Sediment Quality ObjectivesSediment Quality ObjectivesIndirect Effects ProjectIndirect Effects Project

Ben Greenfield

Aroon Melwani

John Oram

Mike ConnorSan Francisco Estuary Institute (SFEI)

Presentation OverviewPresentation Overview

Project conceptual framework

– Description of Multiple Lines of Evidence

Use of information in assessment context Methodological issues and results

– Empirical and mechanistic approaches

– Problems of scale, target species

– BAF vs. BSAF

Pollutant GroupsPollutant Groups

Non-ionic organicsPCBsDDTsChlordanesDieldrin

Methylmercury

DioxinsPBDEs

Conceptual ModelConceptual ModelC

hem

ical

upt

ake

via

diet

, res

pira

tion

S e d im e ntC o n ce n tra tion

W a te rC o n ce n tra tion

In verte b ra teC o n ce n tra tion

F ishC o n ce n tra tion

Effects Thresholds For Wildlife/Fish

Effects ThresholdsFor Humans

Exposure Assessment

Effects Assessment

Multiple Lines of Evidence ApproachMultiple Lines of Evidence ApproachC

hem

ical

upt

ake

via

diet

, res

pira

tion

S e d im e ntC o n ce n tra tion

W a te rC o n ce n tra tion

In verte b ra teC o n ce n tra tion

F ishC o n ce n tra tion

Effects Thresholds For Wildlife/Fish

Effects ThresholdsFor Humans

Exposure Assessment

Effects Assessment

Sources of VariabilityExposure:•Diet•Lipids & Weight•Spatial movement•Chemical Partitioning

Effects:•Consumption Rate•Size•Risk management goals

Uncertainty will be addressed by:•Using multiple lines of evidence

•Incorporating several thresholds into each line of evidence•Unlikely risk

•Potential risk to high-risk consumers

•Potential risk to average consumers

•High risk to average consumers

Indirect Effects Weight of EvidenceIndirect Effects Weight of Evidence

Fish Concentration

SedimentConcentration

LaboratoryBioaccumulation

Concentration

Fish Concentration

SedimentConcentration

LaboratoryBioaccumulation

Concentration

Human Lines of Human Lines of EvidenceEvidence

Fish and Wildlife Fish and Wildlife Lines of EvidenceLines of Evidence

Indirect Effects Approach Indirect Effects Approach Compared to Rest of SQO ProgramCompared to Rest of SQO Program

Similarities:– Integrate multiple lines of evidence– Use ordinal scale ranking based on thresholds– Both exposure and effects are important

Changes:– All lines of evidence are measures of exposure

– Effects thresholds are determined from literature/expert opinion– If local effects information are available, they would be included on a

case-by-case basis

– All effects assessments are specific to individual contaminants (mixtures not accounted for)

– Addition of laboratory bioaccumulation component

Multiple Effects Thresholds:Multiple Effects Thresholds:Fish Targets for Human HealthFish Targets for Human Health

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Screening values for human consumption of edible fish tissue – Tissue thresholds developed using USEPA and CalEPA

reference doses and cancer slope factors – Separate thresholds will be calculated assuming varying

levels of risk • Cancer Risk 1x10-4 - 1x10-6

Assuming 70 kg adult with 70 yr lifetime Consumption rate assumptions will also be varied

– OEHHA consumption rate of 21 g/d.– USEPA consumption rate of 17.5 g/d.– Other consumption rates will be considered

• E.g., 6.3 g/d rate for all anglers consuming fish in SF Bay• E.g., 142.4 g/d EPA rate for subsistance fishers

Multiple Effects Thresholds:Multiple Effects Thresholds:Fish Targets for Human HealthFish Targets for Human Health

Development of four categories– Category 1 = Unlikely risk

• Below all thresholds

– Category 2 = Potential risk to high-end consumers• Above threshold using higher consumption rate assumption and

protective allowable risk (10-6)

