Skip to main content

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

Abstract

Background

The spatiotemporal variation of observed trace gases (NO2, SO2, O3) and particulate matter (PM2.5, PM10) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM2.5 > 40 µg/m3) and complex pollution episodes (PM2.5 > 35 µg/m3 and O3 > 160 µg/m3) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined.

Results

The highest annual mean concentrations of PM2.5, PM10, NO2 and O3 were found for 2019 over all the cities. The annual mean concentrations of PM2.5, PM10, and NO2 showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O3 levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China’s National environmental monitoring program since 2013. However, a slight increase in annual O3 was observed in 2021. The highest overall means of PM2.5, PM10, SO2, and NO2 were observed over Hefei, whereas the highest O3 levels were found in Nanjing. Despite the strict control measures, PM2.5 and PM10 concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O3 and PM2.5 pollution days were higher in Hangzhou and Shanghai.

The in situ data for SO2 and NO2 showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data.

Conclusions

Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O3, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.

Background

Rapid urbanization and industrialization, deteriorating the air quality of most Chinese cities have resulted in adverse impacts on public health [1,2,3]. According to the World Health Organization [WHO] standards, only 1% of Chinese megacities meet the safe city criteria in terms of air quality [4]. Atmospheric aerosols unswervingly impact the earth’s radiation budget, ecological environment, human health, and climate [5]. Atmospheric pollution is caused by higher concentrations of various trace gas species including the oxides of nitrogen (NOx) and sulfur (SOx), tropospheric ozone (O3), volatile organic compounds (VOCs), and airborne particulate matter [PM], which pose serious threats to human health [6,7,8]. The primary emissions from anthropogenic sources are the trace gases such as NO2, SO2, and carbon monoxide (CO) [9]. Industrial emissions, fossil fuel combustion, and biofuels act as the major sources of SO2 and CO [10]. Both CO and NOx act as the main precursors of O3, whereas NOX and SO2 are significant towards the production of secondary inorganic aerosols [11]. The pollution sources have a significant impact on aerosol properties and are driven by atmospheric oxidation capacity, the intensity of emissions, and meteorological conditions. The role of meteorological parameters on different pollution episodes have been documented in previously reported studies [11]. Despite ample research on the effects of meteorological parameters on atmospheric pollution, significant variation in the intensity and extent of these impacts exists for different seasons and across different regions.

China has established more than 1500 stations for air quality monitoring since 2013, with a prime focus on monitoring different trace gases and particulate matter, i.e., PM2.5, PM10, NO2, SO2, O3, and CO [10,11,12,13]. The measurements taken at these monitoring stations have been used to analyze the characteristics of different air pollution episodes in various regions of China including the Yangtze River Delta (YRD) region [10,11,12, 14].

The YRD region is located adjacent to the North China Plain (NCP) and experiences complicated environmental pollution issues originating from dust plumes, biomass burning, photochemical reactions, and coal combustion [15]. The occurrence of severe haze episodes in the YRD region is one of these environmental pollution issues [16,17,18]. Haze is a condition that obstructs visual range and is defined as the weather phenomenon that leads to a reduction in atmospheric visibility caused by the accumulation of suspended solid or liquid fine particles [19,20,21]. The other major pollution issue in the YRD region is a phenomenon in which high O3 and high PM2.5 coincided beyond the National Ambient Air Quality Standards (NAAQS) [14, 22, 23]. This condition is termed as a complex pollution in this study hereafter, where the daily averaged PM2.5 concentration exceeds 35 μg/m3, and the daily averaged O3 concentration exceeds 160 μg/m3 on the same day.

The effects of cross-boundary air pollution and climatic factors on air quality are reported in different cities of the YRD region [24,25,26,27]. Though these investigations were either limited to specific particulate matter, trace gases, time intervals, or certain regions of YRD, the variation among various cities of YRD and their influential factors are not still clear. Thus, it is imperative to study the recent spatiotemporal distribution and characteristics of the criteria pollutants at different geographical locations within YRD. This is the first comprehensive study analyzing different air pollution episodes across YRD regions using recent datasets from National environmental monitoring centers. The study broadly characterizes the current air quality status, over the four most polluted and populous cities of YRD during 2018–2021. This research (a) examines the recent long-term temporal variation of trace gases and particulate matter (b) impact of different meteorological parameters on pollutants (c) identifies different pollutant sources using PSCF (Potential Source Contribution Function) with HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) back trajectory analysis (d) in situ measurements of SO2 and NO2 are compared with TROPOMI satellite data. Further, haze and complex pollution episodes over the study region were also analyzed. The outcomes of this up-to-date study could provide a wide-ranging logical and scientific basis for decision-making while designing effective air pollution control policies for metropolises.

