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Abstract

Concentration estimates for ambient air pollution are used widely in fields such as environmental epidemiology, health impact assessment, urban planning, environmental equity and sustainability. This study builds on previous efforts by developing an updated high-resolution geospatial database of population-weighted annual-average concentrations for six criteria air pollutants (PM2.5, PM10, CO, NO2, SO2, O3) across the contiguous U.S. during a five-year period (2016–2020). We developed Land Use Regression (LUR) models within a partial-least-squares–universal kriging framework by incorporating several land use, geospatial and satellite–based predictor variables. The LUR models were validated using conventional and clustered cross-validation, with the former consistently showing superior performance in capturing the variability of air quality. Most models demonstrated reliable performance (e.g., mean squared error—based R2 > 0.8, standardised root mean squared error <  0.1). We used the best modelling approach to develop estimates by Census Block, which were then population-weighted averaged at Census Block Group, Census Tract and County geographies. Our database provides valuable insights into the dynamics of air pollution, with utility for environmental risk assessment, public health, policy and urban planning.

Document Type

Article

Publication Date

2025

Notes/Citation Information

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.

Digital Object Identifier (DOI)

https://doi.org/10.1002/gdj3.70005

Funding Information

This work was supported by U.S. Environmental Protection Agency, R835873; U.S. National Institutes of Health, P30 ES026529, P30CA177558; University of Kentucky Office of the Vice President for Research.

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