1 Introduction

The World Health Organization (WHO) has classified the new coronavirus disease 2019 (COVID-19) as a “global pandemic”, indicating a significant risk to public health (WHO, 2020a). As of August 6, 2020, 18,354,342 persons were afflicted, and the COVID-19 was responsible for 696,147 deaths worldwide (WHO, 2020b). The impacted nations enforced lockdowns, limitations on human movement, and bans on commercial, educational, sporting, religious, and cultural activities to contain and reduce the large-scale spread of the COVID-19 (Hu et al., 2021; Wang et al., 2021). The lockdowns and other forced restrictions not only stopped the COVID-19 pandemic from spreading, but they also greatly improved the air quality in these areas and regions, which helped to partially offset the costs associated with implementing these measures during the pandemic (Abdullah et al., 2020; Li et al., 2020).

By the end of January 2020, the Chinese government enforced several stringent restrictions and forced limitations in the national public health response to stop the COVID-19 pandemic from spreading quickly (Tian et al., 2020; Zhang et al., 2021a). The COVID-19 hub in Wuhan and the surrounding areas were placed under lockdown, with limited human movement and the cessation of all non-essential activity. In a matter of days, the lockdown's boundaries were expanded nationwide on January 23, 2020. Because of this kind of environment, transportation was suspended, human mobility was reduced, and industrial production was lowered, all of which contributed to a sharp decline in air quality (Zhang et al., 2021b). According to recent studies, all pollutant concentrations observed a significant reduction and decline during the lockdown period, both regionally and globally (Hua et al., 2020; Orak et al., 2021; Ghasempour et al., 2021). Hasnain et al. (2023) reported an abrupt drop in air pollution during the COVID-19 active-LD phase in the Yangtze River Delta, China. The authors of this study observed a remarkable increase in O3 levels during the corresponding period. Another study documented by Bhatti et al. (2022) also indicated similar findings in Anhui Province of China. A sizeable decline in air pollution was reported by the authors of this study.

One of the main problems in developing nations that seriously endangers public health is air pollution (He et al., 2017). According to Xu and Lin (2017), the main sources of air pollution are the growing economy, the use of fossil fuels in various industries, and the rate of urbanization. The two biggest issues facing the world and local communities in recent years have been environmental degradation and air pollution. The most important pollutants in global urban regions are nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). These pollutants can be impacted heavily by mobile sources (Ghasempour et al., 2021).

Many scholars found that the COVID-19 lockdown and stringent regulations caused a notable drop in industrial and transportation emissions, which in turn caused an improvement in air quality (Tian et al., 2020; Hua et al., 2020; Orak et al., 2021). According to TROPOMI instrument data, during China's lockdown period, there was a significant decrease (40%) in NO2 concentration as compared to the previous year (Bauwens et al., 2020). The primary reasons for this drop in NO2 levels are fewer industrial emissions and less traffic on the roads. Comparable findings were also reported in the analysis of CO, NO2, SO2, and particulate matter (PM10 and PM2.5), which shows that during China's COVID-19 control period, the concentration levels of SO2, NO2, CO, PM10, and PM2.5 dramatically decreased (Chang et al., 2020). According to Chen et al. (2020), the concentrations of NO2, PM2.5, CO, SO2, and PM10, showed declining trends, while O3 levels increased during the China’s COVID-19 period. Studies by Zhu et al. (2021) and Zhang et al. (2022) have given detailed insights into the rise in ozone (O3) levels in Chinese cities during the COVID-19 epidemic. The observed ozone increase can be attributed to reduced nitrogen oxide (NOx) emissions from decreased traffic and industrial activities during the lockdown, leading to a lower O3 titration. Additionally, favorable meteorological conditions, such as increased solar radiation and higher temperatures, have enhanced ozone production through photochemical reactions (Kang et al., 2021; Zhang et al., 2022; Zhu et al., 2021).

