U.S. Department of the InteriorU.S. Geological Survey

Development and application of empirically-derived sediment quality guidelines  Chris Ingersoll USGS, Columbia, MODon MacDonald MESL, Nanaimo, BC

Tri-State Mining District Forum, April 12 to 14, 2005, Missouri Southern State University, Joplin, MO

Topics– Uses of empirically-derived SQGs (E-SQGs)– Overview of methods used to derive E-SQGs– Evaluation of E-SQGs

• Comparability• Reliability• Predictive ability

Derivation of numerical SQGs:Two families of approaches

1. Equilibrium partitioning (EqP):• Theoretical approach with partitioning coefficients and

water quality criteria

• Can help answer the question:

– “Can this contaminant, at this concentration, in this sediment contribute to or cause toxicity?”

• Applied primarily to non-ionic organic compounds and metals

– Non-ionic organic chemicals: Partitioning to organic carbon

– Metals: Simultaneously extracted metals and acid volatile sulfides and (SEM-AVS)

Derivation of numerical SQGs:Two families of approaches (cont.)

2. Empirical:• Based upon empirical observations of associations

between chemical concentrations in whole sediments and measures of biological effects

• Can help answer the question:– “Is this sediment likely to be toxic or not?”

• Applicable to all substances associated with sediments

Biota Biota

Water Sediment Pore Water

Water Column

Koc

Equilibrium partitioning

Water ColumnToxicity

Chemical activity isthe same regardlessof exposure route

0 100 1,000 10,000

Phenanthrene (ppb, dry wt.)

100,000 1,000,000

ERL ERM

ERL @ 6th valueERM @ 27th value

No Effects

Significant effects (n = 53)

AET

“Smart Sediment Assessors do it Both Ways” Walter Berry, USEPA– The best way to use chemistry data is to use SQGs

from both approaches either together or in sequence.– Use E-SQGs to see if there is a likelihood of a problem.

Use the EqP-SQGs to determine chemicals that might be the bad actors.

– Go to SEM-AVS directly if you suspect metals are a problem. Avoid the costs if you suspect metals are not a problem.

– Measure organic carbon and grain size anyway – they are cheap.

– SQGs from both approaches may be fortuitously similar, but beware of critical differences in how they were derived and how they can be applied.

Groups of chemicals with SQGs– Metals

– PAHs

• Individual compounds

• Low and high molecular weight

• Total

– PCBs

– Organochlorine pesticides (limited)

– Phthalates (limited)

– Chlorinated benzenes (limited)

– E-SQGs: Dry-weight concentrations (normally, but to also to organic carbon; Barrick et al. 1988, Ingersoll et al. 1996)

Evaluation of SQGs– Comparability: Similarity among SQGs– Reliability: Agreement between narrative intent

actual outcome based on incidence of toxicity within ranges

– Predictive ability: Ability of SQGs to correctly classify samples as toxic or non-toxic in an independent database

Potential uses of SQGs– Interpret historical data– Identify problem chemicals and areas at site– Decision tool for more detailed study– Identify problem chemicals before discharge– Link contaminant source and sediment– Trigger regulatory action

• Characterization vs. remediation?

Uses of E- SQGs by states or provinces

– States, provinces, or groups that have formally (legally) adopted use of SQGs

• Washington, Indiana, Florida (marine), Colville Tribe– States or provinces that are considering adopting formal use

of SQGs in the next several years• British Columbia, California, Florida (freshwater)

– States or provinces that informally use SQGs • California, Hawaii, Oregon, South Carolina, New Jersey,

Alaska, Texas, Maine, Michigan, Wisconsin, Ohio, New York, Montana, Minnesota, Massachusetts, Ontario, Quebec

Overview of E-SQGs– Screening level concentrations (SLC)– Apparent effect concentrations (AET)– Effect-range low (ERL) and effect-range median

(ERM)– Threshold-effect level (TEL) and Probable-effect

level (PEL)– Logistic regression models (LRM)– Consensus-based SQGs

Effect-range SQGs– Matching toxicity and chemistry with sediments (primarily

field-collected sediments)

– Sort effect data in ascending order

– ERL (effect-range low)

• 10th percentile of effects

• Concentration below which effects occur rarely and above which effects may begin

– ERM (effect-range median)

• 50th percentile of effects

• Concentration above which effects occurred frequently

– Long and Morgan (1990), Ingersoll et al. (1996)

Effect-level SQGs– Used to develop Canadian SQGs (CCME 1999)– Matching toxicity and chemistry with sediments

(primarily field-collected sediments)– Sort effect and no effect data in ascending order– TEL (threshold-effect level)

