Assessment of Hg in Sediment, Lakes - EPA Archives...ns ns ns % Dev. Sed. ns-0.33 ns-0.53 HgT Perch...

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Assessment of Hg in Sediment, Water, and Biota of VT and NH

Lakes

A Collaborative REMAP Project

Neil Kamman, VTDECNew Hampshire Dep’t Environmental Services

Syracuse UniversityUSEPA Region 1 and USEPA - ORD

VT / NH REMAP-Hg ProjectCollaborators

• NHDES• VTDEC• Syracuse University• Dartmouth College• Sci. Museum of Minnesota• US Fish and Wildlife Service• VT Dep’t of Fish and Wildlife• BioDiversity Research Institute

Project inception

• In 1997, we knew very little about Hg levels in lakes of northern New England, outside of Maine.

• Application of sampling and analytical methods for trace metal work were just becoming available outside of academic realm.

• EPA Region 1 developed a strong interest in having a complete picture of Hg contamination across the entire region.

Core program goals for the period 1998 to 2000

• Use USEPA “EMAP” approach to:

• Identify the physico-chemical identity(ies) and hypothesized trophic Hg transfer pathway of lakes which pose a risk of Hg contamination to people and wildlife;

• Model Hg signal in lakes outside the study set, which can identify target waters for further fish/avian assessments;

• Model air deposition using MDN / UMAQL network

• Understand the historical accretion patterns of Hg into lakes as a tool to understand potential gains given reduced Hg deposition.

VT / NH REMAP-Hg ProjectStudy design:

• 90+ lake geographically randomized sample• Hg and meHg collections in water, sediment, plankton,

fish, avian piscivores, sediment cores• Hg-clean methods used for all Hg collections

– Water by CVAFS– Mud and Biota by CVAA

• Dataset describing current Hg conditions, • Design permits geographic analyses, and • Dataset ripe for opportunistic analyses (e.g. mining).

VT / NH REMAP-Hg ProjectParameter list

• Water: Hg, meHg, nutrients, DOC and acid-base chemistry, physicochemical measures

• Sediment: Hg, meHg , solids and organic content• Macrozooplankton – Hg, bulk >200u fraction• Yellow Perch in two size classes, Hg and meHg• Upper trophic level piscivore tissue Hg (e.g.,

loons, kingfisher): blood, feather, egg

Study lake locations

19981998

19991999

VT / NH REMAP-Hg ProjectIn the field

Pontoon-craft

VT / NH REMAP-Hg ProjectIn the field

Considerations for clean water collections

VT / NH REMAP-Hg ProjectIn the field

Clean, undisturbed overlyingwater

Perfect sediment-H2O interface

Core laminae

VT / NH REMAP-Hg ProjectIn the field

VT / NH REMAP-Hg ProjectFindings:

• Cumulative frequency distributions• Hg and lake trophic status• Hg and land-use• Water chemistry and methylation• Predicting tissue Hg• Piscivore Hg risk assessment• Air deposition models• Historic and current Hg accumulations

VT / NH REMAP-Hg ProjectFindings: Water Hg

020406080

100

0 0.5 1 1.5 2

Epilimnetic meHg (ng l-1)

Cum

ulat

ive

%

0

2040

60

80100

0 2 4 6 8

Epilimnetic HgT (ng l-1)

Cum

ulat

ive

%

New Hampshire

Vermont

020406080

100

0 1 2 3 4 5

Hypolimnetic meHg (ng l-1)

Cum

ulat

ive

%

020406080

100

0 10 20 30 40

Hypolimnetic HgT (ng/L)

Cum

ulat

ive

%

VT / NH REMAP-Hg ProjectFindings: Sediment Hg

020406080

100

0 0.2 0.4 0.6 0.8

Sediment HgT (ug g-1)

Cum

ulat

ive

%

New Hampshire

Vermont

Maine

Sediment Sediment methylHgmethylHg ranges from <0.0006 ug granges from <0.0006 ug g--11 to 0.021 ng gto 0.021 ng g--11

VT / NH REMAP-Hg ProjectFindings: Tissue Hg

020406080

100

0 0.2 0.4 0.6 0.8 1

Yellow Perch Avg. HgT (ug g-1)correted to age 4.6 yr fish

Cum

ulat

ive

%

020406080

100

0 0.1 0.2 0.3 0.4

Prey Yellow Perch Avg. HgT (ug g-1)composites of <15cm whole fish

Cum

ulat

ive

%

020406080

100

0 0.5 1 1.5 2

Zooplankton HgT (ug g-1.)

Cum

ulat

ive

%

VT / NH REMAP-Hg ProjectFindings Hg in Yellow Perch

Fish in acidic lakes have elevated Hg in their tissue:

To

tal H

g in

fis

h (

ug

g-1

d.w

.)

0.0

0.2

0.4

0.6

0.8

ANC < 300 ueq l-1 ANC > 300 ueq l-1

VT / NH REMAP-Hg ProjectHg and Lake Trophic Status

• Bloom Dilution– Pickhardt et al. PNAS 2002.

• More algae means less Hg per unit algae• So lakes of elevated trophic status should

show less Hg in the plankton, and therefore less bioaccumulation.