Methods

Description of the study area

Anhui, Jiangsu, Shanghai, and Zhejiang are the major provinces that mainly cover the eastern side of the Yangtze plain. Fertile plains make this area characteristically unique and deem it suitable for a variety of food as well as cash crops. According to statistics, the gross domestic product (GDP) exceeded over USD 2.2 trillion for the YRD region as of 2018 (China Statistics: http://data.stats.gov.cn/english, accessed on 26 December 2021). The total area covered by the YRD region sums up to be 210,700 km2 and supports a huge population numbered about 0.23 billion in 2018 [28]. The vulnerability of the region to excessive atmospheric pollution comes from the high industrial growth, economic activity, and ever-increasing population which enhances the anthropogenic footprint on the environment. The four most important cities of the YRD region have been observed in this study, including Hangzhou, Hefei, Nanjing, and Shanghai. All of these have a very high population density and are the hubs for industrial and economic activities which make them prone to extreme pollution episodes. Figure 1 shows the location of these above-mentioned megacities in the YRD region.

Fig. 1
figure 1

The location of four megacities in YRD region of China

Data sets

In situ measurements and meteorological parameters

In situ measurements were attained via a National online database for air quality analysis and monitoring platform for NO2, PM2.5, PM10, SO2, and MDA8 O3 (Maximum Daily 8-hour Average Ozone). The data are available online and the monitoring stations are managed by China National Environmental Monitoring Center. The instruments deployed at these monitoring centers accord to the China Environmental Protection Standard HJ 664-2013. National ambient air quality standards (GB 3095e2012; China) were used to check the validity of the data, as reported in the previous study [12]. The hourly mean data of 10 different monitoring stations over each city (Additional file 1: Table S1) were used from 2018 to 2021. The city-wide day-to-day mean level of pollutants was estimated by taking an average of all the monitoring stations over a particular city as reported in previous studies [29]. The quality control approaches were applied to filter the problematic data as described in previous studies [30].

Meteorological parameters including temperature, visibility, relative humidity and wind speed were taken into account for the aforementioned time frame. Automatic weather stations installed at respective airports of each city were used to obtain meteorological data.

TROPOMI

TROPOMI level-2 NO2 and SO2 product published by Royal Netherlands Meteorological Institute (KNMI) has been used for the current study. Differential optical absorption spectroscopy (DOAS) algorithm is used to retrieve tropospheric VCDs following three major steps: 1) retrieval of slant column densities (SCDs), 2) differentiating stratospheric SCDs from tropospheric SCDs, and 3) converting SCDs to VCDs using air mass factor (AMF). Owing to the error linked to the calculation of AMF, an obvious underestimation of TROPOMI tropospheric NO2 and SO2 has been reported in recent studies, especially in polluted regions. In order to ensure reliability, the data with quality flag exceeding 0.75 were used for this study.

Methodology

Categorization of different pollution episodes

Haze pollution

To investigate the impact of haze days on PM2.5, PM10, NO2, SO2, and O3 over the study region, the study period has been divided into the haze and non-haze days. Haze days were defined with visibility < 7.5 km, RH < 80%, and PM2.5 > 40 µg/m3, while non-haze days were defined with visibility > 7.5 km, RH < 80%, and PM2.5 < 40 µg/m3 non-haze. A similar categorization of Haze and non-haze days is reported in different studies [19, 20, 31]. The aforementioned categorization for haze and non-haze days has been used for the current study over main cities of the YRD region including Hangzhou, Hefei, Nanjing, and Shanghai.

Complex pollution

The complex pollution days refer to the pollution conditions where the daily averaged PM2.5 concentration exceeds 35 μg/m3, and the daily averaged O3 concentration exceeds 160 μg/m3 on the same day. This categorization is used in this study over cities of the YRD region including Hangzhou, Hefei, Nanjing, and Shanghai.

Principal component analysis

Possible pollutant sources can be explored by employing principal component analysis (PCA) along with the study of the correlations among the pollutant concentrations. PCA was employed using SPSS v17.0 for five air pollutants with daily mean data points for each variable. The numbers of principal components (PCs) are determined by the variability in the data and frequency of inspected elements. PCA exploits the factor loading of each variable, which is why it was used with Varimax rotation [32] as reported in the earlier study [33].

Potential source contribution function

The origin of air masses was determined by performing backward trajectory analysis by employing the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model established by National Oceanic and Atmospheric Administration (NOAA) [34]. A comprehensive understanding of the transport of air masses, their dispersion, and chemical conversion is offered by this model [35] which also highlights the probable causes of aerosol pollutants that impact the air quality. The integration of HYSPLIT backward trajectory with PSCF analysis and daily SO2, NO2, O3, PM2.5, and PM10 measurements over a grid of 0.5*0.5 degrees was carried out to find the source strength of different geographical locations. The trajectory was calculated (72 h for every 6 h) at a height of 500 m above the ground level (AGL) employing meteorological data from the GDAS (Global Data Assimilation System) on 1◦ × 1◦ spatial resolution (accessed from: ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1) for each season from January 2018 to December 2021. Weighting functions were performed to lessen the uncertainty of PSCF and denoted as WPSCF [36, 37].

