Eric J. Weber US Environmental Protection Agency National Exposure Research Laboratory Ecosystems Research Division Athe

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Functional Groups that are Susceptible to Reduction

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

13. 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

14. 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

15. 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

16. 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

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

18. 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

19. 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

20. Transfer Data from D4EM Data Store to Modeling System D4EM Data Store Unit Definition Processor

21. 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

22. 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

23. 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)

24. 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