County-level annual cardiovascular mortality rates and annual average PM2.5 concentrations, 2132 U.S. counties, 1990-2010. Included national emissions by sectors and county-level confounders (annual COPD mortality rates, percent non-white population, and median income). This dataset is associated with the following publication: Peterson, G., C. Hogrefe, L. Neas, A. Corrigan, R. Mathur, and A. Rappold. Impacts of Reductions in Emissions from Major Source Sectors on Fine Particulate Matter-Related Cardiovascular Mortality. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 128(1): 17005, (2020).

Data on county socioeconomic status for 2,132 US counties and each county’s average annual cardiovascular mortality rate (CMR) and total PM2.5 concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the U.S. National Center for Health Statistics, U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system, and the U.S. Census, respectively. A socioeconomic index was created using seven county-level measures from the 1990 US census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This dataset is associated with the following publication: Wyatt, L., G. Peterson, T. Wade, L. Neas, and A. Rappold. The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 136: 105430, (2020).

Data on county socioeconomic status for 2,132 US counties and each county’s average annual cardiovascular mortality rate (CMR) and total PM2.5 concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the U.S. National Center for Health Statistics, U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system, and the U.S. Census, respectively. A socioeconomic index was created using seven county-level measures from the 1990 US census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This dataset is associated with the following publication: Wyatt, L., G. Peterson, T. Wade, L. Neas, and A. Rappold. The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 136: 105430, (2020).

Background: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002–2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12 km spatial resolutions, and satellite-based models at 10 km and 1 km resolutions. Case status was either a coronary artery disease (CAD) index>23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12 km-CMAQ models. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Clinical data are located in: C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Cathgen Satellite data are located in : C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\New Ikm Satellite Data C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Satellite Data CMAQ data are located in C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\CMAQ Data. Format: There are two types of datasets used in this study: clinical data taken from patient records at the Duke Medical Center; and air pollution data (PM2.5) taken from a federal reference monitor located in Raleigh, CMAQ data obtained from collaborators at Georgia Tech and NERL/ORD, and satellite data obtained from collaborators at Harvard. Metadata are in the form of Excel spreadsheets that contain columns of data that specify clinical and exposure information for each individual participating in the study. This dataset is associated with the following publication: McGuinn, L., C. Ward-Caviness, A. Schneider, Q. Di, A. Chudnovsky, J. Schwartz, P. Koutrakis, A. Russell, V. Garcia, W. Krause, E. Hauser, L. Neas, W. Cascio, D. Diaz-Sanchez, and R. Devlin. Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure. ENVIRONMENTAL RESEARCH. Academic Press Incorporated, Orlando, FL, USA, 159: 16-23, (2017).

Background: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002–2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12 km spatial resolutions, and satellite-based models at 10 km and 1 km resolutions. Case status was either a coronary artery disease (CAD) index>23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12 km-CMAQ models. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Clinical data are located in: C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Cathgen Satellite data are located in : C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\New Ikm Satellite Data C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Satellite Data CMAQ data are located in C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\CMAQ Data. Format: There are two types of datasets used in this study: clinical data taken from patient records at the Duke Medical Center; and air pollution data (PM2.5) taken from a federal reference monitor located in Raleigh, CMAQ data obtained from collaborators at Georgia Tech and NERL/ORD, and satellite data obtained from collaborators at Harvard. Metadata are in the form of Excel spreadsheets that contain columns of data that specify clinical and exposure information for each individual participating in the study. This dataset is associated with the following publication: McGuinn, L., C. Ward-Caviness, A. Schneider, Q. Di, A. Chudnovsky, J. Schwartz, P. Koutrakis, A. Russell, V. Garcia, W. Krause, E. Hauser, L. Neas, W. Cascio, D. Diaz-Sanchez, and R. Devlin. Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure. ENVIRONMENTAL RESEARCH. Academic Press Incorporated, Orlando, FL, USA, 159: 16-23, (2017).

