Spatiotemporal impact of the COVID-19 pandemic lockdown on air quality pattern in Nanjing, China

1 Introduction

A novel disease later named coronavirus disease 2019 (COVID-19) was first identified at the end of 2019, in Wuhan, China. Due to this contagious disease, a health emergency had been declared by the World Health Organization all around the world (World Health Organization 2020). More than 210 countries had been affected by COVID-19 within 6 months (Wang C et al., 2020). According to a report, about 18,354,342 were affected by the COVID-19 pandemic on 5 August 2020 (World Health Organization 2020). To control the rapid spread of this virus, forced restrictions and control measures were implemented by many countries and nations (McMahon 2020). In this scenario, the air quality significantly improved during the lockdown time. For instance, during the COVID-19 pandemic lockdown period, a sharp drop in air pollution was noticed in several areas and regions all around the world shown by satellite images (Holcombe and O’Key 2020). The lockdown is vital to the public health response to COVID-19, and it has also brought unexpected benefits for the environment (Abdullah et al., 2020; Bhatti et al., 2022a).

Many studies reported that due to these restrictions, a remarkable reduction was found in the concentrations of almost all air pollutants around the globe (Bao and Zhang 2020; Li at al., 2020; Zambrano-Monserrate and Ruano 2020). For instance, according to Berman and Ebisu's study (2020), due to the COVID-19 pandemic lockdown, a significant decline was reported in the concentration of PM_2.5 (11%) and NO₂ (26%) in the United States. Shakoor et al. (2020) reported that during the COVID-19 pandemic lockdown, the concentrations of CO, NO_2, and PM_2.5 were decreased by 19%, 37%, and 1.1%, respectively, in the United States. Their results demonstrate that the concentrations of CO, NO₂, SO₂, PM_2.5, and PM₁₀ were decreased by 27%, 39%, 18%, 18%, and 38%, respectively, in China. Another study illustrates that in India and China, the lockdown measures brought positive impacts and decrease in PM_2.5 and NO₂ concentrations, to be reduced by an average of 65%, 66%, and 45%, 37%, respectively (Agarwal et al., 2020). Meanwhile, it is evident by the study of Sharma et al. (2020) that during the COVID-19 pandemic lockdown period, due to shut down and strict restrictions, an improvement in air quality levels was observed. Their results indicate that during the lockdown period, the concentrations of PM₁₀, PM_2.5, NO_2, and SO₂ decreased by 24–44%, 23–58%, 30–64%, and 35–70%, respectively. According to Zambrano-Monserrate et al. (2020) just after the execution of lockdown, the concentrations of PM_2.5 and NO₂ were significantly reduced. Another study identified the reduction in pollutant emissions along the Yangtze River Delta using satellite and observed data. The study finds that the water-soluble ions (WSI) were decreased during lockdown and pollutant levels also followed a reduction during the lockdown period (Wang et al., 2021a; Wang et al., 2021b). A similar study is carried out to observe the change of urbanization during COVID-19, and it was observed that pollutant levels in larger cities are more than that in smaller cities (Shen L et al., 2021). Air pollution results in Brescia also show a similar decrease in air pollutants due to lockdown restrictions. A study shows that despite the reduction in emission sources during the lockdown period, which allowed to reach a significant reduction of NOx emissions, the PM levels did not have a similar trend if the annual (not the mean in 3 years) decrease of PM is considered (Bontempi et al., 2022).

To prevent the large-scale spread of the COVID-19 pandemic, China was the first country to carry out a nationwide lockdown to shut down all essential activities such as transportation, educational institutions, markets, and commercial and industrial activities at the end of January 2020 (Tian et al., 2020; Wang Y et al., 2020). Many researchers found that during this period, China found a new pattern of air quality (Chu et al., 2020; Xu et al., 2020; Zheng et al., 2020). Compared to 2017–19, in 366 urban areas and territories across China, the concentrations of particulate matter with an aerodynamic diameter of <10 and 2.5 µm, SO₂, NO₂, and CO were decreased, while the concentrations of O₃ increased (Chen et al., 2020). Due to the reduced economic activities and energy consumption in China during the COVID-19 pandemic, NO₂ concentration was significantly reduced, as reported by NASA (NASA 2020). Meanwhile, concentrations of PM₁₀, PM_2.5, and NO₂ also decreased during the lockdown period, while those of SO₂ and O₃ increased in Hangzhou, China. During the COVID-19 pandemic lockdown in northern China, reduced economic, social, and industrial activities resulted in substantial improvement in air quality (Wang L et al., 2020).

Our study assessed the six air pollutant parameters (PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃) in Nanjing, using daily average data obtained from nine monitoring sites. During the COVID-19 pandemic, our study determined the impact of lockdown and discovered new findings and substantial changes in the selected air pollutant parameters. Compared with a number of studies related to COVID-19, our study provides better results with a new pattern (Agarwal et al., 2020; Anil and Alagha, 2020; Islam et al., 2020; Kumar et al., 2020; Singh and Chauhan 2020; Aamir et al,. 2021; Borhani et al., 2021; El-Sheekh and Hassan 2021; Shen N et al., 2021). The objective of the current study was to 1) evaluate the spatiotemporal impact of the COVID-19 lockdown measures on air quality in different time periods, 2) determine the variation, and 3) find out the air pollutant concentrations from the previous 3 years to the lockdown period. The study provides valuable materials and new findings for the scholars and local bodies to make policies to improve and control air pollution.

