Influence of COVID-19 Event on Air Quality and their Association in Mainland China

Received: May 14, 2020
Revised: June 7, 2020
Accepted: June 7, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Chen, Q.X., Huang, C.L., Yuan, Y. and Tan, H.P. (2020). Influence of COVID-19 Event on Air Quality and their Association in Mainland China. Aerosol Air Qual. Res. 20: 1541–1551. https://doi.org/10.4209/aaqr.2020.05.0224

HIGHLIGHTS

• Air quality is significantly improved due to strict COVID-19 control policy.
• New COVID-19 cases is positively correlated with PM_2.5 concentration in many provinces.
• Going out in the afternoon can reduce the risk of infection due to aerosol transmission.

Keywords: COVID-19; Air quality; PM2.5; China.

INTRODUCTION

A novel and contagious pneumonia broke out in the late of 2019. Then it has been identified as a novel strain of coronavirus, which belongs to the same virus family of acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) (Zheng et al., 2020). This novel coronavirus is named as 2019 novel coronavirus (2019-nCoV) in January 13 and then as coronavirus disease 2019 (COVID-19) in March 6 by World Health Organization (WHO). From early medical reports in middle January, COVID-19 can be transmitted from human to human (Guo et al., 2020; Shereen et al., 2020). In China, the government immediately takes nationwide contingency plans to shut down the traffic and public activities and almost everyone is isolated at home. On June 5, 2020, over 6450000 confirmed cases and over 382000 death are reported by the National Health Commission around the world.

Air pollution is one of the major concerns around the world, especially in developing countries like China and India (Dong et al., 2019). Severe air pollution is a major health risk and can lead to large premature mortality (Schraufnagel et al., 2019). Previous studies have shown that PM_2.5 has an adverse impact on human health and long-term exposure to high PM_2.5 concentration environment will lead to various respiratory diseases because -PM_2.5 carries lots of harmful matters and viruses and can go directly into our lungs (Hayes et al., 2019; McGuinn et al., 2019). As reported in the 6^th Edition of COVID-19 Treatment Regimen (Trial Implementation) published by the National Health Commission of the People’s Republic of China, COVID-19 is mainly transmitted by direct contact and droplet transmission, and aerosol transmission is also a potential way (Wang and Du, 2020). The strict COVID-19 control measures implemented in China in the beginning of February aim to minimize the social contact to stop the spread of COVID-19 and results show that these control measures work very well (Kraemer et al., 2020). For example, Xu et al. (2020a) investigated the impact of COVID-19 epidemic prevention and control actions on air quality of three cities in Hubei province and found that the average concentrations of PM_2.5, PM₁₀, SO₂, CO and NO₂ in February 2020 were much lower than those in February 2017–2019 with an average reduction of 30.1%, 40.5%, 33.4%, 27.9% and 61.4%, respectively. A similar decrease was also reported in Anqing, Hefei, and Suzhou in February 2020 and the overall decreases for PM_2.5, PM₁₀, SO₂, CO and NO₂ are 46.5%, 48.9%, 52.5%, 36.2% and 52.8%, respectively compared to those in the same month in 2017–2019 (Xu et al., 2020b). To understand the influence of strict control policy on air quality, it is necessary to gain a full view of how much improvement is made on air quality by these COVID-19 control measures across China. Also, current studies have shown that COVID-19 transmission is associated with temperature and humidity (Ma et al., 2020; Xie and Zhu, 2020), however, the potential influence of PM_2.5 on COVID-19 is still not clear and it might be important for policy makers to implement proper measures to limit the spread of COVID-19 due to aerosol transmission.

In this study, the concentration of six major air pollutions (PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) measured in 366 urban areas in mainland China during COVID-19 is compared to previous years. The diurnal variations of particulate matter in 31 provinces during February to April are then analyzed. We also explores the potential correlation between PM_2.5 concentrations and COVID-19 infections and give some suggestions to the public to minimize the risk of COVID-19 infections due to aerosol transmission.

