1. Sediment Quality Objectives Indirect Effects Project Ben Greenfield Aroon Melwani John Oram Mike Connor San Francisco Estuary Institute (SFEI)

2. Presentation Overview Project conceptual framework –Description of Multiple Lines of Evidence Use of information in assessment context Methodological issues and results –Empirical and mechanistic approaches –Problems of scale, target species –BAF vs. BSAF

5. Pollutant Groups  Non-ionic organics  PCBs  DDTs  Chlordanes  Dieldrin  Methylmercury  Dioxins  PBDEs

6. Conceptual Model Chemical uptake via diet, respiration Effects Thresholds For Wildlife/Fish Effects Thresholds For Humans Exposure Assessment Effects Assessment

7. Multiple Lines of Evidence Approach Chemical uptake via diet, respiration Effects Thresholds For Wildlife/Fish Effects Thresholds For Humans Exposure Assessment Effects Assessment

8. Sources of Variability Exposure: Diet Lipids & Weight Spatial movement Chemical Partitioning Effects: Consumption Rate Size Risk management goals Uncertainty will be addressed by: Using multiple lines of evidence Incorporating several thresholds into each line of evidence Unlikely risk Potential risk to high-risk consumers Potential risk to average consumers High risk to average consumers

9. Indirect Effects Weight of Evidence Fish Concentration Sediment Concentration Laboratory Bioaccumulation Concentration Fish Concentration Sediment Concentration Laboratory Bioaccumulation Concentration Human Lines of Evidence Fish and Wildlife Lines of Evidence

10. Indirect Effects Approach Compared to Rest of SQO Program Similarities: –Integrate multiple lines of evidence –Use ordinal scale ranking based on thresholds –Both exposure and effects are important Changes: –All lines of evidence are measures of exposure –Effects thresholds are determined from literature/expert opinion –If local effects information are available, they would be included on a case-by-case basis –All effects assessments are specific to individual contaminants (mixtures not accounted for) –Addition of laboratory bioaccumulation component

11. Multiple Effects Thresholds: Fish Targets for Human Health F Screening values for human consumption of edible fish tissue –Tissue thresholds developed using USEPA and CalEPA reference doses and cancer slope factors –Separate thresholds will be calculated assuming varying levels of risk Cancer Risk 1x10 -4 - 1x10 -6 Assuming 70 kg adult with 70 yr lifetime Consumption rate assumptions will also be varied –OEHHA consumption rate of 21 g/d. –USEPA consumption rate of 17.5 g/d. –Other consumption rates will be considered E.g., 6.3 g/d rate for all anglers consuming fish in SF Bay E.g., 142.4 g/d EPA rate for subsistance fishers

12. Multiple Effects Thresholds: Fish Targets for Human Health Development of four categories –Category 1 = Unlikely risk Below all thresholds –Category 2 = Potential risk to high-end consumers Above threshold using higher consumption rate assumption and protective allowable risk (10 -6 ) –Category 3 = Potential risk to average consumers Above threshold using sport fisher consumption rate with intermediate allowable risk (10 -5 ) –Category 4 = High risk to average consumers Sport fisher consumption rate with less protective allowable risk (10 -4 ) F

13. Multiple Effects Thresholds: Sediment Targets for Human Health Numeric targets - again 4 categories Based on field sediment concentrations at which fish tissue concentrations would exceed target concentrations –When local data are available, targets developed for specific water body –When local data are not available, general targets will be recommended These will account for uncertainty and will span a range of conditions Calculated based on concentration ratio between sediment and biota –Using statistical and mechanistic models (more later…) S

14. Multiple effects thresholds: Laboratory Bioaccumulation Targets for Human Health Numeric targets - again 4 categories Based on concentrations observed in 28 day laboratory bioaccumulation tests –Tests on sediments to be evaluated –Important link between sediments and indirect effects Confirm whether specific sediments are likely to cause exposure to biota Also important for contaminants that do not bioaccumulate in finfish (e.g., PAHs) Our current thinking: evaluate risk due to consumption of contaminated shellfish L