– Category 3 = Potential risk to average consumers• Above threshold using sport fisher consumption rate with

intermediate allowable risk (10-5)

– Category 4 = High risk to average consumers • Sport fisher consumption rate with less protective allowable

risk (10-4)

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Multiple Effects Thresholds:Multiple Effects Thresholds:Sediment Targets for Human HealthSediment Targets for Human Health

Numeric targets - again 4 categories Based on field sediment concentrations at which fish tissue concentrations would exceed

target concentrations

– When local data are available, targets developed for specific water body

– When local data are not available, general targets will be recommended

• These will account for uncertainty and will span a range of conditions

Calculated based on concentration ratio between sediment and biota

– Using statistical and mechanistic models (more later…)

S

Multiple effects thresholds:Multiple effects thresholds:Laboratory BioaccumulationLaboratory Bioaccumulation

Targets for Human HealthTargets for Human Health

Numeric targets - again 4 categories Based on concentrations observed in 28 day laboratory bioaccumulation tests

– Tests on sediments to be evaluated

– Important link between sediments and indirect effects

• Confirm whether specific sediments are likely to cause exposure to biota

• Also important for contaminants that do not bioaccumulate in finfish (e.g., PAHs)

Our current thinking: evaluate risk due to consumption of contaminated shellfish

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Thresholds for bird and wildlife consumption of fish or shellfish Thresholds will be calculated and presented in tabular form for sensitive and endangered wildlife species

– Tables can be used by local agencies based on local species

For PCBs and DDT, thresholds will be based on work of Biological Technical Assistance Group (BTAG)

– Low and high Toxicity Reference Values used to establish multiple targets

Field fish samples and laboratory invertebrate samples are to be evaluated as separate lines of evidence

All thresholds will be reviewed by a Bioaccumulation Work Group, formed specifically for the indirect effects task

Multiple Effects Thresholds:Multiple Effects Thresholds:Fish and Laboratory BioaccumulationFish and Laboratory Bioaccumulation

Targets for WildlifeTargets for Wildlife

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L

Sensitive and Endangered Sensitive and Endangered Target SpeciesTarget Species

Least Tern Clapper rail Brown pelican Western snowy plover Bald eagle

Southern sea otter

Harbor seal

Tidewater goby

Salmonids

Multiple Effects Thresholds:Multiple Effects Thresholds:Sediment Targets for WildlifeSediment Targets for Wildlife

Numeric targets Based on field sediment concentrations at which fish

tissue concentrations would exceed target concentrations Calculated based on Biota Sediment Accumulation Factor

– Using statistical and mechanistic models (more later…)

Same approach as with sediment targets for humans. I.e.,…

S

Use in Assessment:Use in Assessment:Integration of Lines of EvidenceIntegration of Lines of Evidence

Four categories for each line of evidence– Category 1 = Unlikely risk– Category 2 = Potential risk to high-end consumers– Category 3 = Potential risk to average consumers– Category 4 = High risk to average consumers

F

S L

4321

43214321

A B C D E

A B C D E

A = Sediment meets SQO with high certainty

(i.e., is protective)

B = Sediment probably meets SQO, but some uncertainty is present

C = Sediment possibly fails SQO, but data are inconsistent

D = Sediment likely fails SQO

E = Sediment highly likely to fail SQO

Five Categories For SQO EvaluationFive Categories For SQO Evaluation

Use In AssessmentUse In Assessment

Fish exposure = 1 Fish exposure = 2

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A A A A 1 A A B C 2 A A A A 2 A B C C 3 A A A B 3 B C C D

Lab

Tes

t E

xpos

ure

4 A A B B

Lab

Tes

t E

xpos

ure

4 C C D D Fish exposure = 3 Fish exposure = 4

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A B C D 1 B C D D 2 B C D D 2 C D D E 3 C D D D 3 D D E E