DATA AND METHODS

Data Collection

In this study, a total number of 366 urban area in mainland China is used for the comparison of air quality. The air quality data used here are the hourly concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃ as well as air quality index (AQI). The AQI is a dimensionless index describing the air quality (the lower the better). It is the maximum of sub-AQI of the above-mentioned six major pollutants (Eqs. (1) and (2)). The daily AQI is calculated from the 24-hour average of PM_2.5, PM₁₀, SO₂, NO₂, CO, and the daily maximum 8-hour O₃ (Xu et al., 2020a). All these data can be obtained from China’s National Environmental Monitoring Center (http://www.cnemc.cn). The time range of the air quality data is from January to April in 2017 to 2020.

where SAQI_p represents the sub-AQI; C_p denotes the concentration of pollutant p; C_low and C_high are the concentration breakpoints lower and higher than C_p, respectively; I_low and I_high are the index breakpoint corresponding to C_low and C_high, respectively.

The daily counts of confirmed COVID-19 cases in all these 366 urban areas across mainland China are collected from an open Github project called Wuhan-2019-nCov, which provides the uptodate COVID-19 statistics across the world. The data is crawled from official reports and detailed information and COVID-19 data can be found at https://github.com/canghailan/Wuhan-2019-nCoV/blob/master/Wuhan-2019-nCoV.json. The time range of the COVID-19 data is same as the air quality data.

Statistical Analysis

A decrease ratio is defined as DR = 1 – X₂₀₂₀/X₂₀₁₉, where X denotes the average concentration of air pollutant and the subscript represents the year. And this ratio shows the degree of major pollutant reduction due to COVID-19 control policies.

The relationship between daily COVID-19 infections and particulate matter concentration may exhibit spatial variation due to changing aerosol source, control policy and other factors. To represent the varying COVID-19 – PM relationship, we use a mixed effects model to analyze the influence of PM concentration on COVID-19 infections. The mixed effects model estimates daily COVID-19 infections for each site i of day j (COVID_i,j) from corresponding PM concentration (PM_i,j) in the form of

where α and β are the fixed intercept and slope independent of time and location, and µ_i and v_i are the random intercept and slope for each site. The random terms (µ_i and v_i) are sites term which reflects the spatial difference of the COVID-19 – PM relationship due to differences in site specific characteristics like aerosol sources, topography, population density, meteorology, etc.  is the scaled PM concentration, and mean(PM) and sd(PM) represents the overall mean PM and its standard deviation. ε_i,j and ∑ denote the error term and the variance-covariance matrix for the day-specific random effects. Only matchups with daily COVID-19 infection number ≥ 1 are included in this mixed effect model analysis and the total number of matchups during the studying period is 4351.

RESULTS AND DISCUSSION

Site-scale Analysis of Air Pollutants

Table 1 shows the ambient air quality standard from the Ministry of Ecology and Environment of the People’s Republic of China (MEE) and the World Health Organization (WHO). The MEE Level I standard is designed for natural reserve and tourist attractions and Level II is for industrial, residence and rural areas. In general, WHO standards for 24h average PM_2.5 (25 µg m^–3), SO₂ (20 µg m^–3), and NO₂ (40 µg m^–3) are lower than MEE Level I standards (35 µg m^–3, 50 µg m^–3, and 80 µg m^–3). The MEE Level I standards for 24 h average PM₁₀ (50 µg m^–3) and O₃ (100 µg m^–3) are in consistent with WHO standards. 

Fig. 1 shows the site-scale concentrations of six major pollutants in February 2017–2019 and February 2020. For 24 h average PM_2.5 and PM₁₀, only the cities in Tibet and Yunnan as well as some northern cities in Heilongjiang meet the WHO and MEE Level I standard in February 2017–2019, and due to the strict control policy in February 2020, many cities in south China meet the standards and the spatial area showing MEE Level II standard decreases significantly. For 24 h average SO₂, most cities across China meets the WHO and MEE Level I standard and only several cities in Shanxi exceed the WHO and MEE Level I standard in February 2017–2019, and in February 2020 all of the cities including cities in Shanxi meet the standard. NO₂ concentrations in Beijing, Tianjin, Hebei, Shanxi, Shaanxi, Shandong Jiangsu and Xinjiang are observed over the WHO and MEE Level I standard concentration in February 2017–2019 and similar to SO₂, all the cities meet the standard in February 2020. O₃ concentrations in most part of China generally meet the WHO and MEE Level I standard except some coastal cities and remote region and the spatial characteristic changes little in February 2020. All of CO concentrations across China meet the WHO and MEE Level I standard in February 2017–2019 and in February 2020. 

Fig. 1. Monthly mean concentration of PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO in (a, c, e, g, i, k) February 2017–2020 and (b, d, f, h, j, l) February 2020.

