development of chemistry indicators scientific steering committee meeting july 26, 2005 sediment...
Post on 18-Dec-2015
213 Views
Preview:
TRANSCRIPT
Development of Development of Chemistry IndicatorsChemistry Indicators
Scientific Steering Committee Meeting
July 26, 2005
Sediment Quality Objectives
For California Enclosed Bays and Estuaries
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Data screening & processing
Strata
Calibration & validation subsets
Existing national SQGs
Calibration of national SQGs
New approaches
Categorical classification
Correlation
Predictive ability
Presentation OverviewPresentation Overview Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Chemistry IndicatorsChemistry Indicators
Several challenges to effective use
– Bioavailability
– Unmeasured chemicals
– Mixtures
ObjectivesObjectives
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
ApproachApproach
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
ApproachApproach
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
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Data screening & processing
Strata
Calibration & validation subsets
Data ScreeningData 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
Data ScreeningData 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
Amphipod Mortality in Sediment (Paired Samples) from California
Amphipod (EE or RA) Mortality (%)
0 20 40 60 80 100
Am
pe
lisca
mo
rta
lity
(%)
0
20
40
60
80
100
Eohaustorius estuarius Rhepoxynius abronius
Data ScreeningData Screening
Analyses Dataset Screening Steps
No. Samples Retained
Chemistry Total Number of Samples 6955
Valid data 6934
Nondetect values 6934
Completeness 5962
Habitat type 2768
Toxicity Total Number of Samples 3349
Valid data 3308
Selection of desire chronic and acute 3242
Lack of ammonia interference 3221
Acceptable control performance 3200
Matched data 1988
Amphipod survival data 1741
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Data screening & processing
Strata
Calibration & validation subsets
StrataStrataAre 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
StrataStrata
Northern CA Southern CA
Chemical Name Units N 50th
Percentile 90th
Percentile N 50th
Percentile 90th
Percentile
Cadmium mg/kg 847 0.21 0.47 1099 0.40 1.34 Copper mg/kg 824 42.10 67.98 1115 90.10 299.00 Mercury mg/kg 886 0.26 0.45 1112 0.28 1.02 Lead mg/kg 833 21.96 39.70 1115 46.30 122.60 Nickel mg/kg 817 86.72 116.03 1099 20.70 36.00 Tributyltin mg/kg 340 1.44 30.00 690 59.35 448.20 Zinc mg/kg 831 114.00 166.00 1115 198.00 388.60 Chlordane ug/kg 866 0.87 7.42 1169 8.84 68.63 Chlorpyrifos ug/kg 2 44.12 66.50 11 0.50 1500.00 DDT ug/kg 866 4.61 24.00 1169 25.84 115.97 Dieldrin ug/kg 879 0.24 2.03 926 0.82 2.80 Mirex ug/kg 667 0.12 1.12 631 0.25 1.00 ppDDE ug/kg 875 1.50 7.44 1162 12.72 68.97 PAHs ug/kg 845 1077.60 2736.68 1097 898.48 8575.66 PCBs ug/kg 759 8.19 35.94 957 31.40 219.33 Eohaustorius estuarius % 479 82.50 96.80 465 87.60 99.00 Rhepoxynius abronius % 105 86.30 100.00 505 80.90 95.80
StrataStrata
Northern and Southern California Eohaustorius estuarius Mortality in Sediment vs. mSQGQ1q
mSQGQ1q
0.01 0.1 1 10 100
Mor
talit
y (%
)
0
20
40
60
80
100
Southern EE mSQGQ1q Northern EE mSQGQ1q
Strata DecisionsStrata 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
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Data screening & processing
Strata
Calibration & validation subsets
Calibration and Validation DatasetsCalibration 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
Validation DatasetValidation 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
Bay/Estuary Samples inBay/Estuary Samples inDatabase After ScreeningDatabase After Screening
Number of Samples
(matched chemistry & toxicity)
Stratum
Calibration/Development
Validation
North 504 298
South 800 328
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Existing national SQGs
Calibration of national SQGs
New approaches
National SQGsNational 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
Empirical SQGsEmpirical SQGs
SQG Metric SourceERM
Effects Range Median
Analysis of diverse studies and effects values
Mean Quotient for Chemical Mixture
Long et al.
Consensus MEC
Mid-range effect concentration
Geometric mean of similar guidelines
Mean Quotient for Chemical Mixture
MacDonald et al, Swartz, SCCWRP
SQGQ-1
Mid-range effect concentration
Subset of chemical guidelines from various sources
Mean Quotient for Chemical Mixture
Fairey et al.