Results and discussion

Temporal variation

The time-series for yearly mean and inter-annual temporal trends of NO2, MDA8 O3, SO2, PM2.5, PM10, and PM2.5/PM10 from 2018–2021 are shown in Fig. 2. The basic statistics (annual mean, maxima, minima, standard deviation, and median) of the daily mean (2018-2021) NO2, MDA8 O3, SO2, PM2.5, and PM10 are shown in Additional file 1: Tables S2–S5 for Hefei, Nanjing, Shanghai, and Hangzhou. The main sources for NO2 production are generally vehicular emissions, biomass burning, fossil fuel combustion, and industrial emissions [36, 38]. The highest annual mean NO2 concentration (42 µg/m3) over Shanghai was observed in 2019, whereas 19% decline was observed from 2019 to 2021. Similar temporal variations were observed over other cities, i.e., the highest annual mean NO2 concentration was observed in 2019 and decreased by 17%, 21%, and 14% over Hangzhou, Nanjing, and Hefei, respectively, from 2019 to 2021. Only in 2019, the mean NO2 concentrations exceed the National Ambient Air Quality Standards (NAAQS< 40 µg/m3) and World Health organization Standards (WHO<40µg/m3) over all the cities. However, for the rest of the years, all the cities meet the NAAQS and WHO air quality standards for NO2. The decreasing trend in NO2 can be accredited to the efficacy of emission control measures and implementation of the Clean Air Action plan of China. The regulation of NOx emissions from power plants played an important role in the reduction of NO2 [39]. Overall, these results show that Hefei (38.4 µg/m3) is the most polluted city of the YRD region compared to the other cities, however, all the cities meet the NAAQS and WHO air quality standards based on annual mean NO2 concentrations from 2018 to 2021.

Fig. 2
figure 2

The inter-annual variation of a NO2, b O3, c SO2, d PM2.5, e PM10 and f PM2.5/PM10 in Hangzhou, Nanjing, Hefei and Shanghai during 2018–2021

SO2 is mainly produced from fossil fuel burning for domestic heating, power plants emissions, oil refineries, and metal smelters [40]. Across the four cities, the highest annual mean SO2 concentration (10 µg/m3) was observed in Nanjing during 2019. The annual mean concentration of SO2 in Nanjing decreased by 45% from 2019 to 2021. The temporal variation of SO2 over different cities of the YRD region showed contrasting results. The maxima for Hangzhou and Shanghai (8.5 and 9 µg/m3) were observed in 2018, whereas the decline was 28% and 38%, respectively, from 2018 to 2021. The annual mean SO2 concentration from 2018 to 2021 in Hefei on a whole ranged from 6 to 7 (µg/m3). The highest value 7 µg/m3 was observed in both 2018 and 2021. The SO2 concentration in all the cities was below the NAAQS (20 µg/m3) and WHO (20 µg/m3) guidelines. Additional file 1: Table S6 shows the China NAAQS and WHO guidelines for different trace gases and particulate matter. Previous studies also reported a decline in mean SO2 concentration over different regions of China [12]. Concerning, the most polluted city during 2018–2021 in terms of SO2 pollution was Nanjing with an overall mean concentration of 7.6 µg/m3. The reduction in SO2 levels over the YRD region can be linked to the strong and effective measures taken to reduce the SO2 level. The previous study reported a decrease in industrial SO2 emissions following the widespread installation of fuel gas desulfurization (FGD) devices in China [41].

Temporal variations of PM2.5 concentrations show a similar pattern as NO2, i.e., the highest and lowest annual mean concentrations in 2019 and 2021, respectively. The annual mean PM2.5 concentration decreased from 2019 to 2021 by 29%, 27%, 26%, and 25% over Hefei, Nanjing, Hangzhou, and Shanghai, respectively. The lowest mean yearly concentration of PM2.5 (26 µg/m3) was observed in Shanghai. The PM2.5 concentration was substantially reduced over all the YRD cities. Similar findings were reported in earlier studies [22, 42]. The air pollution prevention and control action plan launched by the Chinese government in 2013 has helped in significant PM2.5 emission abatements. However, the yearly mean concentration of PM2.5 still exceeds the Grade 1 annual NAAQS (15 µg/m3) and WHO (15 µg/m3) air quality guidelines over all the cities.

The yearly mean PM10 concentration for all the cities varies abruptly, i.e., PM10 concentration increased in 2019 compared to 2018, decreased in 2020, and then again increased in 2021. However, the lowest annual mean PM10 was noted in 2020. The highest annual mean PM10 (72 µg/m3) was observed in 2019 over Hefei compared to the other cities. The annual mean PM10 levels were beyond the grade 1 NAAQS (40 µg/m3) and WHO (15 µg/m3) guidelines over all the cities of YRD. This indicates that all the cities are facing severe air pollution problems in terms of PM2.5 and PM10.

