enterococci e. coli - hunter water€¦ · other than arguably for enterococci and e. coli, the...

WRC Hunter Water Stage 2 Report final v5_3

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Figure 4. Regression Line plots of pathogen levels against flow at the time of measurement Notes:

1. a. = Screened primary; b. = Secondary Effluent; c. = WAS

Microbial Assay Limitations When interpreting the risk estimates, the microbial data acquired in this study and the quality control and assurance above it is essential to recognise that environmental water analysis for indicators and pathogens is a relatively immature technology and is not at the same level of sophistication as with chemicals which suffer well known problems (American Public Health Association, 1998). Advances are being made in quality control(Ferrari et al., 2006; Frimmel, 2006; Philipp et al., 2007; Pinheiro et al., 2008; Warnecke et al., 2003) but there is still some way to go. In this study some low recovery efficiencies were encountered and spike reference material was not available for Campylobacter and C. perfringens. The assays for Cryptosporidium and Giardia were total rather than confirmed counts and irrespective may overestimate the numbers of cysts and oocysts. This was not unexpected and is echoed in a recent review of the general issue of quality control and reference materials (Philipp et al., 2007). Other than arguably for enterococci and E. coli, the quality control of assays for water microorganisms in environmental samples is not ideal. In the course of previous work involving the use of local leading environmental water analysis laboratories(Roser et al., 2002; Roser & Ashbolt, 2007) who did have extensive quality assurance programs in place we encountered a range of limitations on the data generated e.g.:

1. Environmental samples varied enormously and water constituents could compromise assay effectiveness. This effect could be hard to detect because of the difficulty in preparing a range of quality control samples(Roser et al., 2002);

2. The number of laboratories having the required analytical skills for environmental pathogens is still relatively limited.


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3. The problem of viable non-culturable bacteria including pathogens(Gracias & McKillip, 2004; Keep et al., 2006; Kell et al., 1998; Kjelleberg et al., 1987; M.M. Lleò, 2001; Oliver, 2005; Reissbrodt et al., 2002; Stevens & Jaykus, 2004);

4. The promise of PCR technology is not fully realised and cultural v. PCR counts may yield very different estimates of pathogen numbers(He & Jiang, 2005).

5. Use of PCR technology is constrained with water samples by: a. The small size of the sample that can be analysed; b. Uncertainty about how genome copy numbers relate to viable pathogen numbers; c. Uncertainty about which primer sequences to use given that there are a large

diversity of biotypes. Thus the reader must be aware that while we used ‘best available technology’ these technologies have clear limitations.

WWTP Hydraulics WWTP Flows v. Rainfall

Burwood Beach WWTP primary and secondary treated flows responded strongly to rainfall, increasing by a factor of up to 3. The correlations (r2) increased from ca 0.3 between flow and rainfall on the same day to ca 0.6 in relation to the following day’s flow and decreased to 0.3 again for flow two days after the rainfall. Graphic plots showed that response to rainfall was still evident several days later (Figure 5). WAS flow, however, was not significantly correlated with rainfall. Regression graphs yielded r2 values in the range 0.01 to 0.04. Overall nearly 80% in daily primary/secondary flow variation could be described by rainfall on the day the flow was measured and the preceding two days (Appendix 34 Regressions describing WWTP flow and rainfall). During such periods there was potential for dilution of microbial numbers or loss of treatment effectiveness. In practice, however, sampling did not coincide with the largest rainfall events with the possible exception of that on 7/October/2008. Linear regression of enterococci data against rainfall on the previous day suggested a slight dilution effect. However this was not statistically significant (significance prob.> 0.1). Diurnal flow variation showed the well known double peak flow (Figure 6). The 8-9am sampling corresponded to the rising part of the main peak. Irrespective it appeared that although the water quality samples taken at 8 to 9 am were representative of that over the whole day, flows were not fully representative. For this reason when calculating loads we did not combine paired level and flow values but instead resampled the flow PDF.


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Flow During Monitoring Period

Flows during the survey period are shown in Appendix 20 Hydraulic Statistics for BBWWTP Experimental Period. To estimate the degree of partitioning it was necessary to identify which flows were characteristic of the sampling periods and combine them with water quality data. Based on the plot of sampling occasion v. flow, the flow conditions identified as those most characteristic of the sampling days were considered to be those on dry days + some moderately rainy days when the influence of large events had dissipated. Accordingly the table of flow data was examined and that from those days corresponding to the two largest events and the two days immediately following were removed. The statistics for the resulting dry + moderate rainfall days are shown in Table 5. These were used subsequently in flow modelling. Table 5. Dry Weather Flow Representative of the 3 month sampling period

Material Average SD Minimum Maximum units Screened Primary 51.01 9.21 41.70 102.60 ML/dSecondary 47.36 9.05 37.29 98.18 ML/dWAS 2.11 0.95 0.50 4.01 ML/d

Microbial Loads

Loads

The probability density functions describing microbial loads into and out of the WWTP were estimated by:

1. assuming there was no correlation between pathogen content and flow 2. resampling randomly the PDFs and overall flow PDFs and 3. combining these values using @Risk 4.5.

