key: cord-0864551-aygy7wok
authors: Yongjian, Zhu; Jingu, Xie; Fengming, Huang; Liqing, Cao
title: Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China
date: 2020-04-15
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.138704
sha: 191771c6b51c237b5efe67fbd871b9f5eb15e78f
doc_id: 864551
cord_uid: aygy7wok

Abstract The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM2.5, PM10, SO2, CO, NO2 and O3) with COVID-19 confirmed cases. We observed significantly positive associations of PM2.5, PM10, NO2 and O3 in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m3 increase (lag0–14) in PM2.5, PM10, NO2, and O3 was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m3 increase (lag0–14) in SO2 was associated with a 7.79% decrease (95% CI: −14.57 to −1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.

A novel coronavirus disease, namely COVID-19, was first detected in Wuhan city, China in December 2019 Xu et al., 2020) . In subsequent months, it spread rapidly to the rest of China, which has later become a global public health problem (Chen et al., 2020a; Gilbert et al., 2020; Sohrabi et al., 2020) . COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Dong et al., 2020; Sohrabi et al., 2020; Zhou et al., 2020) . Generally, most SARS-CoV-2 infected patients have mild symptoms including fever, dry cough, and sore throat Sohrabi et al., 2020) . However, some patients could have severe and even fatal complications such as Acute Respiratory Distress Syndrome (ARDS) (Chen et al., 2020b; Sohrabi et al., 2020) .

To control the spread of COVID-19, various studies have been conducted to explore important factors affecting the transmission of SARS-CoV-2. Several early studies have demonstrated that human-to-human contact could increase the risk of COVID-19 infection (Chan et al., 2020; Wang et al., 2020) . Besides, population mobility has a significant effect on the COVID-19 epidemic (Kraemer et al., 2020) . In addition, a recent study has shown an association of ambient temperature with the infection of COVID-19 (Xie and Zhu, 2020) . However, the impact of short-term exposure to air pollution lacks careful consideration.

Previous studies have suggested that ambient air pollutants are risk factors for respiratory infection by carrying microorganisms to make pathogens more invasive to humans and affecting body's J o u r n a l P r e -p r o o f

This study included 120 cities (4 municipalities and 116 prefecture-level cities) in the geographic regions of 83.4 ° to 131.6 ° east longitude and 20.0 ° to 51.4 ° north latitude (Fig. 1) . According to the National Health Commission, 79,968 COVID-19 confirmed cases have been identified in the whole of China as of February 29, 2020. Our studied cities covered 70% of confirmed cases. We focused our analysis on these 120 cities because of the limitation of the meteorological data and the air pollution data we have obtained.

Daily confirmed new cases for each city between January 23, 2020 to February 29, 2020 were obtained from the reports released by local health commissions on the official websites. We set January 23, 2020 (i.e., the date of lockdown in Wuhan) as the starting point of our study period to minimize the potential inclusion of imported cases from Wuhan.

Air pollution data were collected from an online platform (https://www.aqistudy.cn) monitoring and analyzing the air quality. Daily concentrations of six air pollutants were measured, including particles with diameters ≤ 2.5 μm (PM 2.5 ), particles with diameters ≤ 10 μm (PM 10 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), and ozone (O 3 ).

Meteorological data on daily mean temperature, relative humidity, air pressure, and wind speed during the study period were obtained from the National Meteorological Information Center (http://data.cma.cn).

J o u r n a l P r e -p r o o f Health Commission in China. So, it is a reasonable choice to apply a moving-average approach to capture the cumulative lag effect of ambient air pollution (Duan et al., 2019; Li et al., 2018; Yang et al., 2020) . Thus, in this study, we used the GAM with a Gaussian distribution family to estimate the associations between the moving average concentrations of air pollutants (lag0-7, lag0-14, lag0-21) and daily COVID-19 confirmed cases (Hastie, 2017; Liu et al., 2020) . Specifically, we examined the effects of six air pollutants in six separate models (i.e., single-pollutant models) to reduce the collinearity since some of these pollutants were highly correlated Dastoorpoor et al., 2019; Phosri et al., 2019 ). The basic model was defined as follows:

