Welcome to the CLU-IN Internet Seminar

Welcome to the CLU-IN Internet Seminar

Biological-based Assays - Indicators ofEcological Stress

Sponsored by: National Institute of Environmental Health Sciences, Superfund Research Program

Delivered: September 23, 2010, 2:00 PM - 4:00 PM, EDT (18:00-20:00 GMT)

Instructors:Bruce Duncan, Senior Ecologist with EPA Region 10's Office of Environmental

Assessment ([email protected])Jim Shine, Associate Professor of Aquatic Chemistry, SRP Grantee

([email protected])Moderator:

Beth Anderson, Program Analyst, Superfund Research Program ([email protected])

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3

Bioavailability of Sediment Contaminants

1. NIEHS-sponsored Bioassay Network

2. Relationships between sediment, water, mussels, SPMEs, & fish

Bruce Duncan, EPA Region 10, Seattle, Office of Environmental Bruce Duncan, EPA Region 10, Seattle, Office of Environmental AssessmentAssessmentRisk Evaluation Unit Risk Evaluation Unit Session IV: Biological-based Assays – Indicators of Ecological StressSession IV: Biological-based Assays – Indicators of Ecological StressEcological Risk: New Tools and Approaches – September 23, 2010Ecological Risk: New Tools and Approaches – September 23, 2010

4

KC Donnelly

5

National Network to Investigate the Utility of Short-term Bioassays for Evaluating

Sediment Quality

Investigate the utility of using SRP-developed assays to characterize the toxicity of complex mixtures in sediment

Hypothesis: SRP-developed assays will detect degraded sediment quality effectively and serve as an additional line of evidence if integrated into risk assessment

6

Background

Superfund Research Program

◦ Created in 1986 under the Superfund Amendments and Reauthorization Act (SARA)

◦ University-based grants program◦ Basic research◦ Complement EPA and ATSDR◦ Under National Institute of Environmental Health

Sciences

7

Background

Collaboration between 5 University based Superfund Research Programs◦ Texas A&M University◦ Duke University◦ Michigan State University◦ University of California – Davis◦ University of California – San Diego

8

Summary Table – “calibration” testing single contaminants and mixtures

BIOASSAY

Chemical

In vivo EROD (EC50)

Fish embryo teratogenicity

(EC10) GJIC

(EC50) CALUX (EC50)

BaP

1 ppb 200 ppb NA 405 ppm (EC40)

Flu

NA NA 4.4 ppm NA

BaP+Flu 1 ppb 100 ppb 4.8 ppm 422 ppm (EC40)

Coal-tar .06 ppm 5 ppb 2.87 ppm 341 ppb

PCB 126 .03 ppb 0.1 ppb NA 49 ppt

PCB 153 NA NA 4.34 ppm NA

PCB mix

-- -- -- --

9

Conclusions Calibration step was completed

◦ Assays were not always more sensitive but can serve as an additional line of evidence

◦ Improved specificity

2nd Phase of project anticipated◦ Aliquots of homogenized sediment will be sent to

Superfund Research Program investigators for analysis

Study will attempt to “crosswalk” with biological effects data from sediment toxicity bioassays

10

Bioavailability of Sediment Contaminants

Relationships between sediment, water, mussels, SPMEs, & fish

Bruce Duncan, EPA Region 10, Office of Environmental Assessment, Risk Bruce Duncan, EPA Region 10, Office of Environmental Assessment, Risk Evaluation Unit Evaluation Unit & Adjunct Professor, Texas A&M University, Health Science Center, School of & Adjunct Professor, Texas A&M University, Health Science Center, School of Rural Public Health, Dept Environmental and Occupational Health Rural Public Health, Dept Environmental and Occupational Health In collaboration with Matt Kelley, Postdoctoral Fellow - Dugas Lab, LSU In collaboration with Matt Kelley, Postdoctoral Fellow - Dugas Lab, LSU Health Sciences Center-ShreveportHealth Sciences Center-Shreveport

11

Partners

◦EPA R10 – deployment, retrieval, design Dive Team, Manchester Lab, Field support, Program volunteers

◦Texas A&M University – design, tissue, water, sediment analysis

KC Donnelly (dec); Matt Kelley; Thomas McDonald

◦Southern California Coastal Water Research Project – SPME design, analysis

Keith Maruya, David Tsukada, Wayne Lao

◦NMFS – juvenile salmon Jim Meador

◦Applied Biomonitoring – mussel prep, measuring, design Michael Salazar, Sandra Salazar