The present study used daily average data from 72 monitoring stations in Jiangsu Province to investigate six air pollutant parameters: NO2, SO2, PM2.5, PM10, CO, and O3. We investigated the levels of these air pollutants in the seven periods: pre, active and post periods of COVID-19, and 2021 (same dates of lockdown). The outcomes were also compared with those of the prior three years, 2017–19 (same lockdown’s dates), for a deeper analysis and to find new findings. Numerous studies on the effects of the COVID-19 pandemic lockdown have been documented in the past. Other studies have mostly focused on either the comparison with previous years or the assessment of air quality from pre-to-active lockdown periods, while most studies have demonstrated that the lockdown measures and strict restrictions led to a decline in air pollutant levels for limited time periods. In contrast to earlier studies, our study spans pre-, active-, and post-lockdown times and provides a deeper analysis with new findings and a new paradigm. It also compares the selected air pollutants with the last three years (2017–19) and the following year 2021. The present study aimed to: (1) evaluate the spatiotemporal effects of the lockdown era of COVID-19 on air quality in the pre, active, post periods of COVID-19, and 2021; (2) identify variations in different air pollutants in various time periods; and (3) investigate the levels of air pollutants in Jiangsu Province during the corresponding dates in the preceding three years, 2017–19. In order to help the scientific community and local authorities develop strategies to enhance and manage air pollution in the coming years, the study offers helpful information and a new paradigm.

2 Materials and methods

2.1 Study area

One of the most significant regions of the Yangtze River Delta (YRD) is Jiangsu Province, which has highly developed industrial sectors with a GDP of 12.82 trillion yuan (about 1.8 trillion US dollars) in 2023. With an area of 107,200 km2, Jiangsu is situated between 30°45′ and 35°20′ N and 116°18′ and 121°57′ E. There are thirteen (13) cities in the province, and Jiangsu plays an important role in urbanization and modernization because of its strong economy and favorable location. Based on the data from 2019, Jiangsu is home to a sizable population of about 80.5 million people. Administrative districts at the county level number 78 in the province. Jiangsu has abundant energy resources, traditional industries, and steel smelting.

Figure 1 depicts the locations of 72 monitoring stations in Jiangsu Province that are currently in operation and collecting air pollutants data. Each of these observation stations belonged to a distinct area (S1). Among these areas, Maigaoqiao is connected with an industrial area, Meadow gate represents a populated area, Xuanwu Lake covers a park area, Shangshan stations belongs to a suburban and mountainous site, Wuzhong district represents a manufacturing zone, Star Lake Gardan is connected with a park and green area, Hongmen station belongs to a central city area, municipal monitoring station represents a municipal area, Huang Chao represents a populated area, New district office belongs to a business zone, Park road covers a green area, Palji Mountain represents a mountainous area, Yangcheng in connected with an industrial and manufacturing zone, Suqian covers an institutional area. The complete detail of all these monitoring stations is given in Table S1.

Fig. 1
figure 1

Location of the study area and the air pollution monitoring stations in Jiangsu Province

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2.2 Air quality data and study period

Daily average data for the six air pollutant factors—SO2 (sulphur dioxide), CO (carbon monoxide), PM10 (particulate matter with diameters of ≤ 10 µm), NO2 (nitrogen dioxide), PM2.5 (particulate matter with diameters of ≤ 2.5 µm), and O3 (ozone)—were analyzed in order to determine the spatiotemporal impact of the lockdown era of COVID-19 on air quality. The China Environmental Monitoring Station (CNEMC, 2019) provided the data from 72 monitoring locations (Fig. 1). These monitoring stations are scattered among 13 cities in Jiangsu province: Suzhou (8), Nantong (5), Lianyungang (4), Xuzhou (11), Changzhou (6), Zhenjiang (4), Taizhou (4), Huaian (5), Yancheng (4), Suqiang (4), and Nanjing, the province's capital (9). The COVID-19 lockdown’s effects on air quality were examined by dividing the data into seven time periods between 2017 and 21: Pre-LD (November 11, 2019–January 24, 2020), active-LD (January 25, 2020–April 8, 2020), post-LD (April 9, 2020–June 22, 2020), post-LD (January 25, 2020–April 8, 2020), (iv) similar dates of active-LD in the subsequent year 2021, and (v–vii) same dates of active-LD in the last three years of 2017–19.

