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Temporal-Spatial Ambient Concentrator Estimator (T-SpACE): Hierarchical Bayesian Model Software Used to Estimate Ambient Concentrations of NAAQS Air Pollutants in Support of Health Studies
Citation:
Hall, EricS. Temporal-Spatial Ambient Concentrator Estimator (T-SpACE): Hierarchical Bayesian Model Software Used to Estimate Ambient Concentrations of NAAQS Air Pollutants in Support of Health Studies. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-18/021, 2018.
Impact/Purpose:
This is an EPA Technical Report which documents the Hierarchical Bayesian Model (Temporal-Spatial Ambient Concentrator Estimator [T-SpACE]) that was used to develop the journal article, "Assessing the Impact of Fine Particulate Matter (PM2.5) Sources on Respiratory-Cardiovascular Chronic Diseases in the New York City Metropolitan Area using Hierarchical Bayesian Model Estimates"
Description:
To fulfill its mission to protect human health and the environment, EPA has established National Ambient Air Quality Standards (NAAQS) on six selected air pollutants known as criteria pollutants: ozone (O3); carbon monoxide (CO); lead (Pb); nitrogen dioxide (NO2); sulfur dioxide (SO2), and; particulate matter (PM). The states are primarily responsible for maintaining and improving air quality and complying with the NAAQS. Ozone (O3) and particulate matter (PM) are two of the criteria pollutants whose levels are regulated by NAAQS. Extensive air pollution monitoring networks have been set-up across the US to understand the levels (concentrations) of these air pollutants across the US and over time. Once collected, this information is reported to EPA and is made available publicly after the data is reviewed and analyzed for quality and accuracy.