12

Site History: Lower Duwamish Waterway

http://dnr.metrokc.gov/wlr/waterres/wqa/wqpage.htm

13

2008 stations

13

14

2009 stations

15

tPA

H (

ng/g

dry

)

0

1000

2000

3000

4000

5000

6000

7000

8000

19000

20000

21000

22000

0-15 cm15-30 cm

K1 B2 B3 T4 T5 P2P1

Sediment - 2008

16

Pore water & Surface Water - 2008

0

200

400

600

800

1000

1200

1400

1600

K1 B2 T4 T5 P1

tPA

H (

ng/L

)

0

50

100

150

200

250

300

K1 B2 B3 T4 T5 P2P1

tPA

H (

ng/L

)

17

Sediment PAH bioavailabilityDesign from the sediment up:

Surface Water

Mussels & SPMEs - top of cages

Mussels & SPMEs - bottom of cages

Sediment

Fish in cages

Porewater

18

Sediment Sampling

19

Pore Water Collection

20

SPMEs – inside cages and in sediment 2008, top and bottom of cages in 2009

21

New for 2009

Mussels – top and bottom of cages, matched to SPMEs

22

NOAA Field Facility - Mukilteo

Fish – juvenile salmonids

23

Fish Transport

24

Cage Deployment

25

Cage Retrieval

26

Fish retrieval/processing

27

Water Sampling

28

Sediment PAH bioavailabilityHow well do SPMEs concord with fish and mussel tissue?

What are relationships between biotic and abiotic media both in/on and above the sediment?

Status on other analyses

29

Some ExpectationsFish tissue PAHs – have seen before, but not often

Sediment/Water – expect higher tPAH concentrations in sediment porewater, perhaps different mix of individual PAHs

Mussels – challenges with mussels in contact with sediment

SPMEs – could show reduced variablity and concordance with mussel tissue

30

Any PAHs in the fish?

*

tPA

Hs

(ng/

g w

et)

0

250

500

750

1000

1250

1500

2250

2500

2750

*

*

*

Fish tissue PAHs – previous & new work

2009

0

250

500

750

1000

1250

1500

31

LDW 2004-2007 Sediment Means 2009 ng/g dwt

Site K1 B2 B3 B4 K1 B2 P1 T4

Total PAHs 1782 2712 1730 1395 2537 3418 2583 1752

Total PCBs 16 1248 1752 383

Sediments any different?

32

LDW 2008 SPME tPAHs (ng/L)

K1 T5 B2 P1

Porewater SPME

sampler58 114 45 38

Water Column SPME

sampler

32 109 28 28

2009

K1 T4 P1

SPME-bottom 99 338 268

SPME-top 58 109 93

SPMEs differ?

33

How did mussels do? growth/survival

0.0

1.0

2.0

3.0

4.0

5.0

6.0

K-t K-b T-t T-b P-t P-b Pier

Station and top or bottom

mm

or

mg

gro

wth

0%

20%

40%

60%

80%

100%

120%

% S

urv

iva

l

mm growth mg wt %surv

34

Mussel Growth – closer lookYellow=bottom; blue=top; green=pier

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

mm growth

mg

gro

wth

P

P

TK

K

T

35

How about relationships among parameters?

36

Sediment–water tPAH relationship

water at hatchery = 48.25

y = 0.0764x - 83.428

R2 = 0.8146

0

50

100

150

200

250

0 1000 2000 3000 4000

Sediment

Wat

erB

PT

K

water at hatchery = 48.25

37

LDW090704 (Sediment) P1 #1

0

50

100

150

200

250

300

350

400

Su

Co

rrec

ted

Co

nc.

(ng

/dry

g)

M 4-23-TAM/Paccar/Bottom (Tissue) LDW0047 Mussel

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Su

. Co

rre

cte

d C

on

c. (

ng

/we

t g

)

Station P1 (Fish Tissue) LDW0003

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Su

. Co

rre

cte

d C

on

c. (

ng

/we

t g

)PAH patterns - Sediment, water and

tissue

LDW090703 (Water) LDW0008

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Su

Co

rre

cte

d C

on

c. (

ng

/L)

