Status: this preprint is currently under review for the journal ACP.
Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation
Lei Kong1,3,Xiao Tang1,3,Jiang Zhu2,3,Zifa Wang1,3,4,Yele Sun1,3,Pingqing Fu5,Meng Gao6,Huangjian Wu1,3,Miaomiao Lu7,Qian Wu1,3,Shuyuan Huang8,Wenxuan Sui1,Jie Li1,3,Xiaole Pan1,3,Lin Wu1,3,Hajime Akimoto9,and Gregory R. Carmichael10Lei Kong et al.Lei Kong1,3,Xiao Tang1,3,Jiang Zhu2,3,Zifa Wang1,3,4,Yele Sun1,3,Pingqing Fu5,Meng Gao6,Huangjian Wu1,3,Miaomiao Lu7,Qian Wu1,3,Shuyuan Huang8,Wenxuan Sui1,Jie Li1,3,Xiaole Pan1,3,Lin Wu1,3,Hajime Akimoto9,and Gregory R. Carmichael10
1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2CAS-TWAS Center of Excellence for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
5Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
6Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR, China
7State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention andControl, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
8Chengdu University of Information Technology, Chengdu 610225, China
9National Institute for Environmental Studies, Onogawa, Tsukuba 305-8506, Japan
10Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA
1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2CAS-TWAS Center of Excellence for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
5Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
6Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR, China
7State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention andControl, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
8Chengdu University of Information Technology, Chengdu 610225, China
9National Institute for Environmental Studies, Onogawa, Tsukuba 305-8506, Japan
10Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, USA
Received: 25 Oct 2022 – Discussion started: 03 Jan 2023
Abstract. The unprecedented lockdown of human activities during the COVID-19 pandemic have significantly influenced the social life in China. However, understanding of the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorology variations during COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations of multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measurement with NOx emissions decreased substantially by ~40 %. However, emissions of other air pollutants were found only decreased by ~10 %, both because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over North China Plain (NCP) during lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions, and even led to substantial increases of O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over NCP, which contributed 90 % of the PM2.5 reductions over most parts of NCP region. Meanwhile, our results also suggest that the local stagnant meteorological conditions together with inefficient reductions in PM2.5 emissions were the main drivers of the unexpected COVID-19 haze in Beijing. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.
Report on the manuscript of acp-2022-729 titled “Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation” written by Kong et al.
This work estimated multiple emissions using the EnKF with the state augmentation method during the COVID-19 pandemic. Then, they assessed the unbalanced emission reduction of different species during the restriction. The manuscript is well organized and contains detailed analysis. However, the authors have some issues to be clarified in the manuscript for the final publication in ACP.
Specific comments
Line 91 & 285 (Weak motivation): The EnKF coupled with the state augmentation used in this study could be a more advanced method than inversed single estimation (Zhang et al., 2020; 2021, Feng et al., 2020, & Hu et al., 2022) in terms of emission estimation for multiple species. However, it could be less cost-effective. Also, as mentioned in the manuscript (line 285), it shows a similar performance with the single estimation of SO2 (Hu et al., 2022). In other words, the inversed single estimation can be better, considering both performance and computational cost. Therefore, considering these issues, the authors need some more explanation or justify the use of your method to enhance the motivation for this work.
Line 126: The authors need to clarify how to consider the non-linearity between NOx emission (for estimation) and NO2 concentration (of observation). Additionally, in-situ NO2 observation may be based on the commercial chemiluminescent instrument. While the instrument system converts NO2 to NO through a molybdenum converter, other species, such as peroxyacetyl nitrates (PANs) and HNO3, are also simultaneously converted to NO. The other species account for a large portion of the converted NO molecules. For example, Dunlea et al. (2007) showed the interference in the chemiluminescence detection accounting for up to 50% of ambient NO2 In other words, the observed NO2 is almost equivalent to ambient NOy. Also, PAN is thermally sensitive (and it is also related to the temperature shown in Figure 10). The rapid decrease in NOx emission is strongly linked to this issue. Therefore, the authors also need to explain how to treat these issues in your calculation.
Line 90: Levelt et al. (2022) is not related to the emission inversion technique. The authors had better discard this paper in the manuscript.
Line 105: Anthropogenic and other emission inventories used in the simulations during the COVID-19 pandemic are based on 2010 or relatively long ago. These emission rates could be significantly higher than those during the COVID-19 pandemic. The higher emission rates are significantly related to the concentration of atmospheric species like O3. Therefore, the authors need to justify the uses of such emission inventories in the simulations.
Line 138: The explanation of EI and MI (from both MET and EMIS change scenarios) is rather complicated. The authors need to clarify it. Tabulating or illustrating the scenarios or cases makes it easier for the readers to understand them.
Line 218: The authors need to define P1, P2, and P3 on Line 218, not on Line 247. Also, the authors need to add some lines for P1, P2, and P3 on the x-axis of Figure 4.
Line 268: The performance in the O3 simulation is relatively poor. As mentioned in the manuscript, the lower performance is related to VOC emission. Since the VOC emission used in the simulations is made for 2010, there are some time gaps. Therefore, the authors need to compare the VOC emission rates with the recent or 2019 database and then make an additional explanation of (expected) ozone concentrations.
Line 289 & 303 – 304: The authors need to explain what causes increases in PM10 during the P3 in Figure 4, Figure 5e, and 5f. The authors explained these are related to the sandstorm. However, the concentration of PM10 during the P3 period was rather low in the NW and Central regions.
Line 295: The explanation is not sufficient for the PM2.5 emission increase in the NCP region. The observation done by Dai et al. (2020) was carried out at a single site in Tianjin.
Line 340: The authors also need to mention that the O3 is under-simulated in all regions (refer to Figure S6).
Minor comments
Line 32: measurement -- > measure or measures
Line 33: remove ‘both’.
Line 261. It is probably Table S1, not Table S4.
Lines 277 and 280: The authors need to confirm the numbers in these lines (and Table 2).
REFERENCE
Dunlea, E. J et al.: Evaluation of nitrogen dioxide chemiluminescence monitors in a polluted urban environment, Atmos. Chem. Phys., 7, 2691–2704, 2007.
A multi-air pollutant inversion system has been developed in this study to estimate emission changes in China during COVID-19 lockdown. The results demonstrate that the lockdown is largely a nationwide road traffic control measurement with NOx emission decresed by ~40 %. Emissions of other species were only decreased by ~10 % due to smaller effects of lockdown on other sectors. Assessment results further indicates that the lockdown only has limited effects on the control of PM2.5 and O3 in China.
A multi-air pollutant inversion system has been developed in this study to estimate emission...