Logistic Regression
Regression model for each chemical
Probability of Toxicity (Pmax) for Chemical Mixture
Field et al.
National SQGsNational 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
Mechanistic SQGsMechanistic SQGs
SQG Metric Source
EqP Organics
Acute and chronic effects
Organic Carbon Normalized
Sum of Toxic Units (TU)
EPA + CA Toxics Rule
EqP Metals
Acid Volatile and Organic Carbon Normalized
Difference Between metal concentration and strong binding capability
EPA
National SQGsNational SQGs
SQGs
Chemicals ERM SQGQ1 Consensus LRM EqP
Organics EqP
Metals Cadmium + + + + + Copper + + + + + Lead + + + + + Mercury + + + Nickel + + + + Tributyltin Zinc + + + + + Chlordane + + Chlorpyrifos DDTs + + + + Dieldrin + + + + + Mirex p’p’ DDE + + PAHs + + + + + PCBs + + + +
Other Metals 3 1 3 4 0 1 Other Organics 0 0 0 0 33 0
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Existing national SQGs
Calibration of national SQGs
New approaches
Calibration of National SQGsCalibration 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
Organics Bioavailability CalibrationOrganics 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
TOC NormalizationTOC Normalization
Relationship to sediment toxicity is not improved by TOC normalization of organics
South
Spe
arm
an C
orre
latio
n (r
)-0.1
0.0
0.1
0.2
0.3
0.4
0.5NonNormCorrelation NormCorrelation
North
ChlordaneDDTs
LPAHHPAH
TPAHPCBs
Spe
arm
an C
orr
ela
tion
(r)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5NonNormCorrelationNormCorrelation
Metal Background CalibrationMetal 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
Reference Element NormalizationReference 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
Iron Normalization ApproachIron 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
Southern California ResultsSouthern California Results
Significant regressions obtained for metals of interests in all strata
SoCal
bl ack=r obust , r ed=98, bl ue=94, gr een=KWNor m
Zi nc
0
100
200
300
i r on2
0 1 2 3 4 5 6 7
Zinc
Iron (%)
Residual CalculationResidual CalculationSoCal
bl ack=r obust , r ed=98, bl ue=94, gr een=KWNor m
Zi nc
0
100
200
300
i r on2
0 1 2 3 4 5 6 7
Zinc
Iron (%)
Residual = actual-predicted concentration
Residual = relative metal enrichment
Used for correlation analysis with amphipod mortality
Iron NormalizationIron Normalization
Relationship to sediment toxicity is not improved by iron normalization of metals
South
ArsenicCadmium
ChromiumCopper
LeadMercury
NickelSilver Zinc
Sp
ea
rma
n C
orr
ela
tion
(r)
0.0
0.1
0.2
0.3
0.4
0.5 NonNormCorrelationNormCorrelation
North
ArsenicCadmium
ChromiumCopper
LeadMercury
NickelSilver Zinc
Spe
arm
an C
orr
ela
tion
(r)
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5NonNormCorrelationNormCorrelation
Normalization SummaryNormalization 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
Calibration of SQGsCalibration 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
CA ERM CalculationCA 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
Substantial differences in some ERM values derived
for California datasets compared to
nationally derived values
Chemical Name NOAA ERM
Southern CA ERM
Northern CA ERM
2-Methylnaphthalene 670.0 23.6 20.2 4,4'-DDE NA 38.3 3.8 Acenaphthene 500.0 24.5 19.0 Acenaphthylene 640.0 47.0 19.8 Anthracene 1100.0 215.5 60.8 Arsenic 70.0 19.1 NA Benz(a)anthracene 1600.0 540.0 169.5 Benzo(a)pyrene 1600.0 630.0 225.3 Cadmium 9.6 1.2 0.6 Chlordane_Z 6.0 23.1 4.0 Chromium 370.0 110.0 291.0 Chrysene 2800.0 739.9 239.0 Copper 270.0 208.0 91.2 DDT_Z 46.1 60.0 13.1