Here, ( ) indicates the log-transformed COVID-19 counts reported on day t in city i (added 1 to avoid taking the logarithm of 0) Zhu and Xie, 2020) . is the intercept.

denotes the linear term of (l+1)-day moving average concentration of air pollutant (lag0-l) in city i Phosri et al., 2019) . Meteorological factors during the same period were controlled for the possible confounding effect, including mean temperature ( ), relative humidity ( ℎ ), air pressure ( ) and wind speed ( ). (•) is the smooth function (thin plate spline function with the maximum 3 degrees of freedom) of a certain meteorological factor Wang et al., 2018; Zhu and Xie, 2020) .

( , −1 ) indicates the log-transformed COVID-19 counts reported on day t-1 in city i to account for the potential serial correlation in our data . In addition, we included city fixed effects ( ) to control for time-invariant city characteristics such as population size and density, and we also included day fixed effects ( ) to control for unobserved factors affecting all cities in each day such as national lockdown (Amuakwa-Mensah et al., 2017; Lu and Lu, 2017) .

Two sensitivity analyses were conducted. First, since the number of confirmed cases in Wuhan city (the worst-hit region in China) was much larger than that in other cities, we excluded Wuhan from our data to test the robustness of our findings. Second, we applied two-pollutant models to examine whether the significant results from single-pollutant models were robust after controlling for other pollutants in the basic model Phosri et al., 2019) .

All analyses in this study were conducted using the "mgcv" package (version 1.8-28) in R statistical software (version 3.5.2). The statistical tests were two-sided, and p < 0.05 was considered statistically significant. Effect estimates were showed as percentage change (%) in daily COVID-19 J o u r n a l P r e -p r o o f confirmed cases per unit increase in pollutant concentration (i.e., 10 μg/m 3 increase in PM 2.5 , PM 10 , SO 2 , NO 2 , O 3 or 1 mg/m 3 increase in CO). 

In the first sensitivity analysis, the relationship between COVID-19 confirmed cases and air pollution was robust after excluding Wuhan from our data (Fig. 3 ). Fig. 4 

In this paper, we used a generalized additive model to explore the relationship between ambient air pollutants and daily COVID-19 confirmed cases. We found significantly positive associations of PM 2.5 , PM 10 , CO, NO 2 and O 3 with COVID-19 confirmed cases, while SO 2 was negatively associated with the number of daily confirmed cases. These findings could provide evidence that air pollution is an important factor in COVID-19 infection.

As demonstrated by previous literature, air pollution is also closely related to respiratory infection caused by other microorganisms (Chauhan and Johnston, 2003; Ciencewicki and Jaspers, 2007; Mehta et al., 2013) . So, we compared our main findings with previous studies to find similarities and differences. Horne et al. (2018) reported that short-term exposure to higher PM 2.5 was associated with more healthcare encounters for acute lower respiratory infection by a case-crossover design. Xie et al.

J o u r n a l P r e -p r o o f possible reason (Berendt et al., 1971 (Berendt et al., , 1972 , and additional research is needed to determine the biological mechanisms behind this phenomenon.

Our study has some implications for the control and prevention of COVID-19. First, governments and the public should pay more attention to regions with high concentrations of PM 2.5 , PM 10 , CO, NO 2 and O 3 , since these regions may suffer more serious COVID-19 epidemic. In other words, reducing air pollutants (not include SO 2 ) could be a useful way to control COVID-19 infection. Additionally, it is noteworthy that SO 2 has a negative association with COVID-19 confirmed cases, and further laboratory research needs to be conducted to elucidate the underlying mechanism.

Our study has several limitations. First, we only focused on the association between air pollutants and COVID-19 confirmed cases and not the causal effect of air pollution on COVID-19 infection.

Second, our data did not include gender-or age-specific confirmed cases, so we could not conduct subgroup analyses. Third, our findings were not globally representative since cities of other countries

were not included in this study. Future studies are needed to overcome these limitations

Our study suggests that there is a statistically significant relationship between air pollution and J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f 

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