Ground-Level NO ₂ Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence

Abstract

Nitrogen dioxide (NO ₂) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO ₂ concentrations over mainland China with full spatial coverage (100%) for the period 2019–2020 by combining surface NO ₂ measurements, satellite tropospheric NO ₂ columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO ₂ estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) μg/m ³. The daily seamless high-resolution and high-quality dataset “ChinaHighNO ₂” allows us to examine spatial patterns at fine scales such as the urban–rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO ₂, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 μg/m ³). During the COVID-19 pandemic, surface NO ₂ concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO ₂ column, implying that the former can better represent the changes in NO _x emissions.

Abstract

Daily seamless 1 km resolution high-quality surface NO ₂ dataset across mainland China is generated from big data using artificial intelligence.