Effects of the COVID-19 on Air Quality: Human Mobility, Spillover Effects, and City Connections

Abstract

We quantify the causal effects of the coronavirus disease 2019 (COVID-19) on air quality in the context of China. Using the lockdowns in different cities as exogenous shocks, our difference-in-differences estimations show that lockdown policies significantly reduced air pollution by 12% on average. Based on the first lockdown city, Wuhan, we present three underlying mechanisms driving our findings: anticipatory effects, spillover effects, and a city’s level of connection with Wuhan. Our findings are more pronounced in cities whose population was more willing to self-isolate or more susceptible to anxiety, or whose government faces less pressure to stimulate economic growth. Overall, this study contributes to the literature by evaluating the unintended consequences of the COVID-19 outbreak for air quality, and provides timely policy implications for policymakers.

Air pollution impacts from COVID-19 pandemic control strategies in Malaysia

“…Previous studies (as shown in Table S1 ) critically investigated lockdown effects toward concentrations of air pollutants ( Abdullah et al., 2020 ; Nakada and Urban, 2020 ; Tanzer-Gruener et al., 2020 ; Tobías et al., 2020 ; Venter et al., 2021 ; Yuan et al., 2021 ); air pollution relationship with COVID-19 cases ( Accarino et al., 2021 ; Tello-Leal and Macías-Hernández, 2020 ); meteorology and air pollutants changes ( Hossain et al., 2021 ; Sulaymon et al., 2021 ); traffic and mobility changes ( Aloi et al., 2020 ); and the application of statistical and modelling ( Bao and Zhang, 2020 ; He et al., 2020 ; Liu et al., 2020b ) while this study focuses mainly on air pollutants effects in cities with the analysis related to population exposure to non-carcinogenic risks. The COVID-19 pandemic affected human activities, primarily when the MCO was implemented to reduce the chain of infection among the population in Malaysia.…”

Lessons from a pandemic for systems-oriented sustainability research

“…Some research building from techniques in economics has examined societal and institutional factors in detail. For example, Liu et al (43) use a difference-in-difference approach to assess the impact of lockdowns on air quality in China, including variables that address changes in mobility and social connections, but with air quality simplified to an aggregate index. Studies have also attempted to calculate changes in mortality from COVID-related declines in air pollution using typical methods from air pollution impact literature, largely without accounting for additional COVID-19-related factors.…”

Analyzing the Contribution of Road Traffic to Changes of Air Pollutants Using Random Forest: Insights from COVID-19 lockdown in Wuhan

“…These do not represent the absolute number of the population, but are relative indexes related to road traffic. The daily In-Migration Index (IMI) and Out-Migration Index (OMI) are chosen to represent the inflow and outflow traffic volume of Wuhan, and the daily Within-City Migration Index (WMI) to represent road traffic in Wuhan [20]. IMI and OMI are the indexation results of the ratio of the number of people who have moved into and out of Wuhan to the total number of residents in Wuhan.…”

“…Therefore, transportation, industrial production and other urban activities fell sharply [8][9]. Moreover, reports of air pollution below normal level emerged, not only in China [10][11][12] but also in many other areas such as South Korea [13], Autoregressive Distributed Lag (NARDL) model have been employed to explain the connection between the COVID-19 lockdown and the dropped air pollution [18][19][20][21]. Random forest (RF) is an ensemble learning method, which consists of several simple decision trees [22].…”