This data set contains R code for a hypothetical exposure model described in the manuscript "A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks". Additionally, this data set contains an Excel file that provides the range of parameters used in Monte Carlo simulations to generate iterations of the exposure network. This dataset is associated with the following publication: Hines, D., R. Conolly, and A. Jarabek. A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 11002-11012, (2019).

Resulting betas (health effects) from a variety of copollutant epidemiologic models used to analyze the impact of exposure measurement error on health effect estimates. This dataset is associated with the following publication: Dionisio , K., H.H. Chang, and L. Baxter. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 15: 114, (2016).

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).

This the raw data that the manuscript: Fluctuating Emissions and Availability of Health Reference Values: Implications for Estimating Acute Exposure and Health Risk is based on. This dataset is associated with the following publication: Stewart, M., J. Hirtz, G. Woodall, C. Weitekamp, and K. Spence. A Comparison of Hourly with Annual Air Pollutant Emissions: Implications for Estimating Acute Exposure and Public Health Risk. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 69(7): 848-856, (2019).

This the raw data that the manuscript: Fluctuating Emissions and Availability of Health Reference Values: Implications for Estimating Acute Exposure and Health Risk is based on. This dataset is associated with the following publication: Stewart, M., J. Hirtz, G. Woodall, C. Weitekamp, and K. Spence. A Comparison of Hourly with Annual Air Pollutant Emissions: Implications for Estimating Acute Exposure and Public Health Risk. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 69(7): 848-856, (2019).

Data for "Breen, M., Wambaugh, J.F., Bernstein, A. et al. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. J Expo Sci Environ Epidemiol 32, 855–863 (2022). https://doi.org/10.1038/s41370-022-00491-0". This dataset is associated with the following publication: Breen, M., J. Wambaugh, A. Bernstein, M. Sfeir, and C. Ring. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 855-863, (2022).

This dataset consists of the data used to create the tables and figures for this paper. This dataset is associated with the following publication: Baxter, L., K. Dionisio, P. Pradeep, K. Rappazzo, and L. Neas. Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 29(4): 557-567, (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).