2.3 Meteorological data

For the three study periods (pre-LD, active-LD, and post-LD) in Jiangsu Province, daily meteorological data, including wind speed, relative humidity, precipitation and air temperature, were obtained from the meteorological data service of NASA (https://power.larc.nasa.gov).

2.4 Data analysis

The six air pollutant parameters (NO2, SO2, O3, PM10, CO and PM2.5) were examined in different periods: pre, active and post periods of lockdown, the lockdown dates in the following year 2021, and the last three years 2017–19. This allowed researchers to better understand the spatiotemporal impact of the lockdown (LD) phase of COVID-19 on Jiangsu's air quality. We investigate the variations in air pollution concentrations to observe how the pollutants have changed; the percentage change and net difference were also given for the study period. To determine if the climatic conditions during the pre-, active-, and post-LD periods were typical, a number of meteorological factors were evaluated, including air temperature, relative humidity, wind speed, and precipitation. We used the library of Geopandas in Python to create a spatial distribution map of all the air pollution components. To depict the regional distribution of the six air pollution parameters over the study period, the data were mapped. Meanwhile, line plots were made to demonstrate how the climatic parameters were normal. During Jiangsu’s lockdown, linear regression analysis was done to look at the relationships between the selected air pollutants and meteorological factors. In this work, Python was used to construct regression plots and ArcGIS 10.2.2 was used to create a study area map.

3 Results and discussion

3.1 Pre to post lockdown changes in pollutant concentrations

The Yangtze River Delta (YRD), the Pearl River Delta (PRD) and Beijing-Tianjin-Hebei region (BTH) have garnered significant attention from scholars and researchers studying air pollution due to their highly populated and economically developed locations. One of the most significant regions of the YRD is Jiangsu Province, which has recently had the worst air quality due to rapid growth in a variety of sectors (Zhang et al., 2020). Table 1 summarizes the statistical analysis for the six criteria air pollutants from before to after lockdown (LD). The findings show that during the pre-active-LD changes in Jiangsu, O3 increased by 58.62% while the levels of SO2, NO2, PM10, PM2.5, and CO fell by an average of − 17.85%, − 38.07%, − 29.52%, − 30.33%, and − 19.05%, respectively. With very minor exceptions, the patterns of particulate matter concentrations (PM2.5 and PM10) in the decrease scenario were similar. NO2 was shown to have a significantly declining value among other pollutants, while SO2 and CO showed mixed reductions during the active-LD period. Jiangsu saw a growing tendency for O3 throughout the same period, in contrast to other pollutants. Supporting our findings, numerous studies (Han et al., 2021; Sulaymon et al., 2021; Zhang et al., 2021b; Zheng et al., 2020) reported that during pre-active-LD changes in China, there was an abrupt fall in the levels of these air pollutants, while O3 levels increased. According to Mor et al. (2021), the active-LD period's reduction in industrial operations, traffic restrictions, and community constraints is responsible for the notable drop in air pollution concentrations.

Table 1 24 h average concentration and pre to post lockdown (LD) variation of pollutants in Jiangsu Province
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In Jiangsu Province, the concentrations of CO, NO2, PM10, and PM2.5, decreased by − 9.21%, − 18.80%, − 14.82%, and − 26.44%, respectively, from pre- to post-LD changes, while the concentrations of SO2 and O3 increased by an average of 0.81% and 59.22% (Table 1). PM2.5 exhibited a strong downward trend from pre- to post-LD, while PM10, NO2, and CO behaved differently in the reduction scenario. SO2 concentrations among other pollutants showed a small increase throughout this period, while O3 concentrations increased significantly. According to a study by Hu et al. (2020), Wuhan, China's O3 concentration levels increased following the shutdown. It can be noted that compared to the pre-active-LD changes, the increment ratio in the O3 levels was comparatively larger during the post-LD period. The drop in PM10 concentration was greater during the active-LD period compared to the post-LD decrease, while PM2.5 showed similar decreasing tendencies in both times. Distinct patterns were observed in the SO2 concentration, while a notable decrease in NO2 was observed in the corresponding times. Of the pollutants that were chosen, CO and O3 exhibited comparable patterns in both timeframes. The spatial distribution of these air contaminants is shown in Figs. 2, 3, 4, 5, 6, 7, where we can observe the clear changes over time in Jiangsu.