Statistics for the resultant PDFs are shown in Table 6 and Table 7. Average and standard deviations for enterococci are closely comparable. However standard deviation and 95th percentiles tend to be larger in the weekly data. Table 6. Pathogen Loadings (Organisms per day) Waste Stream Pathogen Average Standard deviation Median 0.95 percentile

Cryptosporidium total 1.11E+09 9.21E+08 8.41E+08 2.84E+09 Giardia total 3.63E+11 4.10E+11 2.41E+11 1.06E+12 enterococci 3.60E+14 4.66E+14 2.12E+14 1.12E+15 Campylobacter spp. 8.98E+08 6.01E+09 4.64E+07 2.56E+09

Screened Primary

Adenovirus 9.51E+09 3.52E+09 8.96E+09 1.61E+10 Cryptosporidium total 1.81E+09 2.27E+09 1.12E+09 5.42E+09 Giardia total 1.66E+10 2.66E+10 8.37E+09 5.58E+10 enterococci 1.47E+14 1.28E+14 1.10E+14 3.87E+14 Campylobacter spp. 2.38E+08 3.63E+08 1.30E+08 8.02E+08

Secondary Effluent

Adenovirus 3.27E+09 4.56E+09 1.91E+09 9.97E+09 Cryptosporidium total 5.84E+08 3.00E+08 5.32E+08 1.15E+09 Giardia total 1.02E+11 9.66E+10 7.34E+10 2.88E+11 enterococci 3.05E+13 4.45E+13 1.59E+13 1.03E+14 Campylobacter spp. 3.84E+07 4.99E+08 1.21E+06 9.06E+07

WAS

Adenovirus 2.34E+08 1.98E+08 1.83E+08 6.12E+08


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Table 7. Indicators Loadings (Organisms per day) Material Indicator Average SD Median 0.95 percentile

enterococci 2.84E+14 1.29E+14 2.58E+14 5.27E+14 E. coli 4.12E+15 2.18E+15 3.62E+15 8.03E+15 C. perfringens 2.46E+14 5.58E+14 9.13E+13 8.65E+14

Screened Primary

FRNA Coliphage 6.17E+10 7.95E+10 3.85E+10 1.96E+11 enterococci 1.10E+14 1.18E+14 7.42E+13 3.04E+14 E. coli 1.60E+15 1.04E+15 1.32E+15 3.55E+15 C. perfringens 1.27E+14 2.24E+14 5.83E+13 4.50E+14

Secondary

FRNA Coliphage 7.61E+11 1.63E+12 2.79E+11 3.03E+12 enterococci 2.82E+13 2.37E+13 2.12E+13 7.31E+13 E. coli 1.31E+14 1.29E+14 9.22E+13 3.82E+14 C. perfringens 1.89E+13 1.70E+13 1.43E+13 5.02E+13

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FRNA Coliphage 2.36E+11 1.47E+11 1.99E+11 5.36E+11

Decimal Reductions and Partitioning

This same resampling was used to estimate the overall reduction in pathogen and indicator numbers as log10 Decimal Reduction (DR) values (Table 8), and the % of each organism type partitioned into the WAS (Table 9). In respect to treatment effectiveness it can be seen that there is very little difference between input and output load except in the case of the F-RNA coliphage where it seems likely that regrowth occurred. C. perfringens numbers appear to have remained unchanged. Cryptosporidium and Campylobacter appear to have undergone slight increases but this is unlikely to be significant given the high standard deviation and poor/unknown recovery from water samples of markedly different types. Given that the indicator assays are the most robust the data for E. coli, enterococci and C. perfringens are probably most indicative of removal. These indicate modest DRs in the range of 0.03 to 0.4. Compared to its volume (<10% of total) the WAS received a disproportionate load of the pathogens and indicators with the possible exceptions of E. coli and Campylobacter. Most striking was the partitioning of Giardia. This result should be treated as tentative though due to the poor recovery of the protozoans. From this data it was decided that the starting levels used in the QMRA should be those for secondary effluent and WAS in Table 3 and reproduced in Table 10. Table 8. Decimal Reduction Based on Load Partitioning and Change Pathogen Log10Average Log10 SD Log10 Median Log10 0.95 percentile Cryptosporidium total -0.340 0.423 -0.323 0.334 Giardia total 0.440 0.514 0.431 1.246 Enterococci (Daily) 0.183 0.521 0.208 1.031 Campylobacter spp. -0.511 1.169 -0.539 1.436 Adenovirus 0.602 0.427 0.627 1.270 Enterococci (Diurnal) 0.383 0.359 0.393 0.948 E. coli 0.390 0.326 0.406 0.912 C. perfringens 0.026 0.748 0.045 1.153 FRNA Coliphage -1.208 0.568 -1.169 -0.329


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Table 9. % of Pathogens and Indicators partitioned Into WAS Pathogen Average SD Median 0.95 percentile Cryptosporidium total 34% 20% 30% 71% Giardia total 83% 18% 89% 99% Enterococci (Daily) 19% 18% 12% 58% Campylobacter spp. 8% 17% 1% 46% Adenovirus 13% 14% 9% 40% Enterococci (Diurnal) 27% 19% 22% 66% E. coli 9% 9% 6% 26% C. perfringens 26% 23% 20% 76% FRNA coliphage 44% 27% 40% 90%

Pathogen Levels in Effluent and WAS Final PDFs estimated for the simulations are shown in Table 10. Some notable points which needed to be recognised in QMRA sensitivity testing but were not fully appreciated at the project inception were:

• The Campylobacter numbers were well below those reported in the literature. For example in the Netherlands (Koenraad et al., 1994) these pathogens have been reported to be detected in sewage at levels in the range of 102-105.L-1.

• Rotavirus were rarely detected. So Adenovirus was substituted as the model virus of choice for reasons discussed in the Uncertainty section..

• Adenovirus were detected in comparable numbers to those reported in the literature for cultural assays but well below the numbers reported from PCR based assays (He & Jiang, 2005).

• Cryptosporidium and Giardia were comparable to the numbers seen in polluted rivers under storm conditions in Australia (Roser & Ashbolt, 2007), southern highland WWTPs (SCA internal report prepared by UNSW) and in sewage in the Netherlands (Medema & Schijven, 2001) and Canada(Payment et al., 2001). But these levels were 1 to 2 log10 units below those reported by others in the US (Madore et al., 1987) under normal circumstances, and even less than in the case of outbreaks in small communities (Lee et al., 2001). These data suggested the level in effluent may be higher.

• On the other hand Cryptosporidium viability assessments of Southern Highlands isolates indicated the viability of oocysts may only be ca 5% in fresh sewage.

