EPA Risk-Screening Environmental Indicators

High-throughput transcriptomics (HTTr) data generated by US EPA Office of Research and Development, Center for Computational Toxicology and Exposure (CCTE), Biomolecular and Computational Toxicology Division. All data is generated using TempO-Seq targeted RNA-seq technology from in vitro cell culture systems.

The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 1. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models (GCMs) under both mid- and high-emission scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5) using the Weather Research and Forecasting (WRF) model. Scenarios were downscaled from the Community Earth System Model (CESM) and the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model version 3 (CM3). Simulations followed the historical periods 1975-2005 (CESM only) and 1995-2005 (both CESM and CM3), and Representative Concentration Pathways (RCP) 4.5 for 2025-2100 (CESM only), RCP6.0 for 20[...]

The data are part of EPA’s Air Quality Time Series (EQUATES) Project. The data consist of hourly gridded pollutant concentrations estimates by the Community Multiscale Air Quality (CMAQ) model version 5.3.2 (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019. Model data is provided for two spatial domains : the Northern Hemisphere (108 km x 108km horizontal grid spacing) and the Contiguous United States including parts of Canada and Mexico (12km x 12km horizontal grid spacing). Two types of hourly data are provided: three-dimensional air pollutant concentrations and vertical column pollutant totals. Previous studies have used this type of CMAQ 3D and vertical column air quality data to evaluate the modeling system, created model-observed ‘fused’ surfaces, and to analyze spatial and temporal changes in air quality in the upper atmosphere, e.g., https://doi.org/10.1016/j.envint.2019.104909; https://doi.org/10.5194/acp-17-12449-2017; https://doi.org/10.1029/200[...]

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