Appendix 17 Example of Part of Simruns 2_1 Record Table

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1 Appendix 17 Example of Part of Simruns 2_1 Record Table Page 262 of 406

2 Appendix 18 Dose Response Assessment When the pathogen and enterococci level data for the two discharge streams were combined with the timestep reduction data obtained via hydraulic modelling it was possible to obtain bathing contact zone level estimates for these microorganisms for the occasions/periods when diluted discharge plumes reached the shoreline zones. From this data doses could be estimated as outlined in the previous section. This data was then used to estimate values for the probability of infection and illness depending on location, population, pathogen, intake volume, and intake scenario based on the application of dose response information from the literature. It now appears to be possible using this system to estimate both the probability of infection and the probability of illness for a number of index pathogens. In the field of drinking water risk assessment there has been ongoing movement toward the development of a Disability Adjusted Life Year endpoint for risk assessments. This is not directly applicable to the recreational situation as the GI benchmarks for the latter are calculated relative to illness probability per bathing exposure. However the DALY literature includes a breakdown of the different underlying factors which allows conversion of infection probability to gastrointestinal illness probability (e.g. Havelaar & Melse, 2003) General Approach For drinking water the emerging best practice is to transform level data into infection probability estimates and disability life adjusted year estimates or DALYs (Haas & Eisenberg, 2001; Havelaar & Melse, 2003; Pruss & Havelaar, 2001 ; Vijgen et al., 2007). The process involves starting with estimating infection probability and estimating the likelihood of different diseases, residual symptoms and death which may ensure (Havelaar & Melse, 2003; Pruss & Havelaar, 2001 Figure 3.1 ). The situation with bathing water is slightly different because the guidelines currently focus on the probability of gastrointestinal illness per exposure. In the line with this manner of expressing risk the two following procedures were implemented: 1. Infection probability was estimated for each model pathogen using infection rate algorithms (Haas & Eisenberg, 2001 Table 8.1) and multiplying these estimates by illness:infection ratios available from the DALY development process to obtain illness probability estimates; and 2. Total gastronintestinal illness probability was estimates using the enterococci surrogate algorithm developed by Kay and his co-workers (Kay et al., 2004; Kay et al., 1994) to provide a quantitative basis for the current WHO and Australian recreation guidelines (NH&MRC, 2008; World Health Organization, 2003). Estimating Infection Probability The basic techniques for estimating illness probability for individual pathogesn are described in Haas et al. (Haas et al., 1999) and Fewtrell and Bartram (Fewtrell & Bartram, 2001). There are also several examples of their use in the recreation context (Gerba, 2000; Schets et al., 2004). The dose response relationships defining the probability of infection following different doses are shown in Table 1. These dose response algorithms were those selected based in part on their use in the EU MicroRisk project (Medema et al., 2006) which reviewed a range of alternate algorithms. Page 263 of 406

3 Table 1. Infection Probability Algorithms for selected pathogens Pathogen Dose Response Equation Equation Reference coefficients Cryptosporidium Beta-Poisson with unit dose threshold: alpha= 0.115, beta=0.176 (Teunis et al., 2002a; Teunis et al., 2002b) Campylobacter Probability of infection =1-(1+dose/beta) -alpha alpha=0.024, beta=0.011 (Evans, 1996; Van den Brandhof et al., 2003) Rotavirus alpha = 0.253, beta = (Teunis et al., 2002a; Teunis et al., Giardia Adenovirus All pathogens 1 Exponential: Probability of infection = 1-exp(- gamma*dose) Exponential: Probability of infection = 1-exp(- r*dose) Probability of excess gastroenteritis infection/ illness= (1/1+e -d )) and d =a*((b-32)) 0.5 -c gamma = 1.99 E b; Ward et al., 1986) (Rendtorff, 1954) cited in(haas & Eisenberg, 2001) r= 1/k and k = (Haas & Eisenberg, 2001) a= , b = numbers of enterococci, c= (Kay et al., 2004; Kay et al., 1994) Notes: 1. Regarding the use of the enterococci surrogate illness algorithm: a. This relationship is an empirical one and only applies between levels of 32 and 158 enterococci per 100 ml. b. The curve rises steeply from zero and P. of illness is assumed to reach a maximum of at this point though in fact it may continues to increase. c. It was necessary for QMRA purposes to convert the levels of enterococci into doses. It was assumed in line with the estimates of water consumed during bathing (see above) that the amount of water consumed by the bathers averaged 30mL and hence the threshold dose was ca 10 enterococci. Estimating Gastrointestinal Illness Probability for Each Pathogen The literature developed in support of DALYs includes, for most listed pathogens, estimates of the likelihood of gastrointestinal illness following infection. These data have been used to calculate illness likelihood as a fraction of infection based on our interpretation of the data presented in the references in Table 2. Table 2. Illness Probability Factors for Selected Pathogens Pathogen Illness:Infection Ratio Reference Cryptosporidium 0.7 (Havelaar & Melse, 2003) Campylobacter 0.33 (Havelaar & Melse, 2003) Rotavirus 0.9 (Havelaar & Melse, 2003) Giardia 0.7 (Andersson & Bohan, 2001) Adenovirus 0.5 (Van Heerden et al., 2005b) All pathogens 1.0 (Kay et al., 2004; Kay et al., 1994) Estimating Total Gastrointestinal Illness Probability using enterococci as a surrogate pathogen One limitation of QMRA based on index pathogens is that the method derives infection probability estimates for individual pathogens. But the Guideline benchmarks are currently cast in terms of total gastronintestinal illness. A possible method to estimate total GI illness probability is available from the literature. The original papers that the Guideline benchmarks are based on (Kay et al., 2004; Kay et al., 1994) include a dose response relationship which links the probability of total GI and Acute Febrile Respiratory Illness (AFRI) to enterococci levels. The central relationship is as follows (Kay et al. 1994, 2004): b = *(c-32) where b = ln odds of excess gastroenteritis and Page 264 of 406

4 c = numbers of enterococci per 100mL. The form of this algorithm is somewhat different to the normal exponential and beta Poisson distributions because is empirical. Its form means that unlike the latter curves it is characterised by a sharp inception and increase so the difference between water quality associated with minimum risk and maximum risk is relatively small. Key features of note are as follows: 1. The algorithm estimates excess gastroenteritis caused by bathing exposure rather than total gastroenteritis caused by bathing and other sources such as food poisoning; 2. It has two sharp cut-offs corresponding to: o A minimum enterococci level associated with no illness (32 cfu.100ml -1 ); o An enterococci level (158 cfu.100ml -1 ) corresponding to the maximum likelihood of excess gastrointestinal illness (0.388); 3. While illness probability may increase further beyond this value the authors considered it preferable to use the latter as an upper limit of illness. To use the enterococci dose response algorithm in QMRA it was also necessary to assume the curve reflected a certain consumption and work backwards to an infectious dose. It was assumed in line with the normal intake that the curve reflected the number corresponded to the number in 30 ml of seawater. Page 265 of 406

