key: cord-0890484-7nkqebbz
authors: Sulaymon, Ishaq Dimeji; Zhang, Yuanxun; Hopke, Philip K.; Zhang, Yang; Hua, Jinxi; Mei, Xiaodong
title: COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown
date: 2020-11-12
journal: Atmos Res
DOI: 10.1016/j.atmosres.2020.105362
sha: 7c68ea5c4283582dc4e2540a1093d7cac9e93db2
doc_id: 890484
cord_uid: 7nkqebbz

As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM(2.5), PM(10), SO(2), NO(2), CO, and O(3)) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO(2), PM(2.5), PM(10), and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O(3) increased by 149% during the lockdown. After the lockdown, the concentrations of PM(2.5), CO and SO(2) declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO(2), O(3), and PM(10) increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO(2), WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM(2.5) concentrations prior to the lockdown. The highest PM(2.5) concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM(2.5) concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.

government. However, it rapidly spread to the neighboring cities in Hubei province and beyond (Muhammad et al., 2020) . To control the COVID-19 epidemic, a total lockdown in Wuhan was announced by the Chinese government on January 23 and in Hubei province on January 24. After several days, the lockdown was extended across China.

The lockdown measures were implemented primarily to reduce large gatherings and thereby control the spread of the virus (China State Council, 2020; Wang et al., 2020) .

The lockdown in Wuhan was in place until April 6, 2020. During the lockdown period, the control measures included the shutting down of all public transportation systems, schools, businesses centers, parks, non-essential industries, restaurants, and entertainment houses. Globally, about 1,226,813 deaths had been linked with COVID-19 as of November 6th, 2020 (WHO, 2020).

Criteria air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) have serious effects on human health (GBD, 2018; USEPA, 2019) . The adverse health outcomes range from increased emergency department visits, hospitalizations, and death from a variety of cardiorespiratory diseases. The WHO estimates that globally, there are 4.2 million premature deaths per year attributed to air pollution (https://www.who.int/airpollution/ambient/health-impacts/en/). For instance, epidemiological studies have identified significant associations between elevated airborne fine particulate matter (PM 2.5 ) concentrations and acute adverse health effects (e.g., respiratory illness and symptoms, and physiologic changes in pulmonary function (e.g., Ayuni et al., 2014; Pope and Dockery, 2006; Zhang et al., 2018; Croft et al., 2018; Hopke et al., 2019) .

The major sources of NO 2 pollution globally and in particular in China are the sources related to human activities (anthropogenic sources). Previous studies have found the combustion of fossil fuels. The main source of electrical energy are coal-fired power plants that are a major source of NO 2 . In 2019, motor vehicles emitted over six million tons of nitrogen oxides in China (Statistica, 2020) . Also, NO 2 pollution could occur due to the combustion of biomass materials. However, less attention is given to it since such an act is strictly forbidden in Chinese cities and urban areas . Since a positive significant correlation has been established between the pollution level of NO 2 and human population size (Lamsal et al., 2013) , increasing population and traffic sources contribute to the NO 2 pollution level . Existing studies have revealed that air pollution due to NO 2 could trigger the risks of several diseases such as asthma, respiratory disease, and cardiovascular disease and even increase the rate of mortality due to the diseases (He et al., 2020; Lu et al., 2020a; Zhao et al., 2020) . Brønnum-Hansen et al. (2018) reported that life expectancy of people residing in cities and urban areas could be elongated by an additional two years if the NO 2 concentration were reduced to same low level as in rural areas with low populations and vehicular movement.

In this study, changes in the concentrations of the six criteria air pollutants before, during, and after the 2020 COVID-19 lockdown period were investigated. Additionally, the pollutants concentrations during the same lockdown period in the prior three years J o u r n a l P r e -p r o o f Journal Pre-proof were assessed. Also, the relationships between the air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 ) and four meteorological variables (temperature, wind speed, wind direction, and relative humidity) during the three periods were investigated using correlation analysis. This would improve the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest potent ways of reducing air pollution in Wuhan. Furthermore, correlation analyses between the six criteria air pollutants during the three periods were performed to help ascertain the sources of emissions responsible for the reduction in concentrations of air pollutants during the periods. There is a lot of work on air quality during the COVID-19 lockdown period being reported from around the world (e.g., Chen et al., 2020; Mahato et al., 2020; Muhammad et al., 2020; Sharma et al., 2020; Wang et al., 2020) . In Wuhan, there have been prior reports such as Lian et al. (2020) . However, that study focused only on the pre-lockdown and during the lockdown periods and primarily on changes in the air quality index (AQI) rather than on the distributions of the various pollutants. This work is the first study to assess the relationships between the concentrations of the six criteria pollutants and the meteorological variables before, during, and after the COVID-19 pandemic lockdown period in Wuhan. These results would help identify effective control measures in mitigating air pollution in Wuhan and China as a whole especially during winter season.

