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

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Presentation on theme: "Sediment Quality Assessment and New York City Watersheds NYC Watershed/Tifft Science & Technical Symposium September 19, 2013 West Point, New York Stephen."— Presentation transcript:

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

2 1.Importance of Sediment Quality 2.Overview of Sediment Quality Guidelines (SQGs) in New York 3.U.S. EPA National Sediment Inventory Data for Catskill/Delaware watershed AGENDA

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

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

5 Exposure Pathways

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

7 7 Sampling is Hard Work!

8 NY State Approaches 1.Equilibrium Partitioning (EqP) 2.Consensus-based Sediment Quality Guidelines (freshwater sediments) 3.Effects Range Low (ERL)/ Effects Range Medium (ELM) (marine/estuarine sediments) 8

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 (K OC ) 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 9

10 Consensus-based Guidelines Empirical: derived from field-collected data Basis: relates measured concentrations of contaminants in sediments to observed biological effects 10 ERL – Effects Range Low: the 10 th percentile concentration in a range of sediment concentrations for a given contaminant wherein adverse biological effects were observed ERM – Effects Range Median: the 50 th 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 Class A - No Appreciable Contamination (no toxicity to aquatic life) EqP: chronic AWQS/GVs empirically-based: threshold effects concentration (TEC) or Effects Range Low (ERL) Class B - Moderate Contamination (potential for chronic toxicity to aquatic life) contaminant concentrations found between the threshold concentrations which define Class A and Class C Class C - High Contamination (potential for acute toxicity to aquatic life) EqP: acute AWQS/GVs empirically-based: probable effects concentration (PEC) or Effects Range Medium (ERM)

12 Multiple Lines of Evidence Chemical contamination Laboratory toxicity Benthos alteration Possible conclusions +++ Strong evidence for pollution-induced degradation; management actions required. --- Strong evidence against pollution-induced degradation; no management actions required. +-- Contaminants are not bioavailable; no management actions required. -+- Unmeasured contaminant(s) or condition(s) have the potential to cause degradation; no immediate management actions required. --+ Benthos alteration is not due to toxic contamination; no toxic management actions required. ++- Toxic contaminants are bioavailable but in situ effects are not demonstrable – need to determine reason(s) for sediment toxicity. -++ Unmeasured toxic contaminants are causing degradation – need to determine reasons for sediment toxicity and benthos alteration. +-+ Contaminants are not bioavailable; alteration not due to toxic chemicals – need to determine reason(s) for benthos alteration. Sediment quality triad (SQT) decision matrix

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

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

15 National Sediments Inventory Data from 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

16 Bioassay Toxicity Tests – Medium: Bulk sediment – Endpoint: Percent mortality 16 EPA: significant toxicity  20% difference in survival from control Ampelisca abdita (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 50,778 stations 18 NSI Samples

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

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

21 Fish Tissue Species SMB: smallmouth bass BT: brown trout WS: white sucker RB: rock bass

22 health endpoint SV fish tissue conc units % lakes above in EPA study Mean (ppb) Confidence Level(95.0%) Count Mercury noncancer 300ppb Chlordane cancer 67ppb0.3ND 19 DDT cancer 69ppb1.7 pp-DDE pp-DDT pp-DDD ND 19

23 Tissue concentrations (ppb)

24 Mercury Tissue Histogram Hg Screening value = 300 ppb

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

26 Summary 26 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 27

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, The National Study of Chemical Residues in Lake Fish Tissue (EPA-823-R ), U.S. Environmental Protection Agency, September The Incidence and Severity of Sediment Contamination in Surface Waters of the United States, U.S. Environmental Protection Agency, Office of Science and Technology, 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 Slides HSPH Practicum Multivariable Regression Sediment-Toxicity Model Additional GIS Maps C-D Fish Tissue Data Analysis BACK-UP

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

31 31 Mercury Cycle

32 32 Shine (2010) Arsenic Redox Conditions

33 Data Analysis 33 Station Toxic Effect Metal 1Metal 2Metal 3PAHsPCBsDDTTOC A1conc. percentage B 0conc. percentage C 1conc. percentage D 1conc. percentage Binary dependant variable (toxicity) Continuous predictor variables (concentrations) Binary dependant variable (toxicity) Continuous predictor variables (concentrations) Pr(tox=1) = F(β 0 + β 1 chem 1 + β 2 chem 2 + β 3 chem 3 … β n chem n

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

35 Results 35 Model1234 Description All bioassay species, 23 chemicals + 10th root TOC Ampelisca abdita, 23 chemicals + 10th root TOC Ampelisca abdita, sigma PAH + 11 chemicals + 10th root TOC Stepwise backward, Model 3: As, Cd, Cu, Hg, pyrene n1,7891,557 Significant positive variables (α=0.05) Cu, Hg, 10th root TOCCd, Cu, Ni Cd, Cu, Hg Significant negative variables (α=0.05) acenaphthylene, dibenz(a,h)anthracene, napthalene, PCBs As BIC-11,024-9,687-9,756-9,809 HL GOF χ 2 (8), (p-value) (0.052)4.15 (0.843)4.72 (0.787)5.59 (0.6925) Area under ROC curve Toxicity distribution (% stations coded as toxic) BIC: Bayesian Information Criterion (more negative values indicate better model fit) HL GOF: Hosmer-Lemeshow goodness-of-fit test (small p-values indicate a lack of fit) ROC: receiver operating characteristic, plot of sensitivity vs. false positive rate, closer to 1 indicates better accuracy

