 Median 0.12 Median 0.32 Median 0.05 Std Dev 0.03 Std Dev 0.03 Std Dev 0.03 Std Dev 0.05 Minimum 0.07 Minimum 0.07 Minimum 0.27 Minimum 0.03 Maximum 0.15 Maximum 0.19 Maximum 0.35 Maximum 0.16 Count 12.00 Count 12.00 Count 12.00 Count 12.00
142 Descriptive Statistics for Rl 92 Q7 1991
Total NO2 NO3 NH3 Mean 0.10 Mean 0.10 Mean 0.43 Mean 0.16 Std Error 0.0 l Std Error 0.0 l Std Error 0.02 Std Error 0.03 Median 0.09 Median 0.09 Median 0.45 Median 0.13 Std Dev 0.03 Std Dev 0.04 Std Dev 0.06 Std Dev 0.11 Minimum 0.06 Minimum 0.00 Minimum 0.35 Minimum 0.07 Maximum 0.15 Maximum 0.17 Maximum 0.51 Maximum 0.56 Count 15.00 Count 15.00 Count 15.00 Count 15.00
Descriptive Statistics for Rl 92 Q8 1991
Total NO2 NO3 Nill Mean 0.15 Mean 0.05 Mean 0.53 Mean 0.14 Std Error 0.07 Std Error 0.01 Std Error 0.01 Std Error 0.01 Median 0.08 Median 0.04 Median 0.53 Median 0.14 Std Dev 0.22 Std Dev 0.04 Std Dev 0.02 Std Dev 0.03 Minimum 0.00 Minimum 0.00 Minimum 0.50 Minimum 0.07 Maximum 0.80 Maximum 0.13 Maximum 0.55 Maximum 0.19 Count 11.00 Count 11.00 Count 11.00 Count 11.00
Descriptive Statistics for Rl 92 Q9 1992
Total NO2 NO3 Nill Mean 0.12 Mean 0.04 Mean 0.57 Mean 0.19 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.11 Median 0.03 Median 0.58 Median 0.18 Std Dev 0.04 Std Dev 0.05 Std Dev 0.05 Std Dev 0.05 Minimum 0.07 Minimum 0.00 Minimum 0.42 Minimum 0.14 Maximum 0.20 Maximum 0.18 Maximum 0.60 Maximum 0.32 Count l 1.00 Count 11.00 Count 11.00 Count 11.00
143 Descriptive Statistics forR192 QlO 1992
Total NO2 NO3 NH3 Mean 0.11 Mean 0.07 Mean 0.47 Mean 0.17 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.02 Median 0.10 Median 0.06 Median 0.46 Median 0.16 Std Dev 0.04 Std Dev 0.05 Std Dev 0.06 Std Dev 0.05 Minimum 0.06 Minimum 0.03 Minimum 0.39 Minimum 0.09 Maximum 0.18 Maximum 0.16 Maximum 0.56 Maximum 0.25 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for Rl 92 QI I 1992
Total NO2 NO3 NH3 Mean 0.06 Mean 0.14 Mean 0.49 Mean 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.04 Std Error 0.00 Median 0.06 Median 0.17 Median 0.47 Median 0.01 Std Dev 0.04 Std Dev 0.08 Std Dev 0.14 Std Dev 0.02 Minimum 0.00 Minimum 0.00 Minimum 0.19 Minimum 0.00 Maximum 0.15 Maximum 0.24 Maximum 0.76 Maximum 0.05 Count 14.00 Count 14.00 Count 14.00 Count 14.00
Descriptive Statistics for R 192 Q 12 1992
Total NO2 NO3 NH3 Mean 0.03 Mean 0.02 Mean 0.65 Mean 0.01 Std Error 0.01 Std Error 0.01 Std Error 0.01 Std Error 0.00 Median 0.03 Median 0.02 Median 0.65 Median 0.01 Std Dev 0.03 Std Dev 0.02 Std Dev 0.03 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.59 Minimum 0.00 Maximum 0.08 Maximum 0.06 Maximum 0.68 Maximum 0.01 Count 8.00 Count 8.00 Count 8.00 Count 8.00