The city of Wuhan (the capital of Hubei Province and the epicenter of COVID-19 in mainland China) was the focus of this study. The ambient concentrations of the six J o u r n a l P r e -p r o o f Journal Pre-proof criteria air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) prior to, during, and after the COVID-19 lockdown control measures were enacted and enforced in Wuhan by the Chinese government were compared. The pre-lockdown period was from January 1st to January 23rd, 2020, the lockdown (COVID-19 control) period ranged from January 24th through April 5th while the post-lockdown period was from April 6th through June 20th, 2020.

Observations data from the eleven air quality monitoring stations covering this provincial capital city were used. One-hour data for particulate matter (PM 2.5 and PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), and carbon monoxide (CO)

were downloaded from the China's National Environmental Monitoring Center (http://www.cnemc.cn). The data have been validated Zhao et al., 2019) . The citywide daily mean concentrations were estimated by averaging the concentrations at the eleven air quality monitoring stations in Wuhan. In reporting the 24hr average concentrations of the six criteria air pollutants to the public, the Chinese Ministry of Environmental Protection (MEP) uses this same method (Hu et al., 2015) .

Meteorological data were downloaded from the National Data Center of the Chinese Meteorological Agency (http://data.cma.cn).

To study the impacts of the lockdown (LD) measures on air quality in Wuhan, the six criteria air pollutants were examined during the three consecutive periods; Pre-LD (January 1st -23rd, 2020), During-LD (January 24th -April 5th, 2020) and Post-LD Ranks (Kruskal and Wallis, 1952) among Pre-, During-, and Post-LD was performed with pairwise comparison using Dunn's method (Dunn, 1964) . In addition, the 1-hr concentrations of the pollutants for the same lockdown period (i.e. January 24th -April 5th) for each of the last four years (2017-2020) were compared using the Kruskal-Wallis

One Way Analysis of Variance (ANOVA) on Ranks and Dunn's tests. These analyses were conducted to assess the changes in pollutant concentrations over these years and to account for the changing photoperiod and temperatures that occur between January and

June each year.

In order to investigate the relationships between the six air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 ) and the three meteorological variables (temperature, wind speed, and relative humidity), Pearson correlation analysis was conducted for the three study periods using SigmaPlot software (version 14 The trajectories with similar geographical origins were classified by computing the air mass backward trajectories (Khuzestani et al., 2017; Sulaymon et al., 2020) . The calculations of the air mass backward trajectories were achieved using hybrid singleparticle Lagrangian integrated trajectory model (HYSPLIT 4.9 version) . In this study, the Global Data Assimilation System (GDAS) one-degree archive which has been used by the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) model was used. The computation of five-day backward trajectories with hourly interval and arrival height of 500 m above ground level (AGL) at the sampling sites was carried out using a vertical velocity model and 6 hr interval between each starting time at every 24 hr (Sulaymon et al., 2020) .

The daily average temperature, wind speed (WS), wind direction (WD), and relative humidity (RH) from January 1st, 2020, to June 20th, 2020 are presented in Fig. 1 .

During the Pre-LD period, temperatures were lower compared to During-LD period with the highest temperatures being recorded during the Post-LD period. A similar pattern was noted for WS. However, WD in the During-LD period had more frequent winds from the northeast (0-90°). The Post-LD period in terms of WS was relatively calm with highly variable wind directions. The highest and most stable RH values were observed during the Pre-LD period compared to the other two periods that had fluctuating RH values. The mean and standard deviation of temperature, WS, and RH were 11.2 ± 4.9 °C, 2.4 ± 0.9 m/s, and 75.1 ± 13.1%, respectively (Table 1 ). The most common WD across the three periods was northeasterly (0-90°).

The daily meteorological variable values from 2017-2019 (Figs S1-S3) were also compared to the present year (i.e. 2020) during the lockdown period (Table 1) . The average WD was southeasterly throughout with no significant changes in values between 2017, 2019, and 2020. This trend was also found in other variables except that the mean temperature in 2020 was somewhat higher than in the previous years. Thus, there was no significant differences for the meteorological variables among the years.

