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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 Agency

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Developing a FRAMES-Supported Environmental Fate Simulator: A Computational System for Estimating Environmental Concentrations of Organic ContaminantsPresented to the EPA Exposure Science Community of PracticeFeb. 9, 2010

Eric J. Weber

US Environmental Protection Agency

National Exposure Research Laboratory

Ecosystems Research Division

Athens, GA

slide2
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
slide3

Effect/Outcome

EF&T Models

Systems

Models

Environmental Conc.

Biological Event

BBDR

Models

Exposure Models

External Dose

Target Dose

PBPK

Models

The Source-to-Outcome Continuum

Source/Stressor Formation

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

slide4

Why Now?

  • 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 underlying process science and data is available for simulating chemical transformations
  • Hydrolysis
  • Reductive Transformations
  • The modeling/software technology is available
  • FRAMES
  • D4EM

Environmental Fate Simulator

DoD support through the SERDP program

- Physico-chemical properties processor

- Reaction pathway simulator

slide5

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
slide6

What is the FRAMES-Supported Environmental Fate Simulator?

  • 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)

TTh

slide7

Reaction Pathway Simulator

Functional

Group

Analysis

Reaction

Pathways and Products

Environmental Scenario

Reaction Rate Estimation

Generic

OR

Physico-Chemical Properties Processor

User specified

Calculated Data - SPARC and EPI Suite

Measured data (D4EM*)

Conceptual Design of the Environmental Fate Simulator

Environmental Fate Simulation

Outputs of EFS

Inputs to EFS

Summary of Relevant

Compounds:

Parent and Reaction Products

User specified

Parent Compound

Physico-Chemical

Properties

Transformation Rate

Constants

Explicit Uncertainty Assessment

Parameritization of the Environmental Scenario

(D4EM)

*DFEM – Data for Environmental Modeling

slide8

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
slide9

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

slide10

Developing the Underlying Process Science

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

  • The functional groups that are susceptible to reductive transformations
  • The molecular parameters describing the “willingness” of chemicals to accept electrons
  • The predominant chemical reductants in natural systems (i.e., the source of electrons)
  • Pathways for electron transfer
  • Readily measureable indicators of reactivity in natural systems
slide13

Process Elucidation in Anaerobic Sediments

  • 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
slide14

Tools for Calculating Physico-chemical Properties

SPARC: SPARC Performs Automated Reasoning in Chemistry

Prediction of Ionization Constants for 187 Pharmaceuticals

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

  • 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
slide15

Developing the Modeling/Software Technology

Conceptual Multimedia Model

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

slide16

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

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

Connections between modules are checked for validity by the system

slide18

D4EM: Data for Environmental Fate Modeling

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

  • Services Provided by D4EM:
  • Data retrieval
  • Statistical and geo-processing operations
  • Data visualization
  • Model input formatting
  • Metadata generation
slide19

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
slide20

Transfer Data from D4EM Data Store to Modeling System

D4EM

Data Store

Unit Definition Processor

slide21

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?

PCNB

  • 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
slide22

Conducting Spatially-Explicit Risk Assessments

Aquifer redox conditions across the US (USGS)

  • 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

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

contributors laboratory based studies
Contributors (Laboratory-based Studies)
  • 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
  • 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)
contributors to the conceptual design of the efs
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

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