Sediment Mussels

Fish Water

38

K-top

T-top

P-top

0

20

40

60

80

100

120

ng

/L

SPME - PAHs Top 2009

K-top T-top P-top

K-bottom

T-bottom

P-bottom

0

50

100

150

200

250

300

350

ng

/L

SPME - PAHs - Bottom 2009

K-bottom T-bottom P-bottom

PAH patterns - SPMEs

39

Mussels (tPAH)Sediment, water and SPMEs

relationships

40

•

Summary of overall relationships

•puzzling in some respects

•recall the loss of the B site samples which had highest sediment PAHs

41

tPAH in mussels v water, SPME, other mussels

bottom mussel vbottom SPME

top mussel vtop SPME

bottom mussel vwater

top mussel v water

top mussel v bottom mussel

top SPME vbottom SPME

40

60

80

100

120

140

160

180

200

220

0 50 100 150 200 250 300 350 400

media mean value

Mu

ss

el T

iss

ue

Summary -mixed relationships

42

tPAH in mussels v sediment

top mussel

bottom mussel

106

126

146

166

186

206

1500 2000 2500 3000

Sediment mean value

Mu

ss

el T

iss

ue

Summary -mixed relationships –note scale for sediment concentration (missing data from 3400 mg/kg dw sediment)

43

Summary of variability (SEs) for different measures

44

Summary: Reducing variabilitytPAH SE v mean for water, sediment, mussels, SPMEs

0.01

0.1

1

10

100

1000

10000

10 100 1000 10000

media mean value

SE

Water tPAH Sediment tPAH Mussel Tissue top tPAH

Mussel Tissue bottom tPAH SPME tPAH top SPME tPAH bottom

Reference-Lab Pier Mussel Farm T = 0

Sed

SPME

Mussels

Mussel controls

45

Summary: Next steps

PCBsPorewater

Questions

Tools to Assess Metal Bioavailability in Aquatic Ecosystems

Jim Shine

Department of Environmental Health

Harvard School of Public Health

Funding: NIEHS Superfund Research Program 46

Outline

-Introduction: Why Care About Metal Speciation?

- The ‘Gellyfish’: Measurement of Metal Speciation in Aquatic Ecosystems

-Design/Testing

-Field Application I: Metal Speciation in Boston Harbor

-Field Application II: Sensor of Metal Uptake in Mussels

- Concluding Remarks

47

Importance of Metal Speciation:

- Free Metal Ion: A Key Metal Species

- Allows understanding of distribution of metals in a system

-Predictive of transport, fate, biological uptake

- Not a constant fraction of total metal in space and time

- Water quality criteria based on total metals awkward

- Biotic Ligand Model: New generation of WQC

- Based on free metal ion interacting with biota

48

Current Speciation Analytical Techniques

-Difficult, time consuming, expensive, require specialized training

-Can only be done for one metal at a time

-Limit scope of speciation studies (space and time)

- Modeling approaches? Cu2+ = f(Cutotal, DOC)

- Problem: How to generate large enough data sets to be useful

- What is the spatial, temporal variability in speciation?

- What environmental factors affect speciation?

- Need: Simple, inexpensive tool to measure speciation 49

Equilibrium Sampler (Gellyfish) : Design Criteria

- Metal binding resin held within a polyacrylamide wafer

- binding sites: Iminodiacetate (IDA)

- IDA sites equilibrate with free metal ions in the surrounding solution

- Metals back extracted into 5% Nitric Acid

- Metal analysis by ICP-MS

- Knowledge of IDA affinity for metal allows calculation of free metal ion in surrounding solution

Polyacrylamide gelToyopearl AF Chelate-650M resin

40 mm

100 µm

2 mm

GelBond Polystyrene Film

50 mm

50

Iminodiacetate (IDA) Binding Sites

- Not metal specific

- will bind a wide range of transition metals

- Weak affinity for salt cations (Na, Ca, Mg)

51

52

Gellyfish: Laboratory Development and Testing

Step 1: Determine Equilibration Time:

t90 = 26 h 53


Step 2: Establish Thermodynamic Parameters

- Metal affinity for IDA

- Complexation capacity

0.00e+00 5.00e-09 1.00e-08 1.50e-08 2.00e-08 2.50e-08

Free Cu2+ (Mol/L)

0

100

200

300

400

Cu

ID (

µm

ol/

L)

54

0.00e+00 2.00e-06 4.00e-06 6.00e-06 8.00e-06 1.00e-05

Free Zn2+ (Mol/L)

0

100

200

300

400

Zn

ID (

µm

ol/

L)

0.00e+00 1.00e-07 2.00e-07 3.00e-07 4.00e-07 5.00e-07

Free Ni2+ (Mol/L)

0

100

200

300

400

NiI

D (

µm

ol/

L)


Data for other metals….

