1. Environmental risk assessment of chemicals Paul Howe Centre for Ecology & Hydrology, UK

2. Foundation in human health risk assessment Extrapolation from surrogate species to one species (humans) Identification of key endpoint Application of factors to account for specific types of uncertainty Precautionary – all individual humans are valued

3. For example …..Organotins Estimates of Tolerable Daily Intake for use in the risk assessment on the basis of medium-term exposure TDI (mg/kg body weight per day, as chloride) ToxicityUncertainty factor Monomethyltin0.0012Neurotoxicity a 500 Dimethyltin0.0012Neurotoxicity a 500 MonobutyltinNo available data Dibutyltin0.003Immunotoxicity1000 MonooctyltinInsufficient data to establish a TDI; indications that MOT less immunotoxic than DOT Different key toxic endpoints for different organotins Some have insufficient data to make an estimate Uncertainty factors reflect the adequacy of the dataset

4. Environmental risk assessment for chemicals 140,000 chemicals in European consumer products Starting point is prioritisation of effort Screening exercise In theory, we move from consideration of individual humans to populations of all other organisms With small datasets, this is difficult or impossible in practice

5. Small datasets and the use of uncertainty factors Guidance value always based on one study Highly dependent on uncertainty factor applied Unlikely to reflect true measure of risk at the population level for all organisms 10 1 100 Acute Chronic

6. Small datasets and the use of uncertainty factors 10 1 100 Acute Chronic Base set: Uncertainty factor of 1000 applied to the red organism PNEC = 0.0008 UF = 1000

7. Small datasets and the use of uncertainty factors 10 1 100 Acute Chronic Base set plus chronic test on green organism: (not most sensitive) Uncertainty factor of 1000 applied to the red organism PNEC = 0.0008 UF = 1000

8. Small datasets and the use of uncertainty factors 10 1 100 Acute Chronic Base set plus chronic test on green and yellow organisms: Uncertainty factor of 100 applied to the red organism PNEC = 0.008 UF = 100

9. Small datasets and the use of uncertainty factors 10 1 100 Acute Chronic Base set plus chronic test on green, yellow and red organisms: Uncertainty factor of 10 applied to the red organism PNEC = 0.02 UF = 10

10. Variability in the deterministic approach Selection of key study Distinction between what is ‘acute’ and what is ‘chronic’ Selection of uncertainty factors Quality criteria against which studies are judged ‘Flexibility’ in the guidance documentation (not all aspects of study quality defined) Inclusion of factors outside the study (for example consideration of solubility/volatility of the substance)

11. For nonylphenol ……. 4 different key studies selected 4 different uncertainty factors applied …. From a dataset with 17 studies by a group of 6 ‘experts’ in risk assessment worldwide

12. Data rich chemicals Probably <0.1% of all chemicals are data rich Quality measures of individual studies are highly variable Is it sensible to base a guidance value on only one study?

13. Data rich chemicals – deterministic approach Lowest no-observed effect concentration Uncertainty factor of 10 applied (even very data rich substances would have a UF applied) Guidance value developed Value below concentration required by some organisms (Cu is an essential element)

14. Probabilistic approach – copper Uses all of the available data Statistically derived value with error estimation Transparent methodology with a defined protection target (95% of species) In practice, very few chemicals have had guidance values derived this way Few have sufficient data points to fit the distribution (often many of the data are acute rather than chronic tests) Restrictive criteria for the use of the probabilistic approach (minimum number of species or taxonomic groups)

15. Guidance values for inorganic ions 95% point mg/litre N=T Ag0.000544 T1 Cu0.01177 T1 CN0.0218 T2 As0.0338 T2 Zn0.08109 T2 Sn0.438 T2 B126 T3 Mn279 T3 F2251 >T3 It is only realistic to estimate relative hazard/risk as order of magnitude bands Hazard or risk bands determine priorities; they are not accurate or precise risk values

16. Local PEC/PNEC ratios for the various uses of organotins ActivityMMTCDMTCMBTCDBTCMOTCDOTC PVC processing sites (using stabilizers) Large calendering plant (using TGD) 1.30.030.0020.040.50.1 Small spread coating plant (using TGD) 0.80.020.0010.030.230.05 Generic plant (EUSES) 0.0030.00010.000040.0010.002 Comparison with exposure to estimate risk

17. Impacts above the guidance value Total dissolved copper (µg/litre) Effects with high bioavailability in water 1-10significant effects are expected for diatoms and sensitive invertebrates, notably cladocerans. Effects on fish could be significant in freshwaters with low pH and hardness 10-100significant effects are expected on various species of microalgae, some species of macroalgae, and a range of invertebrates, including crustaceans, gastropods and sea urchins. Survival of sensitive fish will be affected and a variety of fish should show sublethal effects 100-1000most taxonomic groups of macroalgae and invertebrates will be severely affected. Lethal levels for most fish species will be reached > 1000lethal concentrations for most tolerant organisms are reached

18. Conclusions In theory, assessments have moved from consideration of individual humans to populations of all other organisms With small datasets, this is difficult or impossible in practice For the vast majority of chemicals a deterministic assessment is carried out The deterministic methodology is a rather precautionary approach with multiple sources of variability For the few data rich chemicals it is possible to use probabilistic methods and such methods tend to use most of the data There need to be enough data points (including species & taxonomic groups) to fit the distribution

19. Conclusions Whatever system is used it needs to be transparent Whether human health or environment, deterministic or probabilistic, the guidance value is compared with an environmental concentration to develop a risk ratio The subsequent ratio can be used to inform risk management With enough data it may be possible to subdivide the data into species sensitivity groupings which can be compared with field observations