5 Appendix 19 Assessment of Winter Model Input Data For Newcastle Coastal Waters (extracted from full draft report see Rayner et al. (Rayner et al., 2009)) Background Burwood Beach Wastewater Treatment Works (WwTW) is located approximately 7 km south-west of the entrance to the Hunter River at Newcastle, and services approximately 70 km 2 of the city of Newcastle. Liquid effluent and sludge are disposed of following secondary treatment via an outfall tunnel extending 1500 m offshore of Burwood Beach and discharging into water of 22 m depth. Liquid effluent is discharged through 9 diffuser heads with a total of 71 ports. Sludge is disposed through a separate pipeline (approximately 100 m in length, with 18 active ports) extending from a tenth diffuser head. The Water Research Laboratory (WRL) of the University of New South Wales has been involved in an ongoing program of oceanographic field work and numerical modelling to simulate near and far field plume behaviour. Initial work was undertaken in 1996, comprising two field exercises, near-field dilution modelling, and the establishment of a steady state three-dimensional (3D) hydrodynamic and far-field water quality model. WRL was commissioned by Hunter Water Corporation (HWC) in 1998 to undertake further monitoring works to investigate the performance of the performance of the effluent and waste activated sludge diffusers and subsequent water quality impacts at the Burwood Beach Ocean Outfall under winter conditions. Since the 1998 study computational power has increased significantly, enabling the modelling capabilities (including both the numerical modelling code and the BBOO model domain) to be upgraded. In 2007, WRL undertook a similar study to determine the performance of the outfall under stratified summer conditions (Glamore et al., 2007) for present (2007) and future (2030) flow rates. This was achieved through the use of dye isotope (Rhodamine WT) tracking in the field coupled with extensive deterministic modelling of the likely plume dynamics to generate concentration statistics. In 2008, WRL, in conjunction with the Water Research Centre (WRC), of the University of New South Wales was commissioned by HWC to undertake additional data analysis and modelling tasks related to the Burwood Beach Ocean Outfall. The outcomes of this study are to be utilised by the WRC to assess infection risk probabilities associated with the full range of Hazardous Event likelihoods both near (50 m) and offshore (200 m) from Dudley Beach, Bar Beach, Burwood Beach and Merewether Baths. For this study, the Winter 1998 and the Summer 2007 datasets are proposed to represent onsite general Winter and Summer conditions. To determine if the existing data represent long term trends, the data were compared to long-term datasets from other representative sites. This analysis was undertaken via a comparison of both wind and current roses, coupled with spectral analysis, to determine the validity of the 1998 and 2007 data sets. In conjunction with the above data assessment, WRL undertook further modelling tasks. Page 266 of 406

6 The winter 1998 data was run with the latest near-field and hydrodynamic models in order to match the modelling done for the summer 2007 data. Further to this, the water quality capabilities of the far-field random walk model (3DRWalk) were also upgraded to include: a range of diurnal solar radiation variables to be applied and linked to particle depth; and, a series of output variables to be generated for each particle and time step. Both spatial and temporal statistical data were extracted at eight representative sites along the Newcastle coast. Conclusions The Water Research Laboratory (WRL), working for the Water Research Centre, was commissioned by Hunter Water Corporation to (i) determine if the short-term datasets obtained in 1998 and 2007 are representative of long-term conditions and (ii) undertake updated numerical hydrodynamic and particle tracking modeling of the 1998 and 2007 conditions. The data analyses indicated that the wind and current conditions measured in 1998 and 2007 were generally representative of average years, although the large amount of variability in the long-term dataset may not be entirely represented. The numerical modelling involved updating the modeling domain for the 1998 data, improving the models ability to simulate particle decay, and outputting the updated particle concentrations at a range of locations. The numerical modeling outputs have been provided to WRC for further analyses. Page 267 of 406

7 Appendix 20 Hydraulic Statistics for BBWWTP Experimental Period The data presented here illustrates the current dry weather flows at BBWWTP and the timing of microbial sampling which by in large was outside of the stormflow periods as planned. Given that pathogens excretion relates to loads on the sewers rather than flow the loads would be expected to be much the same during wet weather but the variation in the flows would make calculation of the statistics difficult. Table 1. Daily Waste Flow Totals and Rainfall Date Inflow Raw ML/d Outflow Secondary ML/d Ouflow WAS ML/d Rainfall mm Timeseries Samples Diurnal Samples 21-Sep Sep X 23-Sep Sep Sep Sep Sep Sep Sep X 30-Sep X X 01-Oct Oct Oct Oct Oct Oct Oct X 08-Oct Oct Oct Oct Oct Oct X 14-Oct Oct Oct Oct Oct Oct Oct X 21-Oct Oct Oct Oct Oct Oct Oct Oct X 29-Oct Page 268 of 406

8 Date Inflow Raw ML/d Ouflow WAS ML/d Outflow Secondary ML/d Rainfall mm Timeseries Samples Diurnal Samples 30-Oct Oct Nov Nov X 03-Nov X X 04-Nov Nov Nov Nov Nov Nov Nov X 11-Nov Nov Nov Nov Nov Nov Nov X 18-Nov Nov Nov Nov Nov Nov Nov X X 25-Nov X 26-Nov Nov Nov Nov Nov Dec X 02-Dec Dec Dec Dec Dec Dec Dec X 09-Dec Page 269 of 406

9 Table 2. Flow Statistics Depending on Size of Rainfall Event Used to Edit Data Set Data Collection Station Data Editing Method Parameter No. of Measurements Average Std Deviation Minimum Maximum Units Raw Inlet (No Edit) Flow ML/d Raw Inlet Manual - No rain on day & previous 2 days Flow ML/d Raw Inlet Manual - Two largest events + following 2 days removed Flow ML/d Raw Inlet Query based - 0 mm rain on previous day Flow ML/d Raw Inlet Query based - up to 15 mm rain on previous day Flow ML/d Raw Inlet Query based - up to 21 mm rain on previous day Flow ML/d Secondary Outlet (No Edit) Flow ML/d Secondary Outlet Manual - No rain on day & previous 2 days Flow ML/d Secondary Outlet Manual - Two largest events + following 2 days removed Flow ML/d Secondary Outlet Query based - 0 mm rain on previous day Flow ML/d Secondary Outlet Query based - up to 15 mm rain on previous day Flow ML/d Secondary Outlet Query based - up to 21 mm rain on previous day Flow ML/d WAS Outlet (No Edit) Flow ML/d WAS Outlet Manual - No rain on day & previous 2 days Flow ML/d WAS Outlet Manual - Two largest events + following 2 days removed Flow ML/d WAS Outlet Query based - 0 mm rain on previous day Flow ML/d WAS Outlet Query based - up to 15 mm rain on previous day Flow ML/d WAS Outlet Query based - up to 21 mm rain on previous day Flow WWTP Gauge (No Edit) Rainfall WWTP Gauge Manual - No rain on day & previous 2 days Rainfall WWTP Gauge Manual - Two largest events + following 2 days removed Rainfall WWTP Gauge Query based - 0 mm rain on previous day Rainfall WWTP Gauge Query based - up to 15 mm rain on previous day Rainfall WWTP Gauge Query based - up to 21 mm rain on previous day Rainfall mm/d Notes: 1. Data in bold was used in partitioning estimation. Page 270 of 406

