1. Australian Centre for Environmetrics

2. Developing Risk-based guidelines for Water Quality Monitoring and Evaluation Prof. David Fox CSIRO Land and Water University of Melbourne Melbourne University Private

3. Australian Centre for Environmetrics http://www.deh.gov.au/water/quality/nwqms/

4. Australian Centre for Environmetrics Chapter 1: Introduction Rational for revision Philosophical basis Chapter 2: Framework Key steps Important issues Chapter 3: Aquatic Ecosystems Types & levels of protection Default & site-specific guidelines Use of biological indicators Chapter 4: Primary Industries Irrigation Livestock aquaculture Chapter 5: Recreational WQ & aesthetics Swimming, boating, etc. Chapter 6: Drinking Water Safety & aesthetics Chapter 7: Monitoring & Assessment Data collection & analysis

5. Australian Centre for Environmetrics

7. Environmental monitoring Aim is to design and conduct scientifically credible programs of environmental surveillance Aim is discover specific violations and force corrective action informationdata Compliance monitoring

8. Australian Centre for Environmetrics Risk-based Approaches Evolution of conventions has a lasting effect on how risk analyses are conducted: USEPA has set mostly conservative defaults. US Nuclear Regulatory Commission generally avoids conservative assumptions, recommending that modelers use default values that are close to the central tendency of parameter estimates (Bier 2003). The Bayesian perspective is that there is a random variable, and the job of the analyst is to characterize how variable it may be. The approaches share a common belief in the epistemic nature of risk: there is a state of nature and the job of the risk analyst is to define it.

9. Australian Centre for Environmetrics LowHigh Level of environmental protection Protector Risk Polluter Risk 'Acceptable' region of protection Max. polluter risk Max. protector risk Risks and Trade-offs Protector risk = prob. ecologically important impact goes undetected Polluter risk = prob. unimportant impact triggers further action

10. Australian Centre for Environmetrics Trigger-values

11. Australian Centre for Environmetrics Trigger-values for physico-chemical stressors

12. Australian Centre for Environmetrics mortality 100% concentration Distribtion of NOECS Setting Risk-based trigger values : Aldenburg & Slob (1993) Assumed log-logistic Dose-response curves for selected species

13. Australian Centre for Environmetrics 0.95 Trigger value Distribution of NOECs for all species Setting Risk-based trigger values : Aldenburg & Slob (1993)

14. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Chronic Acute

15. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Raw Data: x = {129, 18, 150, 400, 810 } Trigger value = 0.49  g/L

16. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Chronic data : denote by X with pdf Acute data : denote by Y distribution of Y/ assumed to be same as distribution of X where is acute to chronic ratio. Given sample of n 1 X observations and n 2 Y observations, the maximum likelihood estimator (mle) for is that value which maximises the likelihood function:

17. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Data: x = {129, 18, 150 } and y = {400, 810} Likelihood function Mle = 7.451

18. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Modified Data: x = {129, 18, 150 } and y = {400 / 7.451, 810 / 7.451} Revised trigger value = 5.34  g/L cf 0.49  g/L (raw data) 5.8  g/L (DEH value) 3.11  g/L (using default = 10)

19. Australian Centre for Environmetrics Bayesian Methods – A Credible Alternative? Bayesian approach: Has advantage of introducing subjective assessment / expert opinion But May be perceived as being difficult to interpret & lacking objectivity. London Court of Appeal: The Times, November 3 1997

20. Australian Centre for Environmetrics Example – Modelling Uranium NOECs A Bayesian Approach http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml

21. Australian Centre for Environmetrics Example – Modelling Uranium NOECs A Bayesian Approach Gamma prior for tau

22. Australian Centre for Environmetrics Example – Modelling Uranium NOECs A Bayesian Approach Non-informative Normal prior for 

23. Australian Centre for Environmetrics Example – Modelling Uranium NOECs A Bayesian Approach Gamma prior for (mean = 20)

24. Australian Centre for Environmetrics A Bayesian Approach Example – Modelling Uranium NOECs

25. Australian Centre for Environmetrics Example – Modelling Uranium NOECs Modified Data: x = {129, 18, 150 } and y = {400 / 6.624, 810 / 6.624} Revised trigger value = 6.64  g/L cf 0.49  g/L (raw data) 5.8  g/L (DEH value) 3.11  g/L (using default = 10) 5.34  g/L (using mle = 7.451) A Bayesian Approach

26. Australian Centre for Environmetrics Reference site – Test site comparisons Reference Site Test Site De facto ‘standard’ Test site median Ref site 80 th. percentile Note: Normal distributions not a prerequisite Common distribution not a prerequisite 80 th. Percentile at reference site must be based on minimum of 24 data values (2 years monthly data)

27. Australian Centre for Environmetrics Reference site – Test site comparisons

28. Australian Centre for Environmetrics Despite early attempts, development and adoption of a ‘standard’ risk metric seems a long way off (never?); Bayesian methods are becoming increasingly popular, although acceptance may be hampered by biases and lack of understanding; More attention needs to be given to appropriate statistical modelling. In particular: -model choice -Parameter estimation -Distributional assumptions -‘Outlier’ detection and treatment -robust alternatives (GLMs, GAMs, smoothers etc). Observations & Challenges