High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city.

The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R, and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m for sulfur dioxide (SO) to 0.92 and 6.25 µg/m for fine particulate matter (PM) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.