Integrating traffic pollution dispersion into spatiotemporal NO2 prediction

Yunhan Wu; Jianzhao Bi; Amanda J Gassett; Michael T Young; Adam A Szpiro; Joel D Kaufman

doi:10.1016/j.scitotenv.2024.171652

Integrating traffic pollution dispersion into spatiotemporal NO₂ prediction

Sci Total Environ. 2024 May 15:925:171652. doi: 10.1016/j.scitotenv.2024.171652. Epub 2024 Mar 13.

Authors

Yunhan Wu¹, Jianzhao Bi², Amanda J Gassett³, Michael T Young³, Adam A Szpiro¹, Joel D Kaufman³

Affiliations

¹ Department of Biostatistics, University of Washington, Seattle, WA, USA.
² Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA. Electronic address: jbi6@uw.edu.
³ Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.

PMID: 38485010
PMCID: PMC11027090 (available on 2025-05-15)
DOI: 10.1016/j.scitotenv.2024.171652

Abstract

Accurately predicting ambient NO₂ concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO₂ prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO₂ models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R² from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R² increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO₂ concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO₂ exposure - especially near major roads in the U.S.

Keywords: Air pollution; Dispersion model; Exposure assessment; Nitrogen dioxide; Spatiotemporal model; Traffic density model.

Abstract

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