National PM_2.5 spatiotemporal model integrating intensive monitoring data and land use regression in a likelihood-based universal kriging framework in the United States: 2000-2019.

ABSTRACT: Nationwide PM_2.5 exposure models typically rely on regulatory monitoring data as the only ground-level measurements. In this study, we develop a high-resolution spatiotemporal PM_2.5 model for the contiguous United States from 2000 to 2019 with dense monitoring data at both regulatory and residential sites. Specifically, we combine publicly-available data from 1843 regulatory monitors with our own set of multiple 2-week measurements at 939 residential locations. As we show, these additional data enhance the spatiotemporal prediction capabilities of the model. The model can handle varying data densities and regional variations; it predicts two-week average PM_2.5 concentrations at fine spatial scale for the contiguous United States. Cross-validation performanc

SUBMITTER: Wang M 

PROVIDER: S-EPMC12060488 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature