Journal
ENVIRONMENTAL RESEARCH LETTERS
Volume 17, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1748-9326/ac4ec0
Keywords
nitrogen oxides; satellite and surface observations; model simulations
Funding
- National Natural Science Foundation of China [41721002]
- Hundred Talents Program of Chinese Academy of Science
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Recent studies have shown the challenges in explaining wintertime tropospheric NO2 changes in China, and emphasized the importance of integrating surface NO2 observations for better analysis.
Recent studies demonstrated the difficulties to explain observed tropospheric nitrogen dioxide (NO2) variabilities over the United States and Europe, but thorough analysis for the impacts on tropospheric NO2 in China is still lacking. Here we provide a comparative analysis for the observed and modeled (Goddard Earth Observing System-Chem) tropospheric NO2 in early 2020 in China. Both ozone monitoring instrument and surface NO2 measurements show marked decreases in NO2 abundances due to the 2019 novel coronavirus (COVID-19) controls. However, we find a large discrepancy between observed and modeled NO2 changes over highly polluted provinces: the observed reductions in tropospheric NO2 columns are about 40% lower than those in surface NO2 concentrations. By contrast, the modeled reductions in tropospheric NO2 columns are about two times higher than those in surface NO2 concentrations. This discrepancy could be driven by the combined effects from uncertainties in simulations and observations, associated with possible inaccurate simulations of lower tropospheric NO2, larger uncertainties in the modeled interannual variabilities of NO2 columns, as well as insufficient consideration of aerosol effects and a priori NO2 variability in satellite retrievals. In addition, our analysis suggests a small influence from free tropospheric NO2 backgrounds in E. China in winter. This work demonstrates the challenge to interpret wintertime tropospheric NO2 changes in China, highlighting the importance of integrating surface NO2 observations to provide better analysis for NO2 variabilities.
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