Lab

Tes

t E

xpos

ure

4 D D D E

Lab

Tes

t E

xpos

ure

4 D E E E

Fish exposure = 1 Fish exposure = 2

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A A A A 1 A A B C 2 A A A A 2 A B C C 3 A A A B 3 B C C D

Lab

Tes

t E

xpos

ure

4 A A B B

Lab

Tes

t E

xpos

ure

4 C C D D Fish exposure = 3 Fish exposure = 4

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A B C D 1 B C D D 2 B C D D 2 C D D E 3 C D D D 3 D D E E

Lab

Tes

t E

xpos

ure

4 D D D E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Clean" Sitee.g., "Clean" Site

Fish exposure = 2

Sediment exposure 1 2 3 4 1 A A B C 2 A B C C 3 B C C D

Lab

Tes

t E

xpos

ure

4 C C D D

Use In Assessment - Use In Assessment - e.g., "Clean" Sitee.g., "Clean" Site

Fish exposure = 2

Sediment exposure 1 2 3 4 1 A A B C 2 A B C C 3 B C C D

Lab

Tes

t E

xpos

ure

4 C C D D

Use In Assessment - Use In Assessment - e.g., "Clean" Sitee.g., "Clean" Site

Fish exposure = 2

Sediment exposure 1 2 3 4 1 A A B C 2 A B C C 3 B C C D

Lab

Tes

t E

xpos

ure

4 C C D D

Use In Assessment - Use In Assessment - e.g., "Clean" Sitee.g., "Clean" Site

Fish exposure = 2

Sediment exposure 1 2 3 4 1 A A B C 2 A B C C 3 B C C D

Lab

Tes

t E

xpos

ure

4 C C D D

Use In Assessment - Use In Assessment - e.g., "Clean" Sitee.g., "Clean" Site

Fish exposure = 1 Fish exposure = 2

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A A A A 1 A A B C 2 A A A A 2 A B C C 3 A A A B 3 B C C D

Lab

Tes

t E

xpos

ure

4 A A B B

Lab

Tes

t E

xpos

ure

4 C C D D Fish exposure = 3 Fish exposure = 4

Sediment exposure Sediment exposure 1 2 3 4 1 2 3 4

1 A B C D 1 B C D D 2 B C D D 2 C D D E 3 C D D D 3 D D E E

Lab

Tes

t E

xpos

ure

4 D D D E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Dirty" Sitee.g., "Dirty" Site

Fish exposure = 4 Sediment exposure

1 2 3 4 1 B C D D 2 C D D E 3 D D E E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Dirty" Sitee.g., "Dirty" Site

Fish exposure = 4 Sediment exposure

1 2 3 4 1 B C D D 2 C D D E 3 D D E E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Dirty" Sitee.g., "Dirty" Site

Fish exposure = 4 Sediment exposure

1 2 3 4 1 B C D D 2 C D D E 3 D D E E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Dirty" Sitee.g., "Dirty" Site

Fish exposure = 4 Sediment exposure

1 2 3 4 1 B C D D 2 C D D E 3 D D E E

Lab

Tes

t E

xpos

ure

4 D E E E

Use In Assessment - Use In Assessment - e.g., "Dirty" Sitee.g., "Dirty" Site

Total PCB Concentrations in California Fishes

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300 350 400 450

Total PCB in fish tissue (ug/kg)

Cu

mu

lati

ve F

req

uen

cy

Least Tern High Effects SV (1632 ug/kg)– 100% meet criteria

Human Health US EPA SV x 10 ~ 90% meet criteria

Least Tern Low Effects ~ 70% meet criteria

Human Health US EPA SV ~ 10% meet criteria

(Fish Species included: Bay Goby, California Halibut, English Sole, Longfin Sanddab, Pacific Sanddab, Pacific Staghorn Sculpin, Shiner Surfperch, Slender Sole, Speckled Sanddab, Starry Flounder, White Croaker, and White Surfperch)

