Sediment Quality Assessment and New York City Watersheds

1. Sediment Quality Assessment and New York City Watersheds Stephen Lewandowski Major, United States Army NYC Watershed/Tifft Science & Technical Symposium September 19, 2013 West Point, New York

2. AGENDA • Importance of Sediment Quality • Overview of Sediment Quality Guidelines (SQGs) in New York • U.S. EPA National Sediment Inventory Data for Catskill/Delaware watershed

3. Sediment Quality Introduction • Serve as “sink” for many chemicals • Ecology and human health effects

4. Sediment Processes Sediment processes affecting the distribution and form of contaminants (CA EPA)

5. Exposure Pathways http://www.itrcweb.org/contseds-bioavailability/images/cs_fig_2_1x.jpg

6. Sources and Receptors Sources, fates, and effects of sediment contaminants (CA EPA)

7. Sampling is Hard Work!

8. NY State Approaches • Equilibrium Partitioning (EqP) • Consensus-based Sediment Quality Guidelines (freshwater sediments) • Effects Range Low (ERL)/ Effects Range Medium (ELM) (marine/estuarine sediments)

9. Equilibrium Partitioning • Mechanistic: uses fundamental knowledge of the interactions between process variables to define the model structure • Basis: non-polar organic contaminants will partition between sediment pore water and the organic carbon content of sediment in a constant ratio ratio of the concentration in water to the concentration in organic carbon is termed the organic carbon partition coefficient (KOC) • Limitations: • does not consider the antagonistic, additive or synergistic effects of other sediment contaminants • does not account for bioaccumulation and trophic transfer to aquatic life, wildlife or humans

10. Consensus-based Guidelines • Empirical: derived from field-collected data • Basis: relates measured concentrations of contaminants in sediments to observed biological effects ERL – Effects Range Low: the 10th percentile concentration in a range of sediment concentrations for a given contaminant wherein adverse biological effects were observed ERM – Effects Range Median: the 50th percentile concentration in a range of sediment concentrations for a given contaminant wherein adverse biological effects were observed • TEC–Threshold Effects Concentration : derived by taking the geometric mean of similar sediment quality guidelines for concentrations of contaminants that below which, no adverse impacts would be anticipated • PEC–Probable Effects Concentration: derived by taking the geometric mean of similar sediment quality guidelines for concentrations of contaminants that above which, adverse impacts would be expected to occur frequently

11. Sediment Classification

12. Multiple Lines of Evidence Sediment quality triad (SQT) decision matrix

13. Multiple Lines of Evidence (California) Steven M. Bay and Stephen B. Weisberg, A framework for interpreting sediment quality triad data

14. MLOE (CA Approach) Steven M. Bay and Stephen B. Weisberg, A framework for interpreting sediment quality triad data

15. National Sediments Inventory • Data from 1980-1999 • More than 50,000 stations • ~4.6 million observations • River, lake, ocean, estuary sediments • Mandated by Water Resources Development Act of 1992 • EPA reports to Congress: 1998 and 2004

16. Bioassay Toxicity Tests • Medium: Bulk sediment • Endpoint: Percent mortality EPA: significant toxicity  20% difference in survival from control Ampeliscaabdita (marine amphipod)

17. Human Health Screening Values (SV)a for Interpreting National Lake Fish Tissue Study Predator Results The National Study of Chemical Residues in Lake Fish Tissue (EPA, 2009)

18. NSI Samples 50,778 stations

19. NSI Stations in NY n = 2,239 (NY) 6 C-D subbasins (HU-8), n = 278

20. Schoharie Reservoir 2 Pepacton Reservoir 3 Cannonsville Reservoir Ashokan Reservoir Roundout Reservoir Neversink Reservoir Catskill Aqueduct Delaware Aqueduct Stations in C-D Watershed Boundary, n = 9

21. Fish Tissue Species SMB: smallmouth bass BT: brown trout WS: white sucker RB: rock bass http://www.tpwd.state.tx.us/huntwild/wild/species/smb/