144 Ax 1.10 Descriptive statistics for Frenchs Forest Reservoir (R283) for quarters 1 to 12.
Descriptive Statistics for R283 Ql 1990
Total NO2 NO3 NH3 Mean 0.05 Mean 0.16 Mean 0.41 Mean 0.02 Std Error 0.01 Std Error 0.02 Std Error 0.02 Std Error 0.00 Median 0.05 Median 0.14 Median 0.40 Median 0.02 Std Dev 0.02 Std Dev 0.06 Std Dev 0.07 Std Dev 0.01 Minimum 0.02 Minimum 0.04 Minimum 0.27 Minimum 0.00 Maximum 0.08 Maximum 0.25 Maximum 0.50 Maximum 0.03 Count 9.00 Count 9.00 Count 9.00 Count 9.00
Descriptive Statistics for R283 Q2 1990
Total NO2 NO3 NH3 Mean 0.06 Mean 0.13 Mean 0.57 Mean 0.02 Std Error 0.01 Std Error 0.03 Std Error 0.05 Std Error 0.01 Median 0.06 Median 0.09 Median 0.56 Median 0.01 Std Dev 0.02 Std Dev 0.09 Std Dev 0.15 Std Dev 0.03 Minimum 0.03 Minimum 0.03 Minimum 0.35 Minimum 0.00 Maximum 0.10 Maximum 0.28 Maximum 0.85 Maximum 0.09 Count 11.00 Count 11.00 Count 11.00 Count 11.00
Descriptive Statistics for R283 Q3 1990
Total NO2 NO3 NH3 Mean 0.17 Mean 0.02 Mean 0.31 Mean 0.24 Std Error 0.03 Std Error 0.01 Std Error 0.02 Std Error 0.03 Median 0.16 Median 0.00 Median 0.29 Median 0.28 Std Dev 0.10 Std Dev 0.05 Std Dev 0.07 Std Dev 0.11 Minimum 0.04 Minimum 0.00 Minimum 0.26 Minimum 0.02 Maximum 0.30 Maximum 0.16 Maximum 0.46 Maximum 0.35 Count 12.00 Count 12.00 Count 12.00 Count 12.00
145 Descriptive Statistics for R283 Q4 1990
Total NO2 NO3 NH3 Mean 0.16 Mean 0.00 Mean 0.23 Mean 0.31 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.00 Median 0.13 Median 0.00 Median 0.24 Median 0.31 Std Dev 0.10 Std Dev 0.00 Std Dev 0.03 Std Dev 0.01 Minimum 0.06 Minimum 0.00 Minimum 0.17 Minimum 0.28 Maximum 0.40 Maximum 0.01 Maximum 0.27 Maximum 0.32 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for R283 QS 1991
Total NO2 NO3 NH3 Mean 0.28 Mean 0.00 Mean 0.15 Mean 0.29 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.27 Median 0.00 Median 0.15 Median 0.29 Std Dev 0.08 Std Dev 0.00 Std Dev 0.01 Std Dev 0.02 Minimum 0.16 Minimum 0.00 Minimum 0.13 Minimum 0.26 Maximum 0.42 Maximum 0.01 Maximum 0.17 Maximum 0.34 Count 12.00 Count 12.00 Count 12.00 Count 12.00
Descriptive Statistics for R283 Q6 1991
Total NO2 NO3 NH3 Mean 0.35 Mean 0.00 Mean 0.18 Mean 0.31 Std Error 0.07 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.27 Median 0.00 Median 0.18 Median 0.31 Std Dev 0.21 Std Dev 0.00 Std Dev 0.02 Std Dev 0.04 Minimum 0.08 Minimum 0.00 Minimum 0.15 Minimum 0.25 Maximum 0.68 Maximum 0.01 Maximum 0.23 Maximum 0.36 Count 10.00 Count 10.00 Count 10.00 Count 10.00