Statewide Assessments Will Be ConductedStatewide Assessments Will Be Conducted

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Methodological issuesMethodological issues

Overall approach for development of biota-sediment relationship

Scale of analysis– At what scale can data be extrapolated for biota-

sediment relationship development?– At what scale should movement range be

extrapolated over? Target fish and laboratory bioaccumulation

species BAF vs. BSAF

Overall Approach to DevelopOverall Approach to DevelopBiota to Sediment RelationshipBiota to Sediment Relationship

Empirical Models – Concentrations in Organisms, Concentrations in Sediment, Other Factors

Mechanistic Models – Quantification of Bioenergetics and Physicochemical Properties and Concentrations. – Data-intensive (e.g., bioenergetics, life

history, chemical-specific properties)

Empirical modeling approach: •Linear Regression Models Using SQO database and other data.

0 2 4 6 8 10

SedimentConcentration

0

10

30

40

Bio

ta

Co

nce

ntr

atio

n

20

4

2

High toxicity Threshold

Low toxicity Threshold

DDTs in San Francisco Bay Macoma clams vs. sediment

R2 = 0.6585

0.1

1

10

100

1 10 100 1000

Sediment DDT (ug/kg dry)

Results are from 28 day laboratory bioaccumulation tests

Tis

sue

DD

T (

ug

/kg

dry

)

•Bivalve concentrations compared to co-located sediments.

•Fish concentrations compared with sediments in a disk centered at each fish sampling location.

•Disk size ranged from 0.5 - 15 km (0.5 km increments)

•No a priori assumptions about fish home range

R2 results of distance relationships of sediment and shiner surfperch data in San Francisco Bay

Total PCBs

Linear regression of Total PCB concentration in sediment vs. Shiner Surfperch tissue in San Francisco Bay (p<0.05)

??

Total DDTs

R2 results of distance relationships of sediment and shiner surfperch data in San Francisco Bay

Linear regression of Total DDT concentration in sediment vs. Shiner Surfperch tissue in San Francisco Bay (p<0.05)

Mechanistic modeling approachMechanistic modeling approach

Calculate Biota-Sediment Accumulation Factors and SQO using mechanistic models at local scales

Demonstrate use of mechanistic model for multiple contaminants in two case studies

Evaluate confounding factors– Water contamination– Home range size– Diet

Using Gobas model (e.g., TrophicTrace, Arnot and Gobas 2004)

Validating with available empirical data

Uptake•Dietary•Gill

Loss•Excretion•Egestion•Gill Elimination•Metabolism

Growth

Chemical properties(e.g., Kow) important

Basic Mechanistic Model Elements

Data NeedsData Needs

Minimum: diet and biology– Dietary preference– Weight, lipid content

Preferrable:– Contaminant concentrations in

sediment, water, inverts, fish

Newport Bay case study: Newport Bay case study: Developing conceptual food web modelDeveloping conceptual food web model

Phytoplankton Algae Benthic Diatoms Debris

Harpacticoid copepods Juv. Striped Mullet

Gammarid Amphipods Polychaetes Topsmelt

ClamsCalanoid Copepods Arrow Goby

Cheekspot Goby Crabs

Pac. Staghorn Sculpin Juv. Calif. Halibut

Shiner Perch Ad. Striped MulletYellowfin Croaker

Slough Anchovy Spotted Sand Bass

Osprey

Brown Pelican Least Tern

Doublecrested Cormorant Humans

Preliminary model kindly provided by M. James Allen, SCCWRP

Species Sed

imen

t

Ben

thic

Alg

ae

Zoo

plan

kton

Epi

bent

hic

Cru

stac

eans

Ann

elid

s

Mol

lusc

s

Hyd

rozo

a

Ech

uroi

dea

Fis

h

%lip

id

mas

s (g

)