Table 10. Final Source Material PDFs

Material Pathogen Parameter Log10 average Log10 SD units enterococci 5.352 0.317 cfu/100mL Cryptosporidium (total) adjusted 1.368 0.409 oocysts/L Giardia (total) 2.23 0.508 cystsl/L Campylobacter spp. 0.425 0.468 mpn/L

Secondary Effluent

Adenovirus 1.586 0.447 pfu/L enterococci 5.914 0.457 cfu/100mL Cryptosporidium (total) adjusted 2.412 0.139 oocysts/L Giardia (total) adjusted 4.55 0.337 cystsl/L Campylobacter spp. -0.2 1.108 mpn/L

WAS

Adenovirus 1.948 0.28 pfu/L


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Appendix 12 Guidelines, Combinatorial Explosion and the Scale of Risk Modelling

I fully support the overall design aims. They are ambitious and appropriate but require good empirical calibration data for each stage in the work. In this section, the definition and description of data source and quality is not sufficiently detailed to allow the reader to evaluate whether the data to underpin the approach is suitable.... In effect, one could conclude that the QMRA is using existing data because it is available without a full analysis of its quality and appropriateness. Both characteristics may, of course, be excellent but the reader does not have a clear audit trail to assess any such judgement at this stage in the report. [This may of course be clear from the parallel investigation cited in the reference listing as Glamore et al. (2008)]

(External reviewer comment on draft)

Elizabeth: “You have to take me to shore! According to the Code of the Order of the Brethren— Barbossa: First, your return to shore was not part of our negotiations nor our agreement, so I 'must' do nothing. And secondly, you must be a pirate for the Pirate's Code to apply, and you're not. And thirdly, the code is more what you call "guidelines" than actual rules. Welcome aboard the Black Pearl, Miss Turner!”

(Pirates of the Caribbean: Curse of the Black Pearl) The Challenge An increasingly difficult challenge encountered by health and environmental impact specialists, as well as regulators, is how to deal with the scale of assessments implicitly demanded by the new generation of Australian and international health risk assessment Guidelines including those used to derive the current assessment (EnHealth Council, 2002; NH&MRC, 2008; World Health Organization, 2003). In addition to documenting many more heads of consideration than in the past these Guidelines, in particular EnHealth guidelines, promote open-endedness without providing a clear means for dealing with the conundrum this generates. Specifically the Guidelines stress that when an assessment has been undertaken there will still be likely many uncertainties and these must be documented clearly and comprehensively. This is reasonable scientifically and promotes a balanced defensible realistic assessment. But this approach also begs the question of why the assessment does not resolve residual uncertainties and how far the information provided should be auditable as alluded to above by the draft’s assessor. The result is that both project proponents and decision makers must reach conclusions on action when a risk assessment is still partially deficient. In our experience compromise is essential and the assessor must do a ‘reasonable’ job. But finding this compromise is not easy and what ‘reasonable’ means in practice remains unclear. One result is that regulators will always want more than is agreed to initially and as more insight into the risks is gained more and more will be expected potentially without end. In quantitative risk assessment one result is that the number of risk scenarios requested to be explored increases exponentially to the point where a full risk assessment is logistically infeasible. This Appendix outlines central features of this vexed issue and how it was managed in this assessment a)generally and b)in respect to the combinatorial explosion in risk scenarios which might have been explored. A subsidiary aim is to outline what the authors understand by ‘reasonable’.


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Why the Issues of Scale and Work Explosion Cannot be Ignored The extent of the assessment scale problem arising from the new Guidelines can be gauged by comparing the size of the 1990 Recreation guidelines (National Health and Medical Research Council, 1990) with those of their modern successor (NH&MRC, 2008). In length the main text is increased by a factor of ca 20 and the considerations raised are far more diverse and scientifically complicated. The older system could probably be extensively automated and indeed some efforts have been undertaken in this regard but the new Guidelines require a lot more than water quality measurement collection and automated interpretation based on a small number of fixed universal rules. Unfortunately it is difficult to see how this extra work can be avoided because the of concepts like ‘Due Diligence’ and ‘Duty of Care’. These underpin ‘Best Practice’ and lead to the use Guidelines as quasi legal documents. As a result Guideline adoption appears inevitable even if it is logistically very challenging. There are also ancillary drivers for adopting NH&MRC (2008) in the medium to long term:

1. Current scientific knowledge, notably of pathogen biology, environmental microbiology, and analytical and interpretation technologies, has increased explosively in the past 20 years. These advances have demonstrated that the old coliform indicator survey approach is narrow, deficient and outdated and its use as the sole water quality measure is simply no longer best practice.

2. The new Guidelines are based not only on a consensus of Australian expertise but also reflect International Global Best Practice because they were developed in a consensus fashion by a collective of all the relevant government and academic experts in this field under the oversight of the World Health Organisation.

3. The new NH&MRC(2008) guidelines do not exist in isolation within Australia. They reflect and are designed to be consistent with:

a. Broad introduction of risk assessment and management principles generally(Standards Australia/Standards New Zealand, 2004b).

b. Adoption of risk assessment and management principles by the environmental health community generally(EnHealth Council, 2001; 2002).

c. The introduction of the closely allied risk assessment and management approaches for other water health related activities in particular drinking water and water reuse(NH&MRC/NRMMC, 2008; NRMMC/EPHC, 2005).

4. The movement in health management generally is toward scientific evidence bases for action (i.e. evidence based medicine) and Recreation Guidelines are very much a Health Guideline and the works suggested are consistent with promoting an evidence based approach.

Implementation This said operational Guideline implementation must still:

1. be logistically feasible and systematic; 2. be affordable; 3. involve defensible Guideline interpretation consistent with best practice; 4. have a clear end point (which may or may not lead to further work).

