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Freshwater Sediment Standards

Freshwater Sediment Standards . Science Panel August 25, 2010 Presenters: Russ McMillan – Department of Ecology Teresa Michelsen – Avocet Consulting. Freshwater Sediment Standards Goal For Today .

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Freshwater Sediment Standards

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  1. Freshwater Sediment Standards Science Panel August 25, 2010 Presenters: Russ McMillan – Department of Ecology Teresa Michelsen – Avocet Consulting

  2. Freshwater Sediment StandardsGoal For Today Are the approaches used to develop chemical and biological criteria scientifically defensible?

  3. Freshwater Sediment StandardsDiscussion Points For Today Introduce regulatory and policy context Present proposed biological and chemical criteria and framework Identify and discuss scientific and technical issues

  4. Freshwater Sediment StandardsPolicy Decisions Consistency with SMS regulatory framework. Adopt biological and chemical criteria. Two tier structure: SQS and CSL. Allowance of some adverse effects. Biological override of chemical criteria.

  5. Proposed Biological Sediment Standards Regulatory Framework • Confirmatory bioassays override chemistry • Two tier structure: SQS and CSL • Bioassay suite – Multiple species/sensitive life-history stages • Minimum of 3 endpoints • Both acute and chronic tests

  6. Proposed Suite of Bioassays • Narrower choice of species than SMS marine criteria due to limited bioassay availability. • Built on EPA and ASTM protocols well established in the Northwest: • Hyalella 10-day mortality – 366 • Hyalella 28-day mortality – 312 • Hyalella 28-day growth – 79 • Chironomus 10-day growth – 525 • Chironomus 10-day mortality – 568

  7. Proposed Suite of Bioassays • Consistent w/SMS designation of sediment quality • SQS: Single SQS level hit • CSL: 2+ SQS level hits; 1+ CSL level hit • Consistent w/SMS sensitive endpoints.

  8. Requirements for Proposed Bioassay Suite Bioassay suite will include sensitive endpoints from chronic and acute tests: • 3 Endpoints • 2 Species • 1 Chronic test • 1 Sublethalendpoint

  9. Bioassays: Acute and Chronic and Endpoint Effects Levels

  10. Proposed Bioassay Suite Question Is the proposed bioassay suite scientifically defensible as being appropriately protective of the benthic community?

  11. Proposed Freshwater Bioassay Interpretation • Differs from SMS marine interpretation due to difficulty in identifying appropriate reference areas. • Due to lack of reference sites, interpretation of an SQS and CSL hit is based on a comparison to control. • Comparison to control is a more conservative interpretation than comparison to a reference sediment.

  12. Bioassay Interpretation: Comparison to Control C=Control, R=Reference, T=Test, F=Final, (SQS &CSL hits statistically sign. diff.)

  13. Question Is it scientifically defensible to base interpretation of a bioassay hit by using a comparison to control rather than a comparison to a reference sediment? Is it scientifically defensible to base the designation of sediment quality on a suite of bioassays comparing test to control without the benefit of a reference sediment?

  14. History of Freshwater SQV Development Early work on FW Apparent Effects Thresholds (AETs) & Floating Percentile Method (FPM; Portland Harbor) throughout the late 1990s 2002 – Formal evaluation of FW AETs and other existing SQV sets (TELs/PELs, etc.) Decision that a new approach was needed: • FW AETs not sufficiently conservative • TELs/PELs, etc., far too conservative • National evaluations were not looking at both types of statistical errors

  15. Statistical Digression False Negative (FN) = Predicting that a sample will be non-toxic when it is actually toxic False Positive (FP) = Predicting that a sample will be toxic when it is actually non-toxic Existing national methods were focused on reducing false negatives at lower screening levels and false positives at upper screening levels, creating substantial errors and inefficiencies in between, where most actual data are located. We focused on reducing both types of errors at the same time, for all levels of effects.

  16. History, cont. 2003 – Developed interim FPM values, used as guidance by Ecology and in the regional dredging manual (SEF) 2007 – Regional Sediment Evaluation Team (RSET) OR/WA workgroup formed to update FPM SQVs 2008 – RSET technical work completed; final SQV selection still under discussion and review 2009/2010 – Ecology begins rule revision, finalizes the values, begins peer review

  17. Past and current review Presentations at 5 national and regional scientific conferences (1999-2009) DEQ-led peer review & public meetings during Portland Harbor (2001 state site) Public and agency review of 2003 Ecology report Presentations at 4 SMARMs (2003-2010) + numerous RSET public meetings SMS Advisory Workgroup peer review of approach and draft SQV report (2010) Science Panel and national peer review (2010)

  18. Projects/Guidance to Date 1999, 2003, 2008 Portland Harbor, OR 2001 Onondaga Lake, NY 2003 Los Angeles Harbor, CA 2004 San Francisco Bay/Oakland Harbor, CA 2003 Draft Ecology Freshwater Guidelines, WA 2006 Interim Sediment Evaluation Framework Guidelines for WA/OR/ID 2010 Updated Freshwater Guidelines, WA

  19. Question 1 General Approach: - Is it appropriate to use sediment bioassays to represent effects to the benthic community? - Is the use of a multivariate model to empirically derive chemical SQVs scientifically defensible?

