Decision Science Core ABSTRACT The Texas A&M University Superfund Research Center will investigate the impacts of environmental emergency-related contamination events across the source-to-outcome continuum, including fate and transport, human health hazard, and mitigation of contamination and toxicity. In order to achieve the Center's ultimate goal of improving decision-making after an environmental emergency, the conclusions drawn from these projects will need to be interpretable to first responders, impacted communities, and government bodies involved in site management and cleanup. Therefore, the entire Center will be supported by a Decision Science Core, which has expertise in synthesizing relevant scientific data and conclusions for use by those involved in decisions related to risk management. The overall objective of the Decision Science Core is to provide novel modeling services for supporting the cohesion, relevance, and implementation of project findings in the context of environmental decision-making. Directed by Dr. Weihsueh Chiu at Texas A&M University and in collaboration with Dr. Gregory Characklis at the University of North Carolina at Chapel Hill, the Core will provide numerous methods and services to the Center researchers under three specific aims will facilitate interaction among Center projects, while also serving as a bridge to the Community Engagement Core and Research Translation Core. Key services provided will include toxicokinetic modeling (Aim 1), human health risk modeling (Aim 2), and economic modeling (Aim 3).
In Aim 1, toxicokinetic modeling services will be provided to the projects to extrapolate between exposure doses and internal concentrations in cells or tissues, taking into account chemical absorption, distribution, metabolism, and excretion. Toxicokinetic modeling is an essential part of moving towards a new paradigm in evaluating hazard and risk using in vitro assays.
In Aim 2, human health risk modeling will be used to make inferences about hazard or risk in the human population based on experimental or observational data, serving as an essential bridge between scientific data and environmental policy decisions. In the context of Superfund, human health risk modeling is used to demonstrate that exposure standards or environmental remediation decisions both protect human health and reduce toxicity or risk.
In Aim 3, economic modeling of costs and benefits will be provided as a key input into environmental policy decisions, from planning and priority setting to establishing environmental remediation or exposure standards. The delivery of cost-effective environmental solutions is of keen interest not only to government agencies, but also to affected communities and stakeholders, many of whom will bear at least some of the cost, either directly or indirectly. As a whole, these modeling services will support the Center's overall goal by helping to interpret and translate research project findings into information that can be used by varied stakeholders, from communities to federal and state decision-makers, to assess the human health and economic impact of contaminant exposures after an environmental emergency, and enhance the planning, emergency response, as well as long-term recovery and remediation efforts related to environmental disasters.

Public Health Relevance

Decision Science Core NARRATIVE The Decision Science Core helps the Center to determine the overall impacts from chemical exposures following an environmental disaster. Specifically, using a number of computer-based models, the Decision Science Core helps the Center investigators to convert environmental and biological measurements into predictions of chemical-related health effects and economic costs. This information is critical to first responders, communities, and government agencies in environmental disaster planning, response, and recovery.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Hazardous Substances Basic Research Grants Program (NIEHS) (P42)
Project #
1P42ES027704-01
Application #
9257877
Study Section
Special Emphasis Panel (ZES1)
Project Start
Project End
Budget Start
2017-09-01
Budget End
2018-03-31
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Texas A&M University
Department
Type
DUNS #
020271826
City
College Station
State
TX
Country
United States
Zip Code
77845
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Onel, Melis; Beykal, Burcu; Wang, Meichen et al. (2018) Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques. ESCAPE 43:421-426
Guyton, Kathryn Z; Rusyn, Ivan; Chiu, Weihsueh A et al. (2018) Application of the key characteristics of carcinogens in cancer hazard identification. Carcinogenesis 39:614-622
Li, Gen; Shabalin, Andrey A; Rusyn, Ivan et al. (2018) An empirical Bayes approach for multiple tissue eQTL analysis. Biostatistics 19:391-406
Zheng, Xueyun; Dupuis, Kevin T; Aly, Noor A et al. (2018) Utilizing ion mobility spectrometry and mass spectrometry for the analysis of polycyclic aromatic hydrocarbons, polychlorinated biphenyls, polybrominated diphenyl ethers and their metabolites. Anal Chim Acta 1037:265-273
MacLean, Brendan X; Pratt, Brian S; Egertson, Jarrett D et al. (2018) Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions. J Am Soc Mass Spectrom 29:2182-2188
Horney, Jennifer A; Casillas, Gaston A; Baker, Erin et al. (2018) Comparing residential contamination in a Houston environmental justice neighborhood before and after Hurricane Harvey. PLoS One 13:e0192660
Brewster, Chase S; Sharma, Virender K; Cizmas, Leslie et al. (2018) Occurrence, distribution and composition of aliphatic and polycyclic aromatic hydrocarbons in sediment cores from the Lower Fox River, Wisconsin, US. Environ Sci Pollut Res Int 25:4974-4988
Luo, Yu-Syuan; Hsieh, Nan-Hung; Soldatow, Valerie Y et al. (2018) Comparative analysis of metabolism of trichloroethylene and tetrachloroethylene among mouse tissues and strains. Toxicology 409:33-43
Meyer, Michelle; Hendricks, Marccus; Newman, Galen et al. (2018) Participatory Action Research: Tools for Disaster Resilience Education. Int J Disaster Resil Built Environ 9:402-419

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