California Halibut 0.1 0.9 0.8 1463.3Yellowfin Croaker 0.05 0.25 0.45 0.1 0.1 0.05 1.7 385.0Topsmelt 0.23 0.6 0.05 0.12 1.6Striped Mullet 0.3 0.55 0.05 0.05 0.05 1229.6Arrow Goby 0.35 0.1 0.55 1.2California Killifish 0.1 0.2 0.25 0.45 1.5 7.0Shiner Surfperch 0.1 0.6 0.15 0.15 0.6 8.5Staghorn Sculpin 0.75 0.05 0.05 0.15 1.3 1.8Spotted SandBass 0.25 0.35 0.2 0.2 0.9 599.0

Dietary items

Newport Bay case study: Newport Bay case study: Assembling key parametersAssembling key parameters

Site

B ay

B ioregion

Spatial

Species

Feeding G uild

Fish vs. Invertebrate

Taxonom ic

W hat scale is m ost appropriate?

Develop BSAFs to Set Up SQOs at Develop BSAFs to Set Up SQOs at Appropriate ScaleAppropriate Scale

Macoma nasuta tissue data indicate different results for different water bodies. E.g., total PAHs tissue concentrations lower at given sediment concentration in San Francisco Bay - suggest water body specific BSAFs

Macoma nasuta - Total HPAHs

R2 = 0.1982 (SF)

R2 = 0.7042 (SD)

R2 = 0.0027 (TOM)

R2 = 0.3771 (SP)

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Sediment Concentration (log x+1, ug/kg, dry wt.)

Biv

alve

Tis

sue

Con

cent

ratio

n (lo

g x+

1, u

g/k

g, d

ry w

t.)

San Diego

San Pedro

SF

Tomales

Linear (SF)

Linear (San Diego)

Linear (Tomales)

Linear (San Pedro)

Prey For Humans and Wildlife

SedimentLinkage

Identify Good Target SpeciesIdentify Good Target Species

LimitedVariation in

Diet orHome Range

•Macoma nasuta is a good species for Laboratory Bioaccumulation test

-Recommended for bed sediment testing (EPA guidance)-Deposit feeder with high contaminant tolerance-Large California database available

•Species with existing data in SQO database

Species DDT PCB Chlordane DieldrinAll fish 0.27* 0.32* 0.22 0.41

California Halibut 0.03 0.13 0.07English Sole 0.50* 0.14 Neg. Slope

Shiner Surfperch 0.75* 0.55* 0.49* 0.83*Staghorn Sculpin 0.04 0.70* Neg. Slope

Starry Flouer 0.84* 0.76* 0.93*White Croaker Neg. Slope 0.02 0.03 0.15

California Halibut 0.19 0.63White Croaker 0.16 Neg. Slope 0.70* 0.004

California Halibut 0.46* 0.18

San Francisco Bay

San Pedro Bay

San Diego Bay

Starry Flounder

Summary of regression analysis of individual fish species vs. summed contaminant concentrations in sediment collected within 2 km of fish samples

* = significant linear relationship (p<0.05)

Shiner surfperch

Composites of 20 fish

Sou

th B

ay

Oak

land

San

Lea

ndro

Bay

S.F

. Wat

erfro

nt

Ber

kele

yS

an P

ablo

Bay

0

200

400

600

800

White croaker

Composites of 5 fish

Sou

th B

ay

Oak

land

San

Lea

ndro

Bay

S.F

. Wat

erfro

nt

Ber

kele

yS

an P

ablo

Bay

0

200

400

600

800 Spatial patterns in total PCB concentrations and stable isotope signatures suggest site fidelity for shiner perch in the San Francisco Estuary

Delta 15 N

13 14 15 16 17 18 19

Del

ta 1

3 C

-19.0

-18.5

-18.0

-17.5

-17.0

-16.5

-16.0

-15.5

Berkeley

Oakland

San FranciscoWaterfront

San Leandro Bay

San Pablo Bay

South BayTot

al P

CB

s

Map of San Francisco Bay showing locations of sediment, Shiner surfperch and Macoma nasuta collections used for empirical modeling of Biota Sediment Accumulation Factors