Fig. 2
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Daily average concentration of PM10 during the all-study periods in Jiangsu Province

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Fig. 3
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Daily average concentration of PM2.5 during the all-study periods in Jiangsu Province

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Fig. 4
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Daily average concentration of SO2 during the all-study periods in Jiangsu Province

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Fig. 5
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Daily average concentration of NO2 during the all-study periods in Jiangsu Province

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Fig. 6
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Daily average concentration of CO during the all-study periods in Jiangsu Province

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Fig. 7
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Daily average concentration of O3 during the all-study periods in Jiangsu Province

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There are a number of reasons for the variances in the drops in various air pollutants that occurred during the active-LD period of COVID-19. The sources of pollution's emissions differ. For instance, particulate matter (PM) can come from a variety of sources, including industrial operations, construction activities, dust, and wildfires, whereas nitrogen dioxide (NO2) is mostly released by automobile exhaust and industrial activity. As a result, lockdown procedures may or may not be successful in lowering emissions from various sources. The chemical reactivity and atmospheric lifespan of pollutants vary as well. Certain pollutants may experience swift chemical reactions in the environment and have brief lifespans, whilst others could last for extended periods of time. Pollutant interactions can also affect the concentrations of individual pollutants. For instance, through chemical reactions and physical processes, the presence of certain pollutants can affect the chemistry of the atmosphere and the behavior of others.

3.2 Changes in pollutant concentrations during the equivalent lockdown period over the previous three years (2017–19)

Table 2 summarizes and presents the spatiotemporal variations and changes of the six criteria air pollutants from the previous three years to active-LD. The findings indicate that during Jiangsu's active-LD time, NO2, SO2, PM10, CO and PM2.5 levels reduced by an average of − 28.55%, − 27.66%, − 33.23%, − 13.92% and − 29.83%, respectively, while the levels of O3 increased by up to 6.59%, in comparison to the previous year 2019. According to the findings, stringent regulations and control measures limit traffic, industries, and construction projects, which lowers the concentration of different air pollutants during the lockdown period in 2020 compared to 2019. The highest and lowest declining trends for CO and PM10, respectively, were noted throughout this time. Among other pollutants, such as NO2, SO2, and PM2.5 showed mixed tendencies, while O3 presented an upward trend. According to a study (Wang et al., 2020), during China's active-LD season, the levels of PM2.5, PM10, NO2, CO and SO2, decreased while levels of O3 increased in comparison to the previous year 2019. According to another study published by Mahato et al. (2020), during Delhi, India's active-LD era, the concentration levels of SO2, NO2, PM2.5, PM10 and CO were lower than they were in 2019, while O3 was higher.

Table 2 24 h average concentration and variation of pollutants during 2017–20 (same dates of active-LD) in Jiangsu Province
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Our findings indicate that the concentrations of PM2.5, PM10, NO2, SO2, and CO reduced by an average of -33.16%, -36.51%, -34.95%, -50.11%, and -23.60%, respectively, O3 levels increased by up to 3.53% during the previous three years to active-LD changes, in Jiangsu (Table 2, Figs. 2, 3, 4, 5, 6, 7). In Anhui Province, which is likewise located within the YRD zone, Bhatti et al. (2022) also discovered a noteworthy decrease in air pollutant concentrations during the active-LD period compared with the preceding three years (2017–19). This abrupt drop in air pollutant concentrations during the COVID-19 lockdown period compared with the previous three years was caused by a reduction in industrial, vehicular, construction, and heating activities. It is noteworthy that during both times, every air pollutant shown similar trends. SO2 was observed to have significantly decreased from the previous three years to active-LD, while particulate matter, NO2, and CO indicated almost comparable declining trends with minor variations. The declining spell in PM10 and PM2.5 concentrations was similar, indicating that urban areas were likely the source of both pollutants. We found that during both times, O3 levels increased. Jiangsu experienced notable drops in air pollution levels during the previous three years to active-LD changes, according to comparison analysis between 2019 to active-LD and the prior three years to active-LD changes. In contrast, O3 showed the reverse pattern in this analysis. Moreover, the concentration of SO2 exhibited the greatest decrease, while NO2 presented mixed performance during the corresponding periods.