The highest annual mean concentration of MDA8 O3 (106 µg/m3) was observed in 2019 over Nanjing. There has been an unprecedented reduction in average O3 concentrations during 2020 over all the cities of YRD, while the previously reported studies depicted a continuous increase of approximately 5% in annual mean concentration from 2013 to 2019 over different parts of China [22, 43]. However, a recent study also reported an exceptional reduction of approximately 6% in summer time MDA8 O3 during 2020 compared to 2019 over different Chinese cities [44]. This unprecedented decline in MDA8 O3 during 2020 can be linked to the fact that a recent study reported a shift in ozone sensitivity from the VOC (volatile organic compounds) limited regime to the transitional regime over eastern China [45] indicating that concurrent control of both NOx and VOCs would benefit in ozone reductions. Therefore, concurrent reduction of both NOx and VOCs during 2020 due to ongoing mitigation plan and new stringent control measures for 2020 introduced by the Chinese Ministry of Environment and Ecology in 2020 [46] may be helped in the reduction of ozone level. However, the MDA8 O3 levels again showed a slight increase during 2021 compared to 2020 over Hefei, Nanjing, and Hangzhou; whereas, Shanghai showed a continuous reduction in MDA8 O3 concentration. Therefore, it is still not clear which specific measures have helped in the reduction of O3 and requires further studies over different parts of China.

The daily average concentrations of PM10 and PM2.5 from 2018 to 2021 were used to analyze the ratio of PM2.5 to PM10 (PM2.5/PM10) to assess PM pollution over the studied cities (Shanghai, Nanjing, Hefei, and Hangzhou). Domestic heating and lower temperatures contributed to high PM2.5/PM10 values and their rate of change during winters in general. Due to high penetration rates and enhanced diffusion, smaller particles are more harmful compared to large particles. Therefore, it is imperative to reduce the proportion of PM2.5 in PM10 by applying control strategies especially focusing on the reduction by vehicular discharge and industrial emissions which mainly contribute to PM2.5. Domestic heating, traffic, spatial location, and meteorology should be simultaneously considered while conducting the measures to reduce PM pollution. The larger PM2.5/PM10 are observed in Shanghai and Hefei. This is an indication that fine particulate matter from anthropogenic sources mainly influences the air quality of Hefei and Shanghai.

It is worth discussing here that COVID-19 lockdown also played a significant part in the decline of the annual mean concentration of trace gases and particulate matter during 2020. Several studies reported the impact of COVID-19 lockdown on the concentration of trace gases and particulate matter over the YRD region [47,48,49]. Therefore, the annual time-series of particulate matter and other trace gases were also generated excluding the COVID-19 lockdown period (i.e., 24th of January–31st of March) from each year (Additional file 1: Figure S1). The results depict that the fewer decline in annual mean concentration during 2020 is observed in time-series excluding lockdown period as compared to time-series including the COVID-19 lockdown period. Additional file 1: Table S7 shows the change in percentage of annual mean concentrations from 2019 to 2020 for whole year and excluding COVID-19 lockdown period. The results indicate that for both whole year and excluding COVID-19 lockdown period the change in percentage of trace gases and particulate matter is negative, i.e., decline in concentration is observed. However, the results indicate that decline in concentration of NO2, SO2, PM2.5 and PM10 is relatively less when COVID-19 lockdown period is excluded. This shows the importance of COVID-19 lockdown which resulted in improvement of air quality. Contrasting results were noticed for O3 which showed higher decline in concentration when COVID-19 lockdown period is excluded. The previous study over Nanjing also showed increase in concentration of O3 during COVID-19 lockdown period [47].

Haze pollution

Haze formation is one of the phenomena that is largely impacted by the meteorological conditions of the locality. This haze impacts pollutants distribution, residence-time and photochemical reactions, thereby altering the atmospheric composition and impacting air quality [50, 51]. The study period is categorized into haze and non-haze days according to the categorization described above in the methodology section. Fig. 3 shows the number of haze days for all the four cities studied during the four consecutive years. Evidently, Hefei and Nanjing have the highest number of haze days, while Shanghai and Hangzhou have considerably fewer haze days compared to other cities. However, the general trend shows an average drop in haze days occurrence from 2018 to 2021.

Fig. 3
figure 3

The number of haze days over different cities of YRD region

Further, the impact of the haze condition on the concentration of trace gases and particulate matter is also examined. Table 1 depicts the percent change in mean concentrations of NO2, SO2, O3, PM2.5, and PM10 during haze days.

Table 1 Comparison of criteria pollutants during haze and non-haze days (2018–2021) over the YRD region cities

The concentration of all the particulate matter and trace gases except O3 is higher during haze days. This increase in concentration is mainly because meteorological conditions like low wind speed and high relative humidity were noted during haze days, which were favorable to the accumulation of air pollutants [52]. However, O3 concentrations tend to be higher during non-haze days compared to haze days over all the cities. During haze days, the light intensity is low and the conditions are less likely to favor photochemical reaction, hence the rate of O3 formation is low compared to non-haze sky conditions when ample sunlight is available [53]. The highest change is observed in the concentration of PM2.5.