146 Descriptive Statistics for R283 Q7 1991
Total NO2 NO3 NH3 Mean 0.40 Mean 0.00 Mean 0.33 Mean 0.39 Std Error 0.02 Std Error 0.00 Std Error 0.01 Std Error 0.05 Median 0.42 Median 0.00 Median 0.34 Median 0.32 Std Dev 0.09 Std Dev 0.00 Std Dev 0.05 Std Dev 0.18 Minimum 0.21 Minimum 0.00 Minimum 0.25 Minimum 0.30 Maximum 0.52 Maximum 0.00 Maximum 0.39 Maximum 0.85 Count 15.00 Count 15.00 Count 15.00 Count 15.00
Descriptive Statistics for R283 Q8 1991
Total NO2 NO3 NH3 Mean 0.13 Mean 0.00 Mean 0.39 Mean 0.34 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.08 Median 0.00 Median 0.39 Median 0.33 Std Dev 0.11 Std Dev 0.02 Std Dev 0.02 Std Dev 0.04 Minimum 0.04 Minimum 0.00 Minimum 0.35 Minimum 0.29 Maximum 0.43 Maximum 0.05 Maximum 0.42 Maximum 0.39 Count 11.00 Count 11.00 Count 11.00 Count 11.00
Descriptive Statistics for R283 Q9 1992
Total NO2 NO3 NH3 Mean 0.14 Mean 0.01 Mean 0.42 Mean 0.33 Std Error 0.02 Std Error 0.00 Std Error 0.02 Std Error 0.02 Median 0.12 Median 0.00 Median 0.42 Median 0.36 Std Dev 0.07 Std Dev 0.01 Std Dev 0.07 Std Dev 0.08 Minimum 0.04 Minimum 0.00 Minimum 0.26 Minimum 0.14 Maximum 0.28 Maximum 0.03 Maximum 0.58 Maximum 0.38 Count I 1.00 Count 11.00 Count I 1.00 Count 11.00
147 Descriptive Statistics for R283 Q 10 1992
Total NO2 NO3 NH3 Mean 0.18 Mean 0.00 Mean 0.38 Mean 0.38 Std Error 0.03 Std Error 0.00 Std Error 0.01 Std Error 0.01 Median 0.18 Median 0.00 Median 0.38 Median 0.39 Std Dev 0.10 Std Dev 0.01 Std Dev 0.03 Std Dev 0.04 Minimum 0.06 Minimum 0.00 Minimum 0.32 Minimum 0.32 Maximum 0.39 Maximum 0.01 Maximum 0.42 Maximum 0.44 Count I 1.00 Count 11.00 Count 11.00 Count 11.00
Descriptive Statistics for R283 Q 11 1992
Total NO2 NO3 NH3 Mean 0.13 Mean 0.14 Mean 0.45 Mean 0.08 Std Error 0.06 Std Error 0.03 Std Error 0.03 Std Error 0.03 Median 0.07 Median 0.13 Median 0.43 Median 0.02 Std Dev 0.24 Std Dev 0.11 Std Dev 0.13 Std Dev 0.10 Minimum 0.01 Minimum 0.00 Minimum 0.27 Minimum 0.00 Maximum 1.00 Maximum 0.30 Maximum 0.67 Maximum 0.30 Count 15.00 Count 15.00 Count 15.00 Count 15.00
Descriptive Statistics for R283 Q 12 1992 Total NO2 NO3 NH3 Mean 0.03 Mean 0.11 Mean 0.54 Mean 0.01 Std Error 0.01 Std Error 0.03 Std Error 0.04 Std Error 0.00 Median 0.03 Median 0.12 Median 0.56 Median 0.01 Std Dev 0.02 Std Dev 0.10 Std Dev 0.11 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.36 Minimum 0.00 Maximum 0.06 Maximum 0.23 Maximum 0.66 Maximum 0.02 Count 9.00 Count 9.00 Count 9.00 Count 9.00