3.3 Active to post lockdown changes and the previous three years (2017–19)

From active to post-LD, an increasing trend was found for PM10 concentration (3.02%), while the levels of PM2.5 decreased (− 10.43%) in Jiangsu (Table 3, and Figs. 2, 3). The findings reveal that the concentration levels of NO2, O3, SO2 and CO increased by an average of 6.16%, 29.80%, 11.76%, and 1.47%, respectively, during active to post-LD changes in Jiangsu (Table 3, and Figs. 4, 5, 6, 7). During this period, all air pollutants revealed increasing trends except for PM2.5. According to Sulaymon et al. (2021), the PM10, SO2, and CO concentrations increased, while PM2.5 and NO2 decreased during the period of post-LD in Wuhan, China. The increase in the levels of different air pollutants can be attributed to the emissions from vehicles and industries following the shutdown. PM2.5 levels decreased from active to post-LD changes, but the decrease in PM2.5 concentration was greater during the active-LD phase in Jiangsu. Among other pollutants, PM10 and CO showed a slight increase, while SO2 and NO2 showed mixed rise during this window of time. Moreover, O3 revealed continuously increasing trend.

Table 3 24 h average concentration and variation of pollutants (2017–20 to post-LD) in Jiangsu Province
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Our results reveal that except for O3, all the other pollutants were decreased at a significant level, PM10 (− 34.59%), PM2.5 (− 40.14%), SO2 (− 44.25%), NO2 (− 30.94%) and CO (− 22.47%) during the previous three years (2017–19) to post-LD changes in Jiangsu Province (Table 3 and Figs. 2, 3, 4, 5, 6, 7). The maximum reduction was found for SO2, while PM2.5, PM10, NO2, and CO presented the second, third, fourth and fifth decreasing values respectively, during this period. NO2, SO2, PM10, and CO showed different patterns during the changes of active to post-LD and the previous three years to post-LD changes, while PM2.5 and O3 exhibited similar trends in both periods. The concentration of O3 was found to have significantly increased during the period of post-LD, measuring 34.38%, when compared to the historical data (2017–19), in Jiangsu. According to Fu et al. (2020), compared with the previous four years (2016–19), the levels of O3 increased during and after the lockdown periods in South China. It should be noted that the post-LD phase had a higher increase ratio in O3 levels than the active-LD phase.

3.4 Active to post lockdown in 2021 changes and the previous three years (2017–19)

Between active-LD and 2021 (the same dates of active-LD), Jiangsu had a change in the pattern of air pollution levels. The findings show that during the following year 2021, the concentrations of PM10, NO2, PM2.5, and CO increased by 26.51%, 15.13%, 2.63%, and 2.94%, respectively, compared with the phase of active-LD, while a decline in SO2 and O3 levels was observed by − 6.10% and − 8.29%, respectively, in Jiangsu (Table 4 and Figs. 2, 3, 4, 5, 6, 7). PM10 and PM2.5, two of the selected pollutants, showed different rising tendencies during this window of time. PM10 revealed a significant growing trend, while PM2.5 presented a small increase. There was a significant increase in NO2, while compared to the other pollutants SO2 and O3 presented different tendencies, to be reduced in Jiangsu. The findings suggest that the 2020 active-LD period's declining SO2, NO2, CO and PM concentrations can be associated to the COVID-19 pandemic’s stringent limitations and lockdown policies as opposed to the 2021 increase in different air pollutant levels. Among the six pollutants, only SO2 demonstrated a consistently declining trend from active-LD to 2021, while O3 exhibited different trends during both phases.