Complex pollution episodes

The higher concentrations of O3 and PM2.5 have adversative effects on community health and the environment [14, 54]. The complex pollution episode is basically the co-occurrence of both PM2.5 and MDA8 O3 at higher levels. The complex pollution episodes are identified in the study period according to the categorization described in the methodology section. Fig. 4 shows the percentage of occurrence of O3 polluted days (O3 > 160 μg/m3), PM2.5 polluted days (PM2.5 > 35 μg/m3) and complex polluted days (O3 > 160 μg/m3 and PM2.5 > 35 μg/m3) over Shanghai, Nanjing, Hefei and Hangzhou.

Fig. 4
figure 4

The percentage of occurrence of O3 polluted days, PM2.5 polluted days and complex polluted days over different cities of YRD from 2018 to 2021

The results show that the highest number of PM2.5 polluted days is observed in Hefei (41%), whereas the O3 and complex polluted days are lowest in Hefei. The highest percentage of O3 polluted days (14%) occurred in Nanjing compared to other cities. However, the highest percentage of complex polluted days occurred in Hangzhou (5.8%) and Shanghai (5%), compared to Hefei (3%) and Nanjing (4%). The primary pollutants concentration (NO2 and SO2) is lower in Hangzhou and Shanghai as depicted in the previous section but the percentage of complex pollution days is higher. This is an indication that despite a reduction of primary pollutants in the main cities of China, the formation of the secondary pollutant is still a concern. During the complex pollution, days mean OX (O3+NO2) is compared for different cities, which is an indicator of atmospheric oxidation capacity. The mean OX levels (atmospheric oxidation capacity) during complex pollution episodes indicate that Hangzhou and Shanghai have higher oxidation capacity during complex pollution days compared to Nanjing and Hefei as shown in scatter plot of daily mean PM2.5 and MDA8-O3 with atmospheric oxidation capacity (Ox) in Fig. 5. It is pertinent to mention that PM10 and MDA8- O3 are also closely linked but this study mainly focused on complex pollution episodes, i.e., the co-occurrence of both PM2.5 and MDA8 O3 at higher levels, therefore, the scatter plots of daily mean PM2.5 and MDA8-O3 are presented in this study. The higher oxidation capacity can enhance the rate of secondary pollution formation. Similar results were reported in previous studies conducted in China [52].

Fig. 5
figure 5

The scatter plot of daily mean PM2.5 and MDA8-O3 with atmospheric oxidation capacity (Ox) is indicated by color. The dashed lines depict the NAAQS grade-II standards for O3 and PM2.5

Influence of meteorology

Meteorological factors play a key role in trace gas and aerosol distribution in the atmosphere and substantial pieces of evidence can be found in the literature pertaining to the impact of meteorology on local air quality [55]. Therefore, inter-annual variation in different meteorological parameters like temperature, relative humidity, and wind speed is observed. Fig. 6 shows box plots of meteorological parameters from 2018 to 2021. The results show that there is no significant change in inter-annual variation of temperature, relative humidity, and wind speed. The meteorological conditions are almost identical from 2018 to 2021 over YRD region cities. This provided limited assurance that the air quality trends are not partially influenced by year-to-year meteorological differences. Therefore, inter-annual variations in trace gases and particulate matter are possibly due to changes in emission sources and strict measures taken by the Chinese Government.

Fig. 6
figure 6

Box plot of different meteorological parameters from 2018 to 2021 over different cities of YRD

However, the meteorological conditions at the sub-seasonal scale of a locality are considered to be a major driving force towards the distribution of pollutants in terms of residence-time and chemical behavior in the lower atmosphere. Therefore, the pairwise correlation analysis at a seasonal scale was used to understand the relationship among meteorological factors and five criteria pollutants (NO2, SO2, O3, PM2.5, and PM10). The correlation coefficients between pollutants and meteorological factors are depicted in Table 2. Strong negative correlation of wind speed was observed with NO2, especially during summer and winters, compared to other parameters. The wind speed is negatively correlated with primary pollutants because lower wind speeds result in the accumulation of different primary pollutants [55]. The secondary pollutant O3 is also weakly and negatively correlated with wind speed except in the autumn season. The trend is similar over all the cities.

