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ERMS Activity 5.8 Validation (input from PROOF) • Two main issues: – How can we control monitor estimated environment risks? – Check the validity of the currently applied risk values • The work… • Next steps… The work… • Theoretical work – to establish the conceptual basis for integration of risk assessment and biomarker based monitoring • Laboratory studies – conducted field relevant exposures to examine relationships between biomarkers, fitness effects, and predicted ecological risks • Field studies – Participated in field relevant exposures to validate biomarkers to oil industry discharges in relation laboratory exposures • Applied Statistical extrapolation methods (SSDs) – to link biomarker response levels to ecological risk levels – to validate fitness response levels to existing risk curves • Integrate biomarker distributions in ERMS models – application of the findings to extend the risk models Key terms • Fitness – Refers to the ability of the organism to successfully grow and reproduce and maintain the population of which it forms part • Biomarkers Ecosystem – Refers to any biological response in a living organisms that results from the exposure to a pollutant chemicals Level of Biological organization Individuals NB ! Cells Molecules – – Fitness data are currently used for Environmental Risk Assessment Population Population Fitness Population Population Uptake of chemicals Biomarkers Fitness is difficult to measure in the field – Fitness and Biomarkers often differ in time and level of biological organization – Biomarkers can be Early warning of Ecosystem health Biochemical responses Early effects time Late effects A key conceptual element: How is Biomarkers linked with Ecosystem health • What characterizes a healthy ecosystem? – The constituent animals, plants and microbes must, on the whole, be healthy • How do we characterize ecosystem health? – Biomarkers measure exposure to pollutants and give an assessment of the health status of individual animals – By measuring the health status of a range of species representing different phylogenies and feeding types, we can use a weight of evidence approach to envisage the ecological concequences of pollutant exposures » Depledge & Galloway, Front Ecol Environ 2005; 3(5): 251-258. Link between environmental risk and monitoring …enables predictions of ecosystem health which can be control monitored Animal Fitness Environmental Risk Risk probability or Environmental impact factors Laboratory testing Prediction (prognosis) Field measurement (diagnosis) Biomarker signals Survival and reproductive capacity Under Construction Approach: Compare (statistically) biomarker-response-distributions and fitness-SSDs Species Sensitivity Distributions for fitness and biomarker responses 1 11 1 Alkaline unwinding DNA strand breakreponse Combinedstability risk curve Lysosomal response Lysosomal stability Lysosomal stability response Alkaline unwinding reponse Combined risk curve Combined curve Combined riskrisk curve 0.9 0.9 Fitness 0.8 Potetially affected fraction of species 0.8 0.8 0.8 (=risk curve) 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 Above threshold level 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 5% 0 0 1 0 1 1 Limit line Andel potensielt påvirkete arter Potetially affected fraction species Potetially Potetially affected affected fraction fraction of ofof species species 0.9 0.9 Under threshold level 0 1 10 10 10 Oil 10 Threshold level 100 100 100 1000 1000 1000 100 1000 Concentration THC Concentration Concentration THC THC Concentration THC concentration (mg total hydrokarbons pr. liter) 10000 10000 10000 10000 Construct Biomarker Sensitivity Distributions (BSDs) ! • Statistical evaluation of biomarker response levels versus risk levels • Comparison of • Species Sensitivity Distributions applied in risk assessment (based on literature data) • and Biomarker response distributions (based on our experimental data) 1. 2. 3. 4. Development of SSDs for different oil component groups Construction of risk curve for the different exposures Construction of one average risk curve for all exposures Construction of BSDs for different types of biomarkers: DNA damage, oxidative stress, lysosomal stability and PAH metabolites 5. Comparison of biomarker response levels and risk levels From SSD based on EC50 to risk curve 1 0,9 0,8 Chronic SSD 0,7 SSD based on EC50 values PAF 0,6 Devide by 100 0,5 0,4 0,3 0,2 corresponding PAF 0,1 exposure concentration 0 1 10 100 1000 Concentration 10000 100000 3. Construction of one average risk curve for all exposures Risk curve for Goliath and Statfjord exposures 1 Potentially affected fraction of species 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 10 100 Concentration THC Concentration THC 1000 10000 4. Construction of BSDs • Results from different experiments are combined in BSDs • The sensitivity of the species tested (Cod, Shrimp, Sheepshead minnow, Sea urchin, Mussel & Scallop) represents the sensitivity of all species • Four oil types and one produced water exposure • Lowest Observable Effect Concentrations (LOECs) in biomarkers are applied • BSDs indicates the variation in lowest exposure levels where the specific biomarkers come to expression for