BSAF vs. BAF

1. BSAF = Lipid-normalized tissue conc./ organic carbon-normalized sediment conc.

2. BAF = Tissue conc. / sediment conc.

DDTs in San Francisco Bay Macoma clams vs. sediment

R2 = 0.6585

R2 = 0.2541

0.1

1

10

100

1 10 100 1000

Sediment DDT (ug/kg)

Tis

sue

DD

T (

ug

/kg

)

BAFBSAF

Lipid and organic carbon normalization (BSAF)does not improve relationship compared to BAF

Results and RecommendationsResults and Recommendations

Overall approach for development of biota-sediment relationship– Empirical (statistical) and mechanistic models

Target species– E.g., Shiner surfperch, Macoma clams

Scale of analysis– Develop biota-sediment relationships that are water-

body specific BAF vs. BSAF

– Collect data for BSAF (lipid, sediment OC) but consider using BAF only

Empirical BSAF and BAF models– Linear Regression (with varying home range size)– Calculation of average and distribution of BSAFs using

summary statistics

Mechanistic BSAF models– Using established modeling approach (Frank Gobas)

Species and spatial issues– Macoma nasuta, shiner surfperch reasonable– Sediment range optimization routine

Model Methods ToolkitModel Methods Toolkit

Example shows prey tissue targets for least terns –Similar tables for other sensitive and endangered species

–Only use species that reside in a given water body

Low and high Toxicity Reference Values from BTAGTarget fish concentrations based on body weight (e.g., 40 g)

e.g., Least Tern high effect threshold =

TRV high * Weight / Consumption rate

= 1.5 mg/(kg*d) * 40 g / 31.1 g/d = 1.928 mg/kg = 1928 ppbChemical Low effect threshold High effect threshold

ppb ppb Total DDT 12 1928 PCBs 116 1632

Fish and LaboratoryFish and LaboratoryTargets for WildlifeTargets for Wildlife

Example of CalculationsExample of Calculations

Yellow values = observed in CA fish

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L

Contact InformationContact InformationBen Greenfield: ben@sfei.orgMike Connor: mikec@sfei.orgwww.sfei.org

Acknowledgements

Steve Bay, Doris Vidal, Jim Allen, Steve Weisberg, SCCWRP Frank Gobas and Jon Arnot, Simon Frasier University Ned Black, Michael Anderson, Laurie Sullivan, Katie Zeeman,Robert Brodberg and other members of Bioaccumulation Work Group Chris Beegan, SWRCB Sarah Lowe, Bruce Thompson, Meg Sedlak, SFEI

Bioaccumulation Work GroupBioaccumulation Work GroupName Affiliation

Bill Paznokas CA Department of Fish & GameMichael Anderson CA Department of Toxic Substances ControlLaurie Sullivan National Oceanograpahic and Atmospheric AdministrationDenise Klimas National Oceanograpahic and Atmospheric AdministrationRobert Brodberg Office of Environmental Health Hazard AssessmentFred Hetzel San Francisco Bay - Regional Water Quality Control BoardKaren Taberski San Francisco Bay - Regional Water Quality Control BoardBeth Christian San Francisco Bay - Regional Water Quality Control BoardNaomi Feger San Francisco Bay - Regional Water Quality Control BoardTerri Reeder Santa Ana Region - Regional Water Quality Control BoardJim Allen Southern California Coastal Water Research ProjectDarcy Jones State Water Resources Control BoardNed Black U.S. Environmental Protection AgencyTerry Fleming U.S. Environmental Protection AgencyDan Russell U.S. Fish & Wildlife ServiceKatie Zeeman U.S. Fish & Wildlife ServiceSonce de Vries U.S. Fish & Wildlife Service / U.S. EPA