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Developing a FRAMES-Supported Environmental Fate Simulator: A Computational System for Estimating Environmental Concentrations of

Organic Contaminants

Presented to the EPA Exposure Science Community of Practice

Feb. 9, 2010

Eric J. Weber

US Environmental Protection AgencyNational Exposure Research Laboratory

Ecosystems Research DivisionAthens, GA

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General Outline for Today’s Presentation

• Address the Primary Questions– What is it?– Why do we need it?– Why now?

• Development of the Underlying Process Science• Development and Application of the Model/Software Technology• The Integration of this Knowledge for Conducting Spatially-Explicit

Risk Assessments

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The Source-to-Outcome Continuum

Source/Stressor Formation

Environmental Conc.

External Dose Target Dose

Biological Event

Effect/Outcome

EF&T Models

Exposure Models

PBPKModels

BBDRModels

SystemsModels

One of the primary goals of the Computational Toxicology Research Program is to improve the linkages across the source-to-outcome continuum

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Why Now?

Environmental Fate Simulator

DoD support through the SERDP program

- Physico-chemical properties processor

- Reaction pathway simulator

Targeted ORD Research Programs

-ExpoCastTM:

Providing an overarching framework for the science required to characterize biologically-relevant exposure in support of the computational toxicology program.

-Managing Chemical Risk (PoBNS):

Providing the tools and models for prioritizing chemicals for exposure and effects testing

-SP2 – LTG2:

Developing the data and models for spatially-explicit risk assessments

The modeling/software technology is available

-FRAMES

-D4EM

The underlying process science and data is available for simulating chemical transformations

-Hydrolysis

-Reductive Transformations

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Desired Capabilities of the Frames-based Environmental Fate Simulator

• Provide rate constants for model input and reactivity based binning

• Provide dominant transformation pathways and products as a function of environmental conditions

• Provide seamless input to chemical exposure models

• Conduct uncertainty and sensitivity analyses

• Provide access to measured and calculated physico-chemical properties

– A growing realization that measured data does not necessarily equate to good data

• Provide access to spatially-explicit environmental characterization and source data

– A need for the capability to conduct spatially-explicit risk assessments

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What is the FRAMES-Supported Environmental Fate Simulator?

TTh

1st Generation:

– Physico-chemical properties processor: A tool for accessing computational tools (e.g., SPARC and EPI Suite) and web accessible data bases (D4EM) of measured data to provide the physico-chemical data required for predicting chemical F&T

– Physico-chemical properties database: A depository for the calculated and measured data accessed through the Web

– Reaction pathway simulator: Based on functional group analysis and knowledge of the environmental system of interest, will provide the transformation products and rates for reductive transformation and hydrolysis

2nd Generation:

– Provide for the seamless parameterization of EF&T models that estimate the environmental concentration (EC) of organic chemicals (e.g., WASP, EXAMS, PRZM, BASINS, HSPF, MMSOILS, 3MRA, MULTIMED)

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Environmental Fate Simulation

Summary of Relevant Compounds:

Parent and Reaction Products

Physico-ChemicalProperties

Transformation RateConstants

Outputs of EFS

Explicit Uncertainty Assessment

Inputs to EFS

User specifiedParent Compound

Physico-Chemical Properties Processor

Calculated Data - SPARC and EPI Suite

Measured data (D4EM*)

Conceptual Design of the Environmental Fate Simulator

*DFEM – Data for Environmental Modeling

Reaction Pathway Simulator

FunctionalGroup

Analysis

Reaction Rate Estimation

Reaction Pathways and

Products

Environmental Scenario

User specified

OR

Generic

Parameritization of the Environmental Scenario

(D4EM)

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Development of the EFS requires:

-Knowledge of the current models, data needs and exposure scenarios used by the Program Offices

-Knowledge of the processes controlling chemical F&T

-The capturing of these processes in mathematical expressions and model code

-Software engineering to construct the RPS, provide the linkages to available calculators and data bases of measured data, and to provide for the seamless parameritization of EF&T models

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An example of a generic scenario is the standard pond scenario used by OPP for pesticide risk assessment

A single rain event causes pesticide runoff from a 10 hectare agricultural to a one hectare, 20,000 cubic meter volume, 2 m deep water body.

First Tier Screening Level: GENEEC requires Kd and degradation rate by summing rate constants for aerobic metabolism, abiotic hydrolysis and direct photolysis

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1) The functional groups that are susceptible to reductive transformations

2) The molecular parameters describing the “willingness” of chemicals to accept electrons

3) The predominant chemical reductants in natural systems (i.e., the source of electrons)

4) Pathways for electron transfer

5) Readily measureable indicators of reactivity in natural systems

What do we need to know to predict the reaction pathways and rates for reductive transformations?

Developing the Underlying Process Science

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Reductive Dehalogenation

Hydrogenolysis

Vicinal Dehalogenation

R X + 2e + 2H+ R H + X

C C

X X

+ 2e C C + 2X__

_

Nitroaromatic Reduction

Ar NO2 + 6e + 6H+_

Aromatic Azo Reduction

N NAr Ar + 4e + 4H+_/ ArNH2 + H2NAr/

NH2 + 2H2OAr

_

Functional Groups that are Susceptible to Reduction

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Sulfoxide Reduction

R1 S R2

O

+ 2e + 2H+ R1 S R2 + H2O

N-Nitrosoamine Reduction

N

N

O

R2R1

+ 2e + 2H+ NR2R1

H

+ HNO

Quinone Reduction

OO + 2e + 2H+ OHHO_

_

_

Functional Groups that are Susceptible to Reduction (Cont’d)

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time (min)

0 2 4 6 8 10 12

e- acc

epte

d by

pC

NB

(uM

)

