Sediment Quality Assessment and New York City Watersheds
NYC Watershed/TifftScience & Technical Symposium
September 19, 2013 West Point, New York
Stephen LewandowskiMajor, United States Army
1. Importance of Sediment Quality2. Overview of Sediment Quality Guidelines
(SQGs) in New York3. U.S. EPA National Sediment Inventory Data for
Catskill/Delaware watershed
AGENDA
Sediment Quality Introduction
• Serve as “sink” for many chemicals• Ecology and human health effects
3
4Sediment processes affecting the distribution and form of contaminants (CA EPA)
Sediment Processes
Exposure Pathways
http://www.itrcweb.org/contseds-bioavailability/images/cs_fig_2_1x.jpg
6Sources, fates, and effects of sediment contaminants (CA EPA)
Sources and Receptors
7
Sampling is Hard Work!
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)
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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 humans9
Consensus-based Guidelines
• Empirical: derived from field-collected data• Basis: relates measured concentrations of contaminants
in sediments to observed biological effects
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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
Sediment Classification
Class A - No Appreciable Contamination (no toxicity to aquatic life)
EqP: chronic AWQS/GVsempirically-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)
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
Steven M. Bay and Stephen B. Weisberg, A framework for interpreting sediment quality triad data
Multiple Lines of Evidence (California)
MLOE (CA Approach)
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Steven M. Bay and Stephen B. Weisberg, A framework for interpreting sediment quality triad data
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
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Bioassay Toxicity Tests
– Medium: Bulk sediment– Endpoint: Percent mortality
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EPA: significant toxicity 20% difference in survival from control
Ampelisca abdita (marine amphipod)
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)
50,778 stations18
NSI Samples
n = 2,239 (NY)6 C-D subbasins (HU-8), n = 278
NSI Stations in NY
Stations in C-D Watershed Boundary, n = 9
2
3Cannonsville Reservoir
Pepacton Reservoir
Schoharie Reservoir
Ashokan ReservoirRoundout Reservoir
Neversink Reservoir
Delaware Aqueduct
Catskill Aqueduct
Fish Tissue Species
SMB: smallmouth bassBT: brown troutWS: white suckerRB: rock basshttp://www.tpwd.state.tx.us/huntwild/wild/species/smb/
health endpoint
SV fish tissue conc
units% lakes above in EPA study
Mean(ppb)
Confidence Level(95.0%) Count
Mercury noncancer 300 ppb 48.8 484.29 43.55 217
Chlordane cancer 67 ppb 0.3 ND 19
DDT cancer 69 ppb 1.7
pp-DDE 5.37 1.07 19
pp-DDT 12.79 5.69 19
pp-DDD ND 19
Tissue concentrations (ppb)
Mercury Tissue Histogram
Hg Screening value = 300 ppb
Mercury Tissue by Station
Screening value = 300 ppb
vic. Esopus Creek (Catskill)
Pepacton (Delaware)
Ashokan (Delaware)
Ashokan (Delaware)
Neversink (Delaware)
Roundout (Delaware)
Summary
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• 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
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
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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.
Back-Up Slides
• HSPH Practicum Multivariable Regression Sediment-Toxicity Model
• Additional GIS Maps• C-D Fish Tissue Data Analysis
BACK-UP
Biotic Ligand Model
Site on fish gill (or other receptor) is a ligand too
Gill is primary site of toxic action for most metals, especiallyfor freshwater organisms and acute toxicity
Shine (2010)
31
Mercury Cycle
32Shine (2010)
Arsenic Redox Conditions
Data Analysis
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StationToxic Effect
Metal 1 Metal 2 Metal 3 PAHs PCBs DDT TOC
A 1 conc. conc. conc. conc. conc. conc. percentage
B0 conc. conc. conc. conc. conc. conc. percentage
C1 conc. conc. conc. conc. conc. conc. percentage
D1 conc. conc. conc. conc. conc. conc. percentage
Binary dependant variable (toxicity)Continuous predictor variables (concentrations)
Pr(tox=1) = F(β0 + β1chem1 + β2chem2 + β3chem3… βnchemn
Data Management
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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
Results
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Model 1 2 3 4
DescriptionAll 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
n 1,789 1,557 1,557 1,557Significant positive variables (α=0.05) Cu, Hg, 10th root TOC Cd, Cu, Ni Cd, Cu, Ni Cd, Cu, Hg
Significant negative variables (α=0.05)
acenaphthylene, dibenz(a,h)anthracene, napthalene, PCBs
As As As
BIC -11,024 -9,687 -9,756 -9,809HL GOF χ2 (8), (p-value) 15.41 (0.052) 4.15 (0.843) 4.72 (0.787) 5.59 (0.6925)
