Neighborhood-scale ambient NO2 concentrations using TROPOMI NO2 data: Applications for spatially comprehensive exposure assessment

Abstract

This study estimated long-term average ambient NO₂ concentrations using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO₂ data and land use information at the spatial resolution of 500 m in California for the years 2018-2019. Our satellite-land use regression model demonstrated reasonably high predictive power with cross-validation (CV) R² = 0.76, mean absolute error (MAE) = 1.95 ppb, and root mean squared error (RMSE) = 2.51 ppb in a comparison between measured and estimated NO₂ concentrations. Exploiting the high-resolution NO₂ estimates, we further investigated the representativeness of ground NO₂ monitors for population exposures and examined the spatial variation of NO₂ in relation to parcel-level property data for exposure attributions. The ground NO₂ monitors were the most representative of population exposures in Los Angeles and San Diego counties, supported by population-weighted average NO₂ concentrations (satellite-derived estimations) similar to arithmetic average NO₂ concentrations (ground measurements). On the contrary, the exposure assessment using the ground monitors was the least representative and protective in Humboldt, San Luis Obispo, and Yolo counties with population-weighted average NO₂ greater than arithmetic average NO₂ by 82.2 % (1.85 ppb), 67.1 % (1.89 ppb), and 58.2 % (2.48 ppb), respectively. In a case study of LA County, we identified comparatively high NO₂ concentrations for the property types of food processing facilities and high-density residential complexes (such as high-rise apartments and apartments). This finding provides evidence that these emerging sources may be crucial to mitigate cumulative NO₂ exposures and subsequent health risks from a regulatory perspective.

Keywords: Air quality management; Exposure assessment; Land use regression; Parcel data; Satellite remote sensing; TROPOMI NO(2).