Documentation » Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., v1: Air Quality Data for Health-Related Applications

Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., v1 (2000 – 2016)

The data set authors and their affiliations are:

Yaguang Wei¹, Xiaoshi Xing², Alexandra Shtein¹, Edgar Castro¹, Carolynne Hultquist^2,3, Mahdieh Danesh Yazdi^1,4, Longxiang Li¹, and Joel Schwartz¹

¹ Harvard T.H. Chan School of Public Health, Boston, MA, United States
² Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States
³ School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
⁴ Stony Brook University, New York, United States

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

Wei, Y., X. Qui, M. D. Yazdi, A. Shtein, L. Shi, J. Yang, A. A. Peralta, B. A. Coull, and J. Schwartz. 2022. The Impact of Exposure Measurement Error on the Estimated Concentration–Response Relationship between Long-Term Exposure to PM2.5 and Mortality. Environmental Health Perspectives, 130(7): 077006. https://doi.org/10.1289/EHP10389.

Potential Use Cases:

The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American Community Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research.

References:

Wei, Y., X. Qiu, M. B. Sabath, M. D. Yazdi, K. Yin, L. Li, A. A. Peralta, C. Wang, P. Koutrakis, A. Zanobetti, F. Dominici, and J. D. Schwartz. 2022. Air Pollutants and Asthma Hospitalization in the Medicaid Population. American Journal of Respiratory and Critical Care Medicine, 205(9):1075–1083. https://doi.org/10.1164/rccm.202107-1596OC.
Jbaily, A., X. Zhou, J. Liu, T.-H. Lee, L. Kamareddine, S. Verguet and F. Dominici. 2022. Air pollution exposure disparities across US population and income groups. Nature, 601:228–233. https://doi.org/10.1038/s41586-021-04190-y.
Qiu X, Y. Wei, M. Weisskopf, A. Spiro, L. Shi, E. Castro, B. Coull, P. Koutrakis, and J. Schwartz. 2022. Air pollution, climate conditions and risk of hospital admissions for psychotic disorders in U.S. residents. Environmental Research, 216(Part 2):114636. https://doi.org/10.1016/j.envres.2022.114636.

Acknowledgements:

The ZIP code polygon and point data from Esri ArcGIS Data and Maps, 2000 to 2016, were provided by Jeff Blossom, Center for Geographic Analysis, Harvard University. This work was made possible by the National Institutes of Health (NIH) grants R01ES032418 and ES-000002. This work was also supported by the U.S. Environmental Protection Agency (EPA) grants RD-8358720 and RD-83587201-0. The contents are solely the responsibility of the grantee 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 publication.