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  • 1. 3/2/2014 Development of the Environmental Fate Simulator (EFS):  A  tool for predicting the degradation pathways of organic  chemicals in groundwater aquifers Process  Scientists: Caroline Stevens Said Hilal Dalizza Colón Jack Jones Eric Weber Ecosystems Research  Division  US Environmental  Protection Athens, GA Multi‐Media Modelers: Gene Whelan Justin Babendreier Software  Engineers: Kurt Wolfe Rajbir Parmar Mike Galvin Mitch Pelton  (PNNL) The EFS will be  publicly  available 1
  • 2. 3/2/2014 What is the need for the  Environmental Fate Simulator? The Problem: Current tools available to EPA for  conducting exposure and health  (human and ecological) assessments  are not adequate: • TSCA inventory :  − > 85,000 chemicals  − High quality data for < 2% • New Chemicals (PMN Program):  − 20 to 30 new chemicals per  week • FIFRA inventory: − ~ 1,100 agrochemicals − High quality pchem data for  nearly 100% Our Response: Development of the Environmental  Fate Simulator (EFS): • High throughput computational  system for  providing molecular  and environmental descriptors for  consumption by EF&T models Requires: Knowledge of the process science  controlling chemical fate and  transport The ability to encode this  information into a readable format Integration of existing  cheminformatics applications and  modeling software technologies What is it that requires automation? Exposure/Testing  Scenario: Chemical Structure of  Parent Chemical Reaction  Medium The information required  to simulate this scenario: What is needed to  automate this process: • Knowledge of the process  science underlying  transformation pathways Cheminformatics  applications for encoding   the process science • Molecular descriptors  necessary for predicting  mobility and reaction rates Access to physico‐chemical  calculators Estimated Concentrations of  • Environmental descriptors  necessary for predicting  the Parent Chemical and  reaction rates Predicted Transformation  Products • Parameritization of EF&T  models Software for providing  access to data from  online databases Software providing  seamless  parameritization of  EF&T models 2
  • 3. 3/2/2014 The EFS represents the integration of the most robust process  science available with state‐of‐the‐art cheminformatics  application and modeling software technologies Process  science Java‐based  cheminformatics  applications  Modeling software technologies  developed through ERD‐Athens  Integrated Environmental  Modeling (IEM) Program EFS Cheminformatics: the generation, storage, indexing and  search of information relating to chemical  structure  and chemical processes  5 Example of an EFS Workflow Chemical Editor (CE): Provides options for  chemical entry Reaction Pathway  Simulator (RPS): Generates potential  transformation products  based on user‐specified  conditions Structure‐based Database  (SBD):  populated with calculated  and measured physico‐ chemical properties of  parent and potential  transformation products Earth Systems  Model: Data  Mining for  environmental  descriptors Physicochemical  Properties Calculator  (PPC): Molecular descriptors  for the parent chemical  and predicted  transformation products Reaction Rate  Calculator: Parameritization and  Execution of QSARs  and Algorithms 3
  • 4. 3/2/2014 Tautomer Identification/distribution 4
  • 5. 3/2/2014 MarvinSketch:  Calculation of  pKa values The selection of the environmental  conditions will determine which reaction  libraries will be executed in the Reaction  Pathway Simulator Reaction Libraries consisting of one‐ step reactions and reaction rules for  various transformation pathways: Chemical Processes: • Reduction • Hydrolysis • Photolysis Biological Processes: • Aerobic Biotransformation • Anaerobic Biotransformation 5