The remainder of the article is organized as follows: section 2 presents the study area, air quality data, study period, data analysis, and the model. Section 3 presents the results and discussion. Section 4 discusses the conclusions.

2 Materials and methods

2.1 Study area

Nanjing, the capital of Jiangsu Province, is one of the most economic hubs of the Yangtze River Delta (YRD) with a large population and well-developed industrial and manufacturing zones. Due to rapid development in different sectors, the city has been facing extreme air pollution in recent years (Zu et al., 2017). Nanjing lies between latitude 31°14′ and 32°37′ and longitude 118°22′ and 119°14′ with 11 districts that can be divided into three regions, urban, rural, and suburban, covering an area of 6596 km². With a density of 8500 people/km², the population of Nanjing was approximately 8.34 million according to 2019. The city has four distinct seasons having a subtropical monsoon climate (Nanjing Municipal Government 2018). The main and vital industries in Nanjing are automobile, electronics, steel smelting, and petrochemical.

Presently, a total of nine monitoring stations are working to monitor and record air pollutant concentrations in Nanjing, and their locations are shown in Figure 1.

FIGURE 1

FIGURE 1. Geographical location and the air pollution monitoring sites in Nanjing.

2.2 Air quality data and study period

To assess the impact of nationwide lockdown on air pollution, daily average concentrations of the six air pollutants, namely, PM₁₀ (particulate matter with diameters of ≤10 µm), PM_2.5 (particulate matter with diameters of ≤2.5 µm), SO₂ (sulfur dioxide), NO₂ (nitrogen dioxide), CO (carbon monoxide), and O₃ (ozone) were collected from the Nanjing Environmental Monitoring Centre. A total of nine monitoring stations are working to collect and record air pollutant concentrations in Nanjing. According to the Technical Regulation for Selection of Ambient Air Quality Monitoring Stations of China, these nine monitoring stations belong to different areas that are Xianlin, Pukou, Xuanwuhu, Ruijinlu, Caochangmen, Maigaoqiao, Zhonghuamen, Shanxilu, and Aotizhongxin. In our study, the data were divided into six periods, pre-lockdown: 11 November 2019 to 24 January 2020 (75 days), lockdown period: 25 January 2020 to 8 April 2020 (75 days), post-lockdown: 9 April 2020 to 22 June 2020 (75 days), P4–P6: same dates of lockdown in 2017, 2018, and 2019.

2.3 Data analysis

In this study, summary statistics were calculated during the study period for different variables. This study investigates variations in concentrations of air pollutants in multiple periods as described in the abovementioned lines. In our analysis, the net difference and percentage change were also examined. Box plots were generated to represent the concentrations of these pollutants during the study period. In addition, during the lockdown period, to investigate the relationships between the air pollutants, linear regression analysis was performed.

2.4 HYSPLIT model

The backward trajectories were calculated to study the transport pathways of air mass reaching Nanjing using the NOAA HYSPLIT model (https://ready.arl.noaa.gov/HYSPLIT.php). The model is widely used in atmospheric research and pollution process analysis. The backward trajectories were plotted to identify the sources of air mass in the three periods, pre-LD, active-LD, and post-LD in Nanjing. These trajectories were analyzed for 72 h, starting every 6 h each day. Stein et al. (2015) discussed the details of the HYSPLIT model.

3 Results and discussion

3.1 Understanding the pattern of particulate matter

Due to the large-scale spread of the COVID-19 pandemic, the Government of the People’s Republic of China imposed a lockdown in late January 2020. Consequently, pollution levels during the lockdown period across the country have significantly reduced. According to many studies, a sudden decrease in pollutant concentrations was observed all around the world (Abdullah et al., 2020; Bao and Zhang 2020; Li et al., 2020; Zambrano-Monserrate and Ruano 2020; Bhatti et al., 2022b). The concentration patterns of particulate matter and variations are shown in Figure 2 and Tables 1, 2, 3. Our results demonstrate that compared to the pre-lockdown period, a notable reduction in the concentrations of particulate matter was observed in Nanjing, China. According to the obtained results, the concentrations of PM₁₀ and PM_2.5 reduced by −33.03% and −35.41%, respectively, during the COVID-19 lockdown period in Nanjing. According to the study by Shakoor et al. (2020), an approximately 38 and 18% reduction in the concentrations of PM₁₀ and PM_2.5 were recorded, respectively, during the lockdown period in China. Another study stated by Berman and Ebisu (2020) revealed that during the COVID-19 pandemic lockdown period in the United States, there was a decline in PM_2.5 concentration (11%). In urban areas and regional areas, road dust, transportation, and industrial and construction activities are the main and important sources of particulate matter. The reduction in these sources caused a massive decrease in the concentrations of particulate matter in many areas during the COVID-19 pandemic lockdown (Mor et al., 2021). In addition, the study showed that the concentrations of PM₁₀ and PM_2.5 decreased by -15.50% and -32.77%, respectively, from pre- to post-periods in Nanjing. In these findings, the reduction in the concentration of PM_2.5 was higher than that of PM₁₀.