10 Appendix 21 Hazardous Event Risks and the Guideline Water Quality Categories The enterococci dose response algorithm is based on a range of epidemiology + water quality data collected at the 4 beaches in the United Kingdom. The data prior to refining into a dose response curve is shown graphically in Figure 1 with permission from the senior author. It is presented to help explain to the reader some of the seeming discrepancies between event risk probability and the 95 th Percentile water quality categories in the Guidelines which are largely based on this data. Key points to note are: 1. The data here though relatively raw, places the Guideline values in context. 2. The challenge for the UK study team who acquired this data was to credibly detect bathing measuring gastrointestinal illness. As a result the dose response curves are necessarily conservative (i.e. they did not quantify completely the risks from bathing given enterococci levels lower than ca mL -1 or higher than ca mL -1. The reasons for their conservative nature are: a. There is already substantial GI in the community due to such effects as person to person transmission, food poisoning etc. which raised the threshold of detection for bathing acquired illness alone. b. The population sample size were limited (ca 1000). c. For ethical reasons they could not study bathers at poor quality beaches which effectively reduced the upper detection limit. 3. Nevertheless the UK team were largely successful in detected a gastrointestinal illness effect. They detected clear evidence of illness at the enterococci.100ml -1 band and the detectable risk peaked at ca 38% slightly higher than indicated by the graph shown. Because of these constraints the data does not mean that below this threshold there was no elevated risk from bathing or that similarly above 80 there was no further increase in risk. Rather risks outside these bands could not be estimated because of the survey constraints. 4. The data here shows that the impact of bathing water quality becomes evident at the enterococci.100ml -1 band where the risk jumps by ca 8% compared to the 1% risk limit associated with the Guideline Category A. The discrepancy arises because the Guideline captures mainly periods of good water quality punctuated by transient peaks >40 at the 95 th percentile. The use of this high percentile allows for Hazardous Events to be incorporated in the Guidelines to some extent in the absence of other data. 5. This data underlies the all pathogens algorithm used to estimate Total Gastrointestinal Illness. The algorithm risk probability curve has a steep slope which is likely an artefact of the curve fitting and conservative interpretation of the data where a threshold of 32 enterococci.100ml -1 was estimated. 6. The water quality categories in the Guidelines are based on this data. It can be seen from this Figure where the < mL -1 category A comes from. The category B threshold of mL -1 can also be understood as reflecting a combination of good water quality below the threshold and other times when the point risk is higher as indicated in the figure. 7. The combination of QMRA and hydraulic modelling allowed us to identify occasions when the level of enterococci was likely < mL -1 and such periods were identified as posing a significant risk higher than 5%. This seems contrary to the Category B 95 th percentile threshold for 5% risk of mL -1. But Figure 1 shows that risk of illness from seawater contact at this level itself is likely to be well in excess of 20%. Page 271 of 406

11 This last point illustrates that the overall estimated risk associated with a category A and B waters and presented in the Guidelines is a different concept to specific Hazardous Event risks which are more akin to the short periods of higher risk with air pollutants and need to be monitored and managed separately. For this reason it was necessary to develop a distinct approach and we opted for the Exceedence Probability estimation because it is already in use in water related risk assessments. Further discussion is provided in Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event Characterization. A point to note in hindsight is that the former NHMRC Guidelines(National Health and Medical Research Council, 1990) enterococci geometric mean guideline of mL -1 appears to be problematic as it necessarily reflects both good water periods and many occasions when enterococci levels are > mL -1 and clearly have quite a high associated gastrointestinal risk. This is noteworthy because it suggests a need to re-interpret historical data(armstrong et al., 1997). This has been done within this study (Section 3.1). Despite this guideline tightening Newcastle beaches appear to still be of excellent quality under normal/baseline conditions based on our review of the historical data and a cursory examination of recent Beachwatch reports (NSW Department et al., 2008) (note the Box plot upper limits correspond to 90 th percentile rather than the 95 th percentile). Figure 1. Plot of Observed Gastrointestinal Rates Observed at UK Beaches (Kay et al., 1994) Page 272 of 406

12 Appendix 22 Exceedence Probability Statistics and Risk Benchmarking Hazardous Event Definition During project scoping we examined the recreation Guidelines at length (NH&MRC, 2008; World Health Organization, 2003) and undertook discussion with Hunter Water, NSW Health and DECC on risk benchmarks against which we should report Hazardous Events. While the Guidelines promoted quantitative assessment of risks, and identified overall water quality benchmarks (1% and 5% gastrointestinal illness probability) had been developed, it was found that benchmarks designed for Hazardous Events with a recurrence interval less than 5% had not been developed. What was unavailable in particular were short term targets of the kind illustrated by air contaminant concentration benchmarks e.g. for sulphur dioxide, nitrogen dioxide, ozone and PM (Environment Australia, 2009). Air pollution managers have long recognised that air quality managers need to control both long term and short term (i.e. transient Hazardous Event) quality. The latter arise when point emission plume direction, mixing and atmospheric stratification channel contaminants toward inhabited areas for short periods. This has led to the construction of very tall smoke stacks with dispersion enhancing structures where toxic gases such as SO 2 are emitted. It has also led to the development of both long term (1 year average) and (higher) short term (1 day to 1 hour average) air quality benchmarks. Within the new recreational Guidelines long term water quality benchmarks are proposed (1% or 5% overall at the 95 th percentile) but shorter term benchmarks are not as yet. This may reflect the fact that the 95 th percentile rules have been developed with water quality data sets comprising < 100 measurements in mind. This situation contrasts with air pollution monitoring where continuous monitoring is well developed and short term events can be often detected in real time. Analogous microbial water quality monitoring is still too expensive and slow to be applied in the case of pathogen or indicator monitoring though there have been efforts with rapid/real time detection of events in mind (Campbell & Mutharasan, 2008; Khan et al., 2007a; Shaban & Malkawi, 2007). Quantifying Plume Impacts Using Exceedence Probability Statistics Setting health benchmarks is not the role of risk assessors and project proponents but rather the role of regulators and government advisory committees. However, operational risk assessments can provide test cases, and if properly designed, can act as models for how to generate and communicate critical information, risk management options and data gaps. In response to the lack of short term benchmarks we considered the latter to be essential preliminary support tasks. The issues identified as needing resolution in the process were: 1. The Guidelines are focused on total Gastrointestinal illness probability whereas QMRA traditionally has been developed with annual risk of infection in mind; 2. Though models are available for quantitative risk estimation it was unclear in advance which statistics should be reported in the present instance; 3. A complication arising from the focus on Hazardous Events was that as with air pollutants, plume impact would vary greatly depending on the intensity of predisposing factors such as the wind speed, current and degree of destratification on any given occasion; 4. The risk estimates needed to be communicated in a concise manner but still capture the variable magnitude of event risks in a manner which could be used by the regulators and HWC in developing risk related and water quality targets and working benchmarks. This latter is an important challenge for policy and administration (Pollard et al., 2001). Page 273 of 406

