Mass Balance Models for Persistent, Toxic Bioaccumulative Chemicals (PBTs) in the Great Lakes: Application to Lake Ontario Joseph V. DePinto LimnoTech

Mass Balance Models for Persistent, Toxic

Bioaccumulative Chemicals (PBTs) in the Great Lakes:

Application to Lake Ontario Joseph V. DePinto

LimnoTechAnn Arbor, MI

Russell G. Kreis, Jr.U.S. EPA

Grosse Ile, MI

Great Lakes Research Session 233rd ACS National MeetingChicago, ILMarch 28, 2007

Outline

Overview of PBTs in Great Lakes– Legacy chemicals– Chemicals of

emerging concern Chemical Mass

Balance Models PBT management in

Lake Ontario (LaMP)– Development,

Calibration/Confirmation of LOTOX2

– Application of LOTOX2

1980s Brought Focus on “Toxic Substances” in the Great Lakes

What is a “Toxic” Substance? PBT

Is Persistent in the environment– Half-life > 8 weeks in any medium (IJC definition)

Tends to be Bioaccumulative– Characteristic of hydrophobic substances– Often not well-metabolized within organism

Elicits a Toxic response in exposed biota

Critical PBTs in Great Lakes Basin – Legacy Contaminants

(IJC Virtual Elimination Task Force, 1991)

Typical Great Lakes Legacy Toxic Substance

Historically very high emissions and loadings, followed by significant decrease in loadings through ‘70s and ‘80s

Very Hydrophobic – Strongly associated with particulate matter

Semi-volatile – subject to long-range atmospheric transport

Very Bioaccumulative – Human exposure largely through fish consumption

Typical Historic Pattern of PCB Loadings

Hydrophobic Chemicals Accumulate in Lake Sediments

Typical Great Lakes Toxic Substance



Semi-volatile – Atmospheric inputs were a significant source of PCBs to Great Lakes in late 1980s

– subject to long-range atmospheric transport

Percent Contribution of Atmospheric Deposition of Dioxin to Lake Ontario

Typical Great Lakes Toxic Substance



Semi-volatile – subject to long-range atmospheric transport

Very Bioaccumulative – Human exposure largely through fish consumption

Food Web Bioaccumulation

Biomagnification in Lake Ontario Food Web (IJC, 1987)

BAF for PCBs in Lake Ontario lake trout

6 x 106 L/Kg (ww)

Fish Concentrations Responded to Chemical Bans and Load Reductions

Chemicals of Emerging Concern in the Great Lakes

Tend to have similar properties as Legacy Contaminants but with recent and/or ongoing environmental release

Examples:– Polybrominated diphenylethers (PBDEs) – class of

chemicals used as flame retardants, plastics in consumer electronics, wire insulation

– Perfluoro octane compounds (PFOS/PFOA) – class of chemicals with wide use as surfactants and cleaners, 3M ScotchguardTM, insecticides

– Pharmaceuticals and Personal Care Products (PPCP) – tremendous number of human and veterinary drugs

Links to more information:– http://www.epa.gov/oppt/ – http://www.atsdr.cdc.gov/

http://www.epa.gov/oppt/

http://www.atsdr.cdc.gov/

Mass Balance Model Concept

Substance X

System BoundaryExternal Loading

Transport In Transport OutTransformations/Reactions

Rate of Change of [X] within System Boundary (dCX/dt) =

(Loading) (Transport) (Transformations)

Control Volume

Value of Models for PBT Management

Models can help evaluate and measure the success of load reduction programs– Provide a reference by forecasting the ramifications of

no further action– Explain/normalize the small scale, stochastic variability

in monitoring data so that longer term, system-wide trends can be seen

– Explain time trends of long-term monitoring Models can aid assessments for which there is no

actual environmental experience– Estimate impact of new chemicals– Forecast impact of unusual limnological factors (e.g.,

ANS invasions, major storm events, climate change)– More localized system responses to watershed load

reductions Models can help guide monitoring programs to be

more efficient and effective

Lake Ontario Lakewide Management Plan (LaMP)