- Metals Tested: Cu, Pb, Ni, Zn, Cd

55


Step 3: Incorporate Results into a Computer Model

- Spreadsheet Based

- Accounts for Salinity, pH Effects

- Accounts for metal:metal competitive interactions 56


Step 4: Challenge Gellyfish/Model

A) Effect of varying salinity on Cu Uptake

57

1e-10 1e-09 1e-08 1e-07 1e-06

[Zn2+] (Mol/L)

0.1

1

10

100

Cu

ID (

µm

ol/

L)

Modeled

Measured

Step 4: Challenge Gellyfish/Model, cont’d


B) Effect of high Zn2+ on Cu uptake by Gellyfish

58

0.1 1 10 100 1000

Modeled Concentration (µmol/L)

0.1

1

10

100

1000

Mea

sure

d C

onc.

(µ

mol

/L)

Pb

Cu

Zn


C) Mixed metal:metal competition experiments

Step 4: Challenge Gellyfish/Model, cont’d

59

Field Experiment I: Spatial/Temporal Dynamics of Metal Speciation in Boston Harbor

- What is the concentration of free metal ions in Boston Harbor?

- Is this a problem?

- How does speciation vary during a year?

- Is there spatial variability in metal speciation?

- What factors most influence changes in metal speciation?

60

Sample Locations:

Mystic River

Inner Harbor

Fort Point Channel

Savin Hill Cove

Marina Bay

61

Results: Total Dissolved Copper

Date

0

50

100

150To

tal

Dis

solv

ed C

u (

nM

ol/

Kg

)

Fort Pt Channel

Inner Harbor

Marina Bay

Mystic River

Savin Hill

J F M A M J J A S O N D

Acute WQC

Chronic WQC

- Water Quality Criteria (WQC) exceedances:

- 1 location (Marina Bay)62

Results: Free Cu2+:

Date

0

10

20

30

40

50

Fre

e C

u2

+

(pm

ol/

kg)

Fort Pt Channel

Inner Harbor

Marina Bay

Mystic River

Savin Hill


- More accurate assessment of effects

- Potential effects level: 10 pMol/kg (acute)

- exceeded for many months

- exceeded at multiple locations 63

Date

1e-12

1e-11

1e-10

1e-09

1e-08

1e-07

Fre

e M

e2

+ (

pm

ol/k

g)

Cu

Zn

Pb

Ni

Cd


Data For Other Metals: Free Metal Ion

Location: Fort Point Channel

- Correlations between metals?

- Independence of metal behavior? 64

Date

0.01

0.1

1

10

100

1000

Me

2+ /

Me to

tal (

%)

Cu

Zn

Pb

Ni

Cd


Data For Other Metals: % Free Metal Ion

Location: Fort Point Channel

- Similar effects of environmental factors on speciation?65

What We Can Do (And Have Done) With the Data…

-Correlation Structure of total metals and free metal ions

- Spatial and Temporal Autocorrelation Structure

- temporal component of variance larger- informs monitoring strategies

- Factors Influencing Metal Speciation (Regression Analyses)

- highlight: importance of antecedent rain/DOC interaction- rain associated DOC has less affinity for metals- more nuanced modeling needed?

- Ligand Specificity Experiments

- Are natural ligands metal specific?- to what extent can one metal alter the speciation of other metals?

66

67

( a) Cu

5. 0E- 11

1. 5E- 10

2. 5E- 10

3. 5E- 10

4. 5E- 10

5. 5E- 10

1. E- 09 1. E- 08 1. E- 07 1. E- 06

Total Zn Addi t i on (M)

Cu2+ i

n Su

rrou

ndin

g

Solu

tion

(M)

( c) Pb

5. 0E- 11

2. 5E- 10

4. 5E- 10

6. 5E- 10

8. 5E- 10

1. E- 09 1. E- 08 1. E- 07 1. E- 06

Total Zn Addi t i on (M)Pb

2+ i

n Su

rroun

ding

Solu

tion

(M

)

(d) Ni

2. 0E- 10

8. 0E- 10

1. 4E- 09

2. 0E- 09

2. 6E- 09

3. 2E- 09

1. E- 09 1. E- 08 1. E- 07 1. E- 06


Ni2+ i

n Su

rrou

ndin

g

Solu

tion

(M)