13 The need for special consideration of rare high impact events in the present situation is shown conceptually by the work of Haas(Haas, 1997) which demonstrated that very large data sets may be required to obtain a complete picture of the scale and frequency of Hazardous Events. The relevance of this matter to coastal zone bathing is shown by analysis of large indicator data sets. These confirm the occurrence of hotspots and the limitations of traditional microbial water monitoring (Kim & Grant, 2004). The approach identified that seemed to meet these needs, and subsequently was adopted, was the construction of Exceedence Probability plots and the collection of associated statistics. Rainfall and flood exceedence probability plots have been widely used in water management due to their use in Australian Rainfall and Run-off (Pilgrim & Doran, 1997) and are familiar to civil engineers. They capture concurrently the increasing impact and decreasing likelihood of hazardous (hydrological) events. Their calculation is relatively simple. Using large data sets the probability of an event is calculated. The range of risk data for each scenario is then presented as risk Exceedence Probability plots. Risk estimates are plotted against one minus the percentile recurrence as a log(x) log(y) or log (x) linear (y) graphs. These are the origins in reports of benchmarks like the a 150 mm, 1 in 10 year 24 hour rainfall event. Strictly this does not mean that 150 mm of rain occurs in 24 hours every 10 years but rather the probability of rainfall exceeding 150 mm in 24 h is 0.1 y -1. Exceedence probability analysis arises out of Extreme Value Theory. Presentation of data can be a via a number of formats. This type of analysis is widely used in actuarial and financial risk analysis as well as flooding where small sample sizes can lead to invalid extrapolations e.g. (Embrechts et al., 1999; Wood et al., 2005). It has also been applied to groundwater quality assessment (Ong'or et al., 2007). As with the current study it has been applied to both human health risk assessment (Chowdhury et al., 2004) and ocean discharge impacts(huang et al., 1996). Overall exceedence probability style analysis seemed well suited to the needs of the current microbial risk assessment and it appeared from the literature and our own work that it was well suited to Scenario analysis as well (e.g. Figure 3)(Nilsson et al., 2007). So we adapted the method as follows. Through the hydraulic plume modelling we obtained for each exposure scenario a total of 8516 fifteen minute timestep estimates of the extent of dilution + inactivation of pathogens at each exposure point. By combining these incremental estimates with source, intake and dose response data, and assuming that most of a person s intake occurred in a single accidental swallowing event within one of these 15 min increments, we then modelled the risk and collected percentile statistics down to an exceedence probability of ca 0.01% (the th percentile dilution/inactivation). Page 274 of 406

14 Appendix 23 Wastewater and WAS Quality This Appendix records the water quality data collected for the STP. It was compiled as an Excel Pivotable. Table 1. Main Timeseries Measurements Analyte Date time QA_QC Sign Units Volume Screened Primary Secondary WAS enterococci 22/09/2008 9:00 Rep1 CA cfu 100mL 9.10E+04 EQ cfu 100mL 3.50E E+05 Rep2 EQ cfu 100mL 3.00E E+06 30/09/ :00 Rep1 EQ cfu 100mL 9.40E E E+06 Rep2 EQ cfu 100mL 4.00E+05 7/10/2008 7:30 Rep1 EQ cfu 100mL 1.10E E E+05 Rep2 EQ cfu 100mL 2.70E E+06 13/10/ :00 Rep1 EQ cfu 100mL 2.20E E E+06 Rep2 EQ cfu 100mL 2.60E+05 20/10/2008 8:30 Rep1 CA cfu 100mL 7.30E E+05 EQ cfu 100mL 1.80E+05 Rep2 CA cfu 100mL 8.20E+05 EQ cfu 100mL 2.80E+05 28/10/2008 9:00 Rep1 CA cfu 100mL 7.30E+05 EQ cfu 100mL 4.20E E+05 Rep2 EQ cfu 100mL 2.20E+05 3/11/2008 9:00 Rep1 CA cfu 100mL 1.10E+06 EQ cfu 100mL 5.90E E+06 10/11/2008 8:00 Rep1 EQ cfu 100mL 4.70E E E+06 17/11/2008 8:00 Rep1 EQ cfu 100mL 4.40E E E+06 1/12/2008 8:00 Rep1 EQ cfu 100mL 1.40E E E+06 Giardia total adjusted Adenovirus adjusted 8/12/2008 9:00 Rep1 CA cfu 100mL 9.00E+05 EQ cfu 100mL 4.70E E+05 22/09/2008 9:00 Rep1 EQ cysts 1 L 2.61E E E+05 Rep2 EQ cysts 1 L 1.66E E+05 30/09/ :00 Rep1 EQ cysts 1 L 1.29E E E+04 Rep2 EQ cysts 1 L 2.67E+02 7/10/2008 7:30 Rep1 EQ cysts 1 L 2.75E E E+04 Rep2 EQ cysts 1 L 2.69E E+04 13/10/ :00 Rep1 EQ cysts 1 L 7.08E E E+04 Rep2 EQ cysts 1 L 2.93E+02 20/10/2008 8:30 Rep1 EQ cysts 1 L 3.94E E E+04 Rep2 EQ cysts 1 L 5.35E E+04 28/10/2008 9:00 Rep1 EQ cysts 1 L 7.07E E E+04 Rep2 EQ cysts 1 L 6.70E+01 3/11/2008 9:00 Rep1 EQ cysts 1 L 9.30E E E+04 10/11/2008 8:00 Rep1 EQ cysts 1 L 2.53E E E+04 17/11/2008 8:00 Rep1 EQ cysts 1 L 2.61E E E+04 24/11/2008 8:00 Rep1 EQ cysts 1 L 2.35E E E+04 1/12/2008 8:00 Rep1 EQ cysts 1 L 3.26E E E+04 8/12/2008 9:00 Rep1 EQ cysts 1 L 1.01E E E+04 (blank) (blank) 22/09/2008 9:00 Rep1 EQ pfu 1 L 1.39E E E+02 Rep2 EQ pfu 1 L 1.48E+02 LT pfu 1 L 2.90E+01 30/09/ :00 Rep1 EQ pfu 1 L 2.23E E+02 LT pfu 1 L 2.78E+01 Rep2 EQ pfu 1 L 5.56E+00 7/10/2008 7:30 Rep1 EQ pfu 1 L 1.80E E E+01 Page 275 of 406