different species • Biomarkers which respond at low levels but do not respond at higher concentrations are not suited for this. At levels higher than the LOEC the biomarkers must still respond. BSD: DNA damage related biomarkers 1 1 Shrimp- -Goliat Goliat Shrimp 0.90.9 Potentially affected fraction of species Potentially affected fraction of species Sheepshead -- North Sheepshead North Sea Sea oil oil 0.80.8 Cod Cod -- Goliat Goliat 0.70.7 Cod - Statfjord B Cod - Statfjord B 0.6 0.6 Cod - North Sea oil 0.5 0.5 Cod - North Sea oil Scallop - Goliat 0.4 0.4 Scallop - Goliat 0.3 0.3 Shrimp - Statfjord 0.2 0.2 Sea urchin - Statfjord Sea urchin - Statfjord 0.1 0.1 DNA damage DNA damage Combined risk curve Shrimp - Statfjord Mussel - Statfjord Mussel - Statfjord 5% level 0 1 0 1 10 100 10 100 THC Concentration Concentration THC Concentration THC 1000 1000 10000 10000 BSD: Oxidative stress related biomarkers 1 1 Cod - Statfjord 0.9 Cod - Statfjord Potentially affected fraction of species Potentially affected fraction of species 0.9 0.8 Shrimp - Goliat 0.8 Shrimp - Goliat 0.7 0.7 Mussel Statfjord Mussel Statfjord 0.6 0.6 Cod - Goliat Cod - Goliat 0.5 0.5 0.4 0.4 Sea Sea urchin urchin -- Statfjord Statfjord Oxidative stress Oxidative risk stress Combined curve 0.3 0.3 Shrimp Shrimp -- Statfjord Statfjord 0.2 0.2 0.1 0.1 Scallop--Goliat Goliat Scallop 5% level 00 1 10 Concentration 100 THC Concentration THC Concentration THC 1000 10000 BSD: General toxicity related biomarkers (Lysosomal stability) 1 1 Sea urchin - Statfjord B 0.9 0.9 Potentially affected fraction of species Potentially affected fraction of species Sea urchin - Statfjord B 0.8 0.8 0.7 0.7 Shrimp - Statfjord B Shrimp - Statfjord B 0.60.6 0.50.5 0.40.4 Scallop - Goliat Scallop - Goliat 0.30.3 0.2 0.2 Lysosomalstability stability response Lysosomal response Combined risk curve Shrimp - Goliat Shrimp - Goliat 0.1 0.1 5% level 0 0 1 1 10 10 100 100 Concentration THC Concentration THC Concentration THC 1000 1000 10000 10000 BSD: Exposure related biomarkers (PAH metabolites) 1 1 0.9 Cod - Goliat Potentiallyaffected affectedfraction fraction species Potentially ofof species Cod - Goliat 0.9 0.8 0.8 Sheepshead - North Sea A 0.7 0.7 Sheepshead - North Sea A 0.6 0.6 Cod - Statfjord B Cod - Statfjord B 0.5 0.5 0.4 0.4 0.3 Sheepshead- -North NorthSea SeaBB Sheepshead PAH metabolites Combined risk curve PAH metabolites 0.2 0.2 Cod - North Sea A Cod - North Sea A 0.1 0.1 0 00.1 0.1 5% level 1 10 1 100 10 Sum PAH concentration (water) Sum PAH concentration (water) 100 5. “A Biomarker Bridge”: Comparison of biomarker response levels and risk levels 1 0.9 0.8 Ecological risk (PAF) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Fraction of biota with observed DNA damage (shrimp/mussel/sea urchin/cod/carp) 0.8 0.9 1 Conclusions – part 1 • How can we control monitor estimated environment risks? – A bridge that links environmental risk and biomarkers has been constructed – It allows to express environmental risk with biomarker values – It makes it possible to control measure accepted risik with biomarker measurements in the field – There is a need for biomarker- and fitness- measurements for produced water for more animal species to build a sufficiently robust BSD (approx. 15 species) which can be used to characterize ecosystem health – Biomarker data from Svan field study and preliminary results from PROOF Drilling Discharges project indicate that the approach will be applicable also for drilling discharges (effects in the water column) Validation by comparing to laboratory experiments • • • • Produced water – low dose Log term (chronic) exposures Vulnerable life stages Fitness LOECs From SSD based on EC50 to risk curve 1 0,9 0,8 Chronic SSD 0,7 SSD based on EC50 values PAF 0,6 Devide by 100 0,5 0,4 0,3 0,2 corresponding PAF 0,1 exposure concentration 0 1 10 100 1000 Concentration 10000 100000 Risk curve, BSDs and LOEC curve for Goliath and Statfjord exposures 1 Sheepshead - North sea A (6 wks) Potentially affected fraction of species 0.9 0.8 Sheepshead - North sea B (5 wks) 0.7 0.6 Shrimp - Statfjord B (3 months) 0.5 0.4 Shrimp - Goliat (3 months) 0.3 Oxidative stress DNA damage Lysosomal stability response LOECs 0.2 Mussel - Statfjord B (3 months) 0.1 0 1 10 100 Concentration THC 1000 10000 Fitness LOECs plotted in the normal distribution of the SSD Normal distribustion of species sensitivity • Normal distribution 0.07 species sensitivity Experimental LOECs 0.06 probability 0.05 0.04 0.03 0.02 0.01 0 1 10 100 concentration THC (mg/l) 1000 10000 Conclusions - part 2 • Checking the validity of existing ERA values – A validation of the presently used SSD for Risk assessment has been done based on relevant laboratory experiments – The LOEC values obtained from these fitness studies were lower than the present SSD Next steps • Biomarkers integrated in ERA – BSDs should be fully developed for PW discharges – BSDs should be considered developed for drilling discharges – The concept should be taken into account in next revision of monitoring programmes related to oil and gas discharges • The Validation results with chronic exposures and vulnerable life stags should be – taken into account in the evaluation of application factors for ERA related to oil and gas discharges • Thank you!