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Development of Chemistry Indicators

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Sediment Quality Objectives For California Enclosed Bays and Estuaries Development of Chemistry Indicators Scientific Steering Committee Meeting – PowerPoint PPT presentation

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Title: Development of Chemistry Indicators


1
Development of Chemistry Indicators
Sediment Quality Objectives For California
Enclosed Bays and Estuaries
Scientific Steering Committee Meeting July 26,
2005
2
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

3
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

4
Chemistry Indicators
  • Several challenges to effective use
  • Bioavailability
  • Unmeasured chemicals
  • Mixtures

5
Objectives
  • 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

6
Approach
  • 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

7
Approach
  • 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

8
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Data screening processing Strata Calibration
validation subsets
9
Data 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 sumsDDTs, PCBs, PAHs, Chlordanes

10
Data 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



11
Data Screening
12
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Data screening processing Strata Calibration
validation subsets
13
Strata
  • Are 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

14
Strata
15
Strata
16
Strata 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

17
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Data screening processing Strata Calibration
validation subsets
18
Calibration 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

19
Validation 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

20
Bay/Estuary Samples in Database After Screening
Number of Samples (matched chemistry toxicity) Number of Samples (matched chemistry toxicity)
Stratum Calibration/ Development Validation
North 504 298
South 800 328
21
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Existing national SQGs Calibration of national
SQGs New approaches
22
National 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

23
Empirical SQGs
SQG Metric Source
ERM 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.
24
National 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

25
Mechanistic 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
26
National SQGs
27
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Existing national SQGs Calibration of national
SQGs New approaches
28
Calibration 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

29
Organics 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

30
TOC Normalization
Relationship to sediment toxicity is not improved
by TOC normalization of organics
31
Metal 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

32
Reference 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

33
Iron 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

34
Southern California Results
Significant regressions obtained for metals of
interests in all strata
35
Residual Calculation
Residual relative metal enrichment Used for
correlation analysis with amphipod mortality
36
Iron Normalization
Relationship to sediment toxicity is not improved
by iron normalization of metals
37
Normalization 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

38
Calibration 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

39
CA 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 gt 2x
    nontoxic median
  • CA ERM median concentration from screened toxic
    samples
  • At least 10 toxic samples required for ERM
    calculation

40
Substantial differences in some ERM values
derived for California datasets compared to
nationally derived values
41
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Existing national SQGs Calibration of national
SQGs New approaches
42
New 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

43
Kappa Statistic
  • Developed in 1960-70s
  • 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

44
Kappa
  • Evaluates agreement between 2 methods of
    classification
  • Chemical SQG result
  • Toxicity test result
  • Magnitude of error affects score

45
  Chemical 1 Good Association Between
Concentration and Effect (most of errors in cells
adjacent to diagonal)
 
46
Chemical 2 Poor Association Between
Concentration and Effect (more errors in
categories distant from diagonal)
 
47
Kappa 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

48
New 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

49
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Categorical classification Correlation Predictive
ability
50
Evaluation 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

51
Evaluation of SQGs
  • Categorical (ability to classify each station
    into one of four toxicity response categories)
  • Kappa value
  • Level 1lt10 mortality, Level 210-20, Level
    320-40, Level 4gt40
  • SQG thresholds optimized for best score
  • Spearmans correlation coefficient
  • Nonparametric measure of association
  • Independent of Kappa calculation
  • Validation
  • Used same thresholds selected for calibration
    dataset

52
SQG Evaluation North
53
SQG EvaluationSouth
54
SQG ValidationNorth
All top ranked SQGs validate
55
SQG ValidationSouth
All top ranked SQGs validate
56
Significance 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

57
SQGs Applied to So CA Data
58
Predictive Ability
Negative Predictive Value C/(CA) x 100 (percent
of no hits that are nontoxic) Nontoxic
Efficiency SpecificityC/(CD) x 100 (percent of
all nontoxic samples that are classified as a no
hit) Positive Predictive Value B/(BD) x
100 (percent of hits that are toxic) Toxic
Efficiency SensitivityB/(BA) x 100 (percent of
all toxic samples that are classified as a hit)
59
South 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

60
South mERMq
  • Plots of efficiency vs. specificity or
    sensitivity illustrate tradeoffs in SQG
    performance at different thresholds

61
South Candidate SQGs
  • Mean weighted Kappa shows improved overall
    utility for distinguishing both nontoxic and
    toxic samples

62
North Candidate SQGs
  • Mean weighted Kappa shows improved specificity
    and toxic efficiency

63
Evaluation 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

64
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

65
Conclusions
  • 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

66
Presentation Overview
  • Objectives
  • Data preparation
  • SQG calibration and development
  • Validation
  • Conclusions
  • Next steps

Thresholds Benthos
67
Options 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

68
National vs. CA data
North
South
  • Narrower contamination range in CA
  • High range threshold (1.5) of limited utility

69
Benthos
  • 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?

70
Benthos
  • Preliminary data analysis
  • Used existing benthic response index (BRI) data
    for So. Calif. and San Francisco Bay
  • South San Francisco Bay (North) n83
  • Southern California (South) n203
  • Examined three aspects of relationship with
    chemistry
  • Strength of relationship with SQGs and chemicals
  • Relative sensitivity of response compared to
    toxicity
  • Chemical drivers

71
Benthos
72
Benthos
  • Significant correlations are present between BRI
    scores and SQGs or individual chemicals

73
Benthos
  • Strong correlation between benthic response and
    amphipod mortality
  • Benthic response when no toxicity is evident

74
Relative 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)

75
Relative Sensitivity of Benthos Response
  • Toxicity and benthos responses occur over similar
    contamination ranges

76
Chemical Correlations North
Benthos
Chlordane, copper, and zinc show different
relative influence on effects
Toxicity
77
Chemical Correlations South
Benthos
Cadmium, DDTs, and zinc show different relative
influence on effects
Toxicity
78
Recommendations
  • 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
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