PM_2.5, PM₁₀, SO₂, NO₂ and CO are concentrated mainly in the north part of China, especially Beijing-Tianjin-Hebei region, where their concentrations in February 2017-2019 are around 100 µg m^–3, 160 µg m^–3, 40 µg m^–3, 60 µg m^–3, and 1.8 mg m^–3, respectively. The spatial distribution of high O₃ concentration is contrary to that of PM_2.5, PM₁₀, SO₂, NO₂ and CO because heterogeneous reactions largely reduced surface O₃ in polluted regions of China (Li et al., 2018; Li et al., 2019a). The O₃ concentration in Beijing-Tianjin-Hebei region is around 60 µg m^–3 in February 2017–2019. Fig. S1 shows the monthly variation of six major pollutant concentrations in February during 2017 to 2020. From Fig. S1, a decrease trend of the concentration of PM_2.5, PM₁₀, SO₂, NO₂ and CO is observed but the decrease trend is mild. 

While in February 2020, a sharp fall of the concentration of PM_2.5, PM₁₀, SO₂, NO₂ and CO is observed across the whole country. In Beijing-Tianjin-Hebei region, the PM_2.5, PM₁₀, SO₂, NO₂ and CO concentrations decrease from 100 µg m^–3,150 µg m^–3, 20 µg m^–3, 40 µg m^–3, and 1.2 mg m^–3 in February 2019 to 70 µg m^–3, 80 µg m^–3, 10 µg m^–3, 25 µg m^–3, and 0.5 mg m^–3 in February 2020, respectively. Such a pollutant reduction is directly related to the strict COVID-19 control measures, because the measures restrict many of the public transport, production and business activities and minimize almost all kinds of social activities that may cause direct contact between people.

To take a further look at air quality variation between months, we compare the PM_2.5 concentration from January to April in 2019 and 2020 (Fig. 2). Before the implementation of strict COVID-19 control policy at the beginning of February 2020, an identical spatial distribution of PM_2.5 is observed in January 2020 compared to January 2019. The high concentration of PM_2.5 in wintertime is mostly due to the large amount of anthropogenic pollutants emitted from heating, industry and traffic and also due to the adverse weather conditions in favor of the massive accumulation of pollutants (Deng et al., 2019; Yang et al., 2019). As time goes, the intensification of atmospheric movements accelerates the diffusion of pollutants and a decrease trend of PM_2.5 concentration from January to April is found (Su et al., 2017; Li et al., 2019b). For example in Tianjin, the monthly mean PM_2.5 concentrations from January to April 2019 are 81 µg m^–3, 78 µg m^–3, 54 µg m^–3, and 49 µg m^–3, respectively, and those from January to April 2020 are 102 µg m^–3, 62 µg m^–3, 43 µg m^–3, and 40 µg m^–3, respectively. In April 2020, the PM_2.5 concentration in most part of China has been significantly decreased compared to 2019. However, PM_2.5 concentration in Northeast China met an increase. For example in Harbin, the PM_2.5 concentration increases from 36 µg m^–3 in April 2019 to 91 µg m^–3 in April 2020. This is mostly due to the slowdown of the COVID-19 control policy, and farmers went out and burned their straw, which led to the significant increase of PM_2.5 concentration. The adverse weather condition also contributes to this increase (Li et al., 2016; Yin et al., 2017). 

Fig. 2. Monthly variation of PM_2.5 concentration during (a, c, e, g) January–April 2019 and (b, d, f, h) January–April 2020 in mainland China.

Provincial-scale Analysis of Air Pollutants

Table 2 shows the decrease ratios of the provincial mean air pollutant concentrations during February to April in 2020 compared to 2019. For PM_2.5, an overall decrease ratio of 14% is observed on national scale. There are 12 provinces showing a decrease ratio over 20% including Tianjin (20%), Hebei (29%), Shanxi (24%), Shanghai (21%), Jiangsu (31%), Zhejiang (22%), Anhui (30%), Shandong (29%), Henan (33%), Hubei (23%), Shaanxi (22%), and Qinghai (22%). For example in Shanghai, Hubei and Qinghai, the PM_2.5 concentrations decrease from 50 µg m^–3, 27 µg m^–3, and 49 µg m^–3 in February–April 2019 to 39 µg m^–3, 21 µg m^–3, and 38 µg m^–3 in February–April 2020, respectively. Insignificant decrease of PM_2.5 concentration (DR < 5%) is observed in Jiangxi (5%), Guangdong (4%), Guangxi (0%), Chongqing (5%), and Gansu (4%). And there are three provinces showing a slight increase of PM_2.5 concentration including Guizhou (9%), Yunnan (6%), and Xinjiang (3%), where the PM_2.5 concentrations increase from 27 µg m^–3, 32 µg m^–3 and 64 µg m^–3 in February–April 2019 to 30 µg m^–3, 34 µg m^–3 and 66 µg m^–3 in February–April 2020. 

