Shen, F., Hegglin, M. I.
ORCID: https://orcid.org/0000-0003-2820-9044, Luo, Y., Yuan, Y., Wang, B.
ORCID: https://orcid.org/0000-0003-1403-1847, Flemming, J., Wang, J., Zhang, Y., Chen, M., Yang, Q. and Ge, X.
(2022)
Disentangling drivers of air pollutants and health risks change during the COVID-19 lockdown in China.
npj Climate and Atmospheric Science, 5 (1).
54.
ISSN 2397-3722
doi: 10.1038/s41612-022-00276-0
Abstract/Summary
The COVID-19 restrictions in 2020 have led to obvious variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China’s 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23rd January to 7th March 2020 led to increases in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.7%, and -0.9% for CNY, COVID lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP’s contribution highlights the effectiveness of the Chinese government’s air quality regulations on NO2 reduction.
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| Item Type | Article |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/106479 |
| Identification Number/DOI | 10.1038/s41612-022-00276-0 |
| Refereed | Yes |
| Divisions | Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology Henley Business School > Digitalisation, Marketing and Entrepreneurship |
| Publisher | Nature Publishing Group |
| Download/View statistics | View download statistics for this item |
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