Annual Mean PM_2.5 Components (EC, NH₄, NO₃, OC, SO₄) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1

Particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) increases mortality and morbidity. PM_2.5 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 PM_2.5 components, their US-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM_2.5 mass. The first national super-learned models were developed across the US for hyperlocal estimation of annual mean elemental carbon (EC), ammonium (NH₄⁺), nitrate (NO₃^-), organic carbon (OC), and sulfate (SO₄^2-) concentrations across 3,535 urban areas at a 50-m spatial resolution, and at a 1-km resolution for non-urban areas from 2000 to 2019. Using ensembles of machine learning models and ~82 billion predictions across 20 years, hyperlocal super-learned PM_2.5 components are now available for further research. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM_2.5 components. It is anticipated for this work to be a critical milestone for conducting new studies on individual and combined health risks of PM_2.5 components, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM_2.5 composition. Urban planners and regulators may also use these predictions for selecting locations of new daycares, schools, nursing homes, or air-quality monitors.

This work aimed to predict annual mean levels of several major PM_2.5 components that contribute substantially to PM_2.5 mass (EC, NH₄⁺, NO₃^-, OC, and SO₄^2-) across 20 years from 2000-2019 using machine-learning models (ML), combined using either a generalized additive model (GAM) ensemble geographically-weighted-averaging (GAM-ENWA) or super-learning (SL) in the contiguous United States. Since majority of the US population (~80%) lives in urban areas, models were developed for urban areas at 50 m spatial resolution across 3,535 urban areas, and in non-urban areas at 1 km spatial resolution. Authors divided monitoring data into training and test sets, developed multiple ML models using the training set, predicted for each grid using each ML model, and ensembled the predictions of the ML models. The best model was chosen based on the performance in the test set.

Download the Annual Mean PM_2.5 Components (EC, NH₄, NO₃, OC, SO₄) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1. The files in the table below are in RDS format. The urban file sizes range from approximately 2.5 to 3.8 GB and the non-urban file sizes are about 150 MB.

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. James^8,1, R. Habre⁹, Q. Zhu⁶, J. S. Apte^10,11, Z. J. Andersen², X. Xing¹², C. Hultquist^12,13, I. Kloog¹⁴, F. Dominici^1,15, P. Koutrakis¹, J. Schwartz¹. 2022. Annual Mean PM_2.5 Components (EC, NH₄, NO₃, OC, SO₄) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1. (Preliminary Release). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/7wj3-en73. Accessed DAY MONTH YEAR.

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
⁵PERFORM Centre, Concordia University, Montreal, Quebec, Canada
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
¹²The Center 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, NY, United States
¹⁵Harvard Data Science Initiative, Cambridge, MA, United States

This project was supported by Cyprus Harvard Endowment Program for the Environment and Public Health, US Environment Protection Agency grant RD-8358720, National Institute of Health grant P30 ES000002 and R01 ES032418-01.

This is a preliminary open data release, pending peer review of the associated journal article. Following the peer review process, data curation will be completed by the NASA Socioeconomic Data and Applications Center (SEDAC) and the data will be disseminated through the SEDAC catalog.