Table 4 24 h average concentration and variation of pollutants (2017–21) in Jiangsu Province
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All pollutant concentrations in Jiangsu showed a significant decline from the last three years to 2021 (the same dates of LD). During the corresponding period, there was an approximate drop of − 31.40%, − 53.16%, − 19.68%, − 21.35%, − 25.11%, and − 5.05% in the concentration levels of PM2.5, SO2, PM10, CO, NO2, and O3. (Table 4 and Figs. 2, 3, 4, 5, 6, 7). The results demonstrate that an abrupt decline in SO2 was found, while CO, PM2.5, PM10, and NO2 showed mixed reductions. There was a small rise in the concentration of O3. It should be noted that excluded SO2 and O3, the other pollutant levels increased during the changes of active-LD to 2021, while the levels of the six pollutant parameters were considerably reduced during the last three years to 2021 changes. Except for O3, Jiangsu observed a notable drop in air pollution during the period of active-LD in 2020 compared to the three years prior to the decline in 2021. Overall, our findings reveal that during the COVID-19 pandemic, the lockdown policies and stringent regulations had a major impact on changing and declining the levels of air pollution.

3.5 Co-relationships between air pollutants

Figure 8 shows the relationships between the six criteria air pollutants during Jiangsu’s active-LD era. The daily (24-h) average concentrations of PM2.5 and PM10 showed a strong correlation (R2 = 0.55). PM10 and SO2 had a high correlation (R2 = 0.52) with NO2 (R2 = 0.34). Due to the mutual pollution sources of PM10 and PM2.5, including industrial, vehicle, and construction emissions, there is a strong link between the two. The results of the correlation analysis demonstrated that during the active-LD phase, there was a strong correlation between PM10 concentration and NO2 concentration (R2 = 0.39), but a weak correlation between PM10 and O3 (R2 = 0.16). The daily average concentrations of PM2.5 showed a weak correlation with SO2 (R2 = 0.10) and NO2 (R2 = 0.04), while PM2.5 and CO had a strong significant correlation (R2 = 0.74). Moreover, there was a poor correlation between SO2 and CO (R2 = 0.12) and O3 (R2 = 0.21), but a strong correlation between the daily (24-h) average concentration of SO2 and the NO2 concentration (R2 = 0.68). The strong correlation between SO2 and NO2 can be attributed to their mutual pollution sources in urban areas. NO2, CO, and O3 had the following relationships: R2 = 0.05, R2 = 0.02, and R2 = 0.01). These findings imply that when the levels of other pollutants decreased, the concentration of O3 increased.

Fig. 8
figure 8

Co-relationships between air pollutants

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3.6 Role of meteorological parameters during the three periods

Jiangsu Province's daily mean for air temperature, relative humidity, wind speed, and total rainfall during the periods of pre-, active-, and post-LD are presented in Fig. 9. Because they have an impact on air pollution emissions, transportation, formation, and deposition both directly and indirectly, meteorological parameters are important in determining ambient air quality (Zhang et al., 2015). Overall, with only minor variations, air temperature showed an increasing tendency from the periods of pre- to post-LD. The air temperature constantly increased during the active-LD and post-LD periods, but initially showed a falling trend during the phase of pre-LD. An increased upward mixing of air pollution characteristics is made possible by the atmosphere being subverted by the higher air temperature (Mandal et al., 2021). Hence, higher air temperatures promote the reduction of air pollution concentrations (Ravindra et al., 2019). The pattern for relative humidity was different. Figure 9 makes it clear that relative humidity had an increasing tendency throughout the pre-LD era and, with some changes, it demonstrated a nearly similar pattern during the period of active-LD. According to Yoo et al. (2014), relative humidity aids in lowering the concentrations of air pollutants. The findings show that relative humidity showed an increasing trend during the post-LD period, after initially declining. Zhang et al. (2015) discovered that in the North China Plain (NCP) region, the O3 mixing ratio clearly depended on both temperature and relative humidity.