FIGURE 2

FIGURE 2. Daily average concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃ during the all-study periods in Nanjing.

TABLE 1

TABLE 1. Daily average concentration and variation of pollutants in different time periods in Nanjing.

TABLE 2

TABLE 2. Daily average concentration and variation of pollutants during 2017–20 in Nanjing.

TABLE 3

TABLE 3. Daily average concentration and variation of pollutants in different time periods in Nanjing.

Our results demonstrate that in comparison to last year (2019), PM₁₀ and PM_2.5 concentrations showed decreasing trends, reduced by an average of -39.23% and -41.10%, respectively. Compared with the last 3 years (2017–19), Nanjing experienced a high reduction in the concentrations of PM₁₀ and PM_2.5 by −40.60% and −40.02%, respectively. It can be seen that PM₁₀ and PM_2.5 showed similar decreasing trends in this period. A similar pattern was evident by Chen et al. (2020) compared to the corresponding years (2017–19), and the concentrations of particulate matter (PM₁₀, PM_2.5) showed a diminishing tendency during the COVID-19 pandemic lockdown period in China. Furthermore, Mahato et al. (2020) reported that during the lockdown period, a significant improvement in the particulate matter was recorded as compared to the previous 3 years (2017–19) in megacity Delhi, India. Islam et al. (2020) found that PM₁₀ and PM_2.5 showed a momentous drop from last year (2019) to the lockdown period in Bangladesh. Due to the execution of lockdown in different areas and regions of the world, all essential activities were stopped, such as transportation, construction, and educational, cultural, and industrial activities, which caused a fall in air pollutant levels.

Moreover, compared with the lockdown period, a slight increase in PM₁₀ concentration (2.84%) was found, while in contrast, PM_2.5 showed a decreasing trend (−14.34%) after the lockdown period in Nanjing. Compared with the lockdown period, the increase in PM₁₀ concentration can be attributed to the increase in transportation and industrial and construction activities after the lockdown (Mor et al., 2021). In comparison to the preceding 3 years (2017–19), a substantial reduction in PM concentrations was seen after the COVID-19 pandemic lockdown in Nanjing. The results indicate that compared with those in the last 3 years (2017–19), PM₁₀ and PM_2.5 showed decreasing trends, reduced by an average of −38.91% and −48.62%, respectively. We can observe that the reduction ratio in PM_2.5 concentration is greater than that of PM₁₀. In summary, a drastic drop and an improvement in PM concentration indicate that the COVID-19 pandemic lockdown measures have a considerable impact on changing and improving PM concentration in Nanjing.

3.2 Sulfur dioxide

The results demonstrate that during the lockdown period due to the COVID-19 pandemic, the concentration of SO₂ decreased by −21.26% as compared to that of the pre-lockdown period in Nanjing. The SO₂ concentration pattern is shown in Figure 2, and variations in SO₂ during the study period are presented in Tables 1–3. Coal, diesel, power plants, vehicular traffic, and industrial and manufacturing activities are the main sources of SO₂ (Lu et al., 2013). During the lockdown period, these types of activities reduced significantly, which resulted in a decline in SO₂ concentration. Shakoor et al. (2020) reported similar findings that the concentration of SO₂ decreased by 18% during the lockdown period, compared to the pre-lockdown period in China. Another study documented by Sharma et al. (2020) witnessed that during the lockdown period, SO₂ showed a declining trend as compared to that in the pre-lockdown period. Additionally, the results indicated that compared with the pre- and during-lockdown periods, the city found an increasing trend in SO₂ concentration (13.26%). Meanwhile, compared with last year (2019) (same dates of lockdown), a significant decreasing trend was observed in SO₂ concentration by approximately −43.01%. According to Wang Y et al. (2020) and Islam et al. (2020), compared to last year (2019), SO₂ concentration has decreased during the lockdown period in China and Bangladesh. Mahato et al. (2020) also found similar results that in comparison to the last year, SO₂ concentration was reduced during the said time period in Delhi, India.

The current study found that in comparison to the previous 3 years (2017–19), SO₂ showed a high decreasing trend of approximately −54.19% in Nanjing as shown in Table 2. We can observe that in these findings, the reduction spell in the concentration of SO₂ was greater than that of the previous 3 years than last year (2019) in the city. According to Chen et al. (2020), compared to the last 3 years (2017–19), SO₂ concentration has reduced in China. Similar results were reported that compared to the last 3 years during the said time period, SO₂ concentration was decreased in megacity Delhi, India (Mahato et al., 2020). Our results indicate that the concentration of SO₂ increased by 28.55% from the active- to post-lockdown period, while compared with the last 3 years 2019–17 (same time frame of lockdown), the SO₂ concentration showed a declining trend (-41.12%) after the lockdown period in Nanjing (Table 3). Overall, the results revealed that the COVID-19 pandemic lockdown has noteworthy impacts on changing and diminishing SO₂ concentration during the study period in Nanjing.