The Guidelines are unfortunately vague on these points so we have addressed them in the following manner:

1. The Enhealth HRA assessment scheme has been adopted and adapted based on previous risk assessment projects (Roser et al., 2007);


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2. An initial work scoping was undertaken for HWC (Roser & Stuetz, 2008b) to provide a basis for discussion and scoping with stakeholders in line with HRA starting point;

3. How guideline interpretation and the focus on Hazardous Events corresponds to Guideline requirements has been documented (Appendix 03 Justification for Project Design);

4. This Appendix outlines the origin of, and problems arising from, combinatorial explosion, including why they is important and how they impact on the project and how they have been addressed.

Combinatorial Explosion and Risk Assessment Combinatorial is a well known but often overlooked challenge in problem solving where there are clearly definable rules and outcomes but where there are also many alternative possible answers to be considered. It is illustrated well by a simple comparison of board games for example Tic Tac Toe and Chess(Cipra, 1996). Tic Tac Toe despite its simplicity has 15 possible combinations which can be considered when leading to a conclusion on the next move. In this case the number of combinations can still be managed through exhaustive scenario analysis. Chess also has a finite number of possible moves. However, the number of possible games (scenarios) that a (checkmate) risk assessor needs to consider is so great that to look even 15 moves in advance requires theoretically considering ca 1024 options. The same challenge applies to risk assessments. Combinatorial explosion is well known in quantitative risk assessment because it prevents comprehensive sensitivity analysis and this is an underlying problem for policy making (Ferson et al., 2004; Frey, 1992). Some illustrative examples from environmental/health/engineering fields where compromise has been necessary are:

1. biodegradation pathway analysis (Fenner et al., 2008); 2. assessing the reliability of engineered systems (Davis et al., 1999); 3. prioritizing chemical risk management in a consistent fashion(Lerche et al., 2002);

Combinatorial Explosion in the Current Assessment In the case of the current assessment the potential number of primary simulations implied by the risk estimation approach agreed to was 4(or 5) pathogens X 2 waste streams(WAS, secondary) X 2 seasons (winter, summer) X 2 years (2007, 2030) X 2 populations (50 and 200 m from shore) X 4 locations per population (4 Beaches) X 4 inactivation conditions (conservative, 3, 15, 75 Megajoules.m-2 ) = 1024. Additional scenarios need to be evaluated for sensitivity analysis e.g. it is known that culture methods may underestimate Adenovirus numbers by a 100-1000 fold (He & Jiang, 2005) and this needs to be addressed via sensitivity analysis. Unlike the hydraulic modelling, our QMRA is not yet set up for batch processing and so is much more laborious. Finally in the case of the hydraulic modelling combinatorial explosion meant that compromise was central to modelling microbial particle fate and transport within the coastal zone over a reasonable time. As a result, it was necessary to start QMRA before all hydraulic modelling scenarios had been run. A total of ca 200 QMRA risk simulations was proposed as being feasible and allowed for in project planning but this number is less than the total possible above and selectivity in scenario construction was essential. From previous experience WRC did not see limiting the numbers of scenarios to this as being a major obstacle to understanding risk sufficient for decision making as scenarios tend to provide decreasingly less information once the major concerns have been studied. For example in the present instance Bar Beach showed very similar though slightly lower risks to Merewether Baths. This was explicable because of their relative locations. Doing more Bar Beach scenarios in the assessment revision was accordingly not seen as being greatly informative. And exploring all possible combinations and permutations was seen as a poor use of project resources and un-necessary to identify the primary risk issues if any (For example if high risk is found under high inactivation conditions or low risk under conservative conditions, additional novel information or insight would be unlikely to be gained by assessment of intermediate inactivation scenarios).


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That said it was impossible to fully predict in advance which risk outcomes would be most important from the point of view of different stakeholders. And as this integrated approach was somewhat novel it was unclear what simulations would be most informative. So it was essential to run all the hydraulic modelling scenarios as a contingency even if not all are were used in the end. Further Notes About Combinatorial Explosion This problem of work overload goes by a number of names including ‘Combinatorial Explosion’ and the ‘Curse of Dimensionality’. There is even a discipline on the problem known as Computational Complexity Theory. Wikipedia provides useful summary descriptions of some of the related concepts in plain English (Annonymous, 2009):

“The curse of dimensionality is the problem caused by the exponential increase in volume associated with adding extra dimensions to a (mathematical) space. The term was coined by Richard Bellman. The curse of dimensionality is a significant obstacle to solving dynamic optimization problems by numerical backwards induction when the dimension of the 'state variable' is large. It … complicates machine learning problems that involve learning a 'state-of-nature' (maybe infinite distribution) from a finite (low) number of data samples in a high-dimensional feature space and nearest neighbor search in high dimensional space’. “In mathematics a combinatorial explosion describes the effect of functions that grow very rapidly as a result of combinatorial considerations. Examples of such functions include the factorial function and related functions. Pathological examples of combinatorial explosion include functions such as the Ackermann function.” “Computational complexity theory is a branch of the theory of computation in computer science that investigates the problems related to the resources required to run algorithms, and the inherent difficulty in providing algorithms that are efficient for both general and specific computational problems.”