The statistical analyses for the air pollutants during each of the three periods are summarized in Table 2 . The detailed results are presented in Tables S1-S6. Daily mean (Table S1 ). A larger difference of ranks (892) was observed between Pre-LD vs During-LD.

The differences in the median values of PM 10 between the three periods are statistically significant ( Table 2) (Table S2) showed that all of the pairwise differences were significant. Contrary to PM 2.5 , Post-LD is greater than During-LD, although the difference (152) is small compared to that of Pre-LD vs During-LD and Pre-LD vs Post-LD whose differences were 822 and J o u r n a l P r e -p r o o f Journal Pre-proof 670, respectively. The slight difference between the median values of Post-LD vs During-LD was due to the ease of lockdown as life activities returned to normal in Wuhan.

The ANOVA on ranks showed that there exists a statistically significant difference in the median values of SO 2 (Table 2 ). Contrary to PM 2.5 and PM 10 , the median values of SO 2 increased with significant differences between Pre-LD vs During-LD (6.0-7.0) and Pre-LD vs Post-LD (6.0-7.0) ( Table 2 ). According to the Dunn's test (Table S3) CO behaved similarly to PM 2.5 . The median values declined monotonically with significant differences between Pre-LD vs Post-LD (1.10-0.70) and Pre-LD vs During-LD (1.10-0.90). All the pairwise differences were also statistically significant. Pre-LD is greater than Post-LD (1336). A significant difference of ranks (703) was observed between Pre-LD vs During-LD periods while a smaller but significant difference was also recorded between Pre-LD vs Post-LD periods (Table S5) Table 2 ). There was a significant difference between During-LD vs Post-LD (58.0-72.0). From the Dunn's test, all of the pairwise differences were statistically significant (Table S6) . Post-LD is greater than During-LD with difference of ranks (369), smaller compared to that of Post-LD vs Pre-LD (1711) There would also be an increase in ozone production through the January to June period J o u r n a l P r e -p r o o f due to increases in the photoperiods and resulting increased temperatures. Comparisons among the prior years reported below provide an accounting for the changes in photochemical activity.

The PM 2.5 /PM 10 ratio decreased from 0.84 to 0.74 (Fig. 4) while the SO 2 /NO 2 ratio increased from 0.16 to 0.37 after the lockdown was put in place (Fig. 4) . The increase in the ratio of SO 2 /NO 2 results from both the increase in SO 2 likely from increased coal use (Dai et al., 2019; Song et al., 2017; Wang et al., 2020) and the decrease in NOx from the reduced traffic volume. Compared to lockdown period, both PM 2.5 /PM 10 and SO 2 /NO 2 ratios reduced during the Post-LD period (Fig. 4) . The continuous increase in the concentrations of NO 2 , O 3 , and PM 10 immediately after the lockdown period is a strong indication that there is need to implement some control strategies to continue the reductions in source emissions of these pollutants, otherwise, we would return to the same polluted world we had before COVID-19.

To assess the patterns of concentrations variation of the six criteria pollutants over the last four years (2017-2020), the 1-hr concentrations of the pollutants for the same lockdown period (i.e. January 24th -April 5th) (Fig. 5) Table 4 ). The Dunn's tests also showed that all of the pairwise differences were significant with largest difference between 2017 vs 2020 (2629), followed by 2018 vs 2020 (2446), and 2019 vs 2020 (2208) ( Table S10 ).

The significant differences found between 2020 and each of the prior years indicated that (Table S11 ). In 2020, there was reduction in the concentrations of CO during the lockdown period compared to the previous years.

Contrary to the other pollutants, the Kruskal-Wallis test showed that O 3 increased monotonically with differences from one another as had been seen over the recent years Table S12 ). The substantial increase in the O 3 concentrations during the 2020 lockdown period was clearly related to the NOx emissions reductions while sufficient VOCs remained available.

The maximum PM 2.5 and PM 10 concentrations (Table 4) (Table 4) to 37 μg/m 3 (45.6%) and 59 μg/m 3 (61.4%), respectively. These results show that significant improvements in ambient air quality were achieved when the lockdown and related reductions in emissions were implemented. Therefore, reduced emissions will clearly lead to improved air quality in Wuhan although other measures will be required to control the ozone concentrations.

