Air Quality Data for Health-Related Applications

The data set authors and their affiliations are:

Di, Q.^{1, 3}, Y. Wei¹, A. Shtein¹, X. Xing², E. Castro¹, H. Amini⁵, C. Hultquist^{2, 4}, L. Shi⁶, I. Kloog^{5, 7}, R. Silvern⁸, J. Kelly⁹, M. B. Sabath¹⁰, C. Choirat¹⁰, P. Koutrakis¹, A. Lyapustin¹¹, Y. Wang¹², L. J. Mickley¹³, Y. Daouk², and J. Schwartz^{1, 14}.

¹Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
²Center for International Earth Science Information Network (CIESIN), Columbia Climate School, Columbia University, Palisades, NY, United States
³Vanke School of Public Health, Tsinghua University, Beijing, China
⁴School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
⁵Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
⁶Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
⁷Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
⁸Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States
⁹U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning & Standards (OAQPS), Research Triangle Park, NC, United States
¹⁰Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
¹¹National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC), Greenbelt, MD, United States
¹²University of Maryland, Baltimore County, Baltimore, MD, United States
¹³John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
¹⁴Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States

For more information about the data, see this readme text file and the peer reviewed open access article:

Di Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz. 2019. An Ensemble-based Model of PM2.5 Concentration Across the Contiguous United States with High Spatiotemporal Resolution. Environment International, 130:104909. https://doi.org/10.1016/j.envint.2019.104909.

Acknowledgements:

This work was supported by U.S. EPA grants RD-834798, RD-835872, and 83587201, and Health Effects Institute (HEI) grant 4953-RFA14-3/16-4 and assistance award CR-83467701. The HEI is an organization jointly funded by the U.S. EPA and certain motor vehicle and engine manufacturers. The computations were run on the Odyssey cluster supported by the Faculty of Arts & Sciences (FAS) Division of Science, Research Computing Group at Harvard University. The data conversion work from RDS to GeoTIFF with QA/QC was supported by the National Institutes of Health, National Institute of Environmental Health Sciences (NIH/NIEHS) grant R01ES032418. The contents are solely the responsibility of the grantees and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the data or documents. The authors also thank Gregory Yetman (CIESIN) for his help with the data conversion process.

Potential Use Cases:

It is anticipated for this work to be used for conducting new studies on individual and combined health risks of total PM2.5 concentration, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM2.5. The high resolution PM2.5 estimates allow epidemiologists to assess the health effects of PM2.5 with higher accuracy, and the PM2.5 estimates further help correct residual exposure measurement errors. Epidemiological studies may also use these predictions to inform public health decisions, air quality standards, and assessments of long-term health impacts of PM2.5 exposure.

Select References (for all other references see readme text file):

1. Heo, S., and Bell, M. L. (2023) Investigation on urban greenspace in relation to sociodemographic factors and health inequity based on different greenspace metrics in 3 US urban communities. Journal of Exposure science & Environmental Epidemiology, 33, 218-228. https://doi.org/10.1038/s41370-022-00468-z.
2. Ma, Y., Zang, E., Opara, I. et al. (2023) Racial/ethnic disparities in PM2.5-attributable cardiovascular mortality burden in the United States. Nature Human Behaviour. https://doi.org/10.1038/s41562-023-01694-7.
3. Mork, D., Braun, D., and Zanobetti, A. (2023). Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias. Environment International, 171, 107694. https://doi.org/10.1016/j.envint.2022.107694.
4. Qiu, X., Shi, L., Kubzansky, L. D., Wei, Y., Castro, E., Li, H., Weisskopf, M. G., and Schwartz, J. D. (2023). Association of long-term exposure to air pollution with late-life depression in older adults in the US. JAMA Network Open, 6(2), e2253668. https://doi.org/10.1001/jamanetworkopen.2022.53668.
5. Wen, J., and Burke, M. (2022). Lower test scores from wildfire smoke exposure. Nature Sustainability, 5, 947-955. https://doi.org/10.1038/s41893-022-00956-y.