Even high speed computers can be overwhelmed unless their programming involves specially developed shortcuts (Cipra, 1996). A possible future solution is the introduction of Bayesian analysis and networks (Zhu & McBean, 2007). But in the current instance as each scenario was of potential interest an overall amalgamation of risk estimation by such systems was not seen as sufficient. In any case aggregation methods suffer from hiding the variance arising from different input assumption sets. Some Literature on How to Deal with Combinatorial Explosion Grindal describes a possible approach to better computer software testing(Grindal, 2007). The problem of real world decision making in response to risk is analysed by Pomerol who noted the lack of work on reconciling decision theory and pragmatism (Pomerol, 2001). His example of train crash management unfortunately was not useful in the present instance though as it considered human decisions rather than responding to multiple potential risks. His paper does, however, illustrate the use of scenario development. More applicable is the analysis of Jarke et al. (Jarke et al., 1998). This paper discusses a range of aspects to how management of change can be informed by ‘Scenario’ development and exploration. This paper was also the outcome of a consensus development process. Specifically it presented workshop outcomes and ideas rather than a final answer on how to best develop “Scenarios’ and appears to be applicable to the current risk assessment. Some notable observations made relevant to the present assessment and consistent with our approach of exploring a reasonable but not exhaustive number of informative scenarios were:


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1. Agreed scenario development is seen in Decision Theory as the answer to efficiently dealing with combinatorial explosion of ‘what-if’ combinations;

2. Developing ‘Rich picture’ scenarios and detailed complex models is a useful outcome of problem scoping but it generally leads to combinatorial explosion in system behaviours;

3. Though combinatorial explosion arises even when there is even a modest amount of detail to be considered without such analysis “situational bias, tacit knowledge and implicit assumptions may narrow the search space to less than the really important scenarios”.

Their conclusion appeared to be that Scenario development and analysis is not perfect but it is still a powerful tool for understanding and managing what-if questions and it is the best we have at present. So intelligent compromise in the extent of scenario development is essential and by implication inevitable. The paper also summarised the benefits and limitations of scenario analysis (Table 1). The arguments identified in favour of them were much the same as the benefits we had seen arising from Scenario analysis. Further we had made efforts to address most of the problems associated with Scenario use (Table 2). Table 1. Advantages and Problems of Using Scenarios Task Pro scenario Arguments Contra Scenario Arguments Analysis • Uncover hidden requirements

• Envision future system usage • Provide rationale for design proposal • Make requirements behavioural content more concrete • Enriched context information helps uncover risk org.

problems, etc. • Help envisage the potential of a problem

• Coverage problem: how many scenarios?

• Content problem: how much to capture?

• May result in overlooking concurrency

• Requires much domain knowledge Design • Illustrate trade-off between design solutions

• Validate design using scenarios • Management of scenarios becomes

complex Quality management

• Communication aid between stakeholders • Facilitate documentation • Verify/validate Fitness for use • Justify needs • Understand and resolve conflicting quality

requirements

• May oversimplify problems and project risks

• Cost, time, and manpower-intensive

Notes: 1. Extracted from Table 2 of (Jarke et al., 1998).


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Table 2. How Scenario limitations have been addressed in the current assessment Task Contra Scenario

Arguments How Addressed

• Coverage problem: how many scenarios?

All permutations and combinations were considered and a range of high priority ones was developed in consultation with stakeholders.

• Content problem: how much to capture?

The focus on plume transport related Hazardous Events is explicit. The uncertainty analysis identified gaps which in hindsight could be addressed depending on the perceived need. The analysis necessarily took an ‘Environmental Management System’ approach of doing a reasonable overall assessment which was systematic and targeted and most importantly open to further improvement. Some such revision followed the development of the draft report.

• May result in overlooking concurrency

The use of enterococci as surrogate pathogens was designed to capture the total gastrointestinal risk.

Analysis

• Requires much domain knowledge

The use of exposure pathway analysis appeared to provide a structure for identifying essential domain knowledge as well as data gaps which could not be easily addressed.

Design • Management of scenarios becomes complex

Data was extensively managed using database software. Risk outputs are presented in a fashion designed to facilitate side by side comparison of the different risks.

• May oversimplify problems and project risks

Validation has been undertaken as far as possible. Uncertainties are identified for consideration in line with risk assessment best practice.

Quality management

• Cost, time, and manpower-intensive

A detailed process of assessment planning was implemented. This Appendix outlines the logistics issues identified.


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Appendix 13 Selected Excerpts from WRL Modelling (Glamore et al., 2008)

Complementing the current assessment is a previous WRL report (Glamore et al., 2008). The independent reviewer commented there should perhaps be a more extended audit trail. For the full report readers are referred to HWC. To illustrate the kind of work undertaken in support of hydraulic modelling in lieu of reproducing the full document, extracts are reproduced here from the Powerpoint presentation to DECC and Health. These figures illustrate/outline/summarise: 1. The study site including diffuser location and design; 2. The Finite Element model mesh; 3. The three hydraulic model components JETLAG, RMA-10 and #DRWALK 4. Verification modelling of plume surfacing and migration; 5. Examples of Current and Windspeed data modelling data. Diffuser Locations


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Outfall Design

Summary of Hydraulic Modelling The modelling of plume behaviour involved three components:

Note that in the current document and the revised hydraulic modelling the use of T90s was replaced with S90s.


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The Finite Element Model Mesh The figure here shows the model mesh encompassing the beaches in the Newcastle area.

Note: that for the third Dimension of the Finite Element Model Mesh Vertical layers were defined at the surface, 2 m, 5 m, 10 m, 20 m depth, 2 m from the bottom, and at the bottom. Depths in the model ranged from 0.5 m to 63 m, relative to Australian Height Datum (AHD).


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Rhodamine Dye Tracing Results The profiles show dispersion of the rhodamine tracers introduced into the waste streams on different days during 2007. This data was used in model calibration.

Example of Jetlag Initial Mixing Outputs for Different Days These plots illustrate plume depth and dilution corresponding to different effluent discharge rates.


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Example of data and Comparison of Current and Velocity (validation work) These plots show current direct modeled and the actual corresponding wind and current vectors at the time.


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Example of Input Current Wind and Thermistor Data and Output Model Data The bottom plot shows the modeled current in comparison to the actual measured current and an example of when stratification was limited.


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Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event Characterization

“Exceptional Circumstances” Benchmarks Though they do not explicitly propose Exceedence Probability analysis the Guidelines lay the groundwork for them as follows:

1. The Guidelines identify a special category ‘Exceptional Circumstances’ which correspond to Hazardous Event periods, stating that the normal classification may be inapplicable and indicating the need for management without quantitatively defining water quality benchmarks suited to use as management triggers.

