February 2023 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) PURPOSE To provide annual PM2.5 component concentration data for the contiguous U.S. at resolutions of 50m in urban areas and 1km in non-urban areas for public health research to estimate effects on human health, and for other related research. DESCRIPTION The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of these chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. Since the majority of the U.S. population (approximately 80%) lives in urban areas, models were developed for urban areas at 50m spatial resolution across 3,535 urban areas, and in non-urban areas at 1km spatial resolution. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. 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. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the units for the PM2.5 Components are μg/m^3. ACCESSING THE DATA The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/aqdh-pm2-5-component-ec-nh4-no3-oc-so4-50m-1km-contiguous-us-2000-2019/data-download DATA FORMAT This archive contains data in RDS (tabular) format, a file format native to the R programming language, but can also be opened by other languages such as Python. The data files are compressed zipfiles. Downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or similar application. Users should expect an increase in the size of downloaded data after decompression. DATA UNITS The unit for PM2.5 Components is micrograms (one-millionth of a gram) per cubic meter air (µg/m^3). SPATIAL EXTENT Contiguous United States, 50m in urban areas and 1km in non-urban areas. DISCLAIMER CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN. USE CONSTRAINTS This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided. CITATION(S) Data Set: Amini, H.1, 2*, M. Danesh-Yazdi1, Q. Di3, W. Requia4, Y. Wei1, Y. AbuAwad5, L. Shi6, M. Franklin7, C.-M. Kang1, J. M. Wolfson1, P. James8,1, R. Habre9, Q. Zhu6, J. S. Apte10,11, Z. J. Andersen2, X. Xing12, C. Hultquist12,13, I. Kloog14, F. Dominici1,15, P. Koutrakis1, and J. Schwartz1. 2022. Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/7wj3-en73. Accessed DAY MONTH YEAR. 1 Harvard T.H. Chan School of Public Health, Boston, MA, United States 2 Department of Public Health, University of Copenhagen, Copenhagen, Denmark 3 Vanke School of Public Health, Tsinghua University, Beijing, China 4 Fundação Getúlio Vargas, Brasilia, Brazil 5 Swiss Federal Statistical Office (FSO), Neuchâtel, Switzerland 6 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States 7 Department of Statistical Sciences, University of Toronto, Toronto, Canada 8 Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States 9 Keck School of Medicine, University of Southern California, Los Angeles, CA, United States 10 Department of Civil and Environmental Engineering, University of California, Berkeley, CA, United States 11 School of Public Health, University of California, Berkeley, CA, United States 12 The Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States 13 School of Earth and Environment, University of Canterbury, Christchurch, New Zealand 14 Icahn School of Medicine at Mount Sinai, New York, NY, United States 15 Harvard Data Science Initiative, Cambridge, MA, United States Scientific Publication: 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 U.S. Research Square. https://doi.org/10.21203/rs.3.rs-1745433/v2. REFERENCES: 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. ACKNOWLEDGEMENTS 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, and the National Institutes of Health (NIH) grants 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.