3.3 Nitrogen dioxide

The NO₂ concentration patterns are shown in Figure 2, and variations in NO₂ are presented in Tables 1–3. Generally, NO₂ is released from vehicular emissions, fossil fuels, natural fires, lightning, and soils (Reddy et al., 2012). The results of our study demonstrate that compared with the lockdown period, a sizeable decline was observed in NO₂ concentration by approximately -39.79 in Nanjing. This reduction in NO₂ concentration could be attributed to the reduced industrial and vehicular emissions during the lockdown period in Nanjing. The most momentous similarity with our results is from the study by Shakoor et al. (2020) that the NO₂ concentration was reduced by −39% during the lockdown period in China. Another study documented by Agarwal et al. (2020) also supported our results that during the COVID-19 control period, NO₂ levels decreased by −37%, in China. According to Bao and Zhang (2020) during the lockdown period, a significant decline was observed in NO₂ concentration in many areas and regions across China. All of these findings support our results that NO₂ concentration was reduced during the lockdown period in Nanjing. In addition, our findings demonstrate that after the lockdown, NO₂ concentration decreased by approximately -21.32% in comparison to the pre- and during-lockdown periods in Nanjing. The reduction ratio in NO₂ was quite lower after the lockdown than during the pre- and during-lockdown periods and during the lockdown in comparison to the pre-lockdown period.

The results indicate that compared with the last year (2019), there was a marked decline in NO₂ concentration, to -27.68%, during the lockdown period, while in comparison to the previous 3 years (2017–19), NO₂ has been reduced by up to -33.60%. Chen et al. (2020) reported that in comparison to the last year (2019), NO₂ showed a declining trend during the lockdown period in China. Islam et al. (2020) also found that compared to the last year (2019), an approximately 35–45% reduction in NO₂ concentration has been observed in Bangladesh. According to Fu et al. (2020), in comparison to the last 5 years (2015–19), NO₂ concentration was at its lowest level during the lockdown period, and while compared with the last year (2019) measurements, NO₂ was reduced by approximately 34.7% in North China. Another study revealed that compared to the last 3 years (2017–19), NO₂ was reduced significantly during the lockdown period in Delhi (India) (Mahato et al., 2020). Our results demonstrate that compared with the lockdown period, there was a slight increase in NO₂ concentration by 4.68% after lockdown while compared to the last 3 years (2017–19), and NO₂ has been reduced by an average of -30.50% in Nanjing. The increasing spell in NO₂ concentration after lockdown compared with the lockdown period could be attributed to the winter–summer change (Ghosh et al., 2017). In summary, the results show that the COVID-19 pandemic lockdown and forced restrictions have a momentous impact on changing and upgrading Nanjing’s air pollution.

3.4 Carbon monoxide

CO is mainly a colorless gas emanated from biofuel burning, vehicular emission, agricultural waste burning, and combustion of fossil fuels (Hasnain et al., 2021). Figure 2 shows the concentration pattern of CO, and variations in CO are presented in Tables 1–3. According to our findings, from pre to during lockdown, CO levels decreased by approximately -20.65% in Nanjing. This reduction in CO concentration can be attributed to the restriction on transportation and industrial activities during the COVID-19 control period. According to Sulaymon et al. (2021), during the lockdown period, the concentration of CO reduced in Wuhan, China. A significant decrease in CO concentration was also observed from pre to active lockdown in Delhi, India (Mahato et al., 2020). Compared with the pre and lockdown periods, CO showed an increasing trend after the lockdown period. The results reveal that the increase in the concentration of CO was 3.03% after the lockdown. This shows that the increase in CO levels was due to regular actions such as transportation and industrial activities after the lockdown.

The results demonstrate that compared with last year (2019), the city experienced a sizeable decline in the levels of CO. During this window of time, the reduction in CO was −17.05% in Nanjing. Islam et al. (2020) reported that during the lockdown phase, CO exhibited a declining trend compared with the previous year in Bangladesh. Moreover, compared with the last 3 years, the concentration of CO decreased a significant level. We found a remarkable change and decrease in CO concentration by up to −23.16% in Nanjing. It can be noted that the declining value in the concentration of CO was higher in the previous 3 years to the COVID-19 control period than in the last 1 year to the COVID-19 period. A study reported by Hasnain et al. (2021) supports our findings, which indicates that from the previous 3 years to the lockdown period, the concentration level of CO has decreased. Furthermore, the concentration of CO showed an increasing trend from the lockdown period to the post-lockdown period, which increased by approximately 16.44%. Compared with the previous 3 years, CO presented an opposite pattern, to be decreased by up to −10.53% in Nanjing. The results suggest that the COVID-19 lockdown has a large impact on CO concentration.

3.5 Ozone

In the analysis of O₃, compared with the pre-lockdown period, we found a significant increase in O₃ levels during the COVID-19 period in Nanjing. The results indicate that from pre to during the lockdown periods, the levels of O₃ increased by up to 104.85%, and while compared with the pre and during the lockdown periods, the rising value in the concentration of O₃ was 84.60% in Nanjing. Figure 2 shows the concentration pattern of O_3, and variations in O₃ are presented in Tables 1–3. Previously, it has been reported that during the lockdown period, the concentration level of O₃ increased in China (Fu et al., 2020; Hasnain et al., 2021). Another study documented by Islam et al. (2020) showed that during the COVID-19 period, O₃ concentration decreased in Bangladesh.