2. The 95th percentile in the benchmark definition endeavours to capture some of the variation arising from rarer poorer water quality situations.

3. Because the new water quality benchmarks are more strict and the introduction of risk assessment and principles is a marked change from previous guidance, some change in Guideline use beyond the old compliance approach seems implied.

4. The Guidelines (Figure 1.2)(NH&MRC, 2008) indicate the need to consider the risk X likelihood product. This specific concept is not discussed further but the hybrid risk matrices are employed (water quality X sanitary status) indicating concurrence with this general approach.

5. In discussions NSW Health and DECC did not identify benchmarks relating to ‘Exceptional Circumstances’ beyond the total Gastrointestinal illness Probability values of 1% and 5% per bathing exposure e.g.:

a. for short term poor water quality situations analogous to transient elevated air contaminant exposure identified above;

b. for specific pathogens. But they did concur that Hazardous Events were a primary concern of the assessment.

6. Examination of general risk management guidelines in AS/NZS 4360 (Section 6)(Standards Australia/Standards New Zealand, 2004a) indicated to us that the risks arising from bathing during periods when there was onshort transport of plumes might be considered ‘High’ because:

a. From the WRL report and the hydraulic data presented here, plume events appear to occur with recurrence intervals of <1 per year making their Likelihood Level ‘A’(‘Almost Certain’);

b. The consequence of gastronenteritis appears to correspond to at least the Level II of Health and Safety Consequences i.e. “Objective but reversible disability requiring hospitalization” for a proportion of the population due to the more severe secondary effects of gastroenteritis such as reactive arthritis and Guillaine-Barre(Pruss & Havelaar, 2001 ) .

7. Beachwatch has set a precedent by developing Hazardous Event based rules for special predictable circumstances associated with stormwater impacts (Armstrong et al., 1997).

It is also notable that prior to the present project WRL had reported water quality at the 99th as well as 95th percentile levels of thermotolerant coliforms and enterococci. This contrasts with the actual content of the earlier NH&MRC Guidelines (National Health and Medical Research Council, 1990) which only recommend the use of median, geometric mean and 80th percentiles.


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Risk Statistics and Multimodal Distributions When undertaking QMRA, the output risk takes the form of a probability density function. Where the range of possible input values is narrow and Hazardous Events are assumed to simply be extensions of the measured PDF, risk estimates tend to fall in a narrow band and follow a distribution function like that reproduced in Figure 1a. Microbial levels and risk, however, tend to have a high variance and so at a minimum the PDFs tend to be more like Figure 1b which is lognormal in shape and has a resulting right hand skew. In this situation the mode and median consequence may not be good estimators of the average risk. Water microbe populations are known to approximate a log normal rather than normal distributions (Roser & Ashbolt, 2007) as does risk e.g. (Schonning et al., 2007) . As well, the probability of a high risk may be low while consequence is disproportionately high. More problematic still is the situation illustrated by Figure 1c. This plot shows the PDF where the impact consequence is not fully offset by the decreasing probability of occurrence as the distribution approaches its extremes. This may arise in such situations where microbial levels and risks follow a bimodal or multimodal distribution. This is the situation Hazardous Event analysis has to allow for and it has several important implications:

1. Random sampling will disproportionately sample water quality values around the dominant modal value, and rarer secondary peaks may not be well characterised or even identified;

2. Modelling may greatly underestimate consequence probability if it does not capture the variance arising from the secondary peak.

3. Where there is potential for a Hazardous Event, superimposed on Baseline risk conditions, there is the potential for a sudden jump in exceedence probability.

To address this, ideally risk assessment should be based on a very large data sets which included these secondary modes illustrated in Figure 1c. In practice such large data sets are largely unavailable for any given location. In the present assessment, however:

1. The risk from WAS and the treated effluent streams needed to be considered separately though their impact of beachwater quality could not be distinguished;

2. The available 2001-2006 enterococci data totalled only ca 200 measurements for each beach greatly constraining out ability to distinguish any secondary event peaks against ‘Baseline’ contamination.

Nevertheless there are other means of identifying them. Firstly it may be possible to divide a data set into Baseline and event samples if the events can be distinguished on first principles or through large surrogate data sets. Beachwatch management of stormwater impacts on beaches is an example of both approaches (Armstrong et al., 1997):

1. Monitoring of stormwater quality and hydrology has allowed beachwater quality to be related to rainfall events;

2. Rainfall has become the surrogate measure of high microbial risk. A third source of event data appears to be model outputs as in the present instance.


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Figure 1. Possible Risk Distributions Pertinent to Decision Making – reproduced from (Pollard et al., 2001) Baseline + Event Modelling and Exceedence Plots To address this challenge of detecting and communicating small high impact events of the kind illustrated in Figure 1c’s format, the following strategy was developed:

1. Similar to previous QMRA, each scenario was modelled as the product of ‘Baseline’ and ‘Event’ conditions over three months.

2. The input assumptions were identical for the Baseline and Event conditions for any given scenario except that:

a. For ‘Baseline’ conditions a point microbial Dilution+Inactivation factor of 105 (effluent) or 106 (WAS) was assumed to be operating in the coast zone. These factors were estimated empirically based on comparison of the difference between the level of enterococci in the discharge streams and the levels observed at the beaches in Beachwatch monitoring. Further details of the process and its rationale are provided in Appendix 01 Newcastle Beachwater Quality and Baseline Reductions;

b. For Event conditions the Dilution+inactivation increment data set generated for each hydraulic model scenario was randomly sampled. Depending on the scenario this yielded either:

i. No reduction estimate because no ‘particles’ were detected in the model cell in which case the Baseline value was substituted into the QMRA simulation;

ii. Reductions from the model output in the range of 102 to 104 where there was assumed to be no solar inactivation (conservative) and dilution only occurred;

iii. Reductions from the model output in the range of 102 to 106 depending on the combined effect of dilution and inactivation;

3. The proportion of 15 minute intervals where the exposure point was influenced by Baseline reductions or event reductions was taken as the proportion of increments where no bacterial


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model particles were unequivocally detected (i.e. < 4 particle per exposure zone cell). Depending on the Scenario assumptions particles were detected on as few as 0.00% of occasions (15 min increments) and as many as 19% in the Conservative scenarios(about 1700). In the latter case there were often many more than 4 particles detected per increment and the apparent extent of (microbial) particle transport to the bathing zone varied by 5 orders of magnitude between different scenarios.