Future fine particulate matter (PM2.5) concentrations and health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographics. In this study, a human-earth system model is used to estimate US state-level PM2.5 mortality costs from 2015 to 2050 considering current major air quality and energy regulations. The Logarithmic Mean Divisia Index is applied to quantify the contributions of socioeconomic and energy factors to future changes in PM2.5 mortality costs. National PM2.5 mortality costs are estimated to decrease by 25% from 2015 to 2050, primarily driven by decreases in energy intensity and decreases in PM2.5 mortality cost per unit consumption of electric sector coal and transportation liquids. These factors together contribute to 68% of the net decrease, primarily because of technology improvements and air pollutant emission regulations. Furthermore, the results suggest that states with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PM2.5 mortality costs. In contrast, states with larger projected decreases in mortality costs have smaller increases in population and per capita GDP and greater decreases in electric sector coal share and PM2.5 mortality cost per unit fuel consumption. This dataset includes source code, input data, and model output from the Global Change Assessment Model (GCAM-USA) human-earth system model used in this study. It also includes Excel workbooks and R scripts used in producing the figures in the manuscript. This dataset is associated with the following publication: Ou, Y., S. Smith, J.J. West, C. Nolte, and D. Loughlin. State-level drivers of future fine particulate matter mortality in the United States.. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 14(12): 124071, (2019).

Future fine particulate matter (PM2.5) concentrations and health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographics. In this study, a human-earth system model is used to estimate US state-level PM2.5 mortality costs from 2015 to 2050 considering current major air quality and energy regulations. The Logarithmic Mean Divisia Index is applied to quantify the contributions of socioeconomic and energy factors to future changes in PM2.5 mortality costs. National PM2.5 mortality costs are estimated to decrease by 25% from 2015 to 2050, primarily driven by decreases in energy intensity and decreases in PM2.5 mortality cost per unit consumption of electric sector coal and transportation liquids. These factors together contribute to 68% of the net decrease, primarily because of technology improvements and air pollutant emission regulations. Furthermore, the results suggest that states with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PM2.5 mortality costs. In contrast, states with larger projected decreases in mortality costs have smaller increases in population and per capita GDP and greater decreases in electric sector coal share and PM2.5 mortality cost per unit fuel consumption. This dataset includes source code, input data, and model output from the Global Change Assessment Model (GCAM-USA) human-earth system model used in this study. It also includes Excel workbooks and R scripts used in producing the figures in the manuscript. This dataset is associated with the following publication: Ou, Y., S. Smith, J.J. West, C. Nolte, and D. Loughlin. State-level drivers of future fine particulate matter mortality in the United States.. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 14(12): 124071, (2019).

The data consists of a series of tables containing individual identifiers; countrywide high-resolution (1 km × 1 km) modeled PM2.5 from a model built by Harvard collaborators; daily concentrations of relative humidity (RH) and temperature; and troponin I measurements. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data can be accessed with an approved IRB. Format: The data consists of a series of tables containing individual identifiers; countrywide high-resolution (1 km × 1 km) modeled PM2.5 from a model built by Harvard collaborators; daily concentrations of relative humidity (RH) and temperature; and troponin I measurements. This dataset is associated with the following publication: Wyatt, L., G. Kamat, J. Moyer, A. Weaver, D. Diazsanchez, R. Devlin, Q. Di, J. Schwartz, W. Cascio, and C. Ward-Caviness. Associations between short-term exposure to PM2.5 and cardiomyocyte injury in myocardial infarction survivors in North Carolina. Open Heart. BMJ Publishing Group Ltd, London, UK, 9: e001891, (2022).

Dataset includes CMAQv5.3.1 code used for emission reduction simulations and output from CMAQ for each pollutant at the county level.

This file provides annual average concentration of PM2.5, by county, for the year 2008. Definition of each variable is provided in the first row for each column. This file was prepared for use with BenMAP-CE version 1.3.4. Information on BenMAP-CE (including downloads of the current version of the model, manuals and training materials) available at: https://www.epa.gov/benmap. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.