The correlations between the concentrations of the six criteria air pollutants and the three meteorological variables (T, WS, and RH) during the three periods of study were quantified using Pearson correlation analysis ( After the lockdown period, wind speed was weakly related to all air pollutants except NO 2 . The relationship between RH and the air pollutants throughout the three periods were negative except CO before and after the lockdown periods. Prior to the lockdown period for instance, only SO 2 had strong negative relationship with relative humidity, weak negative correlations were observed for the other pollutants. All pollutants except CO had strong negative relationship with RH during and after the lockdown periods.

The results of PM 2.5 and O 3 for Pre-LD, During-LD, and Post-LD periods are illustrated in Figs. 6 and 7 (Fig. S4) , SO 2 (Fig. S5) , NO 2 (Fig. S6) and CO (Fig. S7 ) were similar to that of PM 2.5 but the lowest CO concentrations were associated with the southwesterly wind.

The peak values of O 3 were related to southwesterly wind followed by easterly wind while the least values were attributed to the northerly wind (Fig. 7) . In the case of During-LD, the highest PM 2.5 concentrations were associated with southwesterly wind followed by easterly wind while westerly wind was responsible for the lowest PM 2.5

values. The results of PM 10 , SO 2 , and O 3 were similar as their highest concentrations were related to southerly winds (including southeast, south, and southwest winds).

Northwesterly wind was responsible for the lowest concentrations of PM 10 and O 3 while the lowest SO 2 concentrations were associated with the westerly wind. The peak values of NO 2 and CO were attributed to easterly wind followed by southeasterly wind while their lowest concentrations were related to the westerly wind. Considering the Post-LD period, the highest concentrations of PM 2.5 , PM 10 , and CO were associated with northwesterly wind while easterly wind was responsible for the peak values of SO 2 , NO 2 , and O 3 . The least concentrations of all the pollutants were related to the westerly wind except NO 2 , whose least value was attributed to the northeasterly wind.

The results revealed that air pollutants are being greatly influenced by certain wind directions compared to other directions. This could be due to two factors. Firstly, the emission of pollutants and their precursors in the up wind areas of wind from certain wind directions are larger in intensity than other areas. This leads to regional transportation of pollutants. Secondly, the lower the speed of the wind from a certain direction, the more the air pollutants accumulate.

The correlations among the six criteria air pollutants in Wuhan during the three periods in 2020 are presented in Table 6 . For the Pre-LD period (January 1st-23rd, 2020), J o u r n a l P r e -p r o o f the hourly PM 2.5 concentrations were strongly correlated with hourly PM 10 concentrations (r 2 = 0.890) and not correlated with the other pollutants. The hourly PM 10 concentrations were weakly correlated with the hourly concentrations of NO 2 (r 2 = 0.183) and SO 2 (r 2 = 0.084). SO 2 was weakly correlated with NO 2 (r 2 = 0.121). In addition, the correlations between NO 2 and CO (r 2 = 0.177) and NO 2 and O 3 (r 2 = 0.181) were also weak.

During the lockdown period (January 24th to April 5th), the hourly PM 2.5 concentrations were strongly correlated with PM 10 (r 2 = 0.654), but only weakly correlated with the other pollutants [NO 2 (r 2 = 0.173), CO (r 2 = 0.248), and SO 2 (r 2 = 0.081)]. The PM 10 concentrations were weakly correlated with the concentrations of NO 2 (r 2 = 0.184), SO 2 (r 2 = 0.183), CO (r 2 = 0.213), and O 3 (r 2 = 0.077). SO 2 was weakly correlated with CO (r 2 = 0.314), NO 2 (r 2 = 0.086), and O 3 (r 2 = 0.083) ( Table. 6 ). The correlation between NO 2 and CO was weak (r 2 = 0.157). There were very low correlations between NO 2 and O 3 and between CO and O 3 .

Considering the Post-LD period (i.e. from April 6th to June 20th, 2020), PM 2.5 was strongly correlated with PM 10 (r 2 = 0.593) but only weakly correlated with the other pollutants [NO 2 (r 2 = 0.182). SO 2 (r 2 = 0.119). CO (r 2 = 0.216), and O3 (r 2 = 0.021)] (Table. 6 ). The PM 10 was weakly correlated with NO 2 (r 2 = 0.266), SO 2 (r 2 = 0.293), CO (r 2 = 0.056) and O 3 (0. 001). In addition, SO 2 was weakly correlated with NO 2 (r 2 = 0.052) and O 3 (r 2 = 0.028). The other correlations were also low. Thus, there is very little signal of possible sources in the interspecies correlations.