Area under ROC curve 0.72 0.695 0.696 0.678
Toxicity distribution (% stations coded as toxic)
51.7 26.1 26.1 26.1
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
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
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Variable Odds Ratio 95% Conf. Interval
∑ PAH# 1.01 1.00 1.03
PCBs 0.96 0.51 1.82
DDT## 2.47 0.05 131.74
root10_TOC 0.25 0.02 2.71
Metals
ARSENIC** 0.93 0.91 0.96
CADMIUM* 1.44 1.11 1.87
CHROMIUM 1.00 0.99 1.00
COPPER* 1.01 1.01 1.02
LEAD 1.00 1.00 1.00
MERCURY 1.30 0.94 1.78
NICKEL* 1.02 1.00 1.03
SILVER 0.98 0.81 1.19
ZINC 1.00 1.00 1.00* Significant positive effect at α=0.05** Significant negative effect at α=0.05
# PAHsacenapththeneanthracenebenzo(a)anthracenedibenz(a,h)anthracenebenzo(a)pyrenechrysenefluorantheneindeno(1,2,3-c,d)pyrenenaphthalenephenanthrenepyrene
## DDT IsomersDDD, p, p’DDE, p, p’DDT, p, p’
Selected ModelDV: Ampelisca abdita toxicity IVs: ∑PAH + 11 chemicals + 10th root TOC
Discussion
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• 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
Limitations
• 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
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Impacts confidence and generalizablility
Maintain large n with a complete representation of chemicals
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
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Chemical class chemcode freqDDE, p,p' DDT PP_DDE 22,771DDT, p,p' DDT PP_DDT 22,330DDD, p,p' DDT PP_DDD 22,103Dieldrin Insecticide DIELDRIN 34,007Lead Metal LEAD 58,389Copper Metal COPPER 56,496Cadmium Metal CADMIUM 55,665Zinc Metal ZINC 54,846Mercury Metal MERCURY 53,069Chromium, total Metal CHROMIUM 52,657Arsenic Metal ARSENIC 47,845Nickel Metal NICKEL 45,551Silver Metal SILVER 29,850Antimony Metal ANTIMONY 16,535
Fluoranthene PAH FLUORANTHN 21,319Pyrene PAH PYRENE 21,121Chrysene PAH CHRYSENE 20,885Phenanthrene PAH PHENANTHRN 20,586Anthracene PAH ANTHRACENE 20,205Acenaphthene PAH ACENAPTHEN 20,075Naphthalene PAH NAPTHALENE 20,014Benzo(a)anthracene PAH BAA 19,760Benzo(g,h,i)perylene PAH BGHIP 19,513Benzo(a)pyrene PAH BAP 19,465Fluorene PAH FLUORENE 19,213Acenaphthylene PAH ACENAPTYLE 19,203Benzo(k)fluoranthene PAH BKF 14,397Benzo(b)fluoranthene PAH BBF 14,383Methylnaphthalene, 2 PAH METHNAP_2 12,977Perylene PAH PERYLENE 182Methylnaphthalene, 1 PAH METHNAP_1 141Methylphenanthrene, 1 PAH METPHENAN1 117Dibenz(a,h)anthracene PAH unk 0Dimethylnaphthalene, 2,6 PAH unk 0Indeno(1,2,3-c,d)pyrene PAH unk 0Polychlorinated biphenyls PCB PCB_SUM 31,738Biphenyl PCB BIPHENYL 151
Predictive Variable Selection
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Chemistry: Inorganic
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• Metals– As– Cd– Cr– Cu– Pb– Hg– Ni– Zn
• Polycyclic aromatic hydrocarbons (∑)– Acenapththene– Anthracene– Benzo(a)anthracene– Benzo(a)pyrene– Fluoranthene– Naphthalene– Phenanthrene– Pyrene
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Chemistry: Organic
•Organochlorines▫Polychlorinated
biphenyls (PCBs, total) ▫DDT (∑)
▫DDD, p, p’ ▫DDE, p, p’ ▫DDT, p, p’
C-D Watershed Layer
NSI Stations
NSI Fields
> 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. :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 pctlipid exsampid 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 2.52 BDACE 1 0.4 3.03 BLC 5 2.1 5.14 BRT 9 3.8 8.95 BT 40 16.9 25.86 CARP 1 0.4 26.37 CCHUB 2 0.8 27.18 CMSH 1 0.4 27.59 LLS 4 1.7 29.210 LMB 1 0.4 29.711 LT 10 4.2 33.912 PICK 3 1.3 35.213 RB 27 11.4 46.614 RT 13 5.5 52.115 SMB 51 21.6 73.716 WEYE 4 1.7 75.417 WS 29 12.3 87.718 YB 13 5.5 93.219 YP 16 6.8 100.0-- ---- Case Summary ---- -- Valid Missing Total# of cases 236 0 236
$tissue-------------------------------------------------------------- Frequencies ---- -- Value # of Cases % Cumulative %1 HV 22 9.3 9.32 SF 207 87.7 97.03 WH 7 3.0 100.0-- ---- Case Summary ---- -- Valid Missing Total# of cases 236 0 236
> frequencies(smptiss9[c("stationid")] , r.digits = 1)$stationid-------------------------------------------------------------- Frequencies ---- -- Value # of Cases % Cumulative %1 AS001 59 25.0 25.02 AS002 57 24.2 49.23 HE001 7 3.0 52.14 NV001 47 19.9 72.05 PP001 2 0.8 72.96 RR001 50 21.2 94.17 TR001 12 5.1 99.28 UE001 2 0.8 100.0
Recommended Target Species for Lakes and Reservoirs
health
endpoint
SV fish tissue conc units
% lakes above in EPA study Mean
Standard Error
Standard Deviation
Minimum
Maximum
Count
Confidence Level(95.0%)
Mercury noncancer 300 ppb 48.8 484.29 22.09 325.47 50.001880.
00217.0
0 43.55
PCBs cancer 12 ppb 16.8 2110.42 538.98 2349.34204.0
09353.
00 19.00 1132.35
Chlordane cancer 67 ppb 0.3 ND 19.00
DDT cancer 69 ppb 1.7
pp-DDE 5.37 0.51 2.22 2.00 12.00 19.00 1.07
pp-DDT 12.79 2.71 11.80 2.00 43.00 19.00 5.69
pp-DDD ND 19.00
