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.