The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach

Abstract

We quantify the impact of the Wuhan Covid-19 lockdown on concentrations of four air pollutants using a two-step approach. First, we use machine learning to remove the confounding effects of weather conditions on pollution concentrations. Second, we use a new augmented synthetic control method (Ben-Michael et al. in The augmented synthetic control method. University of California Berkeley, Mimeo, 2019. https://arxiv.org/pdf/1811.04170.pdf) to estimate the impact of the lockdown on weather normalised pollution relative to a control group of cities that were not in lockdown. We find NO ${}_2$ concentrations fell by as much as 24 $\mu$ g/m ${}^3$ during the lockdown (a reduction of 63% from the pre-lockdown level), while PM10 concentrations fell by a similar amount but for a shorter period. The lockdown had no discernible impact on concentrations of SO ${}_2$ or CO. We calculate that the reduction of NO ${}_2$ concentrations could have prevented as many as 496 deaths in Wuhan city, 3368 deaths in Hubei province and 10,822 deaths in China as a whole.

Keywords: Air pollution; Covid-19; Health; Machine learning; Synthetic control.

Fig. 1

The annual average observed concentrations of SO${}_2$, NO${}_2$, CO and PM10 in Wuhan and 29 control cities between 2013 and 2019. Note: Wuhan is denoted by the red line

Fig. 2

Daily averages of observed and weather normalised concentrations of SO${}_2$, NO${}_2$, CO and PM10 in Wuhan between January 2013 and February 2020

Fig. 3

The comparison of daily observed and weather normalised concentrations of SO${}_2$, NO${}_2$, CO and PM10 in Wuhan between 21st December 2019 and 3rd February 2020

Fig. 4

Ridge ASCM results on weather normalised NO${}_2$ concentrations in Wuhan. Note: Left hand figure shows point estimate ± one standard error of the ATT

Fig. 5

Ridge ASCM results on weather normalised SO${}_2$ concentrations in Wuhan. Note: Left hand figure shows point estimate ± one standard error of the ATT

Fig. 6

Ridge ASCM results on weather normalised CO concentrations in Wuhan. Note: Left hand figure shows point estimate ± one standard error of the ATT

Fig. 7

Ridge ASCM results on weather normalised PM10 concentrations in Wuhan. Note: Left hand figure shows point estimate ± one standard error of the ATT

Fig. 8

The in-time placebo test results of NO${}_2$wn using 21st January 2019 (left) and 21st January 2018 (right) as Wuhan lockdown date. Note: Both figures show point estimate ± one standard error of the ATT

Fig. 9

The in-time placebo test results of PM10wn using 22nd January 2019 (left) and 22nd January 2018 (right) as Wuhan lockdown date. Note: Both figures show point estimate ± one standard error of the ATT

Fig. 10

The results of in-place placebo test on NO${}_2$wn. Note: We randomly assign the lockdown policy to one of the other 29 control cities and compare with Wuhan (in red)

Fig. 11

The results of in-place placebo test on PM10wn. Note: The left figure plots the results using all 30 cities, the right figure plots the results after dropping Shijiazhuang, Jinan, Hangzhou, Huhehaote

Fig. 12

The results of alternative control group tests on NO${}_2$wn (left) and PM10wn (right). Note: “Appendix” Table 6 defines each control group

Fig. 13

The location of Wuhan and the 29 control cities