Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

Min Wu; Xisheng Hu; Zhanyong Wang; Xiaoying Zeng

doi:10.1016/j.ecolind.2023.109862

Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

Ecol Indic. 2023 Feb:146:109862. doi: 10.1016/j.ecolind.2023.109862. Epub 2023 Jan 5.

Authors

Min Wu^{1

2}, Xisheng Hu², Zhanyong Wang², Xiaoying Zeng^{3

2}

Affiliations

¹ Department of Transportation Engineering, Fujian Forestry Vocational Technical College, Nanping 353000, China.
² College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
³ Department of Rail Transit, Fujian Chuanzheng Communications College, Fuzhou 350007, China.

Abstract

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O₃, the annual average concentration of NO₂, PM_2.5 and SO₂ showed a decreasing trend during 2016 and 2019; NO₂, PM_2.5 and SO₂ show a trend of "low in summer and high in winter"; the diurnal variation of NO₂ concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO₂ and NO₂ had four primary periods, while O₃ only had two primary periods. The high NO₂ concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM_2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO₂ and SO₂ decreased considerably during lockdown, whereas PM_2.5 and O₃ increased dramatically. The contribution rates of transportation activities to the NO₂, O₃, PM_2.5 and SO₂ were estimated be 9.4 % ∼ 17.2 %, -76.4 % ∼ -42.9 %, -39.5 % ∼ -22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and -10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

Keywords: Continuous wavelet transform; NO2; PM2.5; Transportation.