148 Axl.11 Descriptive statistics for Warringah Reservoir (R131) for quarters 1 to 12.
Descriptive Statistics for R131 QI 1990
Total NO2 NO3 NH3 Mean 0.11 Mean 0.27 Mean 0.31 Mean 0.03 Std Error 0.01 Std Error 0.01 Std Error 0.02 Std Error 0.01 Median 0.1 Median 0.29 Median 0.29 Median 0.02 Std Dev 0.04 Std Dev 0.05 Std Dev 0.10 Std Dev 0.03 Minimum 0.06 Minimum 0.09 Minimum 0.09 Minimum 0.00 Maximum 0.2 Maximum 0.31 Maximum 0.58 Maximum 0.12 Count 18 Count 18 Count 18.00 Count 18.00
Descriptive Statistics for Rl 31 Q2 1990
Total NO2 NO3 NH3 Mean 0.21 Mean 0.35 Mean 0.32 Mean 0.04 Std Error 0.01 Std Error 0.03 Std Error 0.01 Std Error 0.01 Median 0.22 Median 0.33 Median 0.31 Median 0.03 Std Dev 0.04 Std Dev 0.14 Std Dev 0.07 Std Dev 0.03 Minimum 0.14 Minimum 0.21 Minimum 0.22 Minimum 0 Maximum 0.27 Maximum 0.68 Maximum 0.52 Maximum 0.09 Count 22.00 Count 22.00 Count 22.00 Count 22.00
Descriptive Statistics for R131 Q3 1990
Total NO2 NO3 NH3 Mean 0.46 Mean 0.02 Mean 0.27 Mean 0.28 Std Error 0.02 Std Error 0.01 Std Error 0.00 Std Error 0.01 Median 0.48 Median 0.01 Median 0.26 Median 0.29 Std Dev 0.12 Std Dev 0.04 Std Dev 0.02 Std Dev 0.05 Minimum 0.18 Minimum 0.00 Minimum 0.24 Minimum 0.16 Maximum 0.68 Maximum 0.15 Maximum 0.32 Maximum 0.38 Count 24 Count 24 Count 24 Count 24
149 Descriptive Statistics for R13 l Q4 1991
TOTAL NO2 NO3 NH3 Mean 0.44 Mean 0.00 Mean 0.23 Mean 0.29 Std Error 0.02 Std Error 0.00 Std Error 0.0 l Std Error 0.00 Median 0.44 Median 0.00 Median 0.24 Median 0.29 Std Dev 0.1 l Std Dev 0.00 Std Dev 0.03 Std Dev 0.02 Minimum 0.25 Minimum 0.00 Minimum 0.17 Minimum 0.25 Maximum 0. 75 Maximum 0.01 Maximum 0.27 Maximum 0.33 Count 24.00 Count 24.00 Count 24.00 Count 24.00
Descriptive Statistics for R 131 Q5 1991
TOTAL NO2 NO3 NH3 Mean 0.56 Mean 0.00 Mean 0.14 Mean 0.29 Std Error 0.02 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.57 Median 0.00 Median 0.14 Median 0.29 Std Dev 0.10 Std Dev 0.00 Std Dev 0.02 Std Dev 0.03 Minimum 0.32 Minimum 0.00 Minimum 0.11 Minimum 0.24 Maximum 0.69 Maximum 0.01 Maximum 0.17 Maximum 0.39 Count 24.00 Count 24.00 Count 24.00 Count 24.00
Descriptive Statistics for R13 l Q6 1991
TOTAL NO2 NO3 NH3 Mean 0.68 Mean 0.01 Mean 0.17 Mean 0.33 Std Error 0.02 Std Error 0.00 Std Error 0.0 l Std Error 0.01 Median 0.68 Median 0.01 Median 0.16 Median 0.33 Std Dev 0.07 Std Dev 0.00 Std Dev 0.03 Std Dev 0.03 Minimum 0.55 Minimum 0.01 Minimum 0.13 Minimum 0.27 Maximum 0.80 Maximum 0.01 Maximum 0.24 Maximum 0.36 Count 24.00 Count 24.00 Count 24.00 Count 24.00
150 Descriptive Statistics for Rl31 Q7 1991
TOTAL NO2 NO3 NH3 Mean 0.66 Mean 0.00 Mean 0.33 Mean 0.32 Std Error 0.0 I Std Error 0.00 Std Error 0.0 I Std Error 0.00 Median 0.67 Median 0.00 Median 0.34 Median 0.32 Std Dev 0.06 Std Dev 0.00 Std Dev 0.05 Std Dev 0.03 Minimum 0.50 Minimum 0.00 Minimum 0.25 Minimum 0.28 Maximum 0.77 Maximum 0.00 Maximum 0.38 Maximum 0.38 Count 30.00 Count 30.00 Count 30.00 Count 30.00
