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Development of Chemistry Indicators

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  1. Sediment Quality Objectives For California Enclosed Bays and Estuaries Development of Chemistry Indicators Scientific Steering Committee Meeting July 26, 2005

  2. Data screening & processing Strata Calibration & validation subsets Existing national SQGs Calibration of national SQGs New approaches Categorical classification Correlation Predictive ability Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps

  3. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps

  4. Chemistry Indicators • Several challenges to effective use • Bioavailability • Unmeasured chemicals • Mixtures

  5. Objectives • Identify important geographic, geochemical, or other factors that affect relationship between chemistry and biological effects • Develop indicator(s) that reflect relevant biological effects caused by contaminant exposure • Develop thresholds and guidance for use in MLOE framework

  6. Approach • Use CA sediment quality data in developing and validating indicators • Address concerns and uncertainty regarding influence of regional factors • Document performance for realistic applications • Investigate multiple approaches • Both mechanistic and empirical methods • Existing methods used by other programs • Existing methods calibrated to California • New approaches

  7. Approach • Evaluate SQG performance • Use CA data • Use quantitative and consistent approach • Select methods with best performance for expected applications • Describe response levels (thresholds) • Consistent with needs of MLOE framework • Based on observed relationships with biological effects

  8. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Data screening & processing Strata Calibration & validation subsets

  9. Data Screening • Appropriate habitat and geographic range • Subtidal, embayment, surface sediment samples • Chemistry data screening • Valid data (from qualifier information) • Nondetect values (estimated) • Completeness (metals and PAHs) • Minimum of 10 chemicals: metals and organics • Habitat type (surface, embayment, subtidal) Standardized sums:DDTs, PCBs, PAHs, Chlordanes

  10. Data Screening • Toxicity data screening • Valid data • Selection of candidate acute and chronic toxicity test • Lack of ammonia interference • EPA toxicity test thresholds • Acceptable control performance • Matched data (toxicity and chemistry) • Same station, same sampling event • Test method: amphipod mortality only • Eohaustorius or Rhepoxynius

  11. Data Screening

  12. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Data screening & processing Strata Calibration & validation subsets

  13. Strata Are there differences in contamination among regions of CA that are likely to affect the development of a chemical indicator? • Geographic Strata • North (North of Pt. Conception) • South (South of Pt Conception • Habitat Strata • Ports, Marinas, Shallow • Magnitude of contamination • Relationship between contamination and toxicity

  14. Strata

  15. Strata

  16. Strata Decisions • Treat North and South as separate strata • Different contamination levels and sources • May be different empirical relationships with effects • Adequate data for statistical analyses • Do not distinguish among habitat regions • Limited data for some habitats • Added complexity of application

  17. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Data screening & processing Strata Calibration & validation subsets

  18. Calibration and Validation Datasets • Calibration/development dataset • Screened data minus withheld validation data • Calibration of SQGs • Development of new SQGs • Comparison of performance • Validation dataset • Confirm performance of candidate SQGs

  19. Validation Dataset • Independent subset of SQO database plus new studies • Approximately 30% of data, selected randomly to represent contamination gradient • North and South data are proportional between the calibration/development and validation datasets

  20. Bay/Estuary Samples inDatabase After Screening

  21. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Existing national SQGs Calibration of national SQGs New approaches

  22. National SQGs • Two main types of approaches • Empirical and Mechanistic • Empirical • Intended to aid in prediction of potential for adverse impacts • Derived from analysis of extensive field datasets • Various approaches for development of chemical values • Little explicit consideration of bioavailability • Incorporate a wide range of chemicals • Work best when applied to mixture of contaminants in a sediment

  23. Empirical SQGs

  24. National SQGs • Mechanistic • Intended to assess potential for impacts due to specific chemical groups, not predict overall effects • Derived using equilibrium partitioning and toxicological dose-response information • Incorporate water quality objectives • Explicit consideration of bioavailability • Applicable to a restricted range of chemicals • Work best when applied to specific contaminants

  25. Mechanistic SQGs

  26. National SQGs

  27. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Existing national SQGs Calibration of national SQGs New approaches

  28. Calibration of National SQGs Objective: Improve empirical relationship between chemistry and effects by modifying national SQGs to address potential sources of uncertainty • Variation in bioavailability of organics • Variation in natural background concentration of metals • CA-Specific variations in chemical mixtures Differences in organic carbon content of sediment influences exposure Metal content of sediment matrix varies according to particle type and source material Relative proportions of contaminants within regions of State may differ from national average

  29. Organics Bioavailability Calibration • TOC normalization to represent changes in bioavailability • Conc./TOC • Evaluate whether predictive relationship for chemical classes is improved after normalization • Correlation analysis • Use normalized values as basis for SQG calibration if there is evidence of improved predictive relationship

  30. TOC Normalization Relationship to sediment toxicity is not improved by TOC normalization of organics

  31. Metal Background Calibration • Metals occur naturally in the environment • Silts and clays have higher metal content • Source of uncertainty in identifying anthropogenic impact • Background varies due to sediment type and regional differences in geology • Need to differentiate between natural background levels and anthropogenic input • Investigate utility for empirical guideline development • Potential use for establishing regional background levels

  32. Reference Element Normalization • Established methodology applied by geologists and environmental scientists • Reference element covaries with natural sediment metals and is insensitive to anthropogenic inputs • Regression between reference element and metal developed using a dataset of uncontaminated samples • Regression line indicates natural background metal concentration for different sediment particle size composition • Use of iron as reference element validated for southern California • 1994 and 1998 Bight regional surveys

  33. Iron Normalization Approach • Log transformed data • Selected subset of “reference” stations from SQO database • Least potential for anthropogenic metal enrichment • Nontoxic stations in lowest 30th percentile of DDT, PCB, and PAH concentrations • Reviewed selected stations using GIS to eliminate redundant and likely impacted sites • Calculated regressions • Used residuals from regression as normalized values • Compared relationship of normalized/non -normalized data to toxicity

  34. Zinc Iron (%) Southern California Results Significant regressions obtained for metals of interests in all strata

  35. Zinc Residual = actual-predicted concentration Iron (%) Residual Calculation Residual = relative metal enrichment Used for correlation analysis with amphipod mortality

  36. Iron Normalization Relationship to sediment toxicity is not improved by iron normalization of metals

  37. Normalization Summary • TOC and iron normalization are apparently not effective for improving relationships between chemistry and toxicity • Have not pursued use of normalized data in calibrating/developing SQGs • Iron normalization may be useful for establishing background metal levels

  38. Calibration of SQGs • Adjustment of models or chemical specific values based on California data • Logistic Regression Model (Pmax) • Excluded individual chemical models with poor fit • Antimony, Arsenic, Chromium, Nickel • Adjusted Pmax model to fit CA data (N, S, All) • ERM • Derived CA-specific values using modified method of Ingersoll et al. • Sample-based analysis

  39. CA ERM Calculation • Select paired chemistry and amphipod toxicity data by stratum • Log transform all chemistry data • Classify samples as toxic/nontoxic based on 20% mortality threshold • Calculate median concentration of the nontoxic samples • Select only those toxic samples where concentration of individual chemicals > 2x nontoxic median • CA ERM = median concentration from screened toxic samples • At least 10 toxic samples required for ERM calculation

  40. Substantial differences in some ERM values derived for California datasets compared to nationally derived values

  41. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Existing national SQGs Calibration of national SQGs New approaches

  42. New SQG Characteristics • Categorical classification and multiple thresholds • Based on individual chemical models or values • Thresholds can be adjusted • Accept continuous and categorical data • Some type of weighting based on strength of relationship • Compatible with multiple line of evidence assessment framework • Capability to include/adapt to new contaminants of concern • Adaptable to different application objectives • Able to use toxicity and benthic community impact data in development • Result reflects uncertainty of empirical relationship

  43. Kappa Statistic • Developed in 1960-70’s • Peer-reviewed literature describes derivation and interpretation • Used in medicine, epidemiology, & psychology to evaluate observer agreement/reliability • Similar problem to SQG development and assessment • Accommodates multiple categories of classification • Multiple thresholds can be adjusted by user • Categorical or ordinal data • Result reflects magnitude of disagreement (can be used to weight values) • Sediment quality assessment is a new application

  44. Toxicity Result SQG Result High Moderate Marginal Reference High Moderate Low Reference T3 T2 T1 Kappa Evaluates agreement between 2 methods of classification • Chemical SQG result • Toxicity test result • Magnitude of error affects score

  45. Toxicity Kappa = 0.48  SQG Category High Moderate Marginal Reference High 60 30 20 1 Moderate 33 50 25 0 Low 10 14 65 6 Reference 3 7 20 25 Chemical 1Good Association Between Concentration and Effect(most of errors in cells adjacent to diagonal)

  46. Toxicity Kappa = 0.27  SQG Category High Moderate Marginal Reference High 60 1 20 30 Moderate 33 50 0 25 Low 14 10 65 6 Reference 20 7 3 25 Chemical 2 Poor Association Between Concentration and Effect(more errors in categories distant from diagonal)

  47. Kappa Analysis Output • Kappa (k) • Similar to correlation coefficient • Confidence intervals • Multiple thresholds • Optimized for correspondence to effect levels • Applied to other data to predict effect category (cat) • E.g., Category 1, 2, 3, or 4

  48. New Kappa SQGs • Derived Kappa and thresholds for target chemicals using amphipod mortality data • As, Cd, Cr, Cu, Pb, Hg, Ni, Ag, Zn , t chlordane, t DDT, t PAH, t PCB • Calculated Kappa score for each chemical in sample • k x cat • Mean weighted Kappa score • Average of k x cat • Each constituent contributes to final classification in a manner proportional to reliability of relationship • Mixture joint effects model • Maximum Kappa • Highest Kappa score for any individual chemical • Independent mixture effects model

  49. Presentation Overview • Objectives • Data preparation • SQG calibration and development • Validation • Conclusions • Next steps Categorical classification Correlation Predictive ability

  50. Evaluation Process • Compare performance of candidate SQG approaches in a manner relevant to desired application • Ability to accurately classify presence and magnitude of biological effects based on chemistry • California marine embayment data • Use statistical measures to identify short list of best performing approaches • Categorical classification • Correlation • Validate performance results • Validation dataset • Rank candidate approaches • Examine significance of differences • Predictive ability