O₃ is mainly emitted from sunlight, volatile organic compounds (VOCs), and nitrogen oxides (NOx) (Lal et al., 2020). Compared with last year (2019), O₃ showed a similar trend, to be increased by an average of 18.03% in Nanjing (Table 2). From the previous 3 years to the lockdown period, our study showed that the increasing value for O₃ was 10.83%. According to Fu et al. (2020), during the lockdown period, O₃ levels increased in Guangxi, South China compared with that in the previous 4 years. During the lockdown period in Wuhan, Sulaymon et al. (2021) reported that O₃ concentration was increased. Compared to last year (2019), the increasing spell in the concentration of O₃ was greater than in the previous 3 years to the lockdown period in Nanjing.

Moreover, our results reveal that the concentration of O₃ continuously increased during the lockdown period to post-lockdown period in Nanjing. The decreasing value during this period was 37.36% while compared with the last 3 years to the post lockdown period, and O₃ levels increased by approximately 52.23%. It should be noted that in opposition to other pollutants, O₃ is the only pollutant that showed an increasing trend during the all-study periods in Nanjing. Previously, it was reported by Hasnain et al. (2021) that during the post-lockdown period, the concentration of O₃ had increased. The results suggest that from the COVID-19 control period to the post-lockdown period, the increase in the concentration of O₃ was due to the winter–summer change.

3.6 Backward trajectory analysis

The backward trajectories were plotted to trace the sources of air masses during the pre-LD, active-LD, and post-LD periods in Nanjing (Figure 3). The backward trajectories were analyzed for 72 h, starting every 6 h each day. We can see that during the pre-LD period, the trajectories originated from Siberia and passed through Mongolia and northwestern China. During the active-LD period, the air mass comes from the northwest of Mongolia and passed through the Liaoning Province before arriving in Nanjing. During the post-LD period, the air mass comes from Inner Mongolia and passed near the Beijing–Tianjin–Hebei (BTH) region and the East China Sea before reaching Nanjing. The air masses that are coming from Mongolia and passing near the Liaoning Province and the BTH region affect the air quality of Nanjing. The HYSPLIT model reveals the influence of long-range transport of air mass over Nanjing.

FIGURE 3

FIGURE 3. HYSPLIT backward trajectories over Nanjing (A) pre-lockdown, (B) during the lockdown, and (C) post-lockdown.

3.7 Co-relationships between air pollutants

The correlations between the daily average concentration of air pollutants in Nanjing during the lockdown period are shown in Figure 4. There was a significant correlation between PM₁₀ and PM_2.5 (r = 0.71), while there was also a strong correlation between PM₁₀ and SO₂ (r = 0.42), with NO₂ (r = 0.30). The results show that the strong correlation between PM₁₀ and PM_2.5 can be attributed to mutual pollution sources. The correlation between PM₁₀ and CO was (r = 0.45), and O₃ (r = 0.13). In contrast to the correlation between PM₁₀, SO₂, and NO₂, during the lockdown period, a weak correlation was observed between PM_2.5 and SO₂ (r = 0.04) with NO₂ (r = 0.01). PM_2.5 concentration showed a positive correlation with the concentration of CO (r = 0.36), with O₃ (r = 0.03). Moreover, SO₂ was highly correlated with NO₂ (r = 0.70), SO₂ with CO (r = 48), and SO₂ with O₃ (r = 0.03) during the lockdown period in Nanjing. This strong correlation between SO₂ and NO₂ reveals that it was due to their reduced mutual pollution sources during the lockdown period in the city. The concentration of NO₂ was significantly correlated with CO concentration (0 = 0.40), while there was no correlation between NO₂ and O₃ and CO with O₃. The results indicate that the concentration of CO was positively correlated with all other pollutants except with O₃, while the concentration of O₃ showed weak or no correlation with other pollutants. It suggests that the concentration of O₃ increased with the reduction in the concentrations of other pollutants during the lockdown period in Nanjing.

FIGURE 4

FIGURE 4. Co-relationships between air pollutants during the lockdown period in Nanjing.

4 Conclusions

In general, a pronounced improvement in air quality was observed during the COVID-19 pandemic lockdown period in Nanjing. In order to evaluate the impact of the COVID-19 pandemic lockdown measures on air pollution, this is an excellent opportunity to work in this direction in different parts of the world. In this study, the effects of the COVID-19 pandemic lockdown on air quality were examined using daily average data for the six air pollutants in Nanjing. The results illustrate that compared to the pre-lockdown period, all the air pollutants showed a remarkable decline during the lockdown period, except for O₃ in Nanjing. Among these pollutants, the concentrations of PM₁₀ and PM_2.5 were significantly decreased by an average of -33.03% and -35.41%, respectively. Among other pollutants, compared with the pre-lockdown period, SO₂ (−21.26%), NO₂ (−39.79%), and CO (-20.65%) were also reduced, while O₃ levels significantly increased (104.85%) during the lockdown period. Among the selected pollutants, PM₁₀, SO₂, NO₂, CO, and O₃ increased by 2.84%, 28.55%, 4.68%, 16.44%, and 37.36%, respectively, while PM_2.5 was the only pollutant that increased by an average of 14.34% after the lockdown period. The study revealed that compared with the last year (2019) (same time frame of lockdown), a significant drop and reduction in all pollutants were observed, while O₃ showed an increasing trend. Overall, except for O₃, a substantial decrease in all air pollutants was found during the lockdown period in Nanjing. The results of this research will be helpful for the local bodies of the city and the Chinese administration to develop rules and regulations to improve and upgrade air quality in the future.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.

