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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
F
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)
F
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
L
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
F
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
F
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
F
L
Contact InformationContact InformationBen Greenfield: [email protected] Connor: [email protected]
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