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

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 exam-ined 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.

Automated summary

This study used TROPOMI data and land use information to estimate long-term average ambient NO2 concentrations in California for the years 2018-2019 at a spatial resolution of 500 m. The satellite-land use regression model achieved high predictive power with a cross-validation R2 of 0.76 and low errors in comparison with measured NO2 concentrations. The study also found that the representativeness of ground NO2 monitors varied across counties in California and identified specific property types associated with high NO2 concentrations.