Similar to PM_2.5, the average PM₁₀ concentration decreases by 15% during February to April 2020 compared to that in 2019. There are 12 provinces showing a decrease ratio over 20% including Beijing (27%), Tianjin (28%), Hebei (30%), Shanxi (21%), Jilin (25%), Heilongjiang (28%), Shanghai (21%), Jiangsu (27%), Anhui (22%), Shandong (30%), Henan (29%), and Hubei (25%). In Hebei, Heilongjiang and Shandong, the PM₁₀ concentrations decrease from 114 µg m^–3, 73 µg m^–3, and 109 µg m^–3 in February–April 2019 to 80 µg m^–3, 52 µg m^–3, and 77 µg m^–3 in February–April 2020, respectively. The decrease ratios for PM₁₀ in Beijing, Jilin and Heilongjiang are much larger than those for PM_2.5, and this is probably due to the weaker dust transport in 2020 compared to 2019. Five provinces shows an insignificant variation of PM₁₀ concentration, which are Jiangxi (3%), Guangdong (4%), Guangxi (2%), Hainan (4%), and Guizhou (2%). Only Gansu and Xinjiang meet an increase of PM₁₀ concentration by 3% and 8%, where the PM₁₀ concentrations increase from 84 µg m^–3 and 86 µg m^–3 in 2019 to 174 µg m^–3 and 188 µg m^–3 in.

The national average decrease ratio for SO₂ is 12%. Around half of mainland China (14 of 31 provinces involved in this study) has a decrease ratio between 10% and 20%. Only 6 provinces show a decrease ratio over 20%, which includes Beijing (28%), Tianjin (30%), Hebei (27%), Shanxi (25%), Liaoning (27%), and Shandong (22%), and their SO₂ concentrations decrease from 5 µg m^–3, 12 µg m^–3, 17 µg m^–3, 27 µg m^–3, 23 µg m^–3 and 15 µg m^–3 in February–April 2019 to 4 µg m^–3, 9 µg m^–3, 12 µg m^–3, 20 µg m^–3, 17 µg m^–3 and 11 µg m^–3 in February–April 2020, respectively. Five provinces show an increase of SO₂ concentrations during COVID-19, and they are Heilongjiang (2%), Jiangxi (6%), Hubei (1%), Guangdong (3%), and Tibet (32%). In Jiangxi and Tibet, the SO₂ concentrations increase from 11 µg m^–3 and 5 µg m^–3 in February–April to 12 µg m^–3 and 7 µg m^–3 in February-April 2020. The significant increase of SO₂ may result from the biomass burning emission transported from India during the pre-monsoon period (Cong et al., 2015).

The largest decrease ratio (16%) on national scales is observed in NO₂ concentration. Around 70% of the country (22 of 31 provinces involved in this study) has a decrease ratio between 10% and 20%. Five provinces show a decrease ratio over 20% including Beijing (31%), Hebei (22%), Shanghai (20%), Shandong (24%), and Hubei (32%), where the NO₂ concentrations decrease from 36 µg m^–3, 37 µg m^–3, 39 µg m^–3, 34 µg m^–3 and 28 µg m^–3 in February–April 2019 to 25 µg m^–3, 29 µg m^–3, 31 µg m^–3, 25 µg m^–3 and 19 µg m^–3 in February–April 2020, respectively. Only Guangxi and Qinghai show an insignificant decrease of NO₂ by 1% and 5%, respectively, and no increase is observed.

