  Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants.
However, current ground-level NO2 concentration data are lack of either
high-resolution coverage or full coverage national wide, due to the poor
quality of source data and the computing power of the models. To our knowledge,
this study is the first to estimate the ground-level NO2 concentration in China
with national coverage as well as relatively high spatiotemporal resolution
(0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We
advanced a Random Forest model integrated K-means (RF-K) for the estimates with
multi-source parameters. Besides meteorological parameters, satellite
retrievals parameters, we also, for the first time, introduce socio-economic
parameters to assess the impact by human activities. The results show that: (1)
the RF-K model we developed shows better prediction performance than other
models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average
concentration of NO2 in China showed a weak increasing trend . While in the
economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and
Pearl River Delta, the NO2 concentration there even decreased or remained
unchanged, especially in spring. Our dataset has verified that pollutant
controlling targets have been achieved in these areas. With mapping daily
nationwide ground-level NO2 concentrations, this study provides timely data
with high quality for air quality management for China. We provide a universal
model framework to quickly generate a timely national atmospheric pollutants
concentration map with a high spatial-temporal resolution, based on improved
machine learning methods.
