The data set authors and their affiliations are:
Amini, H.1, 2*, M. Danesh-Yazdi1,3, Q. Di4, W. Requia5, Y. Wei1, Y. AbuAwad6, L. Shi7, M. Franklin8, C.-M. Kang1, J. M. Wolfson1, P. James9,1, R. Habre10, Q. Zhu7, J. S. Apte11,12, Z. J. Andersen2, X. Xing13, C. Hultquist13,14, I. Kloog15, F. Dominici1,16, P. Koutrakis1, and J. Schwartz1.
1Harvard T.H. Chan School of Public Health, Boston, MA, United States
2Department of Public Health, University of Copenhagen, Copenhagen, Denmark
3Stony Brook University, Stony Brook, NY, United States
4Vanke School of Public Health, Tsinghua University, Beijing, China
5Fundação Getúlio Vargas, Brasilia, Brazil
6PERFORM Centre, Concordia University, Montreal, Quebec, Canada
7Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
8Department of Statistical Sciences, University of Toronto, Toronto, Canada
9Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
10Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
11Department of Civil and Environmental Engineering, University of California, Berkeley, CA, United States
12School of Public Health, University of California, Berkeley, CA, United States
13Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States
14School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
15Icahn School of Medicine at Mount Sinai, NY, United States
16Harvard 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 US PM2.5 Trace Elements Super-learned. Research Square. https://doi.org/10.21203/rs.3.rs-2052258/v1.
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.
This work was supported by the Cyprus Harvard Endowment Program for the Environment and Public Health, Novo Nordisk Foundation Challenge Programme grant NNF17OC0027812, U.S. Environmental Protection Agency (EPA) grants RD-8358720 and RD-835872, National Institutes of Health (NIH) grants R01AG074357, R01 HL150119, R01MD012769, R01 ES028033, 1R01AG060232-01A1, 1R01ES030616, 1R01AG066793-01R01, R01ES028033-S1, P30 ES000002, and R01ES032418-01, and the Fernholz Foundation. 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.