In order to trace the sources as well as the transport pathways of air masses during the three periods in 2020 (Pre-LD, During-LD and Post-LD) in Wuhan, the backward J o u r n a l P r e -p r o o f trajectories were plotted (Fig. 8) . During Pre-LD period, four clusters from different wind transport directions were identified. Clusters #1 (53 %) and #3 (19 %) were found to dominate the transport directions as they both emanate from north, although, cluster #3 was a long-range regional transport. The duo of clusters #2 (23 %) and #4 (6 %) were long-range regional transport flowing from the northwest (NW) direction. Considering

During-LD period, clusters #2 (56 %) and #4 (15 %) originated from the north and dominated the transport directions (71 % in total). The remaining 29 % was distributed between clusters #1 (22 %) and #3 (7 %), whose sources originated from the northwest (NW) and west, respectively and both were regional long-range transport. The largest share of the air masses (60 %) during Post-LD period was transported from the northern direction while the remaining 40 % was traced to the southwest (SW) and northwest (NW) directions. The contributions of clusters #1, #2, #3, and #4 were 48, 12, 29, and 11 %, respectively. The trio of clusters #2, #3, and #4 demonstrated long-range regional transport into Wuhan.

In order to ascertain whether there exist unique transport pathways of pollutants into Wuhan, a similar trajectory analysis was carried out for the three periods in 2019 when there were no lockdown control measures in place, and the results are compared to that of 2020. During Pre-LD period of 2019 (Fig. S8) , four clusters from different wind transport directions were obtained. Clusters #1 (51 %) and #4 (13 %) dominated the transport directions as they both originated from north while clusters #2 (17 %) and #3

(18 %) were coming from the northwest (NW) and west, respectively. The trio of clusters #2, #3, and #4 were found to be long-range regional transports into the study area.

Considering During-LD period, clusters #2 (8 %) and #3 (68 %) describe the flows J o u r n a l P r e -p r o o f emanating from the north and dominated the transport directions (76 % in total). Out of the remaining 25 %, cluster #4 (northwest) had 16 % while cluster #3 (west) had 9 % and both exhibited regional long-range transport. During the Post-LD period, four clusters with two major transport pathways were also obtained. Clusters #1 (78 %) and #4 (9 %) dominated the transport directions and emanated from the north. Clusters #2 (10 %) and

#3 (3 %) were approaching Wuhan from the northwest (NW) direction, and both displayed regional long-range transport.

Comparing the results of During-LD period of 2020 to 2019, 56 % of the total trajectories (260) was associated with the local sources in 2020 while 68 % was due to the local sources in 2019. The reduction in 2020 could be due to the control measures such as shutting down of public transport system and non-essential industries in Wuhan.

Above all, there is no significant difference in the transport pathways of pollutants into Wuhan between the two years (2019 and 2020) during the three study periods as local sources dominate the sources of air pollution in Wuhan.

The impact of lockdown on air quality as a result of the COVID-19 pandemic in Wuhan was evaluated by comparing the concentrations of the six criteria air pollutants during January 1 to June 20 from 2017 to 2020. With the lockdown in place, NO 2 , PM 2.5 , and PM 10 declined by 50.6, 41.2, and 33.1%, respectively, compared to Pre-LD period.

The increase in O 3 during the lockdown period while NO 2 decreased indicates that ozone in Wuhan is in a VOC-limited regime coupled with rise in photochemical activity due to increased solar radiation and temperature. However, lockdown 2020 O3 was higher than increases among prior years indicating the strong influence of the reduced NO X emissions. Thus, the lockdown has helped to clarify the nature of ozone formation. These J o u r n a l P r e -p r o o f results suggest the need for careful investigation of VOC emissions and the potential for additional control so as to reduce the increasing ambient O 3 concentrations. Although local air quality seems largely related to local sources, transported pollutants are also important. The increase in NO 2 , O 3, and PM 10 concentrations immediately after the lockdown is a strong indication that additional control strategies must be implemented to continue to improve air quality. Otherwise, we would return to the same polluted world we had before COVID-19.

Zhang, W., Lin, S., Hopke, P.K., Thurston, S.W., van Wijngaarden, E., Croft, D., et al., 2018 . Triggering of cardiovascular hospital admissions by fine particle concentrations in New York state: before, during, and after implementation of multiple environmental policies and a recession. Environ. Pollut. 242, 1404-1416. J o u r n a l P r e -p r o o f 

The authors declare that they have no conflict of interest.

 O 3 increased by 149 % during lockdown form lower NOx and higher solar radiation.  Meteorology has significant impacts in the air pollution concentrations.  Local sources dominated the air pollutant emissions in Wuhan.

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