Table 2 Pearson correlation coefficient between air pollutants and meteorological factors (2018–2021)

Temperature is positively correlated with O3 over all the cities with different values of coefficient. This correlation is relatively stronger in the spring and autumn seasons. Similar results were reported in previous studies [10, 56]. The rate of formation of O3 is higher when temperature increases, as photolytic activity is increased which helps in the formation of secondary pollutant O3 [11, 57]. The correlation among PM2.5 levels and temperature showed a weak negative trend during the autumn season over all the cities. However, the correlations are non-significant during other seasons. The PM10 levels showed a positive correlation with temperature during the spring and summer seasons, with the highest correlation (R = 0.43) found in Nanjing during the spring season. The mean NO2 levels showed weak negative correlations with temperature during the summer and autumn seasons over all the cities; whereas, the mean SO2 levels showed weak negative correlations with temperature during the winter and autumn season, and weak positive correlations during the spring and summer seasons. The relative humidity is negatively correlated with all the pollutants. The concentration of O3 showed a strong negative correlation with relative humidity. The NO2 and PM2.5 are weakly and negatively correlated with relative humidity. However, SO2 and PM10 are found to be moderate and negatively correlated. A similar trend is observed over all the cities.

Generally, the impact of the meteorological parameters on pollutant concentration is similar over all the cities of YRD. The increase in temperature and lower relative humidity favors the accumulation of O3, while low temperature, low wind speeds, and lower relative humidity favor the accumulation of primary pollutants.

Monthly variations

The monthly variations in air pollutants over Shanghai, Nanjing, Hefei, and Hangzhou are investigated. Fig. 7 depicts multiyear (2018–2021) monthly variations in five criteria pollutants over the four cities. The results indicated that the pollutant concentrations are higher during the winter months for all the species (except O3). The concentration of pollutants began to rise in September and peaks were found in December–January. The peak values were observed during January for PM2.5 and PM10 except for Hangzhou where PM10 peaked during December. NO2 concentration peaked in December for all the cities while SO2 remained highest during January for Shanghai and Hangzhou, while for Nanjing and Hefei, it peaked during December. In contrast, O3 concentrations were observed to rise during the summer months with the highest value occurring in the May–June months.

Fig. 7
figure 7

Multiyear (2018–2021) monthly average variations of a PM2.5, b PM10, c NO2, d SO2 and e O3 concentrations in the cities of the YRD region

The concentration of particulate matter peaked during the winter season due to different reasons. These reasons include stagnant meteorological conditions [lower temperature, lower wind speed] of locality [12], higher coal combustion for the heating purpose [58, 59], and formation of new particles and secondary organic–inorganic aerosols [12, 60]. Similarly, the concentration of SO2 and NO2 is also higher during winter months due to increased burning of biomass fuel coupled with reduced air temperature, wind speed, and solar radiations [36].

However, the concentration of O3 is higher in the summer months which can be attributed to the fact that photochemical reactions are its precursors. The monthly mean variation in the O3 levels shows an M-shaped curve, and the O3 peaks were observed in May and September for Shanghai and Hangzhou; whereas, in Hefei and Nanjing, the O3 peaks were observed in June and September. Similar monthly variations of O3 were reported in a study over south China [61]. The monthly variations of pollutants are consistent with findings of previous studies over different cities of China [12, 62].

There are a few anomalies in the results which are worth mentioning. The concentration of NO2, SO2, and PM10 showed a sudden decline in February. This can be linked to the fact that the Spring festival which is the most valuable vacation for Chinese people occurred in February during these years (Additional file 1: Table S8). The majority of migrant residents of megacities return to their hometowns. The closure of industries, offices, and educational institutes is observed over all the regions of China during the spring festival. Therefore, the reduction in anthropogenic activities results in a sudden dip in pollutants concentrations. Similar results were reported in previous studies explaining the impact of the spring festival on pollutants concentration [48, 53].

Fig. 8 shows the percentage of haze and complex pollution days occurrence in different months from 2018 to 2021. It is observed that complex pollution days mainly occurred in summer, whereas the higher percentage of haze days occurred in winter. The maximum haze days occurred in January over all the cities. This occurrence of haze episodes in winter months can be attributed to the fact that fine particulates concentration is higher in winter which plays an important role in haze formation; whereas, the maximum complex pollution days occurred in May over Shanghai and Nanjing, in June over Hefei, and in September over Hangzhou. This distribution of complex days is mainly because O3 and PM2.5 are negatively correlated during winter and autumn. However, the correlation is moderate positive during late spring and summer [63, 64].

Fig. 8
figure 8

Multiyear (2018–2021) monthly variation in percentage (%) of haze and complex pollution days in the cities of the YRD region

TROPOMI validation

The in situ measurements for trace gases including SO2 and NO2 are compared with spatially averaged TROPOMI satellite data of SO2 and NO2 over different cities of YRD region. The multiyear (2018–2021) monthly mean values of SO2 and NO2 are compared with each other. Fig. 9 shows correlation plot among in situ and TROPOMI satellite data.