Author contributions

SF: conceptualization, methodology, data curation, writing—original draft, writing—review and editing, visualization, project administration, and funding acquisition; RAW: data curation, writing—original draft, writing—review and editing, and visualization; AHH: conceptualization, methodology, data curation, writing—original draft, writing—review and editing, supervision, resources, project administration, and funding acquisition; AAH: data curation, writing—original draft, and writing—review and editing; UAB: investigation, data curation, writing—original draft, writing—review and editing, supervision, resources, and project administration; EE: investigation, data curation, and writing—review and editing.

Funding

This research was supported by the Key Fund of the National Natural Science Foundation of China (grant No. 41631175). This research is supported by the Youth Foundation of Hubei Educational Committee, 2021, Science and Technology Research Project, Research on the Deep Active Intelligent Interpretation Method of Unbalanced Remote Sensing Image under “Quantity-Spectrum Synergy”, Young and middle-aged talents Project, No. Q20212904 and The Huanggang Normal University High-level Cultivation Project of 2021, Remote sensing data mining with an unbalanced dataset based on deep learning, NO. 202111804.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Aamir, M., Li, Z., Bazai, S., Wagan, R. A., Bhatti, U. A., Nizamani, M. M., and Akram, S. (2021). Spatiotemporal change of air-quality patterns in Hubei province—a pre-to post-COVID-19 analysis using path analysis and regression. Atmosphere 12 (10), 1338. doi:10.3390/atmos12101338

CrossRef Full Text | Google Scholar

Abdullah, S., Mansor, A. A., Napi, N. N. L. M., Mansor, W. N. W., Ahmed, A. N., Ismail, M., and Ramly, Z. T. A. (2020). Air quality status during 2020 Malaysia Movement Control Order (MCO) due to 2019 novel coronavirus (2019-nCoV) pandemic. Sci. Total Environ. 729, 139022. doi:10.1016/j.scitotenv.2020.139022

PubMed Abstract | CrossRef Full Text | Google Scholar

Agarwal, A., Kaushik, A., Kumar, S., and Mishra, R. K. (2020). Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air. Qual. Atmos. heal. 13, 1167. doi:10.1007/s11869-020-00881-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Anil, I., and Alagha, O. (2020). The impact of COVID-19 lockdown on the air quality of Eastern Province, Saudi Arabia. Air Qual. Atmos. Health 14, 117–128. doi:10.1007/s11869-020-00918-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Bao, R., and Zhang, A. (2020). Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci. Total Environ. 731, 139052. doi:10.1016/j.scitotenv.2020.139052

PubMed Abstract | CrossRef Full Text | Google Scholar

Berman, J. D., and Ebisu, K. (2020). Changes in U.S. air pollution during the COVID-19 pandemic. Sci. Total Environ. 739, 139864. doi:10.1016/j.scitotenv.2020.139864

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhatti, U. A., Wu, G., Bazai, S. U., Nawaz, S. A., Baryalai, M., Bhatti, M. A., Nizamani, M. M., et al. (2022a). A pre-to post-COVID-19 change of air quality patterns in anhui Province using path analysis and regression. Pol. J. Environ. Stud. doi:10.15244/pjoes/148065

CrossRef Full Text | Google Scholar

Bhatti, U. A., Zeeshan, Z., Nizamani, M. M., Bazai, S., Yu, Z., and Yuan, L. (2022b). Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. Chemosphere 288, 132569. doi:10.1016/j.chemosphere.2021.132569

PubMed Abstract | CrossRef Full Text | Google Scholar

Bontempi, E., Carnevale, C., Cornelio, A., Volta, M., and Zanoletti, A. (2022). Analysis of the lockdown effects due to the COVID-19 on air pollution in Brescia (Lombardy). Environ. Res. 212, 113193. doi:10.1016/j.envres.2022.113193

PubMed Abstract | CrossRef Full Text | Google Scholar

Borhani, F., Motlagh, M. S., Stohl, A., Rashidi, Y., and Ehsani, A. H. (2021). Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran, Iran. Environ. Monit. Assess. 193–331. doi:10.1007/s10661-021-09096-w

CrossRef Full Text | Google Scholar

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 Chinafluence of COVID-19 eventon air quality and their association in mainland China. Aerosol Air Qual. Res. 20, 1541–1551. doi:10.4209/aaqr.2020.05.0224

CrossRef Full Text | Google Scholar

Chu, B., Zhang, S., Liu, J., Ma, Q., and He, H. (2020). Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic. J. Environ. Sci. 99, 346. doi:10.1016/j.jes.2020.06.031