23. Tissue concentrations (ppb)

24. Mercury Tissue Histogram Hg Screening value = 300 ppb

25. Mercury Tissue by Station vic. Esopus Creek (Catskill) Roundout (Delaware) Pepacton (Delaware) Neversink (Delaware) Ashokan (Delaware) Ashokan (Delaware) Screening value = 300 ppb

26. Summary • Sediments are an important component of watershed ecosystems • New York State applies screening guidelines derived from both mechanistic and empirical models to classify contamination and potential for toxicity • National sediments database is useful for a historical perspective on contaminants and development of guidelines

27. Acknowledgements United States Military Academy, Dept. of Geography & Environmental Engineering • Environmental Program (Dr. Marie Johnson) • Geospatial Lab (COL Michael Hendricks) Harvard School of Public Health • Professor Jim Shine • Professors Francine Laden and Bob Herrick

28. References Screening and Assessment of Contaminated Sediment. New York State Department of Environmental Conservation, Division of Fish, Wildlife and Marine Resources, Bureau of Habitat, January 24, 2013 (Draft Version 4.0) Technical Guidance for Screening Contaminated Sediments. New York State Department of Environmental Conservation, Division of Fish, Wildlife and Marine Resources, January 25, 1999. • The National Study of Chemical Residues in Lake Fish Tissue (EPA-823-R-09-006), U.S. Environmental Protection Agency, September 2009. • The Incidence and Severity of Sediment Contamination in Surface Waters of the United States, U.S. Environmental Protection Agency, Office of Science and Technology, 1997. • The Incidence and Severity of Sediment Contamination in Surface Waters of the United States, National Sediment Quality Survey: Second Edition, US EPA, 2004.

29. BACK-UP Back-Up Slides • HSPH Practicum Multivariable Regression Sediment-Toxicity Model • Additional GIS Maps • C-D Fish Tissue Data Analysis

30. Biotic Ligand Model Site on fish gill (or other receptor) is a ligand too Shine (2010) Gill is primary site of toxic action for most metals, especially for freshwater organisms and acute toxicity

31. Mercury Cycle

32. Arsenic Redox Conditions Shine (2010)

33. Data Analysis Binary dependant variable (toxicity) Continuous predictor variables (concentrations) Pr(tox=1) = F(β0 + β1chem1 + β2chem2 + β3chem3… βnchemn

34. Surface Chemistry Dataset BioassayDataset Paired Dataset Data Management B1. Include all species or sort C1. Select analytes to retain P1. Drop unmatched observations P2. Reshape data from long to wide B2. Compress from sample-level to station level C2. Drop duplicate entries P3. Remove observations with missing chemical concentrations B3. Threshold for station tox based on mean sample tox C3. Compress to station-level with mean sample concentrations P4. Apply MLRM C4. Merge with bioassay dataset by station B4. Merge with surface chemistry dataset by station

35. Results

36. Model Evaluation • Bayesian Information Criterion (BIC) • more negative values indicate better model fit • Hosmer-Lemeshow goodness-of-fit test • small p-values indicate a lack of fit • Receiver operating characteristic (ROC) • plot of sensitivity vs. false positive rate • closer to 1 indicates better accuracy

37. Selected Model DV: Ampeliscaabdita toxicity IVs: ∑PAH + 11 chemicals + 10th root TOC * Significant positive effect at α=0.05 ** Significant negative effect at α=0.05

38. Discussion • Decent overall model fit and predictive value • High specificity, but low sensitivity • Scientific plausibility • Cadmium, copper, nickel as positive indicators • Arsenic as a negative indicator • Species could be adaptive to As in seawater  convert to arsenobetaine • Suggestive of oxidized conditions: As(V) vs As (III) • Competition for binding sites on sediment particles and biotic ligand receptors • Hg, Pb not significant  may be tightly bound with low bioavailability • Large standard error for DDT

39. Limitations Impacts confidence and generalizablility • Chemical Analysis  Exposure Misclassification • Different methods by study and over time from 1980-1999 • Handling of detection limits/ low concentrations • Bioassay • Species appropriate • Consistent methods and endpoint determination (EPA toxicity classification) • Data set • Spatial resolution: sample vs. station identification • Data input errors • Model: limited in number of parameters; trade-offs in selection of species and chemical predictors; care not to over-fit model  Maintain large n with a complete representation of chemicals