VT / NH REMAP-Hg ProjectLake trophic status

Epi

limne

tic m

eHg

(ng

l-1)

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

D E M O

Trophic State

D E M O

Mea

n ag

e 4.

6yr

Yel

low

per

ch fi

llet (

ug g

-1 w

.w.)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

VT / NH REMAP-Hg ProjectHg and Land Use

• Do forested and wetland rich watersheds deliver more Hg? Literature says they should.

• What about developed watersheds?

VT / NH REMAP-Hg ProjectLand use correlations (Spearman)

-0.41

ns

ns

ns

% Dev.

-0.53ns-0.33nsSed. HgT

-0.239ns-0.2160.38Perch fillet HgT

-0.198nsns0.228meHg

-0.32nsnsnsHgT

E911% Wetland & water

% Ag.% Forested

VT / NH REMAP-Hg Project Water chemistry influences

PC1PC1àà: +Cond, +ANC, +pH, +SO4, +CL: +Cond, +ANC, +pH, +SO4, +CL--

PC1

-4 -2 0 2 4 6 8T

issu

e H

gT u

g g-1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8There is too much There is too much heterogeneity of lake heterogeneity of lake types and factors to yield types and factors to yield satisfactory ‘univariate’ satisfactory ‘univariate’ relationships. relationships.

Joint ‘multivariate’ Joint ‘multivariate’ distributions provide distributions provide insight into how the insight into how the interaction of factors interaction of factors controls Hg.controls Hg.

Modeling compliance with tissue criterionYellow perch fillets <0.3 ug g-1 HgT, Meets EPA Criterion: -1,580 – 82.92(lnANC) + 45.35(lnDOC) + 1,658(ln_pH) - 18.99(lnCond) – 35.09(invrtFlush) Eq. 1. Yellow perch fillets >0.3 ug g-1 HgT, Violates EPA Criterion: -1,494 – 81.94(lnANC) + 48.49(lnDOC) + 1,610(ln_pH) – 18.65(lnCond) – 33.02(invrtFlush) Eq. 2. Where: lnANC = ln (1+acid neutralizing capacity, in mg l-1, measured from the epilimnion) lnDOC = ln (1+dissolved organic carbon, in mg l-1, measured from the epilimnion) ln_pH = ln (1+pH, in standard units, average of total water column) lnCond = ln (1+conductivity, in us cm3, average of total water column) invrtFlush = (Flushing rate, in # yr-1)-2

Modeling compliance with tissue criterion

0

10

20

30

40

50

60

70

80

%

VT NH

MeetsFails

0

10

20

30

40

50

60

%MeetsFails

VT / NH REMAP-Hg ProjectCumulative Risk Index based on Hg in Loon Blood

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

ME

NH

VT

low

moderate

high

xhigh

History of Hg Accumulation:Atmospheric Hg Deposition

• Can wet Hg deposition be modeled given existing MDN/UMAQL network?

• What about dry Hg deposition?

VT / NH REMAP-Hg Project Atmospheric deposition

VT / NH REMAP-Hg Project

• Is the Hg flux rate to VT and NH lake sediments presently increasing or decreasing?

• Can we then infer changes in the atmospheric deposition rate?

VT / NH REMAP-Hg ProjectFindings HgT Fluxes (ug . m-2 . yr-1)

Note the synchrony of flux increase and decrease

Spring Lake Lake Carmi High Pond Branch Pond Willard Pond

Sessions Pond Wallingford Pond Gilman Pond Intervale Pond McConnell Pond

Beaver Lake

0 50 100 150 200

210 P

b Y

ear

1800

1850

1900

1950

2000Wheeler Pond Dudley Pond

0 20 40 60 80100120

Increasing Basin Area / Lake Area

VT / NH REMAP-Hg Project Synopsis:

• Hg concentrations in VT and NH lakes range widely. [ ]’s are greater in NH, and in lake hypolimnia.

• Yellow perch fillets vary w/ age and size, and age-correction most accurately captures variation in Hg accumulation rates across lakes.

• Hg, meHg, and tissue Hg all vary with trophic status. Eutrophic and dystrophic lakes have higher meHg, but only dystrophic lakes show higher tissue Hg.

• MethylHg varies significantly w/ numerous water chemistry parameters. The way these parameters inter-relate is important to understanding in which lakes meHg is more readily produced.

• For VT-NH lakes, the likelihood that yellow perch tissues will violate current criteria can reasonably be modeled using simple parameters.

VT / NH REMAP-Hg Project Synopsis:

VT / NH REMAP-Hg ProjectPubs:

• Paleolimnology– Atmos. Environ. 2003.

• Water and Tissue Chemistry and Statistics: – Env. Tox. Chem. 2004.

• Modeling, air deposition:– Ecotoxicology in prep.

• Modeling: Air+watersheds+in-lake interactions– Ongoing w/ EPA-ORD

How does this all get used??

• TMDL’s– Air dep maps and by-lake estimates provide one estimate of

critical loads – these dep maps are in preparation for all of NE presently

– Additional modeling can and will follow w/ these data• Env. Indicators:

– CDF’s provide reproducible, statistically valid estimates of region-wide contamination signals

– Paleo profiles provide reproducible, statistically quantified and landscape-integrated estimates of deposition, both presently and historically

• NERC Hg project for NE US and SE Canada.• EPA Regional Modeling Project