15. Thresholds for bird and wildlife consumption of fish or shellfish Thresholds will be calculated and presented in tabular form for sensitive and endangered wildlife species –Tables can be used by local agencies based on local species For PCBs and DDT, thresholds will be based on work of Biological Technical Assistance Group (BTAG) –Low and high Toxicity Reference Values used to establish multiple targets Field fish samples and laboratory invertebrate samples are to be evaluated as separate lines of evidence All thresholds will be reviewed by a Bioaccumulation Work Group, formed specifically for the indirect effects task Multiple Effects Thresholds: Fish and Laboratory Bioaccumulation Targets for Wildlife F L

16. Sensitive and Endangered Target Species Least Tern Clapper rail Brown pelican Western snowy plover Bald eagle Southern sea otter Harbor seal Tidewater goby Salmonids

17. Multiple Effects Thresholds: Sediment Targets for Wildlife Numeric targets Based on field sediment concentrations at which fish tissue concentrations would exceed target concentrations Calculated based on Biota Sediment Accumulation Factor –Using statistical and mechanistic models (more later…) Same approach as with sediment targets for humans. I.e.,… S

18. Use in Assessment: Integration of Lines of Evidence Four categories for each line of evidence –Category 1 = Unlikely risk –Category 2 = Potential risk to high-end consumers –Category 3 = Potential risk to average consumers –Category 4 = High risk to average consumers F S L 4321 4321 4321 ABCDE ABCDE

19. A = Sediment meets SQO with high certainty (i.e., is protective) B = Sediment probably meets SQO, but some uncertainty is present C = Sediment possibly fails SQO, but data are inconsistent D = Sediment likely fails SQO E = Sediment highly likely to fail SQO Five Categories For SQO Evaluation

20. Use In Assessment

21. Use In Assessment - e.g., "Clean" Site

26. Use In Assessment - e.g., "Dirty" Site

31. Least Tern High Effects SV (1632 ug/kg)– 100% meet criteria Human Health US EPA SV x 10 ~ 90% meet criteria Least Tern Low Effects ~ 70% meet criteria Human Health US EPA SV ~ 10% meet criteria (Fish Species included: Bay Goby, California Halibut, English Sole, Longfin Sanddab, Pacific Sanddab, Pacific Staghorn Sculpin, Shiner Surfperch, Slender Sole, Speckled Sanddab, Starry Flounder, White Croaker, and White Surfperch) Statewide Assessments Will Be Conducted F

32. Methodological issues Overall approach for development of biota- sediment relationship Scale of analysis –At what scale can data be extrapolated for biota- sediment relationship development? –At what scale should movement range be extrapolated over? Target fish and laboratory bioaccumulation species BAF vs. BSAF

33. Overall Approach to Develop Biota to Sediment Relationship Empirical Models – Concentrations in Organisms, Concentrations in Sediment, Other Factors Mechanistic Models – Quantification of Bioenergetics and Physicochemical Properties and Concentrations. –Data-intensive (e.g., bioenergetics, life history, chemical-specific properties)

34. Empirical modeling approach: Linear Regression Models Using SQO database and other data. 0 2 4 6 810 Sediment Concentration 0 10 30 40 Biota Concentration 20 4 2 High toxicity Threshold Low toxicity Threshold

35. DDTs in San Francisco Bay Macoma clams vs. sediment R 2 = 0.6585 0.1 1 10 100 1101001000 Sediment DDT (ug/kg dry) Results are from 28 day laboratory bioaccumulation tests Tissue DDT (ug/kg dry)

36. Bivalve concentrations compared to co-located sediments. Fish concentrations compared with sediments in a disk centered at each fish sampling location. Disk size ranged from 0.5 - 15 km (0.5 km increments) No a priori assumptions about fish home range

37. R 2 results of distance relationships of sediment and shiner surfperch data in San Francisco Bay Total PCBs Linear regression of Total PCB concentration in sediment vs. Shiner Surfperch tissue in San Francisco Bay (p<0.05) ??