PubMed Abstract | CrossRef Full Text | Google Scholar

El-Sheekh, M. M., and Hassan, I. A. (2021). Lockdowns and reduction of economic activities during the COVID-19 pandemic improved air quality in Alexandria, Egypt. Environ. Monit. Assess. 193, 11. doi:10.1007/s10661-020-08780-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, S., Guo, M., Fan, L., Deng, Q., Han, D., Wei, Y., Luo, J., Qin, G., and Cheng, J. (2020). Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown. Environ. Pollut. 272, 115927. doi:10.1016/j.envpol.2020.115927

PubMed Abstract | CrossRef Full Text | Google Scholar

Ghosh, D., Lal, S., and Sarkar, U. (2017). Variability of tropospheric columnar NO2 and SO2 over eastern Indo-Gangetic Plain and impact of meteorology. Air Qual. Atmos. Health 10 (5), 565–574. doi:10.1007/s11869-016-0451-y

CrossRef Full Text | Google Scholar

Hasnain, A., Hashmi, M. Z., Bhatti, U. A., Nadeem, B., Wei, G., Zha, Y., et al. (2021). Assessment of air pollution before, during and after the COVID-19 pandemic lockdown in nanjing, China. Atmosphere 12, 743. doi:10.3390/atmos12060743

CrossRef Full Text | Google Scholar

Holcombe, M., and O’Key, S. (2020). Satellite images show less pollution over the US as coronavirus shuts down public places. CNN (23 March 2020), Available at: https://go.nature.com/2VzglPH.

Google Scholar

Islam, M. S., Tusher, T. R., Roy, S., and Rahman, M. (2020). Impacts of nationwide lockdown due to COVID-19 outbreak on air quality in Bangladesh: a spatiotemporal analysis. Air Qual. Atmos. Health 14, 351–363. doi:10.1007/s11869-020-00940-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, P., Hama, S., Omidvarborna, H., Sharma, A., Sahani, J., Abhijith, K. V., Debele, S. E., Zavala-Reyes, J. C., Bar-wise, Y., and Tiwari, A. (2020). Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities. Sustain. Cities Soc. 62, 102382. doi:10.1016/j.scs.2020.102382

PubMed Abstract | CrossRef Full Text | Google Scholar

Lal, S., Naja, M., and Subbaraya, B. (2020). Seasonal variations in surface ozone and its precursors over an urban site in India. Atmos. Environ. X. 34, 2713–2724. doi:10.1016/s1352-2310(99)00510-5

CrossRef Full Text | Google Scholar

Li, L., Li, Q., Huang, L., Wang, Q., Zhu, A., Xu, J., Liu, Z., Li, H., Shi, L., Li, R., Azari, M., et al. (2020). Air quality changes during the COVID-19 lockdown over the Yangtze River Delta region: an insight into the impact of human activity pattern changes on air pollution variation. Sci. Total Environ. 732, 139282. doi:10.1016/j.scitotenv.2020.139282

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, Z., Streets, D. G., Foy, B., and Krotkov, N. A. (2013). Ozone Monitoring Instrument observations of interannual increases in SO₂ emissions from Indian coal-fired power plants during 2005e2012. Environ. Sci. Technol. 47 (24), 13993–14000. doi:10.1021/es4039648

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahato, S., Pal, S., and Ghosh, K. G. (2020). Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Environ. 730, 139086. doi:10.1016/j.scitotenv.2020.139086

PubMed Abstract | CrossRef Full Text | Google Scholar

McMahon, J. S. (2020). Coronavirus lockdown likely saved 77,000 lives in China just by reducing pollution. Forbes (16 March 2020). Available at: https://go.nature.com/2BiWBt3.

Google Scholar

Mor, S., Kumar, S., Singh, T., Dogra, S., Pandey, V., and Ravindra, K. (2021). Impact of COVID-19 lockdown on air quality in Chandigarh, India: Understanding the emission sources during controlled anthropogenic activities. Chemosphere 263, 127978. doi:10.1016/j.chemosphere.2020.127978

PubMed Abstract | CrossRef Full Text | Google Scholar

Nanjing Municipal Government (2018). Available at: http://www.nanjing.gov.cn, accessed 12.21.2018.

NASA (2020). Airborne nitrogen dioxide plummets over China. Available at: https://earthobservatory.nasa.gov/images/146362/airborne-nitrogendioxideplummets-over-china.

Google Scholar

Reddy, B. S. K., Kumar, K. R., Balakrishnaiah, G., Gopal, K. R., Reddy, R. R., Sivakumar, V., et al. (2012). Analysis of diurnal and seasonal behavior of surface ozone and its precursors (NOx) at a semi-arid rural site in southern India. Aerosol Air Qual. Res. 12, 1081–1094. doi:10.4209/aaqr.2012.03.0055