  • 6. 3/2/2014 UM‐Pathway Prediction System (UM‐PPS) • Web‐based system for the prediction of microbial biotransformation Database (http://umbbd.ethz.ch) Prediction System (http://umbbd.ethz.ch/predict) 11 Encoding the Process Science MarvinSketch: Translation of  chemical structures into a  readable code SMILES String O=N(=O)C1=CC=CC=C1 SMART Reaction String O=N(=O)C1=CC=CC=C1>>NC1=CC=CC=C1 12 6
  • 7. 3/2/2014 Development of  Reaction  Libraries based  on Chemical  Terms Language Abiotic  Reductions: Data Sources: • Peer‐reviewed  literature • Registration data  submitted to EPA Implementing the Reaction Libraries Functional group transformation  based on execution of reaction  libraries X 7
  • 8. 3/2/2014 Encoding the Process Science Product formation  based on the  execution of the  reduction library Likelihood:   Likely Generation:  95% Accumulation:  10%  15 Prototype EFS:  Environmental Systems Model Environmental Descriptor collection  for site‐specific assessments 8
  • 9. 3/2/2014 Environmental Descriptor collection through  the executions of Data for Environmental  Modeling (D4EM):   an open source software system consisting of a  library of utilities that can be used to access,  retrieve and process model data automatically  from sources on the internet Access the necessary databases for the  collection of the required environmental  descriptors (e.g., pH, aqueous Fe(II) and (DOC)) Identifying Predominant Chemical  Reductants Anaerobic Aquifers and  Sediments Flow Path Aquifer Intrusion of Dissolved Organic Matter Primary Redox Reactions Aerobic Nitrate Reducing Corg CO2 Corg O2 H2 O NO3- Manganese Reducing HCO3- Corg N2 MnO2 Iron Reducing HCO3- Corg HCO3- Mn2+ Fe(OH)3 Fe2+ Sulfate Reducing Corg HCO3- SO42- H2S Methanogenic Corg HCO3 CH4 Working Hypothesis: The reactivity of chemical reductants in natural sediments will  vary as a function of redox zonation as described by the dominant terminal electron  accepting processes (TEAPs) 9
  • 10. 3/2/2014 Formation of Potential Chemical Reductants  as a Function of Redox Zonation Chemical Reductants Mineral Formation Complexation Redox (DOM) O 2 + Fe2+ + HCO32- C O O 2 Fe + 3 Methanogenic Fe Sulfate Reducing Redox Zones Fe Reducing Fe + FeCO3 + H+ O Green Rust Formation O e , H+ [Fe2+Fe3+(OH)8+ [Cl nH2O][Fe42+Fe23+(OH)12]2+ [SO4 nH2O] Surface O - Solution Phase O OH [Fe42+Fe23+(OH)12]2+ [CO3 nH2O] O Fe2+ + HS- FeS + So FeS + + H2S FeS2 SH O OCSPP Harmonized* Test Guidelines Series 835 ‐ Fate, Transport and  Transformation Test Guidelines *Harmonized OPPT, OPP and OECD Test guidelines  Group A — Laboratory Transport Test Guidelines OH H+ OH Environmental conditions  can also be entered by the  user through selection of the  appropriate test OECD test  guideline 835.1230 - Adsorption/Desorption (Batch Equilibrium) (November 2008) 835.1240 - Leaching Studies (November 2008) 835.1410 - Laboratory Volatility (November 2008) Group B — Laboratory Abiotic Transformation Test Guidelines 835.2120 - Hydrolysis (November 2008) 835.2130 - Hydrolysis as a Function of pH and Temperature (January 1998) 835.2210 - Direct Photolysis Rate in Water by Sunlight (January 1998)) 835.2240 - Photodegradation in Water (November 2008) 835.2410 - Photodegradation in Soil (November 2008) 835.Weber- Reduction Group C — Laboratory Biological Transformation Test Guidelines Group D —Transformation in Water and Soil Test Guidelines 835.4100 - Aerobic Soil Metabolism / 835.4200 – Anaerobic Soil Metabolism (October 2008) 835.4300 - Aerobic Aquatic Metabolism / 835.4400 – Anaerobic Aquatic Metabolism (October 2008) Group E — Transformation Chemical-Specific Test Guidelines 835.5045 - Modified SCAS Test for Insoluble and Volatile Chemicals (January 1998) 835.5154 - Anaerobic Biodegradation in the Subsurface (January 1998) 835.5270 - Indirect Photolysis Screening Test: Sunlight Photolysis in Waters Containing Dissolved Humic Substances (January 1998) 10