15 Analyte Date time QA_QC Sign Units Volume Screened Primary Secondary WAS Rep2 EQ pfu 1 L 1.86E E+02 13/10/ :00 Rep1 EQ pfu 1 L 1.08E E+02 LT pfu 1 L 2.90E+01 Rep2 EQ pfu 1 L 1.17E+02 20/10/2008 8:30 Rep1 EQ pfu 1 L 5.00E E+02 Rep2 EQ pfu 1 L 1.68E E+02 28/10/2008 9:00 Rep1 EQ pfu 1 L 1.45E E E+01 Rep2 EQ pfu 1 L 8.33E+00 3/11/2008 9:00 Rep1 EQ pfu 1 L 1.19E+02 10/11/2008 8:00 Rep1 EQ pfu 1 L 1.54E E+01 LT pfu 1 L 2.78E+01 17/11/2008 8:00 Rep1 EQ pfu 1 L 2.49E E E+02 24/11/2008 8:00 Rep1 EQ pfu 1 L 2.09E E E+02 1/12/2008 8:00 Rep1 EQ pfu 1 L 1.83E E E+02 8/12/2008 9:00 Rep1 EQ pfu 1 L 2.43E E E+01 Campylobacter spp. Cryptosporidium total adjusted 22/09/2008 9:00 Rep1 EQ mpn 1 L 3.80E E+01 LT mpn 1 L 4.00E+00 Rep2 GT mpn 1 L 1.10E+03 LT mpn 1 L 4.00E+00 30/09/ :00 Rep1 LT mpn 1 L 4.00E E E+00 Rep2 LT mpn 1 L 4.00E+00 7/10/2008 7:30 Rep1 EQ mpn 1 L 2.90E E+01 GT mpn 1 L 1.10E+03 Rep2 EQ mpn 1 L 2.90E E+01 13/10/ :00 Rep1 LT mpn 1 L 4.00E E E+00 Rep2 LT mpn 1 L 4.00E+00 20/10/2008 8:30 Rep1 EQ mpn 1 L 1.10E+01 LT mpn 1 L 4.00E E+00 Rep2 LT mpn 1 L 4.00E E+00 28/10/2008 9:00 Rep1 LT mpn 1 L 4.00E E E+00 Rep2 LT mpn 1 L 4.00E+00 3/11/2008 9:00 Rep1 EQ mpn 1 L 3.00E E+00 LT mpn 1 L 4.00E+00 10/11/2008 8:00 Rep1 EQ mpn 1 L 3.00E E E+00 17/11/2008 8:00 Rep1 EQ mpn 1 L 3.00E+00 LT mpn 1 L 4.00E E+00 24/11/2008 8:00 Rep1 EQ mpn 1 L 6.00E E E+00 1/12/2008 8:00 Rep1 LT mpn 1 L 4.00E E E+00 8/12/2008 9:00 Rep1 EQ mpn 1 L 3.00E+00 LT mpn 1 L 4.00E E+00 22/09/2008 9:00 Rep1 EQ oocysts 1 L 6.00E+01 LT oocysts 1 L 1.20E E+02 Rep2 LT oocysts 1 L 1.20E E+02 30/09/ :00 Rep1 EQ oocysts 1 L 2.00E+01 LT oocysts 1 L 2.40E E+02 Rep2 EQ oocysts 1 L 1.30E+01 7/10/2008 7:30 Rep1 LT oocysts 1 L 2.40E E E+02 Rep2 LT oocysts 1 L 2.40E E+02 13/10/ :00 Rep1 EQ oocysts 1 L 2.70E E+02 LT oocysts 1 L 2.40E+01 Rep2 EQ oocysts 1 L 4.70E+01 20/10/2008 8:30 Rep1 EQ oocysts 1 L 4.80E E E+02 Rep2 EQ oocysts 1 L 4.80E E+02 28/10/2008 9:00 Rep1 EQ oocysts 1 L 3.40E+01 LT oocysts 1 L 7.00E E+02 Rep2 LT oocysts 1 L 7.00E+00 3/11/2008 9:00 Rep1 LT oocysts 1 L 2.40E E E+02 Page 276 of 406

16 Analyte Date time QA_QC Sign Units Volume Screened Primary Secondary WAS 10/11/2008 8:00 Rep1 EQ oocysts 1 L 4.80E E+01 LT oocysts 1 L 3.45E+02 17/11/2008 8:00 Rep1 EQ oocysts 1 L 2.40E E E+02 24/11/2008 8:00 Rep1 EQ oocysts 1 L 1.30E+01 LT oocysts 1 L 2.40E E+02 1/12/2008 8:00 Rep1 EQ oocysts 1 L 2.00E E+02 LT oocysts 1 L 2.40E+01 8/12/2008 9:00 Rep1 EQ oocysts 1 L 2.70E+01 LT oocysts 1 L 2.40E E+02 Rotavirus adjusted 22/09/2008 9:00 Rep1 EQ pfu 1 L 3.48E+01 LT pfu 1 L 2.90E E+00 Rep2 LT pfu 1 L 2.90E E+00 30/09/ :00 Rep1 EQ pfu 1 L 2.32E+01 LT pfu 1 L 2.78E E+00 Rep2 LT pfu 1 L 2.78E+00 7/10/2008 7:30 Rep1 EQ pfu 1 L 1.74E+01 LT pfu 1 L 2.90E E+00 Rep2 EQ pfu 1 L 1.16E+01 LT pfu 1 L 2.90E+00 13/10/ :00 Rep1 LT pfu 1 L 2.90E E E+00 Rep2 LT pfu 1 L 2.78E+00 20/10/2008 8:30 Rep1 LT pfu 1 L 2.90E E E+00 Rep2 LT pfu 1 L 2.90E E+00 28/10/2008 9:00 Rep1 LT pfu 1 L 2.90E E E+00 Rep2 LT pfu 1 L 2.78E+00 3/11/2008 9:00 Rep1 LT pfu 1 L 2.90E E E+00 10/11/2008 8:00 Rep1 LT pfu 1 L 2.90E E E+00 17/11/2008 8:00 Rep1 LT pfu 1 L 2.90E E E+00 24/11/2008 8:00 Rep1 LT pfu 1 L 2.90E E E+00 1/12/2008 8:00 Rep1 LT pfu 1 L 2.90E E E+00 8/12/2008 9:00 Rep1 LT pfu 1 L 2.90E E E+00 Table 2. Diurnal Variation Study Measurements Diurnal Sampling Run Analyte Date time QA_QC Sign Units Volume Screened Primary Secondary WAS 1 enterococci 29/09/ :00 Rep1 EQ cfu 100mL 8.30E E E+06 29/09/ :00 Rep1 EQ cfu 100mL 5.90E E E+06 29/09/ :00 Rep1 CA cfu 100mL 6.40E+05 EQ cfu 100mL 5.90E E+05 30/09/2008 2:00 Rep1 CA cfu 100mL 6.40E+05 EQ cfu 100mL 5.40E E+05 30/09/2008 6:00 Rep1 EQ cfu 100mL 2.40E E E+06 30/09/ :00 Rep1 EQ cfu 100mL 4.70E E E+06 E. coli C. perfringens Rep2 EQ cfu 100mL 5.30E E E+06 29/09/ :00 Rep1 EQ mpn 100mL 5.73E E E+06 29/09/ :00 Rep1 EQ mpn 100mL 8.09E E E+07 29/09/ :00 Rep1 EQ mpn 100mL 5.20E E E+06 30/09/2008 2:00 Rep1 EQ mpn 100mL 5.83E E E+06 30/09/2008 6:00 Rep1 EQ mpn 100mL 1.71E E E+06 30/09/ :00 Rep1 EQ mpn 100mL 1.05E E E+06 Rep2 EQ mpn 100mL 8.86E E E+06 29/09/ :00 Rep1 EQ cfu 100mL 7.70E E E+05 29/09/ :00 Rep1 EQ cfu 100mL 6.00E E E+05 29/09/ :00 Rep1 EQ cfu 100mL 9.80E E E+05 30/09/2008 2:00 Rep1 EQ cfu 100mL 8.10E E E+06 Page 277 of 406

