Spatiotemporal mapping and assessment of daily ground NO<inf>2</inf> concentrations in China using high-resolution TROPOMI retrievals

Abstract

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.