CrossRef Full Text | Google Scholar

Shakoor, A., Chen, X., Farooq, T. H., Shahzad, U., Ashraf, F., Rehman, A., Sahar, N., and Yan, W. (2020). Fluctuations in environmental pollutants and air quality during the lockdown in the USA and China: two sides of COVID-19 pandemic. Air Qual. Atmos. Health 13, 1335–1342. doi:10.1007/s11869-020-00888-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Sharma, M., Jain, S., and Lamba, B. Y. (2020). Epigrammatic study on the effect of lockdown amid Covid-19 pandemic on air quality of most polluted cities of Rajasthan (India). Air Qual. Atmos. Health 13, 1157–1165. doi:10.1007/s11869-020-00879-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, L., Wang, H., Zhu, B., Zhao, T., Liu, A., Lu, W., and Wang, Y. (2021). Impact of urbanization on air quality in the Yangtze River Delta during the COVID-19 lockdown in China. J. Clean. Prod. 296, 126561. doi:10.1016/j.jclepro.2021.126561

CrossRef Full Text | Google Scholar

Shen, N., Zhao, X., Li, L., Zhou, B., Duan, F., and Zhao, W. (2021). Spatial and temporal variation characteristics of atmospheric NO2 and SO2 in the Beijing-Tianjin-Hebei region before and after the COVID-19 outbreak. Air Qual. Atmos. Health 14, 1175–1188. doi:10.1007/s11869-021-01016-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Singh, R. P., and Chauhan, A. (2020). Impact of lockdown on air quality in India during COVID-19 pandemic. Air Qual. Atmos. Health 13, 921–928. doi:10.1007/s11869-020-00863-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J., Cohen, M. D., Ngan, F., Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J., and Cohen, M. D. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077. doi:10.1175/bams-d-14-00110.1

CrossRef Full Text | Google Scholar

Sulaymon, I. D., Zhang, Y., Hopke, P. K., Zhang, Y., Hua, J., and Mei, X. (2021). COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown. Atmos. Res. 250, 105362. doi:10.1016/j.atmosres.2020.105362

PubMed Abstract | CrossRef Full Text | Google Scholar

Tian, H., Liu, Y., Li, Y., Wu, C. H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., et al. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368 (6491), 638–642. doi:10.1126/science.abb6105

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., Horby, P. W., Hayden, F. G., and Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. Lancet 395, 470–473. doi:10.1016/s0140-6736(20)30185-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, H., Miao, Q., Shen, L., Yang, Q., Wu, Y., and Wei, H. (2021b). Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment. Environ. Pollut. 271, 116298. doi:10.1016/j.envpol.2020.116298

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, H., Miao, Q., Shen, L., Yang, Q., Wu, Y., Wei, H., and Lu, W. (2021a). Characterization of the aerosol chemical composition during the COVID-19 lockdown period in Suzhou in the Yangtze River Delta, China. J. Environ. Sci. 102, 110–122. doi:10.1016/j.jes.2020.09.019

CrossRef Full Text | Google Scholar

Wang, L., Li, M., Yu, S., Chen, X., Li, Z., Zhang, Y., et al. (2020). Unexpected rise of ozone in urban and rural areas, and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou, China: implications for air quality. Environ. Chem. Lett. 18, 1713–1723. doi:10.1007/s10311-020-01028-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., Yuan, Y., Wang, Q., Liu, C. G., Zhi, Q., and Cao, J. (2020). Changes in air quality related to the control of coronavirus in China: implications for traffic and industrial emissions. Sci. Total Environ. 731, 139133. doi:10.1016/j.scitotenv.2020.139133

PubMed Abstract | CrossRef Full Text | Google Scholar

World Health Organization (WHO). (2020). Corona virus disease (COVID19). Available at: https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200805-covid-19-sitrep-198.pdf? sfvrsn=f99d1754_2. Accessed 25 Oct 2020.

Google Scholar

Xu, K., Cui, K., Young, L. H., Hsieh, Y. K., Wang, Y. F., Zhang, J., and Wan, S. (2020). Impact of the COVID-19 event on air quality in central China. Aerosol Air Qual. Res. 20, 915–929. doi:10.4209/aaqr.2020.04.0150

CrossRef Full Text | Google Scholar

Zambrano-Monserrate, M. A., and Ruano, M. A. (2020). Has air quality improved in Ecuador during the COVID-19 pandemic? A parametric analysis. Air Qual. Atmos. Health 13, 929–938. doi:10.1007/s11869020-00866-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Zambrano-Monserrate, M. A., Ruano, M. A., and Sanchez-Alcalde, L. (2020). Indirect effects of COVID-19 on the environment. Sci. Total Environ. 728, 138813. doi:10.1016/j.scitotenv.2020.138813

PubMed Abstract | CrossRef Full Text | Google Scholar

Zheng, H., Kong, S., Chen, N., Yan, Y., Liu, D., Zhu, B., Xu, K., et al. (2020). Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19ficant changes in the chemical compositions and sources of PM_2.5 in Wuhan since the city lockdown as COVID-19. Sci. Total Environ. 739, 140000. doi:10.1016/j.scitotenv.2020.140000

PubMed Abstract | CrossRef Full Text | Google Scholar

Zu, Y., Huang, L., Hu, J., Zhao, Z., Liu, H., Zhang, H., et al. (2017). Investigation of relationships between meteorological conditions and high PM10 pollution in a megacity in the western Yangtze River Delta, China. Air Qual. Atmos. Heal 10, 713. doi:10.1007/s11869-017-0472-1

CrossRef Full Text | Google Scholar