151 Axl.12 Descriptive statistics for Upper Canal at Prospect before chloramination (HPR3) for quarters 1 to 12.
Descriptive Statistics for HPR3 Q 1 1990 Total NO2 NO3 NH3 Mean 0.00 Mean 0.02 Mean 0.19 Mean 0.11 Std Error 0.00 Std Error 0.01 Std Error 0.03 Std Error 0.01 Median 0.00 Median 0.01 Median 0.17 Median 0.12 Std Dev 0.01 Std Dev 0.02 Std Dev 0.08 Std Dev 0.02 Minimum 0.00 Minimum 0.00 Minimum 0.11 Minimum 0.07 Maximum 0.02 Maximum 0.06 Maximum 0.37 Maximum 0.13 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.05 Con (95%) 0.01
Descriptive Statistics for HPR3 Q2 1990 Total NO2 NO3 NH3 Mean 0.00 Mean 0.01 Mean 0.25 Mean 0.07 Std Error 0.00 Std Error 0.00 Std Error 0.04 Std Error 0.02 Median 0.00 Median 0.00 Median 0.20 Median 0.06 Std Dev 0.01 Std Dev 0.01 Std Dev 0.15 Std Dev 0.05 Minimum 0.00 Minimum 0.00 Minimum 0.14 Minimum 0.01 Maximum 0.02 Maximum 0.03 Maximum 0.59 Maximum 0.14 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.08 Con (95%) 0.03
Descriptive Statistics for HPR3 Q3 1990 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.20 Mean 0.05 Std Error 0.00 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.00 Median 0.00 Median 0.21 Median 0.05 Std Dev 0.00 Std Dev 0.00 Std Dev 0.01 Std Dev 0.04 Minimum 0.00 Minimum 0.00 Minimum 0.19 Minimum 0.01 Maximum 0.00 Maximum 0.00 Maximum 0.22 Maximum 0.13 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.02
152 Descriptive Statistics for HPR3 Q4 1991 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.18 Mean 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.01 Std Error 0.00 Median 0.00 Median 0.00 Median 0.20 Median 0.03 Std Dev 0.01 Std Dev 0.00 Std Dev 0.03 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.11 Minimum 0.02 Maximum 0.02 Maximum 0.01 Maximum 0.21 Maximum 0.05 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.01
Descriptive Statistics for HPR3 Q5 1991 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.09 Mean 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.00 Std Error 0.00 Median 0.00 Median 0.00 Median 0.08 Median 0.04 Std Dev 0.01 Std Dev 0.00 Std Dev 0.02 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.07 Minimum 0.01 Maximum 0.02 Maximum 0.00 Maximum 0.12 Maximum 0.04 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) #NUM! Con (95%) 0.01 Con (95%) 0.01
Descriptive Statistics for HPR3 Q6 1991 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.12 Mean 0.08 Std Error 0.00 Std Error 0.00 Std Error 0.0 l Std Error 0.01 Median 0.00 Median 0.00 Median 0.10 Median 0.07 Std Dev 0.00 Std Dev 0.00 Std Dev 0.04 Std Dev 0.02 Minimum 0.00 Minimum 0.00 Minimum 0.08 Minimum 0.04 Maximum 0.00 Maximum 0.00 Maximum 0.19 Maximum 0.10 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.02 Con (95%) 0.01
153 Descriptive Statistics for HPR3 Q7 1991 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.29 Mean 0.04 Std Error 0.00 Std Error 0.00 Std Error 0.01 Std Error 0.00 Median 0.00 Median 0.00 Median 0.30 Median 0.04 Std Dev 0.00 Std Dev 0.00 Std Dev 0.05 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.21 Minimum 0.02 Maximum 0.00 Maximum 0.00 Maximum 0.35 Maximum 0.06 Count 13.00 Count 13.00 Count 13.00 Count 13.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.03 Con (95%) 0.01
Descriptive Statistics for HPR3 Q8 1992 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.35 Mean 0.08 Std Error 0.00 Std Error 0.00 Std Error 0.00 Std Error 0.04 Median 0.00 Median 0.00 Median 0.35 Median 0.05 Std Dev 0.00 Std Dev 0.00 Std Dev 0.01 Std Dev 0.11 Minimum 0.00 Minimum 0.00 Minimum 0.32 Minimum 0.04 Maximum 0.00 Maximum 0.00 Maximum 0.36 Maximum 0.37 Count 9.00 Count 9.00 Count 9.00 Count 9.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.07