  • 11. 3/2/2014 Prototype EFS:  Physico‐Chemical Properties Calculator The number of required calculated  data for a given physico‐chemical  property is based on its intended use Physico‐Chemical Properties Calculator 3-nitro-5-oxo-1,4dihydro-1,2,4triazol-1-ide Chemical Specific Parameters Abbrev Units Molecular Weight Melting Point MW MP g/mole oC Boiling Point Water Solubility Vapor Pressure Molecular diffusivity in water Ionization constant Henry’s Law Constant Octanol Water Partition Coefficient Organic Carbon Partition Coefficient Distribution Coeffecient (pH dependent BP WS VP oC mg/L torr Measured Goal:   •Provide complete  coverage •Consensus approach EPI Suite – Fragment based major species  at pH 7.5 Calculated (EPI Suite) Calculated (SPARC) Calculated (ChemAxon) Calculated (QSAR) SPARC – Mechanistic based ChemAxon – Atom based cm2/sec pKa unitless Atm m3/mole Kow mL/g Koc mL/g KD Available Not Available Chemical Specific mL/g 22 11
  • 12. 3/2/2014 Calculation of P‐Chem Data Base Based on Consensus Approach  ChemAx ChemAx ChemAx Braekevelt et al  AVERAGE (2003) calculated calculated measured KLOP log Kow log Kow log Kow log Kow PHYS log Kow VG log Kow calculated measured log Kow SPARC Name PBDE‐28 PBDE‐47 PBDE‐66 PBDE‐85 PBDE‐99 PBDE‐100 PBDE‐138 PBDE‐153 PBDE‐154 PBDE‐183 PBDE‐209 6.46 7.14 7.22 7.96 7.92 7.95 8.74 8.71 8.73 9.52 12.01 4.638 Calculated log Kow SSE =  EPIsuite EPIsuite 5.88 6.77 6.77 7.66 7.66 7.66 8.55 8.55 8.55 9.44 12.11 ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ 6.84 ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ ‐‐‐‐ 5.97 6.76 6.76 7.54 7.54 7.54 8.32 8.32 8.32 9.10 11.45 2.706 1.297 5.51 6.25 6.25 6.98 6.98 6.98 7.71 7.71 7.71 8.44 10.64 0.915 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 5.85 6.64 6.64 7.43 7.43 7.43 8.23 8.23 8.23 9.02 11.39 0.923 5.94 6.71 6.73 7.51 7.51 7.51 8.31 8.30 8.31 9.10 11.52 • Structure Searching • Data             Analysis 5.94 6.81 7.37 7.32 7.24 7.90 7.82 8.27 1.237 Provide structure SPARC EPIsuite ChemAxon KLOP ChemAxon PHYS ChemAxon VG y = x 5.0 6.0 7.0 8.0 9.0 Measured log Kow Calculation of P‐Chem Data Base Based on Consensus Approach  Compound class KOWWIN SPARC VG KLOP PHYS ALOGP XLOGP2 XLOGP3‐AA PBDEs 0.58 0.76 0.34 0.40 0.34 0.25 0.38 0.39 Phthalate esters 0.78 0.40 0.48 0.79 0.54 0.53 1.17 0.79 PCBs 0.76 0.87 0.57 0.72 0.71 0.73 0.77 0.65 Fused ring  structures 0.29 0.41 0.74 0.85 0.93 1.24 0.36 0.37 Others 0.31 0.86 1.51 0.87 0.61 1.19 1.32 1.09 ALL 0.58 0.74 0.94 0.75 0.64 0.90 0.96 0.78 Root mean square error (RMSE) for log Kow calculated by selected models Results of Consensus Approach for poorly soluble chemicals 12
  • 13. 3/2/2014 Reaction Rate Calculator: Parameritization and Execution of  QSARs and Algorithms Ability to populate and  execute QSARs for  calculating rate constants 2.15 2.98 5.71 DNAN 3.03 QSAR based on irreversible sorption of mono‐ substituted anilines in aerobic sediment Correcting for environmental  conditions Reaction Rate  Calculator: Parameritization and  Execution of QSARs  and Algorithms Temperature: k = Ae − Ea RT where A is the frequency factor or pre‐ exponential factor and Ea is the activation  energy (Default value for Ea = 50 kJ/mol) Sorption: kapp = k (1 + ρ K d ) where k is the first‐order rate constant for  transformation in the aqueous phase, (Kd) is the  sorption coefficient and ρ is the solid‐to‐ solution ratio  Ionization : 1 ⎛ K d ,app = ⎜ pH − pK a ⎝ 1 + 10 ⎛ 10 pH − pKa ⎞ ⎟ K d , HA + ⎜ pH − pK a ⎠ ⎝ 1 + 10 ⎞ ⎟ K d , A− ⎠ where pKa is the negative of the logarithm  of the acid dissociation constant for the  chemical 13
  • 14. 3/2/2014 Required Hallmarks of the EFS: Vibrant – Representing the most current process science and software  technologies available Transparent – Presentation of the meta data High Throughput capability – Relatively short run times  – Allows for operation in batch mode  Accessible – Web‐based Usable – Reasonable run times  – User friendly Flexible – Customized for the user’s need Quality Controlled – Based on peer‐reviewed science  14

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