4. During the stimulation the @Risk inbuilt RiskPercentile() function was used to estimate the following exceedence probability values – 0.5, 0.2, 0.1, 0.05, 0.02, 0.01, 0.008 0.005, 0.002, 0.001, 0.0008, 0.0005, 0.0002, 0.0001.

Exceedence probability plots were prepared for both Baseline, and Baseline + Event, Scenarios. The shapes of the plots reflected the Monte Carlo sampling of, in effect, a bimodal distribution analogous to that shown in Figure 1c. Figure 2 illustrates the bimodal distribution using a simple model of enterococci count frequency and distribution similar to those constructed during the assessment. Baseline level of ca 1 enterococcus per 100 mL is assumed to occur 95% of the time and 5% of the time the exposure point is contaminated by a plume of less diluted/contaminated seawater with ca 1000 enterococci per 100mL. The frequency distribution plot (Figure 2a) can be seen to compare with Figure 1c but the event peak is increasingly hard to distinguish. However when the statistics are rearranged to an exceedence probability format the impact of the event is seen as a clear step change. Such step changes become increasingly evident the smaller the standard deviations of the two underlying distributions and the greater the difference between the modes (Figure 2b). Importantly small events still tend to be visible as a ‘step’ in the exceedence probability plots even when they are no longer in evidence in the Figure 1c/2a format plots. The main change seen is that the ‘step’ is shifted to the right. This difference arises from the use of a logarithmic scale for risk probability. What this presentation format shows clearly is the point along the cumulative probability curve where event related risks become evident.

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Figure 2. Simulated Enterococci Probability Distribution as Mass Function and Exceedence Probability Plots. Notes:


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1. Data combines Baseline (95% of time log10 normal 0±0.5 /100mL) & Event Contamination (5% of time log10 normal 3±0.5 /100mL)

2. Left shows a normal frequency plot. 3. Right plot shows the cumulative frequency plot in exceedence format.

Which Measurements to Plot? In constructing Exceedence Probability plots and collecting the percentile statistics we assumed that sea water consumption occurs in one discrete event effectively corresponding to a 15 minute timestep period per bathing period. Uncertainties associated with this assumption are discussed in the Section 5. Two examples of the exceedence probability output are shown below illustrating the modelling approach and its use in describing variability. The three parameters selected for plotting were:

1. The level of the pathogen of concern; 2. The probability of infection; 3. The probability of gastrointestinal illness.

The first of these was used to illustrate the shape of the pathogen concentration curve e.g. for comparison with that actually observed. The second infection probability is the normal statistic calculated in QMRA. The third illness statistic was calculated to make the outputs consistent with the Guideline focus on daily Gastrointestinal illness probability. The output statistics are calculated on a per timestep basis which are taken to be equivalent to ‘per exposure’ as explained previously. Exceedence Plot Format and Labelling Each exceedence probability plot has been designed to have a largely standard format and has been labelled with a heading which summarises the scenario plotted. The reason for using this shorthand was that the modelling generated large numbers of similar appearing output simulations which needed to be distinguished while the plots were produced on an “industrial” scale. Two labelling formats were used. The more common is shown in Figures 4 and 5. We have used a slightly different labelling system for ‘Baseline’ risk plots (e.g. Figure 3):


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Common features of plots 1. The main outputs of each QMRA model were 3 sets of 10,000 estimates of the a) infection

risk probability, b) illness risk probability and c) microorganism level which formed in practice 3 PDFs. For each of these output sets the model extracted percentiles between 0.2 and 0.9999. These were converted into exceedence probability by subtracting each percentile from 1.0.

2. The risk percentiles and other information for each simulation were both recorded in a database and plotted below in what is essentially a log log cumulative distribution curve.

3. The labels at the head of each plot identify the scenario modelled. 4. The X axis shows the exceedence probability. 5. The right hand Y axis shows the number of pathogens or enterococci per L modelled to

occur for that scenario; 6. The left hand Y axis shows the probability of infection or illness scale; 7. For a given exceedence probability:

a. Blue dots indicate the estimated number of pathogens; b. Pink dots indicate infection probability; c. Green dots indicate gastrointestinal illness probability.

Baseline + Event Plot Headings

8. For the main ‘Baseline + Event’ plots the heading code is as follows: a. “Microorganism_” (one of the five considered); b. “Wastestream_” (effluent or WAS); c. “Location number_Location name_” (1 to 8 plus name and distance from shore); d. “Year of discharge modelled_” (2007 or 2030); e. “Season_” (winter or summer); f. “Solar radiation exposure ( mainly as an S90 in MJ.m-2 - 3, 15, 75 and conservative); g. “Exposed population_”; (shoreline bathers or surfers);

For example “Adenovirus_effluent_1_Merewhether Baths (50m)_2007_Summer_3_Shoreline bathers” means the scenario where shoreline bathers off Merewether Baths during summer under 2007 discharge flow rates consumed Adenovirus from secondary effluent following inactivation at a rate corresponding to an S90 of 3 MJ.m-2 as well as dilution. Baseline Plot Headings

9. The heading code is as follows: a. “Microorganism_” (one of the five considered); b. “Wastestream_” (2nd ary effluent or WAS); c. “Nominal Dilution_n_” (Identifies the scenarios as a nominal dilution plus the

decimal reduction factor of 4, 5, 6 or 7); d. “Exposed population_” (shoreline bathers or surfers);

For example “enterococci_2nd nominal dilution 5_Shoreline bathers” means a scenario where shoreline bathers are exposed to all pathogens (surrogate is enterococci) in secondary effluent following dilution + inactivation totalling 105.