Fig. 9
figure 9

Scatter plot between TROPOMI and in situ measurement of multiyear (2018–2021) monthly mean data of SO2 and NO2 over a Hangzhou, b Hefei, c Nanjing and d Shanghai. The results show that there is strong correlation among both datasets over all the cities

Principal component analysis and correlation analysis

PCA was used to reveal possible sources of pollutants emissions and calculate correlations between them. PCA is a frequently utilized multi-variate statistical analysis tool that is crucial to the study of interrelationships between diverse variables coming from a similar transference path or source [12, 33]. Additional file 1: Tables S9–S12 represents the total variance and rotated component matrix of PCA of atmospheric pollutants concentrations in our study area. Two factors were obtained which explain 83%, 81%, 80%, and 78% total variability over Hangzhou, Shanghai, Nanjing, and Hefei. Table 3 depicts the two factors obtained through PCA.

Table 3 Factor loadings after PCA analysis Varimax rotation

Factor 1 has positive loading of SO2, PM2.5, PM10, and NO2. These all are indicative of primary anthropogenic sources (industrial, coal, biomass and fossil fuel). However, factor 2 has positive loading of O3 reflecting markedly unlike sources that are photochemical reactions. Similar results were observed over all the cities with different values. These results are consistent with findings over urban cities of Lanzhou, Urumqi, Jinan and Shanghai [12, 33, 65, 66]. It is worth mentioning that Lanzhou, Urumqi and Jinan are located at geographically different locations as compared to study area. Fig.10 depicts the correlation matrix of trace gases and particulate matter over cities of the YRD region.

Fig. 10
figure 10

Correlation matrix of daily mean criteria pollutants from 2018 to 2021

The daily mean PM2.5 concentrations are strongly correlated with daily mean PM10 concentration in all the cities. The strongest correlation is observed over Hangzhou as compared to other cities. Particulate matter (PM2.5 and PM10) shows an almost similar correlation trend with NO2 and SO2 over all the cities of the study. The positive correlation between particulate matter (PM2.5 and PM10) with SO2 and NO2 is noted. This indicates that particulate matters (PM2.5 and PM10), and primary trace gases (SO2 and NO2) have similar sources of emissions like fossil fuel combustion and traffic. The recent studies also stated a strong positive correlation between particulate matters (PM2.5 and PM10), and primary trace gases in Beijing [67] and Delhi [68]. The correlation of O3 with other trace gases and particulate matter showed diverse trends over the different cities of this study. A weak positive correlation is observed between particulate matter (PM2.5 and PM10) and O3 over Shanghai; whereas, over Hangzhou and Nanjing this correlation among is particulate matters (PM2.5 and PM10) and O3 weak negative. PM2.5 is negatively correlated with O3 in Hefei, whereas PM10 showed a weak positive correlation. A weak negative correlation is observed among NO2 over all the cities. The correlation coefficient between SO2 and O3 is weak positive over Shanghai and Nanjing; whereas, the correlation coefficient between SO2 and O3 is weak negative over Hefei and Hangzhou.

The results depict that O3 had distinctly different sources as compared to primary trace gases and particulate matters. The negative correlation of O3 with NO2 can be explained by studying the tropospheric chemistry where the reaction of O3 with NO leads to the formation of NO2 thereby depleting O3 [69]. These results comply with previously reported findings [10, 55].

Potential source contribution function

PSCF analyses were performed to identify the potential source areas of NO2, SO2, O3, PM2.5, and PM10 over Hangzhou, Hefei, Nanjing, and Shanghai regions using seasonal in situ measurements of NO2, SO2, O3, PM2.5, and PM10 as input in the PSCF model and 72-h back trajectories obtained from the NOAA HYSPLIT model. The regions with WPSCF less than 0.4 are described as low pollution source, regions with WPSCF ranging from 0.4-0.5 are described as medium pollution source, whereas areas with WPSCF greater than 0.5 are termed as high pollution source regions. The results show that the air quality over Hangzhou, and Hefei, Nanjing, and Shanghai are significantly affected by local [neighboring provinces and cities within China] pollution sources of NO2, SO2, PM2.5, and PM10 pollutants, which are stronger in winter and weaker in summer, whereas an opposite scenario was observed for O3. Figs. 11, 12, 13, 14, 15 (Additional file 1: Figures S2–S16) show PSCF Analysis based on NO2, SO2, O3, PM2.5, and PM10, respectively grouped by season over Nanjing (Hefei, Hangzhou and Shanghai, respectively). Additional file 1: Figure S17 shows different regions in People’s republic of China Figs.12, 13, 14,15 .