Dibenz(a,h)anthracene 260.0 130.0 23.4 Dieldrin 8.0 2.0 0.8 Fluoranthene 5100.0 723.0 410.9 Fluorene 540.0 46.2 NA Lead 218.0 94.5 56.4 Mercury 0.7 0.8 0.7 Naphthalene 2100.0 33.4 42.5 Nickel 51.6 42.0 NA PCB_Z 180.0 125.4 21.3 Phenanthrene 1500.0 275.9 310.6 Pyrene 2600.0 1000.0 480.0 Silver 3.7 1.1 0.4 Tributyltin NA 308.0 30.0 Zinc 410.0 406.9 214.5
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Existing national SQGs
Calibration of national SQGs
New approaches
New SQG CharacteristicsNew SQG Characteristics
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
– 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
Kappa StatisticKappa 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
KappaKappaEvaluates agreement between 2 methods of classification
– Chemical SQG result
– Toxicity test result
– Magnitude of error affects score
Toxicity Result
SQG Result
High Moderate Marginal Reference
High
Moderate
Low
Reference
T1T3 T2
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 1Chemical 1Good Association Between Concentration and EffectGood Association Between Concentration and Effect
(most of errors in cells adjacent to diagonal) (most of errors in cells adjacent to diagonal)
Chemical 2 Chemical 2 Poor Association Between Concentration and EffectPoor Association Between Concentration and Effect
(more errors in categories distant from diagonal) (more errors in categories distant from diagonal)
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
Kappa Analysis OutputKappa 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
New Kappa SQGsNew 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
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
Presentation OverviewPresentation Overview
Categorical classification
Correlation
Predictive ability
Evaluation ProcessEvaluation 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
Evaluation of SQGsEvaluation of SQGs
Categorical (ability to classify each station into one of four toxicity response categories)
– Kappa value
– Level 1=<10% mortality, Level 2=10-20%, Level 3=20-40%, Level 4=>40%
– SQG thresholds optimized for best score
Spearman’s correlation coefficient
– Nonparametric measure of association
– Independent of Kappa calculation
Validation
– Used same thresholds selected for calibration dataset
SQG Evaluation: NorthSQG Evaluation: North
SQGSpearmanCorrelation Kappa
Mean Weighted Kappa 0.54 0.36Northern CA mERMq 0.37 0.29Northern CA Pmax 0.35 0.29Max Weighted Kappa 0.40 0.26mConsensusq 0.29 0.24mERMq 0.37 0.24mSQGQ1q 0.28 0.22National Pmax 0.27 0.19Chronic EqP TU -0.08 0.08Acute EqP TU -0.09 0.08
SQG Evaluation:SouthSQG Evaluation:South
SQGSpearmanCorrelation Kappa
Mean Weighted Kappa 0.46 0.31Max Weighted Kappa 0.43 0.27
Southern CA Pmax 0.32 0.21mERMq 0.29 0.18Southern CA mERMq 0.28 0.18National Pmax 0.22 0.16mSQGQ1q 0.25 0.16mConsensusq 0.22 0.13Chronic EqP TU -0.06 0.04Acute EqP TU -0.08 0.03
SQG Validation:NorthSQG Validation:North
All top ranked SQGs validate
SQGSpearmanCorrelation Kappa
Mean Weighted Kappa 0.47 0.31Northern CA mERMq 0.38 0.26Max Weighted Kappa 0.36 0.22Northern CA Pmax 0.31 0.21mSQGQ1q 0.38 0.21National Pmax 0.30 0.17mERMq 0.31 0.17mConsensusq 0.14 0.12
SQG Validation:SouthSQG Validation:South
All top ranked SQGs validate
SQGSpearmanCorrelation Kappa
National Pmax 0.34 0.24Southern CA Pmax 0.34 0.21mSQGQ1q 0.39 0.21Mean Weighted Kappa 0.36 0.20mERMq 0.29 0.18Max Weighted Kappa 0.34 0.18Southern CA mERMq 0.22 0.15mConsensusq 0.21 0.07
Significance of DifferencesSignificance of Differences
Are the differences in performance significant to the user?
– Do differences in SQG ranking correspond to greater accuracy, applicability, or utility of the SQG?
– Better predictive ability (efficiency)?
– Better sensitivity or specificity?