0

20

40

60

80

100

120Soluble Fe(II) consumed

e- accepted by pCNB=4*[pCHA] + 6*[pCNA]

solu

ble

Fe(

II) c

onsu

med

(uM

)

Process Elucidation in Anaerobic Sediments

              

 

time (min)

0 2 4 6 8 10 12 14 16

C (

uM)

0

5

10

15

20

25

30

SUM

pCNH

pCNApCNB

NO2

CN

N

CN

O

pCNB pCNN pCNH pCNA

2e-

2H+

2e-

2H+

2e-

2H+

HN

CN

OH

NH2

CN

Conclusions:

- Nitroaromatic reduction is facile process in anaerobic sediments

- Soluble Fe(II) is a good predictor of reactivity

- One-electron reduction potentials are good molecular descriptors for predicting activity

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Tools for Calculating Physico-chemical Properties

SPARC: SPARC Performs Automated Reasoning in Chemistry

A "toolbox" of mechanistic perturbation models calibrated on measured data

- Resonance on light absorption spectra

- Electrostatic models on ionization equilibrium constants

- Solvation models (e.g., dispersion, induction, H-bonding, dipole-dipole) on vapor pressure, solubility, Henry’s law constants and GC RTs

NO2

Y

X

SPARC calculates how the X and Y: substituents modify the reactivity of the NO2 group

Prediction of Ionization Constants for 187 Pharmaceuticals

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Conceptual Multimedia

Model

Source Media Biota Receptors Risk/Hazard

The Research Question: How can EPA conduct multi-media, multi- receptor, and multi-pathway risk analyses at the national level

Developing the Modeling/Software Technology

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FRAMES: Framework for Risk Analysis in Multi-Media Environmental Systems

Connections between modules are checked for

validity by the system

A software system that facilitates the linking and execution of individual models

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FRAMES: Framework for Risk Analysis of Multi-Media Environmental Systems

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D4EM: Data for Environmental Fate Modeling

Services Provided by D4EM:

• Data retrieval• Statistical and geo-

processing operations• Data visualization• Model input formatting• Metadata generation

D4EM is a set of reusable components used to automate the retrieval and processing of data for use in executing environmental models

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Examples of Use Cases: A number of operations performed in concert to accomplish a specific task Examples include:

• Downloading data

• File formatting

• Geo-operations

• Logging metadata

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D4EMData Store

Unit Definition Processor

Transfer Data from D4EM Data Store to Modeling System

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The Integration of the Process Science and Modeling Technology Allows for Spatially-Explicit Risk

Assessments

Are public supply wells a source for human exposure to the fungicide pentachloronitrobenzene (PCNB) or its transformation products in Athens-Clarke County?

NO2

Cl

Cl

Cl

Cl

Cl

NH2

Cl

Cl

Cl

Cl

Cl

SurfaceComplexed Fe(II)

What information is required to answer this question?

• The EF&T processes controlling the reactive transport of PCNB in aquifers

• The physico-chemical properties required to simulate these processes

• The redox conditions of the aquifers

• The location of public supply wells relative to the sources of PCNB and human populations/activities

PCNB

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Conducting Spatially-Explicit Risk Assessments

The study of public-supply well vulnerability is one of five national priority topics being addressed by the National Water-Quality Assessment (NAWQA) Program

NO2

Cl

Cl

Cl

Cl

Cl

NH2

Cl

Cl

Cl

Cl

Cl

SurfaceComplexed Fe(II)

Dominant EF&T processes:

• Sorption predicted by Koc (Kow values predicted by SPARC and %OC values available in USGS databases)

• Nitroaromatic reduction rates predicted by E1 values (SPARC) and soluble Fe(II) (USGS)

Exposure information:

• Location of the wells relative to population centers is available

Aquifer redox conditions across the US (USGS)

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Contributors (Laboratory-based Studies)

Dalizza Colón (Fed)• Reactivity of iron oxides • QSAR development for reduction of NACs,

intermediates, and covalent binding of aromatic amines

• Effect of DOM on reduction rates of NACs

Rebecca Adams (TAI)• Azo dye reduction in sediments (Disperse Blue

79)

Mike Elovitz (NRC)• NAC reduction in sediments (TNT)

David Spidle (AI)• Covalent binding of aromatic amines with DOM

Kevin Thorn (USGS)• Characterization of with DOM

of aromatic amine binding sites in DOM by N15 NMR

John Barnett (EPA)• Analytical support

Jean Smolen (NRC), Paul Tratnyek (OGI)• Application of a molecular probe for

distinguishing pathways for electron transfer in sediments (abiotic vs. enzymatic)

Lisa Hoferkamp (NRC)• Identifying dominant chemical reductants in

sediments as a function of redox zonation for NACs

Rupert Simon (NRC)• Column studies of sediments (redox zonation)

Caroline Stevens (Fed)• Incorporating column kinetic results into a

reactive transport model

John Kenneke (EPA post doc)• Identifying dominant chemical reductants as a

function of redox zonation for halogenated methanes and ethanes in sediments

• Identifying molecular descriptors for reduction rates of halogenated methanes and ethanes

Said Hilal (Fed), Butch Carreira (UGA)• Development of SPARC calculators for

molecular descriptors

Judy Zhang (NRC post doc)• Elucidation of pathway for DOM as an electron

transfer mediator in sediments• Identification of readily measureable indicators

of reactivity for chemical reductants in sediments

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Contributors to the Conceptual Design of the EFS

Dalizza Colón

Wayne Garrison

Said Hilal

Jack Jones

Gerry Laniak

Rajbir Parmar

Susan Richardson

Caroline Stevens

John Washington

Jim Weaver

Gene Whelan

Kurt Wolfe