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https://doi.org/10.5194/acp-2020-522
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-522
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Jul 2020

14 Jul 2020

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This preprint is currently under review for the journal ACP.

Quantifying the emission changes and associated air quality impacts during the COVID-19 pandemic in North China Plain: a response modeling study

Jia Xing1,2, Siwei Li3,4, Yueqi Jiang1,2, Shuxiao Wang1,2, Dian Ding1,2, Zhaoxin Dong1,2, Yun Zhu5, and Jiming Hao1,2 Jia Xing et al.
  • 1State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
  • 2State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 5College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China

Abstract. Quantification of emission changes is a prerequisite for the assessment of control effectiveness in improving air quality. However, the traditional bottom-up method for characterizing emissions requires detailed investigation of emissions data (e.g., activity and other emission parameters) that usually takes months to perform and limits timely assessments. Here we propose a novel method to address this issue by using a response model that provides real-time estimation of emission changes based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. We applied the new method to quantify the emission changes in the North China Plain (NCP) due to the COVID-19 pandemic shutdown, which overlapped the Spring Festival holiday. Results suggest that the anthropogenic emissions of NO2, SO2, VOC, and primary PM2.5 in NCP were reduced by 51 %, 28 %, 67 % and 63 %, respectively, due to the COVID-19 shutdown, indicating longer and stronger shutdown effects in 2020 compared to the previous Spring Festival holiday. The reductions of VOC and primary PM2.5 emissions are generally effective in reducing O3 and PM2.5 concentrations. However, such air quality improvements are largely offset by reductions in NOx emissions. NOx emission reductions lead to increases in O3 and PM2.5 concentrations in NCP due to the strongly VOC-limited conditions in winter. A strong NH3-rich condition is also suggested from the air quality response to the substantial NOx emission reduction. Well-designed control strategies are recommended based on the air quality response associated with the unexpected emission changes during the COVID-19 period. In addition, our results demonstrate that the new response-based inversion model can well capture emission changes based on variations in ambient concentrations, and thereby illustrate the great potential for improving the accuracy and efficiency of bottom-up emission inventory methods.

Jia Xing et al.

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Short summary
Quantifying emission changes is a prerequisite for assessment of control effectiveness in improving air quality. However, traditional bottom-up method usually takes months to perform and limits timely assessments. A novel method was developed by using a response model that provides real-time estimation of emission changes based on air quality observations. It was successfully applied to quantify emission changes in the North China Plain due to the COVID-19 pandemic shutdown.
Quantifying emission changes is a prerequisite for assessment of control effectiveness in...
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