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Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., v1 (
2000 – 2019)
The data set authors and their affiliations are:
Heresh Amini1,2,*, Mahdieh Danesh-Yazdi1, Qian Di3, Weeberb Requia4, Yaguang Wei1, Yara AbuAwad5, Liuhua Shi6, Meredith Franklin7, Choong-Min Kang1, Jack Mikhail Wolfson1, Peter James8,1, Rima Habre9, Qiao Zhu6, Joshua S. Apte10,11, Zorana Jovanovic Andersen2, Xiaoshi Xing12, Carolynne Hultquist12,13, Itai Kloog14, Francesca Dominici1,15, Petros Koutrakis1, Joel Schwartz1
1Harvard T.H. Chan School of Public Health, Boston, MA, United States
2Department of Public Health, University of Copenhagen, Copenhagen, Denmark
3Vanke School of Public Health, Tsinghua University, Beijing, China
4Fundação Getúlio Vargas, Brasilia, Brazil
5Swiss Federal Statistical Office (FSO), Neuchâtel, Switzerland
6Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
7Department of Statistical Sciences, University of Toronto, Toronto, Canada
8Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
9Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
10Department of Civil and Environmental Engineering, University of California, Berkeley, CA, United States
11School of Public Health, University of California, Berkeley, CA, United States
12Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States
13School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
14Icahn School of Medicine at Mount Sinai, New York, NY, United States
15Harvard Data Science Initiative, Cambridge, MA, United States
For more information about the data see this readme text file and the preprint of the article:
Amini, H., M. Danesh-Yazdi, Q. Di, W. Requia, Y. Wei, Y. AbuAwad, L. Shi, M. Franklin, C.-M. Kang, J. M. Wolfson, P. James, R. Habre, Q. Zhu, J. S. Apte, Z. J. Andersen, X. Xing, C. Hultquist, I. Kloog, F. Dominici, P. Koutrakis, and J. Schwartz. 2022. Hyperlocal super-learned PM2.5 components across the contiguous US. Research Square. https://doi.org/10.21203/rs.3.rs-1745433/v2.
Potential Use Cases:
It is anticipated for this work to be used for conducting new studies on individual and combined health risks of PM2.5 components, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM2.5 composition. Urban planners and regulators may also use these predictions for selecting locations of new day care centers, schools, nursing homes, or air-quality monitors.
- Jin, T., H. Amini, A. Kosheleva, M. D. Yazdi, Y. Wei, E. Castro, Q. Di, L. Shi, and J. Schwartz. 2022. Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration. Environmental Health, 21:96. https://doi.org/10.1186/s12940-022-00907-2.
- Qiu X., Y. Wei, H. Amini, C. Wang, M. Weisskopf, P. Koutrakis, and J. Schwartz. 2022. Fine particle components and risk of psychiatric hospitalization in the U.S. Science of the Total Environment, 849:157934. https://doi.org/10.1016/j.scitotenv.2022.157934.
- Li J., Y. Wang, K. Steenland, P. Liu, A. van Donkelaar, R. V. Martin, H. H. Chang, W. M. Caudle, J. Schwartz, P. Koutrakis, and L. Shi. 2022. Long-term effects of PM2.5 components on incident dementia in the northeastern United States. The Innovation. 3(2):100208. https://doi.org/10.1016/j.xinn.2022.100208.
This project was supported by the Cyprus Harvard Endowment Program for the Environment and Public Health, U.S. Environmental Protection Agency (EPA) grant RD-8358720, National Institutes of Health grant P30 ES000002 and R01 ES032418-01. 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. The authors also thank Gregory Yetman (CIESIN) for his help on the data conversion process.