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. Amini⁴, C. Hultquist², L. Shi⁵, I. Kloog^{4, 6}, R. Silvern⁷, J. T. Kelly⁸, M. B. Sabath⁹, C. Choirat⁹, P. Koutrakis¹, A. Lyapustin¹⁰, Y. Wang¹¹, L. J. Mickley¹², Y. Daouk², and J. Schwartz^{1, 13}.

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
⁴Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
⁵Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
⁶Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
⁷Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States
⁸U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning & Standards (OAQPS), Research Triangle Park, NC, United States
⁹Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
¹⁰National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC), Greenbelt, MD, United States
¹¹University of Maryland, Baltimore County, Baltimore, MD, United States
¹²John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
¹³Department 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 this open access peer reviewed article:

Di, Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. T. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz. 2020. Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. Environmental Science & Technology, 54 (3): 1372-1384. https://doi.org/10.1021/acs.est.9b03358.

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 NO2 concentration, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of NO2. This high-resolution daily NO2 estimation, along with predicted uncertainty, would help to better assess both long-term and short-term exposures for studies of large cohorts with residents in locations far from or without monitors. Epidemiological studies may also use this data to guide public health decisions, health impact assessments, and understand the effects of localized NO2 pollutant exposure, particularly for small areas, such as over census tracts or ZIP Codes.

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

1. 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, 5, 2074-2083. https://doi.org/10.1038/s41562-023-01694-7.
2. 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.
3. 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.
4. Wang, Y., Liu, P., Schwartz, J., Castro, E., Wang, W., Chang, H., Scovronick, N., and Shi, L. (2023). Disparities in ambient nitrogen dioxide pollution in the United States. Proceedings of the National Academy of Sciences, 120(16), e2208450120. https://doi.org/10.1073/pnas.2208450120.
5. Zheng, Y., McElrath, T., Cantonwine, D., and Hu, H. (2023). Longitudinal associations between ambient air pollution and angiogenic biomarkers among pregnant women in the LIFECODES study, 2006–2008. Environmental Health Perspectives, 131(8), 087005. https://doi.org/10.1289/ehp11909.