40. Conclusions • Able to develop a reasonable MLRM with decent goodness of fit and predictive value • for A. abditatoxic effects from surface sediment chemical concentrations • Big limitations and uncertainty from the data set structure, chemical analysis, bioassays and the statistical model reduce overall confidence • Methodology adds value for investigating data and physical and chemical relationships • Multiple lines of evidence with knowledge of local area should be examined to assess sediment quality

41. 41 Predictive Variable Selection

42. Chemistry: Inorganic • Metals • As • Cd • Cr • Cu • Pb • Hg • Ni • Zn

43. Chemistry: Organic • Organochlorines • Polychlorinated biphenyls (PCBs, total) • DDT (∑) • DDD, p, p’ • DDE, p, p’ • DDT, p, p’ • Polycyclic aromatic hydrocarbons (∑) • Acenapththene • Anthracene • Benzo(a)anthracene • Benzo(a)pyrene • Fluoranthene • Naphthalene • Phenanthrene • Pyrene

46. C-D Watershed Layer

48. NSI Stations

49. NSI Fields

50. > summary(smptiss9) X siteidstudyidstationidsampleidfieldreplabrepsampdate species Min. : 1867 Min. :2400 Min. : 8.00 AS001 :59 A0 : 8 #:236 #:236 Min. :19700000 SMB :51 1st Qu.:25949 1st Qu.:2400 1st Qu.:36.00 AS002 :57 A1 : 8 1st Qu.:19980414 BT :40 Median :26008 Median :2400 Median :36.00 RR001 :50 A2 : 6 Median :19980611 WS :29 Mean :26117 Mean :2400 Mean :35.19 NV001 :47 A3 : 6 Mean :19972687 RB :27 3rd Qu.:28005 3rd Qu.:2400 3rd Qu.:36.00 TR001 :12 A4 : 6 3rd Qu.:19981026 YP :16 Max. :28064 Max. :2400 Max. :36.00 HE001 : 7 A5 : 6 Max. :19981028 RT :13 (Other): 4 (Other):196 (Other):60 tissue noincomp length weight sex pctlipidexsampid HV: 22 Min. : 1.000 Min. :-0.90 Min. : 5.0 F: 1 Min. :0.520 Mode:logical SF:207 1st Qu.: 1.000 1st Qu.:23.15 1st Qu.: 173.8 M: 2 1st Qu.:1.750 NA's:236 WH: 7 Median : 1.000 Median :33.55 Median : 490.0 U:233 Median :2.850 Mean : 1.174 Mean :33.56 Mean : 661.7 Mean :2.968 3rd Qu.: 1.000 3rd Qu.:43.25 3rd Qu.: 920.0 3rd Qu.:3.480 Max. :15.000 Max. :73.60 Max. :5980.0 Max. :6.860 NA's :215 -- SPECIES -- Value # of Cases % Cumulative % 1 BB 6 2.5 2.5 2 BDACE 1 0.4 3.0 3 BLC 5 2.1 5.1 4 BRT 9 3.8 8.9 5 BT 40 16.9 25.8 6 CARP 1 0.4 26.3 7 CCHUB 2 0.8 27.1 8 CMSH 1 0.4 27.5 9 LLS 4 1.7 29.2 10 LMB 1 0.4 29.7 11 LT 10 4.2 33.9 12 PICK 3 1.3 35.2 13 RB 27 11.4 46.6 14 RT 13 5.5 52.1 15 SMB 51 21.6 73.7 16 WEYE 4 1.7 75.4 17 WS 29 12.3 87.7 18 YB 13 5.5 93.2 19 YP 16 6.8 100.0 -- -- -- Case Summary -- -- -- Valid Missing Total # of cases 236 0 236 $tissue ------------------------------------------------------------ -- Frequencies -- -- -- Value # of Cases % Cumulative % 1 HV 22 9.3 9.3 2 SF 207 87.7 97.0 3 WH 7 3.0 100.0 -- -- -- Case Summary -- -- -- Valid Missing Total # of cases 236 0 236