Descriptive Statistics for HPR3 Q9 1992 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.34 Mean 0.05 Std Error NIA Std Error NIA Std Error NIA Std Error NIA Median 0.00 Median 0.00 Median 0.34 Median 0.05 Std Dev NIA Std Dev NIA Std Dev NIA Std Dev NIA Minimum 0.00 Minimum 0.00 Minimum 0.34 Minimum 0.05 Maximum 0.00 Maximum 0.00 Maximum 0.34 Maximum 0.05 Count 1.00 Count 1.00 Count 1.00 Count 1.00 Con (95%) NIA Con (95%) NIA Con (95%) NIA Con (95%) NIA
154 Descriptive Statistics for HPR3 Q 10 1992 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.25 Mean 0.10 Std Error 0.00 Std Error 0.00 Std Error 0.00 Std Error 0.00 Median 0.00 Median 0.00 Median 0.25 Median 0.10 Std Dev 0.00 Std Dev 0.00 Std Dev 0.00 Std Dev 0.01 Minimum 0.00 Minimum 0.00 Minimum 0.25 Minimum 0.09 Maximum 0.00 Maximum 0.00 Maximum 0.25 Maximum 0.10 Count 3.00 Count 3.00 Count 3.00 Count 3.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01
Descriptive Statistics for HPR3 Ql 1 1992 Total NO2 NO3 NH3 Mean 0.00 Mean 0.00 Mean 0.27 Mean 0.05 Std Error 0.00 Std Error 0.00 Std Error 0.00 Std Error 0.01 Median 0.00 Median 0.00 Median 0.27 Median 0.05 Std Dev 0.00 Std Dev 0.01 Std Dev 0.01 Std Dev 0.02 Minimum 0.00 Minimum 0.00 Minimum 0.25 Minimum 0.02 Maximum 0.00 Maximum 0.02 Maximum 0.27 Maximum 0.08 Count 12.00 Count 12.00 Count 12.00 Count 12.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.00 Con (95%) 0.01
Descriptive Statistics for HPR3 Q 12 1993 Total NO2 NO3 NH3 Mean 0.00 Mean 0.01 Mean 0.24 Mean 0.03 Std Error 0.00 Std Error 0.00 Std Error 0.02 Std Error 0.01 Median 0.00 Median 0.00 Median 0.26 Median 0.03 Std Dev 0.00 Std Dev 0.01 Std Dev 0.05 Std Dev 0.02 Minimum 0.00 Minimum 0.00 Minimum 0.13 Minimum 0.01 Maximum 0.00 Maximum 0.03 Maximum 0.28 Maximum 0.06 Count 7.00 Count 7.00 Count 7.00 Count 7.00 Con (95%) 0.00 Con (95%) 0.01 Con (95%) 0.04 Con (95%) 0.01
155 ACKNOWLEDGEMENTS
This study was made possible by the Drinking Water Progamme of Sydney Water, who commissioned the overall study into the control of nitrification within Sydney Water's chloraminated systems. The work was mostly performed at A WT EnSight, under the supervision of Dr Keith Mullette (Chief Scientist and General Manager) and Professor Kevin Marshall (School of Biological Sciences, University of New South Wales).
The following people have all, at some time, been a member of the overall project team and have therefore made particular contributions to the completion of this thesis: Dr Elizabeth Eager, Roslyn Deal, Bruno Nucci, Brett Clark, Dien Huynh, Rebecca Conway and Lena Berg. I am particularly indebted to the work of Elizabeth and Roslyn.
Staff of Sydney Water and AWT EnSight have contributed with routine laboratory analyses, background information or advice and some field work. In particular, Ellis Roberts (statistical) and Dr Nicholas Ashbolt (microbiological) with whom methods and results have been discussed.
The transmission electron microscopy was conducted by the Electron Microscopy Unit at the University of New South Wales, under the supervision of Patrick Marks and the final formatting of the document was made possible by Platinum Business Services of Manly.
The completion of the thesis was supported by my managers at the Department of Land and Water Conservation, following new employment in January 1995. In particular Dr Robert Crouch who has provided encouragement and advice over the final stages of completion.
Finally there are friends and family who have provided encouragement and support during the writing of this thesis. Special mention must go to Sharon Rixon and Kevin Marshall, without whom I would never have seen the "light at the end of the tunnel".
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