Fig. 9
figure 9

Daily mean of the meteorological parameters (air temperature, relative humidity, wind speed, and total rainfall) during the three study periods in Jiangsu Province

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Moreover, Jiangsu observed a comparable wind speed pattern during the same dates, with slight fluctuations. When compared to the pre- and post-LD periods, the difference was discernible only during the active-LD period (Fig. 9). It implies that throughout the active-LD period, wind speed had negligible effect on lowering air pollution levels. The findings indicate that, with very minor exceptions, rainfall patterns throughout the pre- and active-LD periods were similar. During the pre-LD phase, there was initially a minor increase in rainfall, but during the active-LD period, there was mixed performance. Rainfall during the post-LD period exhibited an increasing trend when compared to the periods of pre- and post-LD. It may be concluded that, like other meteorological factors, rainfall had negligible effect on declining air pollution concentration levels during the active-LD phase.

3.7 Relationship between air pollutants and meteorological factors

The generation, dispersion, and transportation of air pollutants are greatly influenced by meteorological factors (Chen et al., 2019; Hasnain et al., 2023). A linear regression analysis was carried out to assess the correlation between meteorological variables and ambient air pollutants (Fig. 10). During Jiangsu's active-LD period, most of the pollutants had a negative correlation with the meteorological factors. PM10 and air temperature had a positive association (R2 = 0.14), as did SO2 and temperature (R2 = 0.30). NO2, on the other hand, had a strong positive correlation (R2 = 0.64) with air temperature. Moreover, during the active-LD period, there was a negative correlation between all air pollutants and other meteorological factors such as relative humidity, wind speed, and rain (Fig. 10). The results of this study closely resemble those of Zhou et al. (2020), who observed a negative relationship between air pollution and meteorological conditions in Naning and Beijing.

Fig. 10
figure 10

Relationship between air pollutant parameters and meteorological factors

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4 Conclusions

While the COVID-19 pandemic has undoubtedly been designated a deadly disease and has killed countless people worldwide, it has also had a good effect on the environment. To determine the spatiotemporal impact of the COVID-19 lockdown (LD) on Jiangsu Province’s air quality, the six air pollutant parameters, SO, NO2, CO, O3, PM10, and PM2.5 were evaluated in the current study. The results revealed that during the period of active-LD, except for O3, all pollutant concentrations had significantly decreased. Except for SO2 and O3, all the remaining four pollutant concentrations dropped from pre-active to post-LD changes. However, the reductions during the active-LD period were significantly larger than the post-LD reduction. Jiangsu had a maximum decrease in the concentrations of SO2, NO2, PM10, PM2.5, and CO from 2019 to active-LD and from the previous three years (2017–19) to active-LD. During the phase of active-LD, O3 levels slightly increased. PM10, SO2, CO, NO2, and O3 levels increased from the active to the post-LD phase, while PM2.5 levels decreased. During active-LD to 2021 changes, the concentration levels of NO2, PM2.5, PM10, and CO increased while those of SO2 and O3 declined. In 2021, there was a notable decrease in all pollutant concentrations as compared to the three years prior, 2017–19. The results of this study demonstrate that, except for O3, the active-LD period observed the greatest fall in pollutant concentrations. This suggests that the COVID-19 pandemic lockdown had a significant impact on the environment and declining the levels of air quality. As discussed in Sect. 1, most of the studies have mainly focused on either the comparison with previous years or the assessment of air pollution from pre-lockdown to active lockdown periods, while other studies have demonstrated that the lockdown measures and strict limitations led to a decline in air quality levels for limited time periods. However, a variety of comparisons were made in the current study, and it is possible to expand its scope to other regions and areas to obtain new results in upcoming years. The scientific community, policy makers, and local authorities may use all these new insights to help develop new laws and guidelines that will protect the environment and enhance air quality in the years to come.