Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China

Zhang, Qingyong; Mao, Xianqiang; Wang, Zhengzao; Tan, Yutong; Zhang, Ziyin; Wu, Yanjie; Gao, Yubing

doi:10.1016/j.envsci.2023.04.009

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Among these six criteria air pollutants, PM 2.5 , PM 10 , NO 2 , SO 2 , and CO showed a significant decreasing trend (P < 0.05; Figure 2), which was consistent with other studies (Bao & Zhang, 2020;He et al, 2020;Huang & Li, 2022;K. Li et al, 2022;Mamtimin et al, 2022;Bagherinia et al, 2023;Gao et al, 2023;Jia et al, Zhang et al, 2023). Compared with the pre-lockdown period, the mass concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , and CO decreased by 46.7%, 40.3%, 48.6%, 45.6%, and 43.2%, respectively, with absolute values of 53.4 μg m -3 , 77.9 μg m -3 , 30.5 μg m -3 , 17.6 μg m -3 , and 0.9 mg m -3 , respectively (Figure 2).…”

Section: Temporal Changes In Air Pollutant Concentrations In Differen...supporting

confidence: 91%

“…This is because the process of resuming social production and daily life is gradual (Cai et al, 2021;J. Yang et al, 2023;Zhang et al, 2023). From the migration data, we also found that the number of individuals traveling across and within the city gradually returned to pre-lockdown levels (Figure S1).…”

Section: Temporal Changes In Air Pollutant Concentrations In Differen...mentioning

confidence: 80%

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Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Wang,

Zhao

et al. 2023

Journal of Environmental Engineering and Landscape Management

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Many studies in China investigated how the lockdown following the coronavirus disease 2019 substantially affected air quality; however, few were conducted in Northeastern China. Here, the changes in six criteria air pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were investigated in Shenyang from January to May 2015–2020. Compared with the pre-lockdown, the mass concentrations of PM2.5, PM10, SO2, NO2, and CO during the lockdown decreased by 40.3% to 48.6%, indicating a positive impact of lockdown policies on reducing pollutant emissions. The responses of PM2.5, PM10, and CO to the lockdown measures in downtown areas were more sensitive than in the suburbs. However, the O3 concentration showed the opposite trend, attributed to the drop in NOx and particulate matters. Compared to the same period in 2015–2019, the proportion of days with good air quality increased from 63.2% to 77.2% during the lockdown and Shenyang experienced no severe pollution. Our results suggest that reducing human activities can improve air quality; however, coordinated control policies of O3, PM2.5, and NO2 are imperative.

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Section: Temporal Changes In Air Pollutant Concentrations In Differen...supporting

confidence: 91%

Section: Temporal Changes In Air Pollutant Concentrations In Differen...mentioning

confidence: 80%

Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Wang,

Zhao

et al. 2023

Journal of Environmental Engineering and Landscape Management

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Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

Zeng,

Chen,

Tang

et al. 2024

Journal of Environmental Management

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Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China

Qin,

Wang,

Dai

et al. 2024

Remote Sensing

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Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

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Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China

Cited by 3 publications

References 44 publications

Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China

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