National PM2.5 spatiotemporal model integrating intensive monitoring data and land use regression in a likelihood-based universal kriging framework in the United States: 2000-2019

Meng Wang; Michael Young; Julian D Marshall; Logan Piepmeier; Jianzhao Bi; Joel D Kaufman; Adam A Szpiro

doi:10.1016/j.envpol.2024.125405

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

Environ Pollut. 2024 Nov 27:125405. doi: 10.1016/j.envpol.2024.125405. Online ahead of print.

Authors

Meng Wang¹, Michael Young², Julian D Marshall³, Logan Piepmeier², Jianzhao Bi², Joel D Kaufman², Adam A Szpiro⁴

Affiliations

¹ Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY, USA; RENEW Institute, University at Buffalo, Buffalo, NY, USA; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA. Electronic address: mwang54@buffalo.edu.
² Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
³ Department of Civil and Environmental Engineering, University of Washington, Seattle WA, USA.
⁴ Department of Biostatistics, University of Washington, Seattle, WA, United States.

PMID: 39613178
DOI: 10.1016/j.envpol.2024.125405

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 performance indicates a spatial R² of 0.93 and a root mean square error (RMSE) of 1.19 (μg/m³), and a temporal R² of 0.85 and RMSE of 2.05 (μg/m³). Regional spatial R² ranged from 0.80 (northwest) to 0.93 (northeast and central). Over time, the average PM_2.5 across the United Stats decreased from 7.6 μg/m³ in 2000 to 4.7 μg/m³ in 2019. Our model effectively captured local PM_2.5 gradients, highlighting its ability to address fine-scale variations related to local sources and roadways.

Keywords: exposure assessment; fine particulate matters; fine-scale monitors; spatiotemporal model.