  20. Use of toxicity tests Marine AETs were derived based on toxicity tests, benthic community studies, and chemistry Benthic community studies were considered equivalent to a chronic bioassay Freshwater benthic community data were searched for but not found in OR, WA, or ID Substantial diversity of freshwater sites compared to marine areas complicates use of benthic community data, if there were any

  21. Floating Percentile Method • Goal: Minimize false negatives and false positives simultaneously • Approach: • Data QA, screening, and summing • Determine “true” toxicity based on bioassay results • Search for the most predictive chemical concentrations, allowing each chemical to move independently to the level at which it appears to be toxic

  22. Features • Uses synoptic chemistry and bioassay field data • Multivariate → considers all chemicals at once • Incorporates measures to address covariance • Requires selection of false negative target • Follows with optimization of false positives • Repeat for a range of false negative targets • Thoroughly evaluates reliability • Now automated using a series of Excel spreadsheets

  23. Covariance All chemicals are addressed simultaneously – avoids inappropriate assignment of toxicity Covariance analysis can be run ahead of time Model results allow visual identification of covariance patterns Appropriate chemical classes can be summed Those that can’t be summed but often covary are subjected to multiple runs with different starting points to find the low concentration for each chemical, then selection of the combination with the highest reliability

  24. Question 2 Data Issues: - Is the data set sufficiently robust and representative? - Has appropriate data screening and QA been conducted?

  25. Data Set – Chemistry • Oregon and Washington • West and east of the Cascade Mountains • Lakes, rivers, small and large • Various geochemical environments • 50 analytes and sums → 105 chemicals • Rigorous QA/QC applied (PSEP QA2) – qualifiers rectified among data sets

  26. Data Set – Bioassay Endpoints • Hyalella 10-day mortality – 366 • Chironomus 10-day mortality – 550 • Chironomus 10-day growth – 504 • Hyalella 28-day mortality – 319 • Hyalella 28-day growth – 79 • Rigorous QA/QC applied – recent ASTM protocols, QA2 review of lab sheets, etc.

  27. Question 3 Reliability Testing: - Is the reliability testing that was conducted an appropriate method for evaluating SQVs? - Is the comparative reliability analysis that was conducted an appropriate way of making decisions while fine-tuning the approach? - Are the reliability measures that were used the right ones and were the relative weights given to them appropriate? - Are there alternative methods of validation that could have been used without collecting additional data?

  28. Reliability • Sensitivity (100% – false negatives) • Efficiency (100% – false positives) • Predicted no-hit reliability • Predicted hit reliability • Overall reliability All measures of reliability were used for ALL effects levels – endpoints given greater weight are shown in yellow

  29. Reliability Measures Diagram

  30. Reliability Goals The RSET Workgroup set the following goals before beginning SQV development:

  31. Freshwater StandardsReliability No Effect Level Values are averages across relevant assays Minor Effect Level

  32. Comparative Reliability Analysis East side vs. west side vs. combined TPH vs. PAH vs. combined Microtox – include? Hyalella growth – include Portland Harbor? Ammonia and sulfides issues N-qualified pesticides Blank-correction standardization Comparison to control vs. reference

  33. SQV Validation RSET made a decision early on to not withhold part of the data set for validation • For such a large heterogeneous area, we needed all the data to develop the best possible model • Most other SQV sets have followed the same approach, with independent validation following after Independent validation will require a large, representative data set, not just a few projects Other validation methods available?

  34. Question 4 Data Interpretation and Regulatory Decision-Making: - Is the overall framework and selection of final SQVs consistent with the SMS and marine SQVs? - Is the approach used to select final SQVs scientifically defensible?

  35. Challenges – Criteria Selection Expectation:SQS values clustered below CSL values SQS CSL

  36. Challenges – Criteria Selection Reality: Differences between bioassays were much greater than differences between endpoints → try species sensitivity distribution approach

  37. “>” values- no toxicity observed for that endpoint up to the listed concentration. Sample concentrations at or above this level should undergo toxicity testing. Approach for selection of CSL: “next significantly different value”

  38. Questions?

  39. Petroleum Toxicity PAHs contribute to the greatest number of errors: • PAHs do not sufficiently capture petroleum toxicity • No form of normalizing or summing solves the problem • Addressed through side-by-side PAH/TPH & combined model runs • Best results when both were included • May be legacy issues with not enough TPH data

  40. Summation of Chemical Classes PAHs/TPH classes PCB Aroclors Dioxins/Furans Chlordanes DDT, DDE, DDD isomers Heptachlors

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