GLWQA mandated Lakewide Management Plan (LaMP) in all Great Lakes

– Lake Ontario LaMP led by Four Party Secretariat– EPA-Reg 2, NYS DEC, Environment Canada, Ontario MOE

Lake Ontario LaMP identified lakewide beneficial use impairments:

– Restrictions on fish consumption– Degradation of wildlife populations– Bird or animal deformities or reproductive problems– Loss of fish and wildlife habitat

Priority LaMP chemicals– PCBs, DDT & metabolites, Dieldrin, Dioxins-Furans,

Mirex-Photomirex, Mercury LOTOX2 model develop to help address several

management questions for critical pollutants in Lake Ontario

Toxic Chemical Questions for Lake Ontario Lakewide Management Plan (LaMP)

1. What is the relative significance of each major source class discharging toxic chemicals into Niagara R. and Lake Ontario?

2. What is the role of toxic chemicals existing in sediments of the system?

3. Can changes in major source categories and sediments be quantitatively related to concentrations in the water column and fish?

4. Can observed trends in toxic chemical concentrations over time be explained?

5. How does a regulatory or remediation action affect the water column and fish tissue concentrations at steady-state and over time?

Information Flow in LOTOX2 Model

LOTOX2 - Time-dependent, spatially-resolved model relating chemical loading to concentration in water, sediments and adult lake trout

Hydraulic TransportModel

Chemical Loading

Sorbent Dynamics Model

Chemical Mass Balance Model

Food Chain Bioaccumulation Model

In situ Solids Levels

Toxicant in dissolved

form

Toxicant on suspended particulates

desorption

sorption

Canadian direct sources

Deep Sediment

diffusive exchange

resuspension

Atmospheric wet & dry deposition

Gas phase absorption Volatilization

settling

Outflow

Dissolved toxicant in

interstitial water

Toxicant on sediment

particulates

desorption

sorption

burial

Su

rficial

Se

dim

ent

Wa

ter Co

lum

n

Canadian tributaries

Niagara river

Hamilton Harbor

US tributaries

US direct sources

Total toxicant in water column

Total toxicant in sediment

Decay

Decay

LOTOX2 Chemical Mass Balance Framework

LOTOX2 Segmentation Scheme - plan view

Surface water column

Deep water column

Surface sediment

Projection of water columnto sediment segments

N

Bioaccumulation Model Framework

Toxicant Concentration

in Phytoplankton

(g/g) (1)


in Large Fish(g/g) (4)


in Small Fish(g/g) (3)


in Zooplankton(g/g) (2)

“Available” (Dissolved) Chemical Water Concentration (ng/L)

Physical-ChemicalModel of

Particulate and Dissolved Concentrations

Uptake UptakeUptakeUptake

Depuration Depuration Depuration Depuration

Predation

PCB Calibration/Confirmation:Historical Simulation

Reconstruction of historical PCB Loading

0

5,000

10,000

15,000

20,000

1930 1940 1950 1960 1970 1980 1990 2000

Year

Wat

ersh

ed P

CB

Lo

adin

g,

kg/y

r

0

2

4

6

8

10

Atm

osp

her

ic G

as P

has

e P

CB

Co

nce

ntr

atio

n,

ng

/m3

Load

Cg

Model Calibration/Confirmation for Water Column PCB

Water Column tPCB Concentrations

0

1000

2000

3000

4000

1980 1985 1990 1995 2000 2005

Year

Co

nce

ntr

atio

n, n

g/m

3LOTOX2 baseNYSDEC dataData (Literature)LOADS dataEnv. Canada (April 2004)

0

50

100

150

200

250

23 24 25 26 27 28 29 30 31 32 33 34 35 36

Segment Number

PC

B C

once

ntra

tion,

ng/

gData (1998) with Std.Dev.,Env. Canada

LOTOX2 Results

Confirmation of Average Surface Sediment Concentrations by Segment (1998)