17 Diurnal Sampling Run Analyte Date time QA_QC Sign Units Volume Screened Primary Secondary WAS 30/09/2008 6:00 Rep1 EQ cfu 100mL 7.20E E E+05 30/09/ :00 Rep1 EQ cfu 100mL 5.60E E E+06 Rep2 EQ cfu 100mL 1.80E E E+05 FRNA Coliphage adjusted 29/09/ :00 Rep1 EQ pfu 1 L 3.33E+03 LT pfu 1 L 8.33E E+00 29/09/ :00 Rep1 EQ pfu 1 L 8.67E+02 LT pfu 1 L 8.33E E+00 29/09/ :00 Rep1 EQ pfu 1 L 5.33E+03 LT pfu 1 L 8.33E E+00 30/09/2008 2:00 Rep1 EQ pfu 1 L 1.10E E+02 LT pfu 1 L 8.33E+00 30/09/2008 6:00 Rep1 EQ pfu 1 L 2.00E E E+02 30/09/ :00 Rep1 EQ pfu 1 L 1.17E E+01 LT pfu 1 L 8.33E+00 Rep2 EQ pfu 1 L 7.33E E+04 LT pfu 1 L 8.33E+00 2 enterococci 2/11/ :00 Rep1 CA cfu 100mL 2.70E+04 EQ cfu 100mL 9.30E E+06 2/11/ :00 Rep1 EQ cfu 100mL 7.00E E E+05 2/11/ :00 Rep1 EQ cfu 100mL 5.20E E E+06 3/11/2008 0:00 Rep1 EQ cfu 100mL 4.80E E E+06 3/11/2008 4:00 Rep1 EQ cfu 100mL 2.00E E E+06 3/11/2008 8:00 Rep1 EQ cfu 100mL 3.60E E+05 3/11/ :00 Rep2 CA cfu 100mL 2.70E E+05 E. coli C. perfringens FRNA Coliphage adjusted EQ cfu 100mL 5.80E+05 2/11/ :00 Rep1 EQ mpn 100mL 9.59E E E+06 2/11/ :00 Rep1 EQ mpn 100mL 1.07E E E+06 2/11/ :00 Rep1 EQ mpn 100mL 1.79E E E+06 3/11/2008 0:00 Rep1 EQ mpn 100mL 7.76E E E+06 3/11/2008 4:00 Rep1 EQ mpn 100mL 7.38E E E+06 3/11/2008 8:00 Rep1 EQ mpn 100mL 8.16E E+06 3/11/ :00 Rep2 EQ mpn 100mL 9.60E E E+06 2/11/ :00 Rep1 CA cfu 100mL 1.80E+06 EQ cfu 100mL 1.30E E+05 2/11/ :00 Rep1 CA cfu 100mL 6.00E+05 EQ cfu 100mL 1.40E E+05 2/11/ :00 Rep1 CA cfu 100mL 8.40E+05 EQ cfu 100mL 1.40E E+06 3/11/2008 0:00 Rep1 CA cfu 100mL 8.40E+05 EQ cfu 100mL 7.20E E+05 3/11/2008 4:00 Rep1 EQ cfu 100mL 4.90E E E+05 3/11/2008 8:00 Rep1 EQ cfu 100mL 6.40E E+05 3/11/ :00 Rep2 EQ cfu 100mL 6.00E E E+06 2/11/ :00 Rep1 EQ pfu 1 L 6.83E E E+05 2/11/ :00 Rep1 EQ pfu 1 L 3.83E E E+05 2/11/ :00 Rep1 EQ pfu 1 L 3.50E E E+05 3/11/2008 0:00 Rep1 EQ pfu 1 L 2.00E E E+05 3/11/2008 4:00 Rep1 EQ pfu 1 L 2.50E E E+05 3/11/2008 8:00 Rep1 EQ pfu 1 L 2.17E E+03 3/11/ :00 Rep2 EQ pfu 1 L 3.00E E E+04 3 enterococci 24/11/2008 8:00 Rep1 EQ cfu 100mL 3.40E E E+06 24/11/ :00 Rep1 CA cfu 100mL 4.00E+04 EQ cfu 100mL 3.40E E+06 24/11/ :00 Rep1 CA cfu 100mL 4.00E+04 EQ cfu 100mL 4.40E E+06 Page 278 of 406