• Geometric mean of 15th percentile of effects and 50th percentile of no effects

• Concentration below which effects occur rarely and above which effects may begin

– PEL (probable-effect level)• Geometric mean 50th percentile of effects and 85th

percentile of no effect• Concentration above which effects occurred

frequently– MacDonald et al. (1996), Smith et al. (1996), Ingersoll et

al. (1996)

Development of consensus-based SQGs– Concentrations of individual contaminants in

sediment above which toxicity frequently observed• TETs: EC & MENVIQ (1992)• SLCs: Persaud et al. (1993)• PELs: Smith et al. (1996) • PELs: Ingersoll et al. (1996)• ERMs: Ingersoll et al. (1996)

– Alphabet soup…

Consensus-based SQGs total PAHs

Derivation Method ug/g OC

Threshold effect concentrations (TEC)• Threshold effect level 87• Equilibrium partitioning 211• Sigma PAH toxicity threshold 393• Effects range-low 350• Screening level concentration 409• Mean - consensus - TEC 290

Median effect concentrations (MEC)• Probable effects level 804• Low apparent effects level 1796• Sigma PAH LC50 2114• Effects range-median 2358• Mean - consensus - MEC 1800

Extreme effects concentrations (EEC)• High apparent effects threshold 10230• Consensus EEC 10000

Swartz (1999)

Consensus-based SQGs– Geometric mean SQGs with similar narrative

intent: • Measure of central tendency• Not weighting outliers

– Provide unifying synthesis of existing SQGs – Account for effects of contaminant mixtures– Reflect concentrations causing or substantially

contributing to toxic effects– Swartz (1999), MacDonald et al. (2000a,b),

Ingersoll et al. (2001)

Consensus-based freshwater SQGs (MacDonald et al. 2000)

– Reliability: • Database of 347 samples with matching toxicity

and chemistry >75% correct prediction of toxic or not toxic.

• >20 samples predicted to be toxic or not toxic.– Reliable PECs:

• Metals: As, Cd, Cr, Cu, Pb, Ni, Zn• PAHs: 7 including total PAHs• Organochlorines: total PCBs, sum DDE

– Predictive ability of PECs (1800+ samples)

Calculating mean SQG quotients– Traditional (Long et al. 1998)

• Divide concentration of chemical by SQG.• Sum individual quotients in a sample (n = 1 to 25).• Calculate mean quotient/sample.• Equally weights all individual chemicals measured.

– By classes (USEPA 2000)• Calculate an average quotient for metals, a quotient

for total PAHs, and a quotient for total PCBs.• Sum individual quotients in a sample (n = 1 to 3).• Calculate mean quotient/sample.• Equally weights contribution of PAHs, PCBs, and/or

metals (assumes joint toxic action among major classes).

Thresholds for PAH-contaminated sediments

2,387 ug/g

5,017 ug/g

1,800 ug/g

2,280 ug/g

23.9 ug/g

50.2 ug/g

18 ug/g

22.8 ug/g

ACUTE:

Spiked sediment: R. abronius 10-d LC50:

EqP: Di Toro and McGrath (2000)

Empirical: MEC (marine)

PEC (freshwater)

10,000 ug/g100 ug/g

SEVERE:

Empirical: EEC (marine)

211 ug/g

865 ug/g

986 ug/g

290 ug/g

160 ug/g

2.11 ug/g

8.65 ug/g

9.86 ug/g

2.9 ug/g

1.6 ug/g

CHRONIC:

EqP: Swartz (1999)

USEPA (1999)

Di Toro and McGrath (2000)

Empirical: TEC (marine)

TEC (freshwater)

500 ug/g4.89 ug/gSite specific:H. azteca 10-d LC50

2 to 10 ug/g

THRESHOLD (dry)

400 to 1000 ug/gSite specific: Benthic community

THRESHOLD (oc)ENDPOINT

2,387 ug/g

5,017 ug/g

1,800 ug/g

2,280 ug/g

23.9 ug/g

50.2 ug/g

18 ug/g

22.8 ug/g

ACUTE:

Spiked sediment: R. abronius 10-d LC50:

EqP: Di Toro and McGrath (2000)

Empirical: MEC (marine)

PEC (freshwater)

10,000 ug/g100 ug/g

SEVERE:

Empirical: EEC (marine)

211 ug/g

865 ug/g

986 ug/g

290 ug/g

160 ug/g

2.11 ug/g

8.65 ug/g

9.86 ug/g

2.9 ug/g

1.6 ug/g

CHRONIC:

EqP: Swartz (1999)

USEPA (1999)

Di Toro and McGrath (2000)

Empirical: TEC (marine)

TEC (freshwater)