(e) Cd

3. 0E- 10

6. 0E- 10

9. 0E- 10

1. 2E- 09

1. 5E- 09

1. 8E- 09

1. E- 09 1. E- 08 1. E- 07 1. E- 06


Cd2+ i

n Su

rrou

ndin

g

Solu

tion

(M)

Results of a Ligand Specificity Study:

-Boston Harbor (Winter)- Water spiked with increasing Zn- Free metal ions of Cu, Pb, Ni, and Cd followed- Results: Free metal ions of Cu, Pb, Ni, Cd increase

- Implication: Ligands not metal specific- Summer Results Different!

Me2+

Phytoplankton

Gellyfish

Concordance?Direct Uptake

Trophic Transfer

Shellfish

Experiment II: Does the Gellyfish Sampler Mimic the Uptake of Metals into Biological Organisms?

Megel

(mol)

Me

org

an

ism

(m

ol/g

)

m = ??

68

Co-Deployment of Gellyfish, Mussels in Boston Harbor and

Massachusetts Bay

Gellyfish Mounted in Baskets

Gellyfish Baskets and Mussel Cages on Deployment Line 69

Locations:

Buoy B

Cape Cod Bay

Deer Island

Inner Harbor (Aquarium)

Outfall (OSM-1, OSM-4, OSM-6)

Quincy Bay

Savin Hill Cove

Sampling Locations:

70

0.00 0.10 0.20 0.30 0.40 0.50(E-1)

Gellyfish Pb (mean µg)

0

2

4

6

8

10M

uss

el P

b (

mea

n µ

g/g)

Mussel Mean Pb = 161.76 Gellyfish Mean Pb + 1.29R2 = 0.95; p < 0.0001

0.00 0.16 0.32 0.48 0.64 0.80

Mean Gellyfish Cu (µg)

3

6

9

12

15

Mea

n M

usse

l Cu

(µg/

g)

Mussel Mean Cu = 4.71 Gellyfish Mean Cu + 7.64R2 = 0.52; p = 0.02

Results: Gellyfish x Mussel Regressions

Pb: R2 = 0.95; p<0.001

Cu: R2 = 0.52; p=0.02

71

Concluding Remarks – Gellyfish

- Simple, effective tool for measurement of metal speciation

- Multiple Uses:

- Biogeochemistry studies

- Surrogate measures of biological uptake

- User groups:

- Geochemists

- speciation studies, competition studies

- Environmental Managers

- monitoring programs

- TMDL assessments

- New Sampler Designs:

- Shorter equilibration times (<10 hrs

- Metal specific ligands (biologically relevant ligands?)72

73

SRP would also like to thank the presenters and moderators of the Ecological Risk: New Tools and Approaches webinar series:

Presenters:Gary Ankley, Toxicologist, USEPA/ORD Mid-Continent Ecology Division

David Barber,* Associate Professor, Toxicology, University of Florida

Nancy Denslow,* Professor Toxicology, University of Florida

Kim Anderson,* Professor, Oregon State University

Celia Chen,* Research Associate Professor, Dartmouth College

Mark Hahn,* Woods Hole Oceanographic Institution

Richard Di Giulio,* Director, Duke University’s Integrated Toxicology Program, Duke University

Bruce Duncan, Senior Ecologist, EPA Region 10

Jim Shine,* Associate Professor of Aquatic Chemistry, Harvard University

Moderators:Heather Henry, Program Administrator, Superfund Research Program

Charles Maurice, Superfund and Technology Liaison, EPA Office or Research and Development

Diane Nacci, Senior Research Biologist, EPA Office or Research and Development

Beth Anderson, Program Analyst, Superfund Research Program

*SRP Grantee (current or former)

Acknowledgements

74

http://www.niehs.nih.gov/research/supported/srp/events/riskelearning/ecorisk.cfm

SRP would like to thank the Ecological Risk Planning Committee:

•Marc Greenberg (EPA, ERT Region 2)

•Heather Henry (NIEHS, SRP)

•Sharon Thoms (EPA, Region 2)

•Jean Zodrow (EPA, Region 10)

And Members of the:

•EPA Ecological Risk Assessment Forum (ERAF)

•DOD Tri-Services Ecological Risk Assessment Working Group (TSERAWG)

Acknowledgements

75

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