The increasing number of chemicals for which SHEDS probabilistic exposure assessment has been performed over the years. This dataset is associated with the following publication: Egeghy , P., L. Sheldon, K. Isaacs , H. Ozkaynak, M. Goldsmith, J. Wambaugh , R. Judson , and T. Buckley. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 124(6): 697–702, (2016).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

input data for air pollution exposure models. This dataset is associated with the following publication: Breen, M., S.Y. Chang, M. Breen, Y. Xu, V. Isakov, S. Arunachalam, M.S. Carraway, and R. Devlin. Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study. ATMOSPHERE. MDPI AG, Basel, SWITZERLAND, 11(1): 65, (2020).

input data for air pollution exposure models. This dataset is associated with the following publication: Breen, M., S.Y. Chang, M. Breen, Y. Xu, V. Isakov, S. Arunachalam, M.S. Carraway, and R. Devlin. Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study. ATMOSPHERE. MDPI AG, Basel, SWITZERLAND, 11(1): 65, (2020).

Links to data for "The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations". This dataset is associated with the following publication: Handa, S., K. Isaacs, J. Wall, A. Larger, S. Burns, L. Koval, K. Baron-Furuyama, C. Elonen, D. Lyons, K. Dionisio, M.B. Horton, and K. Phillips. The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations. Scientific Data. Springer Nature, LONDON, UK, 12: 950, (2025).

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"

The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure predictions. The database currently includes biomonitoring exposure data from three studies: the American Healthy Homes Survey, the First National Environmental Health Survey of Child Care Centers and the Children's Total Exposure to Persistent Pesticides and Other Persistent Organic Pollutants study. Data include the amounts of chemicals found in food, drinking water, air, dust indoor surfaces and urine. The database will eventually include high-throughput exposure predictions for thousands of chemicals based on manufacture and use information. EPA researchers developed high-throughput exposure models to predict exposures for 1,763 chemicals using production volume, environmental fate and transport models, and a simple indicator of consumer product use.The model is being improved by adding more refined indoor and consumer use information since these are also large determinants of exposure. As these models are refined and more exposure data is collected, it will be added to ExpoCastDB.