The national scale decrease ratio of CO is 12%, which is similar to SO₂. There are 15 provinces showing a decrease ratio between 10% and 20%. Six provinces show a decrease ratio over 20%, which includes Tianjin (20%), Shanxi (23%), Fujian (21%), Shandong (21%), Guangdong (21%), and Shaanxi (24%). Their CO concentrations decrease from 1.0 mg m^–3, 1.1 mg m^–3, 0.7 mg m^–3, 0.9 mg m^–3 and 0.9 mg m^–3 in February-April 2019 to 0.8 mg m^–3, 0.9 mg m^–3, 0.6 mg m^–3, 0.7 mg m^–3 and 0.7 mg m^–3 in February-April 2020, respectively. Inner Mongolia, Jilin, Heilongjiang, Hubei, and Yunnan show an insignificant decrease of CO concentration with a decrease ratio of 4%, 3%, 0, 3% and 0, respectively. Only Hainan and Tibet show an increase of CO concentration of 1% and 3%, respectively, where the CO concentrations in February–April 2020 are 0.5 mg m^–3 and 0.6 mg m^–3.

The variation of O₃ concentration is generally the opposite of other pollutants and it shows an increase of 9% on national scale. There are 28 provinces show an increase of O₃ concentration. Five provinces have an increase ratio over 20% including Jiangxi (30%), Hubei (22%), Guangdong (23%), Guangxi (23%), and Hainan (34%), where the O₃ concentrations increase from 55 µg m^–3, 54 µg m^–3, 48 µg m^–3, 40 µg m^–3 and 48 µg m^–3 in February–April 2019 to 65 µg m^–3, 66 µg m^–3, 59 µg m^–3, 48 µg m^–3 and 64 µg m^–3 in February–April 2020, respectively. And nine provinces show an insignificant increase of O₃ concentration (< 5%), which includes Shanxi (1%), Inner Mongolia (3%), Liaoning (5%), Shandong (3%), Henan (3%), Yunnan (1%), Tibet (3%), Shaanxi (5%), and Ningxia (1%). Three provinces meet a decrease of O₃ concentration including Chongqing (8%), Gansu (3%), and Qinghai (2%), where the O₃ concentrations decrease from 45 µg m^–3, 73 µg m^–3 and 79 µg m^–3 in 2019 to 42 µg m^–3, 71 µg m^–3 and 77 µg m^–3 in 2020, respectively.

AQI decreases by 11% on national scale during February to April 2020 compared to 2019. Large decrease ratios (> 20%) are found in Tianijn (20%), Hebei (25%), Heilongjiang (20%), Jiangsu (25%), Anhui (23%), Shandong (25%), Henan (27%) and Hubei (20%), where the AQI decreases from 93, 96, 67, 77, 81, 92, 109, and 73 in 2019 to 75, 72, 54, 58, 62, 69, 80, and 59 in 2020, respectively. There are 10 provinces having a decrease ratio between 10% and 20% and 7 provinces having an insignificant decrease of AQI (< 5%). Guangxi, Guizhou, and Xinjiang show an increase of AQI by 1%, 5% and 4%, respectively. The variation of AQI is generally in consistent with that of PM_2.5, PM₁₀, SO₂, NO₂ and CO, while the opposite of O₃.

In general, insignificant variation of pollutants are mainly found in Fujian, Jiangxi, Guangdong, Guangxi, Hainan, Guizhou, Yunnan, Gansu, and Xinjiang, where the seasonal mean concentration of major pollutants are generally low, while significant variation of pollutants are mainly observed in Beijing, Tianjin, Hebei, Shandong, Shanghai, Anhui, Jinagsu, Henan, and Hubei, where the seasonal mean concentration of major pollutants are generally high. Thus, the strict COVID-19 control policy can effectively improve the air quality in northeast and inland provinces where previous air quality was poor, while the effect of the COVID-19 policy is less significant in southeast coastal and western provinces where previous air quality was good. On the whole, twenty six provinces (84%) meet an AQI improvement due to COVID-19 control policy.

Diurnal Variation of PM_2.5

Fig. 3 shows the diurnal variation of PM_2.5 concentrations in 2019 and 2020, and Fig. S2 presents results of the rest 24 provinces. In general, PM_2.5 concentrations vary with time and region. On national scale, the lowest PM_2.5 concentrations (55 µg m^–3 in February 2020) are distributed in the afternoon while the peak values (70 µg m^–3) are generally distributed in the morning and evening. Such a variation is mostly observed in provinces like Hebei, Shandong, Henan, Shaanxi, and Heilongjiang, where the daily mean PM_2.5 concentration is generally high (> 50 µg m^–3). However, the variation is less obvious in provinces like Guangdong and Zhejiang, where the daily mean PM_2.5 concentration is low (< 30 µg m^–3). Although strict COVID-19 control policy is implemented, the diurnal variation of PM_2.5 remains unchanged. The low PM_2.5 concentration in the afternoon is generally due to that the height of boundary layer in the afternoon is the highest of the day, so the concentration is correspondingly decreased to the lowest value (Guo et al., 2017; Li et al., 2019b). When temperature decreases in the evening, the height of boundary layer decreases and accordingly the concentration increases to the peak value. The concentration increase in the morning is mainly due to the rapid increase of aerosol emissions and slow increase of boundary layer height (Guo et al., 2017). 