500 ug/g4.89 ug/gSite specific: H. azteca 10-d LC50

2 to 10 ug/g

THRESHOLD (dry)

400 to 1000 ug/gSite specific: Benthic community

THRESHOLD (oc)ENDPOINT

MacDonald and Ingersoll (2001)

Average PEC quotient for metals

0.1 1 10 100

Sur

viva

l of

H.

azte

ca (

%)

0

20

40

60

80

100ToxicNon-toxic

PEC quotient for total PAHs

0.1 1 10 100

Sur

viva

l of

H.

azte

ca (

%)

0

20

40

60

80

100

PEC quotient for total PCBs

0.1 1 10 100

0

20

40

60

80

100

A B

C

Mean PEC quotient

0.1 1 10 100

0

20

40

60

80

100

r2 = 0.73P < 0.001

D

10-d survival of H. azteca vs PECs (GCR sediment)

Ingersoll et al. (2002)

0.01 0.1 1 10 100

Inci

denc

e of

toxi

city

(%

)

0

20

40

60

80

10010- to 14-d H. azteca test

Geometric mean of mean PEC-Q

0.01 0.1 1 10 100

Inci

denc

e of

toxi

city

(%

)

0

20

40

60

80

100

28- to 42-d H. azteca test

r2=0.73

r2=0.93

Incidence of toxicity in the HA10 and HA28 test for the national database (160 to 630 samples)

Prediction of toxicity in HA28 test: National database vs. Southeastern United States (643 samples)

r2=0.93

r2=0.89

Geometric mean of mean PEC-Q

0.01 0.1 1 10 100

Inci

denc

e of

toxi

city

(%

)

0

20

40

60

80

100NationalCalcasieu estuary

Prediction of toxicity in the HA28 test: National database vs. Calcasieu estuary, LA (128 samples)

Prediction of toxicity in the HA10 test: National database vs. Columbia River basin (147 samples)

Ind

ex

of

Bio

tic

Inte

gri

ty

Mean ERM Quotients

5

4

3

2

1

0

Carolinian Province EMAP, 1994-96(Dr. Jeff Hyland, NOAA, Charleston, SC)

0.001 0.01 0.1 1.0 10

Benthic-invertebrate colonization studies(USEPA 2005)

ug DDD/goc

0.1 1 10 100 1000

Re

spon

se r

elat

ive

to

con

trol

(%

)

0

25

50

75

100

125

150

175

200 10-d H. azteca survival10-d H. azteca length28-d H. azteca length42-d H. azteca reproductionTotal abundanceNematode abundanceChironomid abundanceOther diptera abundance

Dilution of GCR sediment (%)

0.001 0.01 0.1 1 10

Re

spon

se r

elat

ive

to

con

trol

(%

)

0

25

50

75

100

125

150 10-d H. azteca survival10-d H. azteca length28-d H. azteca length42-d H. azteca reproductionTotal abundanceNematode abundance

A

B

A: DDD-spiked sediment

B: Dilutions of sediment from Grand Calumet River

ug D

DD

/goc

0

50

100

150

200

250

300

350

400

ug D

DD

/L

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.710-d H. azteca survival10-d H. azteca length28-d H. azteca length42-d H. aztaca reproductionTotal abundanceNematode abundanceChironomid abundance Other diptera abundance

ChV IC25 IC50

> >10-d H. azteca(Kemble et al. [76])

0.83

42-d H. azteca (Kemble et al. [76])

Dilu

tion

of G

CR

sed

imen

t (%

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Mea

n P

EC

-Q

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.510-d H. azteca survival10-d H. azteca length28-d H. azteca length42-d H. azteca reproductionTotal abundanceNematode abundance

ChV

10-d H. azteca(Ingersoll et al. [66])

28-d H. azteca(Ingersoll et al. [66])

A

B

Benthic-invertebrate colonization studies(USEPA 2005)

A: DDD-spiked sediment

B: Dilutions of sediment from Grand Calumet River

Conclusions of SETAC workshop1. Empirically-derived SQGs can be used to predict the probability of

the presence or absence of toxic effects with a known level of statistical confidence based on the results of the analyses of the data sets evaluated to date.

2. Mechanistic SQGs based on partitioning theory attempt to causally relate sediment concentration to toxicity. The availability and success or failure depends on the adequacy of the partitioning model and its parameters and the assumption that exposure is either from pore water or sediment particles or both.

3. SQGs should only be used with an understanding of how they were derived, their narrative intent, and their predictive ability.

4. Future evaluations of the predictive ability of SQGs should include controlled benthic community colonization studies and controlled mesocosm studies.