18 Diurnal Sampling Run Analyte Date time QA_QC Sign Units Volume Screened Primary E. coli C. perfringens FRNA Coliphage adjusted Secondary WAS 25/11/2008 0:00 Rep1 EQ cfu 100mL 3.60E E E+06 Rep2 CA cfu 100mL 8.00E+04 EQ cfu 100mL 2.50E E+06 25/11/2008 4:00 Rep1 EQ cfu 100mL 4.70E E E+06 25/11/2008 8:00 Rep1 CA cfu 100mL 3.00E+05 EQ cfu 100mL 8.00E E+05 25/11/ :00 Rep1 CA cfu 100mL 2.00E+05 EQ cfu 100mL 7.90E E+05 24/11/ :00 Rep1 EQ mpn 100mL 3.84E E E+06 24/11/ :00 Rep1 EQ mpn 100mL 6.44E E E+06 25/11/2008 0:00 Rep1 EQ mpn 100mL 6.05E E E+06 Rep2 EQ mpn 100mL 7.38E E E+06 25/11/2008 4:00 Rep1 EQ mpn 100mL 6.44E E E+06 25/11/2008 8:00 Rep1 EQ mpn 100mL 5.56E E E+06 25/11/ :00 Rep1 EQ mpn 100mL 8.84E E E+06 24/11/ :00 Rep1 CA cfu 100mL 6.00E E+05 EQ cfu 100mL 1.30E+04 24/11/ :00 Rep1 CA cfu 100mL 5.80E+04 EQ cfu 100mL 1.10E E+05 25/11/2008 0:00 Rep1 EQ cfu 100mL 1.60E E E+05 Rep2 CA cfu 100mL 2.90E+05 EQ cfu 100mL 1.70E E+04 25/11/2008 4:00 Rep1 EQ cfu 100mL 2.60E E E+05 25/11/2008 8:00 Rep1 EQ cfu 100mL 2.20E E E+05 25/11/ :00 Rep1 EQ cfu 100mL 2.60E E E+05 24/11/ :00 Rep1 EQ pfu 1 L 3.67E E+05 LT pfu 1 L 8.33E+00 24/11/ :00 Rep1 EQ pfu 1 L 3.50E E+05 LT pfu 1 L 8.33E+00 25/11/2008 0:00 Rep1 EQ pfu 1 L 4.33E E+05 LT pfu 1 L 8.33E+00 Rep2 EQ pfu 1 L 4.67E E+05 LT pfu 1 L 8.33E+00 25/11/2008 4:00 Rep1 EQ pfu 1 L 7.50E E E+05 25/11/2008 8:00 Rep1 EQ pfu 1 L 1.03E E E+05 25/11/ :00 Rep1 EQ pfu 1 L 1.12E E E+05 Table 3. Comparison of Replicate Counts Analyte Material Date time Sign Rep1 Rep2 Enterococci (weekly samples) Screened Primary 22/09/2008 9:00 EQ 3.50E E+05 7/10/2008 7:30 EQ 1.10E E+04 20/10/2008 8:30 EQ 1.80E E+05 Secondary 30/09/ :00 EQ 3.50E E+05 13/10/ :00 EQ 2.10E E+05 28/10/2008 9:00 EQ 3.30E E+05 WAS 22/09/2008 9:00 EQ 9.10E E+06 7/10/2008 7:30 EQ 1.80E E+06 20/10/2008 8:30 CA 4.50E E+05 Giardia total adjusted Screened Primary 22/09/2008 9:00 EQ 2.61E E+03 7/10/2008 7:30 EQ 2.75E E+03 20/10/2008 8:30 EQ 3.94E E+03 Secondary 30/09/ :00 EQ 8.70E E+02 13/10/ :00 EQ 1.40E E+02 28/10/2008 9:00 EQ 2.70E E+01 WAS 22/09/2008 9:00 EQ 1.27E E+05 Page 279 of 406

19 Analyte Material Date time Sign Rep1 Rep2 7/10/2008 7:30 EQ 4.92E E+04 20/10/2008 8:30 EQ 9.04E E+04 Campylobacter spp. Screened Primary 22/09/2008 9:00 EQ 3.80E+01 22/09/2008 9:00 LT 4.00E+00 7/10/2008 7:30 EQ 2.90E E+02 20/10/2008 8:30 LT 4.00E E+00 Secondary 30/09/ :00 LT 4.00E E+00 13/10/ :00 LT 4.00E E+00 28/10/2008 9:00 LT 4.00E E+00 WAS 22/09/2008 9:00 EQ 3.60E+01 x 22/09/2008 9:00 GT 1.10E+03 7/10/2008 7:30 EQ 6.40E E+01 20/10/2008 8:30 LT 4.00E E+00 Cryptosporidium total adjusted Screened Primary 22/09/2008 9:00 LT 1.20E E+01 7/10/2008 7:30 LT 2.40E E+01 20/10/2008 8:30 EQ 4.80E E+01 Secondary 30/09/ :00 EQ 2.00E E+01 13/10/ :00 EQ 2.70E E+01 28/10/2008 9:00 LT 7.00E E+00 WAS 22/09/2008 9:00 LT 1.72E E+02 7/10/2008 7:30 LT 3.45E E+02 20/10/2008 8:30 EQ 3.45E E+02 Adenovirus adjusted Screened Primary 22/09/2008 9:00 EQ 1.39E+02 22/09/2008 9:00 LT 2.90E+01 7/10/2008 7:30 EQ 1.80E E+02 Secondary 30/09/ :00 EQ 5.56E+00 30/09/ :00 LT 2.78E+01 13/10/ :00 EQ 1.08E E+02 28/10/2008 9:00 EQ 1.67E E+00 WAS 22/09/2008 9:00 EQ 1.01E E+02 7/10/2008 7:30 EQ 6.67E E+02 20/10/2008 8:30 EQ 1.22E E+02 Rotavirus adjusted Screened Primary 22/09/2008 9:00 LT 2.90E E+00 7/10/2008 7:30 LT 2.90E E+00 20/10/2008 8:30 LT 2.90E E+00 Secondary 30/09/ :00 LT 2.78E E+00 13/10/ :00 LT 2.78E E+00 28/10/2008 9:00 LT 2.78E E+00 WAS 22/09/2008 9:00 EQ 3.48E+01 22/09/2008 9:00 LT 2.90E+00 7/10/2008 7:30 EQ 1.74E E+01 20/10/2008 8:30 LT 2.90E E+00 Enterococci (diurnal sampling) Screened Primary 30/09/ :00 EQ 4.70E E+05 E. coli 3/11/2008 8:00 EQ 3.60E E+05 25/11/2008 0:00 EQ 3.60E E+05 Secondary 30/09/ :00 EQ 3.90E E+05 3/11/2008 8:00 EQ 1.30E E+04 25/11/2008 0:00 CA 1.10E E+04 WAS 30/09/ :00 EQ 1.10E E+06 3/11/2008 4:00 EQ 1.50E E+05 Screened Primary 25/11/2008 0:00 EQ 1.40E E+06 30/09/ :00 EQ 1.05E E+06 3/11/2008 8:00 EQ 8.16E E+06 25/11/2008 0:00 EQ 6.05E E+06 Secondary 30/09/ :00 EQ 5.56E E+06 3/11/2008 8:00 EQ 3.59E E+05 25/11/2008 0:00 EQ 1.99E E+05 WAS 30/09/ :00 EQ 4.10E E+06 Page 280 of 406

