This research project will develop methods to rapidly estimate chemical releases for exposure and risk assessment applications, including manufacturing, processing, and use of a chemical through models known as Generic Scenarios. A Generic Scenario is an EPA Office of Pollution Prevention and Toxics term for a model that describes the release of a chemical during a well-defined industrial activity or set of activities. These activities, in such systems as manufacturing, processing, and use of chemicals, are described by process flow diagrams, process models, stream tables, etc. with mass and energy balance and transfer equations. The rapid estimation of chemical releases expands the system of interest beyond the equipment to worker exposure and ambient environments. The research project answers questions about what amount and concentration of a chemical is predicted to be in environmental exposure pathways such as water releases, indoor air, on surfaces, etc.
The research project involves manufacturing process modeling, chemical use modeling, mass transfer modeling, model input estimation, uncertainty analysis, data source identification and data collection, statistics, computer programming, artificial intelligence, machine learning, knowledge discovery and data mining, and prediction of chemical releases and concentrations for exposure and risk assessment purposes. Modeling involves engineering approximations to equipment operation and release processes, estimating model inputs, and determining relationships between inputs and result uncertainty. Methods of collecting data, for source identification, from EPA databases, and through automated data scraping, will be developed based on model needs. Computer-based statistical methods will be applied as fit for purpose, including machine learning techniques for clustering, classification, and regression. Prediction will be used as appropriate to extrapolate beyond the specific chemicals and circumstances studied in equation-based modeling. The research participant will interact with a team to develop methods and computer tools and to publish appropriate methodology and case study results.
Through engagement with engineering, chemistry, and chemical safety experts the research participant will learn innovative methods and tools in process and fate & transport modeling, data mining, and related disciplines. The research participant will learn about procedures for generating and managing high quality scientific data and models for exposure and risk assessment purposes in the context of a Government research laboratory. Increased experience and knowledge will be obtained on procedures for writing and publishing in peer-reviewing journals. The research participant will learn about topics related to the research areas of chemical releases, exposures, and risk assessment.
The qualified candidate should have received a doctoral degree in one of the relevant fields, or be currently pursuing the degree and will reach completion by the start date of the appointment. Degree must have been received within five years of the appointment start date.
A background and/or experience in process and fate & transport modeling, chemical industrial/occupational hygiene, data mining, statistics, machine learning, computer programming, or mathematics is desired.
Completion of a successful background investigation by the Office of Personnel Management (OPM) is required for an applicant to be on-boarded at EPA. OPM can complete a background investigation only for individuals, including non-US Citizens, who have resided in the US for the past three years.
For technical questions, contact mentor Dr. Raymond Smith, firstname.lastname@example.org
For application questions, contact ORISE, EPArpp@orau.org (Reference Code: EPA-ORD-NRMRL-LMMD-2019-02)