Fig. 3. Diurnal variation of PM_2.5 concentration in 2019 and 2020 on (a) national scale and (b–h) provincial scale.

Association between PM_2.5 Concentration and COVID-19 Infections

Fig. 4 shows the time series variation of new COVID-19 cases and the total confirmed cases. To better discuss the correlation between PM_2.5 concentration and COVID-19 infections, correlations in three time ranges are considered, which are the lockdown (1 February–29 February), main phase (January 20–29 February), and the whole studying period (January 1–30 April). The lockdown refers to the period with nationwide strict COVID-19 control policy, and the main phase refers to the period of the sharp increase of COVID-19 infections. Table 3 shows the statistical results between PM_2.5 concentration and the COVID-19 infections in mainland China. The correlations of fixed effect of PM_2.5 concentration for lockdown, main phase and the whole studying period are 0.279, 0.200, and 0.199 respectively. Most of the estimated slopes and intercepts have a p-value less than 0.001 and only the estimated slope in main phase has a p-value less than 0.01. The small p-value (< 0.05) and the positive correlations observed in different time ranges indicate that PM_2.5 is positively correlated to the new COVID-19 cases, which agrees with the early COVID-19 findings (Xie and Zhu, 2020). To minimize the risk of aerosol transmission, it is not suggested to go out in severe PM_2.5 pollution days. And according to the diurnal variation of PM_2.5 concentration (Fig. 3), it’s better to go out in the afternoon because PM_2.5 concentration in the afternoon is generally the lowest of the day. Also, try to avoid going out in late morning and evening because PM_2.5 concentration is increasing thus the risk of infections increases. 

Fig. 4. Time series variation of COVID-19 cases in mainland China. Lockdown refers to the period with nationwide strict control policy, and main phase refers to period with sharp increase of total cases. 

CONCLUSIONS

This study evaluates the influence of nationwide lockdown on air quality during COVID-19 as well as the association between PM_2.5 and COVID-19 infections in mainland China. Data from 366 air quality monitoring stations during January to April in 2017–2020 are used to investigate the air quality improvement. Compared to 2019, a national scale decrease of 14%, 15%, 12%, 16% and 12% is observed for PM_2.5, PM₁₀, SO₂, NO₂ and CO, respectively, while O₃ has a slight increase of 9%. The diurnal variation of particulate matter concentrations changes less during COVID-19. Lowest PM_2.5 concentrations are mainly distributed in the afternoon while high concentrations are witnessed in the morning and evening. Correlation analysis shows that positive correlations between PM_2.5 concentration and new COVID-19 infections are observed in different time ranges. The preliminary results in this study gives the following suggestions to the public to avoid the potential aerosol transmission of COVID-19:

• do not go out in heavy polluted days and try to minimize the exposure time in high PM₅ concentration environment;
• better to go out in the afternoon, because PM₅ concentration is the lowest of a day;
• avoid going out in late morning and evening, because the PM₅ concentration is increasing.

As for policy maker, the increase of PM_2.5 concentration due to human activities should be avoided, limited or scheduled during this sensitive period to minimize the risk of aerosol transmission of COVID-19. Since the diurnal variation of PM_2.5 concentration may vary with countries and regions, a further study is planned to suggest a more proper time range for people to go outdoors in other part of world. Also, further studies are needed to understand the detailed knowledge of aerosol transmission of COVID-19 so that more reliable and effective measures can be taken to stop the pandemic.

ACKNOWLEDGMENTS

This study is supported by the National Natural Science Foundation of China (52041601). We thank the China’s National Environmental Monitoring Center and the Wuhan-2019-nCov Github project for kindly providing the air quality and COVID-19 infection data.

Aerosol Air Qual. Res. 20 :1541 -1551 . https://doi.org/10.4209/aaqr.2020.05.0224