Examples for the Risk Assessment Outputs The plots below illustrate the range of risks presented, how risk consequence rises with increasing pathogen level and how the likelihood of a bather encountering such conditions when ingesting water decreases.


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In the case of Giardia shown in Figure 1 the Baseline plots are interpreted as follows: • If a shoreline bather consumes seawater where pathogens are reduced in numbers by the

Baseline factor of 105 they have a 1 in 1000 (exceedence) probability of consuming water with >0.07 Giardia per L and the chance they could become ill if they do so at this point is ca 0.00003 (0.003%).

• This is well below both the 1% benchmark (P.=0.01) and 0.1% mooted single pathogen benchmark (P=0.001) and the chance of becoming ill would be seen as ‘tolerable’.

As noted, at the top of each plot is a heading code indicating the Scenario modelled. So for example the:

• Figure 3 heading indicates that the model output plots the risk to shoreline (normal) bathers from Giardia from secondary effluent nominally diluted/reduced in numbers by a factor 105 ;

• Figure 4a heading indicates the model outputs plot the risk to shoreline (normal) bathers at Merewether Baths from Giardia from secondary effluent during summer assuming a conservative inactivation scenario;

• Figure 4b heading indicates the model outputs plot the risk to surfers off Merewether Baths from Giardia from secondary effluent during summer a assuming conservative inactivation scenario.

Figure 4a shows a plot with Baseline plus Hazardous Event conditions included. It indicates that:

• If a surfer consumes seawater with a small portion of diluted effluent, depending on tide, wind and current movement, they would have a 1 in 20 chance (the 95th percentile) of consuming water with 0.1 Giardia per L and the probability they could become ill from Giardia if they did would be ca 0.0003 (0.03%) (this appears tolerable based on the 1%/ P.=0.01 benchmark and illustrates how under ‘average’ conditions the water quality is good based on the criteria in the new guidelines).

• The surfer also has a 1 in 1000 chance of consuming water with > 2 Giardia per L and the (exceedence) probability that they could become ill if they did is > ca 0.01 (1%);

• This 1% figure is the same as the 1% benchmark (P.=0.01) but above the 0.1% illness risk proposed as a possible benchmark for any given pathogen;

• A decision needs to be make by stakeholders as to whether such a low likelihood risk is acceptable/tolerable or otherwise.

The plots do not clarify what probabilities of Gastroenteritis for a single pathogen are tolerable for bathers but they do indicate the levels that need to be considered in decision making. The modelling suggests these conditions would only be notionally encountered on ca 1 in 1000 surfing encounters for an individual i.e. ca once in ten years for a person surfing on 100 days per year. Contaminant plumes would in reality reach the beach more often but the surfer would not typically be there and so be exposed. Other features to note about these plots are seen in Figure 4b, the analogue of Figure 4a for shoreline bathers:

• The surfer’s risks (Figure 4a) are higher and are enhanced by their proximity to the discharge points and more importantly the larger volume they are assumed to consume per bathing exposure. The risk to a normal ‘shoreline bather’ is ca one order of magnitude less than for surfers and arguably well within what is ‘tolerable’.

• There is a noticeable step change in the curves compared with the Baseline plots. This is because, as explained above, when undertaking the simulation it was necessary to make an assumption about the underlying level of baseline contamination. In this case as others it was assumed that the background reduction is 105 in line with the level of enterococci seen in


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seawater. The step is useful because it indicates at which percentile the Baseline risk is overtaken by that arising from on-shore contaminant movement.

Figure 3. Illustrative Exceedence Probability Plot for Giardia Risk Assuming secondary effluent diluted by 105

a.


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b. Figure 4. Illustrative Exceedence Probability Plot for Giardia Risk Assuming secondary effluent diluted by 105 down to and exceedence probability of 0.1-0.2 and beyond that it is reduced by the degree of dilution estimated by hydraulic modelling Notes:

1. Plot a. is for surfers, plot b. is for normal bathers. 2. The left hand part of each curve is the same at in the Baseline scenario.

Figure 5a. and Figure 5b. show analogous plots for WAS. The difference in the step behaviour is much more marked and arises from the assumption that the normal Baseline reduction factor for WAS is ca 106. By contrast with the conservative scenario the minimum reduction detectable is 104. The step change arises when the Baseline sub-distribution PDF is significantly different from the PDF for events as explained above in the discussion of how multimodal distributions appear when plotted. Compared to the secondary effluent the ‘Event’ risk starts at a higher point because the estimated level of Giardia in WAS is 2.2 log10 units greater than in effluent (Table 10). Again most of the time (percentiles up to the 95th percentile or probability of 0.05) the risk of illness is very low. But the risk of infection from Giardia, when such material is transported in-shore, appears to disproportionately higher than would be expected if the Baseline distribution were simply continued e.g. infection risk at the 0.001 exceedence probability value is ca 10%. When interpreting such data it is essential to recognise that Hazardous Event conditions were detected in Figure 5 a only in ca 0.1% of the timesteps (about 10 of 8516 fifteen minute timesteps).


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a.

b. Figure 5. Illustrative Exceedence Probability Plot for Giardia Risk Assuming WAS diluted by 106 down to and exceedence probability of ca 0.01 and beyond that it is reduced by the degree of reduction estimated by hydraulic modelling. Notes:

1. plot a. for surfers in summer where reduction is by dilution only, plot b. for shoreline bathers in winter given high rate inactivation + dilution (S90= 3 MJ.m-2).

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