20 Analyte Material Date time Sign Rep1 Rep2 3/11/2008 4:00 EQ 7.50E E+06 25/11/2008 0:00 EQ 4.43E E+06 C. perfringens Screened Primary 30/09/ :00 EQ 5.60E E+05 3/11/2008 8:00 EQ 6.40E E+05 25/11/2008 0:00 EQ 1.60E E+05 Secondary 30/09/ :00 EQ 4.90E E+04 3/11/2008 8:00 EQ 1.60E E+05 25/11/2008 0:00 EQ 1.50E E+04 WAS 30/09/ :00 EQ 2.20E E+05 24/11/ :00 CA 4.00E E+06 25/11/2008 0:00 CA 2.90E+05 FRNA Coliphage adjusted Screened Primary 30/09/ :00 LT 8.33E E+00 3/11/2008 8:00 EQ 2.17E E+03 25/11/2008 0:00 LT 8.33E E+00 Secondary 30/09/ :00 EQ 1.17E E+03 3/11/2008 8:00 EQ 4.67E E+04 25/11/2008 0:00 EQ 4.33E E+04 WAS 30/09/ :00 EQ 1.67E E+04 3/11/2008 4:00 EQ 2.33E E+04 25/11/2008 0:00 EQ 3.17E E+05 Table 4. Comparison of Replicate Counts Sampling Type QA_QC Analyte Date time Sign Screened Primary Secondary WAS Laborator y Blank Weekly Blank enterococci 22/09/2008 9:00 LT /09/ :00 LT 1 7/10/2008 7:30 LT /10/ :00 LT 1 20/10/2008 8:30 LT /10/2008 9:00 LT 1 Cryptosporidium total 22/09/2008 9:00 LT /09/ :00 LT 0.1 7/10/2008 7:30 LT /10/ :00 LT /10/2008 8:30 LT /10/2008 9:00 LT 0.2 Giardia total 22/09/2008 9:00 LT /09/ :00 LT 0.1 7/10/2008 7:30 LT /10/ :00 LT /10/2008 8:30 LT /10/2008 9:00 LT 0.2 Campylobacter spp. 22/09/2008 9:00 LT /09/ :00 LT 4 7/10/2008 7:30 LT /10/ :00 LT 4 20/10/2008 8:30 LT /10/2008 9:00 LT 4 Adenovirus 7/10/2008 7:30 EQ 94 LT /10/ :00 EQ 6 20/10/2008 8:30 LT 0.5 Page 281 of 406

21 Sampling Type QA_QC Analyte Date time Sign Screened Primary Secondary WAS Laborator y Blank 28/10/2008 9:00 LT 0.5 8/12/2008 0:00 LT 0.5 Poliovirus 7/10/2008 7:30 LT /10/2008 8:30 LT /10/2008 9:00 LT 0.5 Rotavirus 22/09/2008 9:00 LT /09/ :00 LT 0.5 7/10/2008 7:30 LT /10/ :00 LT /10/2008 8:30 LT /10/2008 9:00 LT 0.5 8/12/2008 9:00 LT 0.5 Spike enterococci 30/09/ :00 EQ 27 13/10/ :00 EQ 24 20/10/2008 8:30 EQ /10/2008 9:00 EQ 21 3/11/2008 9:00 EQ 23 Cryptosporidium total 22/09/2008 9:00 EQ /09/ :00 EQ 0.4 7/10/2008 7:30 EQ /10/ :00 LT /10/2008 8:30 EQ /10/2008 9:00 LT 0.2 3/11/2008 9:00 10/11/2008 9:00 EQ 40 24/11/2008 8:00 EQ 0.8 LT 2 Giardia total 22/09/2008 9:00 EQ /09/ :00 EQ 0.4 7/10/2008 7:30 EQ /10/ :00 EQ /10/2008 8:30 EQ /10/2008 9:00 EQ 0.4 3/11/2008 9:00 10/11/2008 9:00 EQ 40 24/11/2008 8:00 EQ 0.6 LT 2 Adenovirus 22/09/2008 9:00 LT /09/ :00 LT 0.5 7/10/2008 7:30 EQ 6 LT /10/ :00 EQ 16 20/10/2008 8:30 EQ 72 28/10/2008 9:00 LT 0.5 Poliovirus 22/09/2008 9:00 EQ 8.5E /09/ :00 EQ 7.6E+ 05 7/10/2008 7:30 EQ 7.3E /10/ :00 EQ 7.4E /10/2008 8:30 EQ 5.9E E E E+ 05 Page 282 of 406

22 Sampling Type QA_QC Analyte Date time Sign Screened Primary Secondary WAS Laborator y Blank Poliovirus recovery 28/10/2008 9:00 EQ 7.4E+ 05 8/12/2008 9:00 EQ 7.1E /09/2008 9:00 EQ /09/ :00 EQ /10/2008 7:30 EQ /10/ :00 EQ /10/2008 8:30 EQ /10/2008 9:00 EQ /12/2008 9:00 EQ 0.69 Rotavirus 22/09/2008 9:00 EQ 28 LT /09/ :00 LT 0.5 7/10/2008 7:30 LT /10/ :00 LT /10/2008 8:30 LT /10/2008 9:00 LT 0.5 Diurnal Blank enterococci 29/09/ :30 EQ /09/ :00 LT C. perfringens 29/09/ :30 EQ /09/ :00 EQ 2 5 E. coli 29/09/ :30 EQ 8 2 LT 1 30/09/ :00 LT FRNA Coliphage 29/09/ :30 LT /09/ :00 LT Spike enterococci 30/09/ :00 EQ E. coli 30/09/ :00 EQ FRNA Coliphage 30/09/ :00 EQ Carryover LT 0.5 C. perfringens 29/09/ :30 EQ 49 FRNA Coliphage 30/09/ :00 EQ 4 29/09/ :30 LT /09/ :00 LT 0.5 Page 283 of 406

23 Appendix 24 Summary of Hydraulic Modeling Outputs This Appendix summarises the raw outputs from each hydraulic model run. Further details are provided in Appendix 25 Summary Tables for Hydraulic Modelling Statistics. What this data shows is: 1. The hydraulic modeling scenario (first 6 columns); 2. The number of timesteps of data generated per hydraulic scenario; 3. The number of timesteps per scenario where simulated particles were detected illustrating how often dilute contamination was detected at the exposure points; 4. The average of the median travel times (median travel time was computationally more straightforward) illustrating the speed at which the contaminants recorded travelled to the exposure points; 5. The sum of particle mass across the whole timeseries for those mesh cells where particles were detected illustrating the relative scale of the contamination at each exposure point under that scenario. 6. Average particle mass illustrating the relative amount of material per exposure point/grid cells (The mass of particles under conservative conditions in those cells where masses were detected is at least 4E9. Smaller averages arise in other scenarios because of the reduction in particle mass due to simulated inactivation). Page 284 of 406