38. Total DDTs R 2 results of distance relationships of sediment and shiner surfperch data in San Francisco Bay Linear regression of Total DDT concentration in sediment vs. Shiner Surfperch tissue in San Francisco Bay (p<0.05)

39. Mechanistic modeling approach Calculate Biota-Sediment Accumulation Factors and SQO using mechanistic models at local scales Demonstrate use of mechanistic model for multiple contaminants in two case studies Evaluate confounding factors –Water contamination –Home range size –Diet Using Gobas model (e.g., TrophicTrace, Arnot and Gobas 2004) Validating with available empirical data

40. Uptake Dietary Gill Loss Excretion Egestion Gill Elimination Metabolism Growth Chemical properties (e.g., K ow ) important Basic Mechanistic Model Elements

41. Data Needs Minimum: diet and biology –Dietary preference –Weight, lipid content Preferrable: –Contaminant concentrations in sediment, water, inverts, fish

42. Newport Bay case study: Developing conceptual food web model Preliminary model kindly provided by M. James Allen, SCCWRP

43. Newport Bay case study: Assembling key parameters

44. Develop BSAFs to Set Up SQOs at Appropriate Scale

45. Macoma nasuta tissue data indicate different results for different water bodies. E.g., total PAHs tissue concentrations lower at given sediment concentration in San Francisco Bay - suggest water body specific BSAFs 22.533.544.555.56 Sediment Concentration (log x+1, ug/kg, dry wt.) Bivalve Tissue Concentration (log x+1, ug/kg, dry wt.) San Diego San Pedro SF Tomales Linear (SF) Linear (San Diego) Linear (Tomales) Linear (San Pedro)

46. Prey For Humans and Wildlife Sediment Linkage Identify Good Target Species Limited Variation in Diet or Home Range

47. Macoma nasuta is a good species for Laboratory Bioaccumulation test - Recommended for bed sediment testing (EPA guidance) -Deposit feeder with high contaminant tolerance -Large California database available Species with existing data in SQO database

48. Starry Flounder Summary of regression analysis of individual fish species vs. summed contaminant concentrations in sediment collected within 2 km of fish samples * = significant linear relationship (p<0.05)

49. Spatial patterns in total PCB concentrations and stable isotope signatures suggest site fidelity for shiner perch in the San Francisco Estuary Total PCBs

50. Map of San Francisco Bay showing locations of sediment, Shiner surfperch and Macoma nasuta collections used for empirical modeling of Biota Sediment Accumulation Factors

51. BSAF vs. BAF 1. BSAF = Lipid-normalized tissue conc./ organic carbon-normalized sediment conc. 2. BAF = Tissue conc. / sediment conc.

52. DDTs in San Francisco Bay Macoma clams vs. sediment R 2 = 0.6585 R 2 = 0.2541 0.1 1 10 100 1101001000 Sediment DDT (ug/kg) Tissue DDT (ug/kg) BAF BSAF Lipid and organic carbon normalization (BSAF) does not improve relationship compared to BAF

53. Results and Recommendations Overall approach for development of biota- sediment relationship –Empirical (statistical) and mechanistic models Target species –E.g., Shiner surfperch, Macoma clams Scale of analysis –Develop biota-sediment relationships that are water-body specific BAF vs. BSAF –Collect data for BSAF (lipid, sediment OC) but consider using BAF only

54. Empirical BSAF and BAF models –Linear Regression (with varying home range size) –Calculation of average and distribution of BSAFs using summary statistics Mechanistic BSAF models –Using established modeling approach (Frank Gobas) Species and spatial issues –Macoma nasuta, shiner surfperch reasonable –Sediment range optimization routine Model Methods Toolkit

55. Example shows prey tissue targets for least terns –Similar tables for other sensitive and endangered species –Only use species that reside in a given water body Low and high Toxicity Reference Values from BTAG Target fish concentrations based on body weight (e.g., 40 g) e.g., Least Tern high effect threshold = TRV high * Weight / Consumption rate = 1.5 mg/(kg*d) * 40 g / 31.1 g/d = 1.928 mg/kg = 1928 ppb Fish and Laboratory Targets for Wildlife Example of Calculations Yellow values = observed in CA fish F L

56. Contact Information Ben Greenfield: ben@sfei.org Mike Connor: mikec@sfei.org www.sfei.org Acknowledgements Steve Bay, Doris Vidal, Jim Allen, Steve Weisberg, SCCWRP Frank Gobas and Jon Arnot, Simon Frasier University Ned Black, Michael Anderson, Laurie Sullivan, Katie Zeeman, Robert Brodberg and other members of Bioaccumulation Work Group Chris Beegan, SWRCB Sarah Lowe, Bruce Thompson, Meg Sedlak, SFEI

58. Bioaccumulation Work Group