Model Calibration/Confirmation - Lake Trout PCB

Model Confirmation 1998-2001

0

2

4

6

8

10

12

14

16

18

20

1930 1940 1950 1960 1970 1980 1990 2000

Year

La

ke

Tro

ut

tPC

B C

on

ce

ntr

ati

on

, m

g/k

g w

wt

Huestis et al., 1996 and Whittle 2003 Data (with Std Dev)EPA data (with Std Dev)LOTOX2 ModelDe Vault et al., 1996Whittle 2003 Data (w/ Std Error)Model Confirmation (Whittle 2003 Data w/ Std Error)Model Confirmation (EPA Data)

Model Confirmation - Lake Trout PCB

LOTOX2

0

2

4

6

8

10

12

14

1975 1980 1985 1990 1995 2000 2005 2010

Year

La

ke

Tro

ut

tPC

B C

on

ce

ntr

ati

on

, m

g/k

g w

wt

Huestis et al., 1996 and Whittle 2003 Data (with Std Dev)EPA data (with Std Dev)LOTOX2 ModelDe Vault et al., 1996Whittle 2003 Data (w/ Std Error)Model Confirmation (Whittle 2003 Data w/ Std Error)OME 2002Env. Canada

Calibration Period

Confirmation Period

ForcastingPeriod

Management Application of LOTOX2: Source Category

and System Response Time

Sediment Feedback Delays Lake Trout Response(all scenarios start at 2000 and run for 50 years)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

Base Forecast (No Action Scenario)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0


Scenario_2 (Natural Attenuation)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0


Scenario_2 (Natural Attenuation)

Scenario_8 (Eliminate all loads)

Influence of Sediment Feedback

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1995 2005 2015 2025 2035 2045

Year

lake

tro

ut

PC

B c

on

c (m

g/k

g w

w)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

LOTOX2 baserunforecast

baserun with NOsediment feedback

Base Forecast

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

Base Forecast

Baseline and Categorical Scenarios(all scenarios start at 2000 and run for 50 years)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Base Forecast

Scenario 7a (Zero all Point Sources)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Base Forecast


Scenario 7b (Scenario 7a + Zero all tributaries)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Base Forecast



Scenario 7c (Scenario 7b + Zero Niagara River)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1990 2000 2010 2020 2030 2040 2050

Year

Lak

e Tr

ou

t P

CB

Co

nc.

(m

g/k

g w

wt)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Base Forecast



Scenario 7c (Scenario 7b + Zero Niagara River)

Scenario 7d (Scenario 7c + Zero all atmospheric loads)

LOTOX2 Findings for Management of PCBs in Lake Ontario Significant load reductions from mid-60s through

80s have had major impact on open water and lake trout rapidly declining trends through that period

Lake is not yet at steady-state with current loads. Time to approximate steady-state with 2000 loads is ~30 years– Slower declines through ‘90s are result of sediment

feedback– Ongoing load reductions take 5-10 years to

distinguish from no post-2000 load reductions Point Sources of PCBs are relatively small fraction

of current total loading– Major non-point sources are upstream lake and

atmospheric gas phase absorption– At present model cannot address problems in

localized areas (tributaries, bays, nearshore areas (AOCs)), where PS reductions will have greatest value

Acknowledgements

USEPA – Region 2 for providing most of the funding for this modeling program and for providing guidance and coordination with data collection activities

Lake Ontario LaMP Workgroup members and other Four Party participants for continued support and input, including data collection and sharing

Other collaborative investigators during model development process, especially:

– Dr. Joseph Atkinson, University at Buffalo– Dr. Thomas Young, Clarkson University– Dr. William Booty, NWRI – Canada

USEPA – GLNPO for providing funding for the POM-LOTOX2 linkage project and for providing guidance based on experiences with mass balance modeling programs for other Great Lakes systems

Gulls Biomagnify PCBs from Fish

Documents

Mass Balance Models for Persistent, Toxic Bioaccumulative Chemicals (PBTs) in the Great Lakes: Application to Lake Ontario Joseph V. DePinto LimnoTech