Need to look at the data
Mean Weighted Kappa NOAA ERM
EqP Acute So CA ERM
SQGs Applied to So CA DataSQGs Applied to So CA Data
Predictive AbilityPredictive Ability
Guideline Value
0 20 40 60 80 100 120
Freq
uenc
y
True Positive(Hit/Toxic)
Toxic Sample Distribution
A
BFalse Negative(No Hit/Toxic)
AB
Threshold
Nontoxic Sample Distribution
True Negative(No Hit/Nontoxic)
DC
False Positive(Hit/Nontoxic)
CD
Negative Predictive Value =C/(C+A) x 100(percent of no hits that are nontoxic)=Nontoxic Efficiency
Specificity=C/(C+D) x 100(percent of all nontoxic samples that are classified as a no hit)
Positive Predictive Value =B/(B+D) x 100(percent of hits that are toxic)=Toxic Efficiency
Sensitivity=B/(B+A) x 100(percent of all toxic samples that are classified as a hit)
South: mERMqSouth: mERMq
SQG performance is threshold dependent
Inverse relationship between efficiency (toxic or nontoxic) and specificity or sensitivity
Improved SQG accuracy when greater efficiency obtained
Improved SQG utility when greater sensitivity or specificity obtained without sacrificing efficiency
South: mERMqSouth: mERMq
Plots of efficiency vs. specificity or sensitivity illustrate tradeoffs in SQG performance at different thresholds
South: Candidate SQGsSouth: Candidate SQGs
Mean weighted Kappa shows improved overall utility for distinguishing both nontoxic and toxic samples
Nontoxic Efficiency vs Specificity
Nontoxic Efficiency
60 70 80 90 100
Spe
cific
ity
0
20
40
60
80
100
NOAA mERMq SouthSo Cal mERMqKappa SouthPMax South
Toxic Efficiency vs Sensitivity
Toxic Efficiency
40 50 60 70 80 90 100
Sen
sitiv
ity
0
20
40
60
80
100
NOAA mERMq SouthSo Cal mERMqKappa SouthPMax South
North: Candidate SQGsNorth: Candidate SQGs
Mean weighted Kappa shows improved specificity and toxic efficiency
Nontoxic Efficiency vs Specificity
Nontoxic Efficiency
60 70 80 90 100
Spe
cific
ity
0
20
40
60
80
100
NOAA mERMq NorthNor Cal mERMqKappa NorthPMax North
Toxic Efficiency vs Sensitivity
Toxic Efficiency
40 50 60 70 80 90 100
Se
nsi
tivity
0
20
40
60
80
100
NOAA mERMq NorthNor Cal mERMqKappa NorthPMax North
Evaluation and Validation SummaryEvaluation and Validation Summary
North
– Mean weighted Kappa has highest performance
– Northern California ERM and Northern California Pmax also perform better than others
South
– Mean weighted Kappa has highest performance
– Max Kappa also performs better than others
Validation results consistent with evaluation
– The approaches are robust
Presentation OverviewPresentation Overview
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next steps
ConclusionsConclusions
Pursue mean weighted Kappa as component of chemistry LOE
– Best relationship with toxicity
– Easily adaptable to new chemicals or different datasets
– Provides information on strength of relationship
Use EqP benchmarks as component of stressor identification, not chemical LOE score
– Predictive value not strong enough
– Provide guidance on calculation and interpretation
Presentation OverviewPresentation Overview
Objectives
Data preparation
SQG calibration and development
Validation
Conclusions
Next stepsThresholds
Benthos
Options for Threshold DevelopmentOptions for Threshold Development
Optimum statistical fit to effects in CA
– Toxicity only?
– Benthos only?
– Combination?
Based on accuracy or error rate
Consideration of national patterns
National vs. CA dataNational vs. CA data
Narrower contamination range in CA
High range threshold (1.5) of limited utility
North South
BenthosBenthos
How should benthic community response be incorporated into the chemical LOE
– In the SQG approach?
– In the thresholds?
Factors to consider:
– Strength of relationship between benthos and chemistry or toxicity
– Relative sensitivity of benthos and toxicity responses
– Nature of association with chemistry
– Are there different drivers?