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 36

37 37 VariableOdds Ratio95% Conf. Interval ∑ PAH # PCBs DDT ## root10_TOC Metals ARSENIC** CADMIUM* CHROMIUM COPPER* LEAD 1.00 MERCURY NICKEL* SILVER ZINC 1.00 * Significant positive effect at α=0.05 ** Significant negative effect at α=0.05 # PAHs acenapththene anthracene benzo(a)anthracene dibenz(a,h)anthracene benzo(a)pyrene chrysene fluoranthene indeno(1,2,3- c,d)pyrene naphthalene phenanthrene pyrene ## DDT Isomers DDD, p, p’ DDE, p, p’ DDT, p, p’ Selected Model DV: Ampelisca abdita toxicity IVs: ∑PAH + 11 chemicals + 10th root TOC

38 Discussion 38 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 Chemical Analysis  Exposure Misclassification – Different methods by study and over time from – 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 39 Impacts confidence and generalizablility  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. abdita toxic 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 40

41 Chemicalclasschemcodefreq DDE, p,p'DDTPP_DDE22,771 DDT, p,p'DDTPP_DDT22,330 DDD, p,p'DDTPP_DDD22,103 DieldrinInsecticideDIELDRIN34,007 LeadMetalLEAD58,389 CopperMetalCOPPER56,496 CadmiumMetalCADMIUM55,665 ZincMetalZINC54,846 MercuryMetalMERCURY53,069 Chromium, totalMetalCHROMIUM52,657 ArsenicMetalARSENIC47,845 NickelMetalNICKEL45,551 SilverMetalSILVER29,850 AntimonyMetalANTIMONY16,535 FluoranthenePAHFLUORANTHN21,319 PyrenePAHPYRENE21,121 ChrysenePAHCHRYSENE20,885 PhenanthrenePAHPHENANTHRN20,586 AnthracenePAHANTHRACENE20,205 AcenaphthenePAHACENAPTHEN20,075 NaphthalenePAHNAPTHALENE20,014 Benzo(a)anthracenePAHBAA19,760 Benzo(g,h,i)perylenePAHBGHIP19,513 Benzo(a)pyrenePAHBAP19,465 FluorenePAHFLUORENE19,213 AcenaphthylenePAHACENAPTYLE19,203 Benzo(k)fluoranthenePAHBKF14,397 Benzo(b)fluoranthenePAHBBF14,383 Methylnaphthalene, 2PAHMETHNAP_212,977 PerylenePAHPERYLENE182 Methylnaphthalene, 1PAHMETHNAP_1141 Methylphenanthrene, 1PAHMETPHENAN1117 Dibenz(a,h)anthracenePAHunk0 Dimethylnaphthalene, 2,6PAHunk0 Indeno(1,2,3-c,d)pyrenePAHunk0 Polychlorinated biphenylsPCBPCB_SUM31,738 BiphenylPCBBIPHENYL151 Predictive Variable Selection 41

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

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

44

45

46 C-D Watershed Layer

47

48 NSI Stations

49 NSI Fields

50 > summary(smptiss9) X siteid studyid stationid sampleid fieldrep labrep sampdate species Min. : 1867 Min. :2400 Min. : 8.00 AS001 :59 A0 : 8 #:236 #:236 Min. : SMB :51 1st Qu.: st Qu.:2400 1st Qu.:36.00 AS002 :57 A1 : 8 1st Qu.: BT :40 Median :26008 Median :2400 Median :36.00 RR001 :50 A2 : 6 Median : WS :29 Mean :26117 Mean :2400 Mean :35.19 NV001 :47 A3 : 6 Mean : RB :27 3rd Qu.: rd Qu.:2400 3rd Qu.:36.00 TR001 :12 A4 : 6 3rd Qu.: YP :16 Max. :28064 Max. :2400 Max. :36.00 HE001 : 7 A5 : 6 Max. : RT :13 (Other): 4 (Other):196 (Other):60 tissue noincomp length weight sex pctlipid exsampid HV: 22 Min. : Min. :-0.90 Min. : 5.0 F: 1 Min. :0.520 Mode:logical SF:207 1st Qu.: st Qu.: st Qu.: M: 2 1st Qu.:1.750 NA's:236 WH: 7 Median : Median :33.55 Median : U:233 Median :2.850 Mean : Mean :33.56 Mean : Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.:3.480 Max. : Max. :73.60 Max. : Max. :6.860 NA's : SPECIES -- Value # of Cases % Cumulative % 1 BB BDACE BLC BRT BT CARP CCHUB CMSH LLS LMB LT PICK RB RT SMB WEYE WS YB YP Case Summary Valid Missing Total # of cases $tissue Frequencies ---- Value # of Cases % Cumulative % 1 HV SF WH Case Summary ---- Valid Missing Total # of cases

51 > frequencies(smptiss9[c("stationid")], r.digits = 1) $stationid Frequencies ---- Value # of Cases % Cumulative % 1 AS AS HE NV PP RR TR UE

52 Recommended Target Species for Lakes and Reservoirs

53 health endpoint SV fish tissue concunits % lakes above in EPA studyMean Standard Error Standard Deviation Mini mum Maxi mum Coun t Confidence Level(95.0%) Mercurynoncancer300ppb PCBscancer12ppb Chlordan ecancer67ppb0.3ND DDTcancer69ppb1.7 pp-DDE pp-DDT pp-DDD ND 19.00

54


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