5. Efforts to estimate sediment toxicity and benthic community effects in relation to SQGs need to account for potentially confounding factors.

Conclusions of SETAC workshop6. The chemical state of the contaminants (e.g., paint chips, lead shot,

tar balls, metal ore) can reduce the predictive ability of the SQGs.7. The presence of an unusual sediment matrix (e.g., black carbon,

peat, wood chips) can also reduce the predictive ability of SQGs.8. Toxic sediment in which only a single empirical SQG is exceeded

should not be assumed to be toxic as a result of the presence of that substance,

9. SQGs for total PAH and total PCBs derived using empirical approaches are similar to guidelines derived using mechanistic approaches with a similar narrative intent. This concordance suggests that these mixtures are causally implicated in the toxicity observed in a substantial number of sediments

10.Existing effects-based SQGs (e.g., ERMs, AETs) are not designed or intended to predict bioaccumulation-based effects. However, Biota-sediment accumulation factors (BSAFs) for non-ionic organic compounds may be used to generate SQGs predictive of effects in sediment-dwelling organisms. Bioaccumulation is only the first step and should be linked to predicted tissue residue effects concentrations.

Research needs identified at SETAC workshop

1.Develop a better understanding of the reasons why benthic communities in estuaries appear to be more sensitive to contaminants in sediment compared to 10-day laboratory tests (e.g., conduct additional data analyses or conduct controlled laboratory and field studies).

2.Develop procedures to better understand and account for the potential influence of confounding factors in toxicity, bioaccumulation, and benthic community studies.

3.Further validate the predictive ability of SQGs in the laboratory and in the field for single chemicals and for complex mixtures.

4.Develop a better understanding of additive, antagonistic and synergistic effects of chemical mixtures in sediments.

Research needs identified at SETAC workshop

5. Further evaluate ability of SQGs to predict chronic and sublethal endpoints and develop methods for conducting toxicity tests with additional species.

6. Further evaluate the predictive ability of SQGs using controlled benthic community colonization studies, controlled mesocosm studies, or in situ toxicity testing

7. Improve our understanding of the relationship between chemical residues and toxic response and improve our ability to account for variation in site-specific bioavailability to improve the potential for developing bioaccumulation-based SQGs.

8. Develop a better understanding of the factors controlling bioaccumulation of metals and the importance of metal tissue residues.

Uses of empirically-derived SQGs– Low-range SQGs (e.g., ERLs, TELs)

• Not predictive of toxicity, but are protective• Classify samples as non toxic• Use to establish background or reference conditions

– Mid-range SQGs (e.g., ERMs, PELs)• More predictive of toxicity, but less protective• Classify samples as toxic (mean quotients or number

of exceedances)• Use to establish samples as intermediate in quality

– Interpret historical data (+)– Identify problem chemicals and areas at site (+)– Decision tool for more detailed study (+)– Identify problem chemicals before discharge (+)– Link contaminant source and sediment (?)– Trigger regulatory action (?)

Conclusions– SQGs not absolute predictors of effects– Not a substitute for biological measures– Incidence and magnitude of toxicity increases with

increasing number of exceedances or mean quotients– Identify increased probability of toxicity– Identify sites and chemicals of concern

Uncertainties– Lack of SQGs for many substances (e.g., pesticides)– Lack of reliable SQGs for bioaccumulation-associated

effects– May not be applicable to low or high TOC sediments– Not applicable to gravel, paint chips, lead shot, tar balls,

metal ore, black carbon, peat, wood chips– 10-d lethality tests may not be protective of field effects

“The weight of evidence required should depend on the weight of the decision”

Dave MountUSEPA, Duluth, MNSETAC short courseNovember 1997

Life cycle of freshwater mussels

Adult

Glochidia

Fish host

Juveniles

Determination of the sensitivity of Ozark mussels to zinc, lead, and cadmium in water and sediment• Task 1: Adapt laboratory methods for conducting

water and sediment toxicity tests with glochidia and juveniles of mussels native to the Ozark Plateau

• Task 2: Evaluate the toxicity of zinc, lead, and cadmium in laboratory exposures with water or sediment to sensitive life stages of mussels native to the Ozark Plateau

• Task 3: Evaluate distributions of mussel species at a metal-contaminated site within the Ozark Plateau

Determination of the sensitivity of Ozark mussels to zinc, lead, and cadmium in water and sediment• Determine if use of existing USEPA WQC for

zinc, lead, or cadmium or data from toxicity tests with surrogate species provides adequate protection of native mussels in the Ozarks

• Collaborators:– USGS Columbia– USFWS Regions 2, 3, 4, and 6– Chris Barnhart, “Southwest” Missouri State University