BenthosBenthos
Preliminary data analysis:
Used existing benthic response index (BRI) data for So. Calif. and San Francisco Bay
– South San Francisco Bay (North); n=83
– Southern California (South); n=203
Examined three aspects of relationship with chemistry
– Strength of relationship with SQGs and chemicals
– Relative sensitivity of response compared to toxicity
– Chemical drivers
BenthosBenthos
BRI Score vs. Mean ERM Quotient for SouthernSan Francisco, California
m ERM q San Francisco South
0.01 0.10 1.00 10.00
BR
I Sco
re
0
20
40
60
80
100
Ref
Rl 1
RL 2
RL 3
BRI Index vs. Mean ERM Quotient for Southern California
m ERM q Southern CA
0.01 0.10 1.00 10.00B
RI S
core
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
BenthosBenthos
Significant correlations are present between BRI scores and SQGs or individual chemicals
BRI Score vs. Zinc in Southern San Francisco, California
Zn (mg/kg) San Francisco South
10 100 1000 10000
BR
I Sco
re
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
BRI Score vs. Zinc in Southern California
Zn (mg/kg) Southern CA
10 100 1000 10000
BR
I Sco
re
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
BRI Score vs. PAHs in Southern California
PAHs (ug/kg) Southern CA
10 100 1000 10000
BR
I Sco
re
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
BRI Score vs. PAHs in Southern San Francisco, California
PAHs (mg/kg) San Francisco South
10 100 1000 10000
BR
I Sco
re
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
BenthosBenthos
Strong correlation between benthic response and amphipod mortality
Benthic response when no toxicity is evident
BRI versus percent mortality of Eohaustorius estuarius in sediment of Southern San Francisco
EE Mortality (%)
0 20 40 60 80 100
BR
I Sco
re S
an F
ranc
isco
Sou
th
0
20
40
60
80
100
Ref
RL 1
Rl 2
RL 3
BRI versus percent mortality of Eohaustorius estuarius in sediment of Southern CA
EE Mortality (%)
0 20 40 60 80 100
BR
I Sco
re S
outh
CA
0
20
40
60
80
100
Ref
RL 1
RL 2
RL 3
Relative Sensitivity of Benthos ResponseRelative Sensitivity of Benthos Response
Use cumulative distribution function to indicate approximate thresholds for increased incidence of impacts (10th percentile) and likely impacts (50th percentile)
Compare results for toxicity and benthos (same dataset)
Pr o
por t
ion
of S
ampl
es
Pr o
por t
ion
of S
ampl
es
Relative Sensitivity of Benthos ResponseRelative Sensitivity of Benthos Response
Toxicity and benthos responses occur over similar contamination ranges
North South SQG 10th
percentile 50th
percentile 10th
percentile 50th
percentile Toxicity Consensus 1.36 1.94 2.13 6.84 ERMq 0.13 0.17 0.13 0.29 SQGQ1 0.16 0.20 0.22 0.54 Benthos Consensus 1.19 1.94 1.83 8.83 ERMq 0.13 0.18 0.10 0.29 SQGQ1 0.18 0.21 0.22 0.62
Chemical Correlations : NorthChemical Correlations : North
achi eved maxi um kappa wei ght ed scor el oc=Nor t h
per cmax SUM
0. 013
0. 025
0. 026
0. 025
0. 494
0. 025
0. 103
0. 090
0. 013
0. 089
0. 083
0. 025
0. 038
Chemi cal Name
Zi nc
TPAH_Z
Si l ver
PCB_Z
Ni ckel
Mer cur y
Lead
DDT_Z
Copper
Chr ysene
Chr omi um
Chl or dane_Z
Ar seni c
per cmax SUM
0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0. 30 0. 35 0. 40 0. 45 0. 50
Benthos
Toxicity
Chlordane, copper, and zinc show different relative influence on effects
achi eved maxi um kappa wei ght ed scor el oc=Nor t h
per cmax SUM
0. 329
0. 013
0. 052
0. 025
0. 077
0. 026
0. 063
0. 208
0. 025
0. 205
Chemi cal Name
Zi nc
TPAH_Z
Si l ver
PCB_Z
Ni ckel
Lead
DDT_Z
Copper
Chr omi um
Chl or dane_Z
per cmax SUM
0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0. 30 0. 35
S. Correlation
S. Correlation
Chemical Correlations: SouthChemical Correlations: South
Benthos
Toxicity
Cadmium, DDTs, and zinc show different relative influence on effects
achi eved maxi um kappa wei ght ed scor el oc=Sout h
per cmax SUM
0. 006
0. 077
0. 174
0. 013
0. 090
0. 013
0. 032
0. 006
0. 052
0. 535
Chemi cal Name
Zi nc
TPAH_Z
Si l ver
PCB_Z
Mer cur y
Lead
DDT_Z
Copper
Chl or dane_Z
Cadmi um
per cmax SUM
0. 0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6
S. Correlation
achi eved maxi um kappa wei ght ed scor el oc=Sout h
per cmax SUM
0. 033
0. 399
0. 124
0. 007
0. 242
0. 131
0. 013
0. 007
0. 046
Chemi cal Name
Zi nc
TPAH_Z
PCB_Z
Ni ckel
DDT_Z
Chr omi um
Chl or dane_Z
Cadmi um
Ar seni c
per cmax SUM
0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0. 30 0. 35 0. 40
S. Correlation
RecommendationsRecommendations
Develop thresholds of application specific to toxicity and benthos
– Need to incorporate both types of responses into assessment
Continue development of a SQG that is best predictor of benthic community impacts
– May respond to different chemical mixtures
– Need revised benthic index data to complete development and evaluation
– Determine whether toxicity and benthos SQGs are needed
– A method to combine the results will be needed to produce a single chemistry LOE score
top related