Fig. 11
figure 11

PSCF analysis based on NO2 grouped by season over Nanjing. The color bar indicates the weights of Pollution source regions

Fig. 12
figure 12

PSCF analysis based on SO2 grouped by season over Nanjing. The color bar indicates the weights of Pollution source regions

Fig. 13
figure 13

PSCF analysis based on O3 grouped by season over Nanjing. The color bar indicates the weights of pollution source regions

Fig. 14
figure 14

PSCF analysis based on PM2.5 grouped by season over Nanjing. The color bar indicates the weights of pollution source regions

Fig. 15
figure 15

PSCF analysis based on PM10 grouped by season over Nanjing. The color bar indicates the weights of pollution source regions

Generally, the wintertime high values of PSCF (> 0.50) over Hangzhou, Hefei, Nanjing, and Shanghai regions indicate the stronger contributions from local source areas (Anhui, Beijing, Chongqing, Fujian, Hebei, Henan, Hubei, Hunan, Guangdong, Jiangsu, Jiangxi, Inner Mongolia, Shanxi, Shaanxi, Shandong, Sichuan, Tianjin, and Zhejiang). These local sources of PM2.5 concentrations more significantly affect the air quality of Hangzhou, Hefei, Nanjing, and Shanghai than regional sources. In spring, the most common local source areas (e.g., Anhui, Fujian, Hebei, Henan, Hubei, Hunan, Guangdong, Jiangsu, Jiangxi, Inner Mongolia, Shanxi, Shandong, and Zhejiang) of NO2, SO2, O3, PM2.5, and PM10 concentrations also substantially affect the air quality of Hangzhou, Hefei, Nanjing, and Shanghai.

The summertime air quality of these four study areas was significantly influenced by O3, whereas the lowest contributions were noted for NO2, SO2, PM2.5, and PM10 concentrations. This is showing the influence of local meteorology on aerosol pollutants and trace gases, i.e., more dispersion of aerosols and trace gases is occurred in summer than winter due to strong solar radiations, high temperatures leading to a high planetary boundary layer [36, 70]. However, the most common potential source areas of O3 concentrations are Anhui, Beijing, Fujian, Hebei, Henan, Hubei, Hunan, Guangdong, Jiangsu, Jiangxi, Shanxi, Shandong, Tianjin, and Zhejiang. In addition, the autumn air quality over Hangzhou, Hefei, Nanjing, and Shanghai are significantly impacted by local pollution sources. Overall, the results show that local sources from mainland China are the major contributors of air pollution over Hangzhou, Hefei, Nanjing, and Shanghai, with PM2.5 pollution levels being higher in winter than in other seasons, whereas O3 pollution is higher in summer.

Conclusions

This study investigates the recent spatiotemporal variations (2018–2021) of trace gases and aerosols along with the impact of trans-boundary air contamination and the effect of meteorological parameters on air quality in the cities of YRD region (Shanghai, Hefei, Hangzhou and Nanjing). The annual mean concentration of NO2 and PM2.5 continuously declines from 2019 to 2021 over all the cities. The unprecedented decline in annual mean O3 concentration is observed in 2020 over the cities of YRD region. The concurrent reduction of both NOx and VOCs during 2020 due to ongoing mitigation plan and new stringent control measures introduced by the Chinese Ministry of Environment and Ecology in 2020 may be helped in the reduction of O3 concentration. The results showed still particulate matters exceeded the NAAQS and WHO guidelines although strict measures have resulted in significant improvement of air quality. The Haze pollution events occurred more frequently in Hefei and Nanjing as compared to Shanghai and Hangzhou. This can be attributed to higher concentration of PM2.5 in Hefei and Nanjing. The complex pollution episodes, i.e., the concurrent occurrence of PM2.5 and O3 are higher in Shanghai and Hangzhou possibly due to higher atmospheric oxidation capacity which enhances the rate of secondary pollution formation. The primary pollutants concentrations were higher in the winter, whereas O3 levels were higher during the summer season. The higher concentration of trace gases and particulate matter were mainly due to trans-boundary transport from adjoining cities and provinces. The increase in temperature and lower relative humidity favors the accumulation of O3, while low temperature, low wind speeds, and lower relative humidity favor the accumulation of primary pollutants. Different cities have different pollution characteristics, requiring continuous monitoring to design city-specific policies and action plans. The results of this study can be used as a strong reference for the future research by scientific community, administration and policymakers, and other stakeholders who might be concerned about air pollution impacts and mitigation over this vast, populous and economically crucial region. Future emission control strategies should be designed considering the synchronized control of O3 and PM2.5, especially for the cities with higher atmospheric oxidation capacity.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

µg/m3 :

Microgram per cubic meter

AGL:

Above ground level

AMF:

Air mass factor

DOAS:

Differential optical absorption spectroscopy

FGD:

Fuel gas desulfurization

GDAS:

Global data assimilation system

GDP:

Gross domestic product

HYSPLIT:

Hybrid single-particle Lagrangian integrated trajectory

MDA8 ozone:

Maximum daily 8-hour average ozone

NAAQS:

National Ambient Air Quality Standards

NCP:

North China Plain

NO2 :

Nitrogen dioxide

NOAA:

National Oceanic and Atmospheric Administration

O3 :

Ozone

PC:

Principal component

PCA:

Principal component analysis

PM:

Particulate matter

PSCF:

Potential source contribution function

SCD:

Slant column density

SO2 :

Sulphur dioxide

TROPOMI:

Tropospheric monitoring instrument

VCD:

Vertical column density

VOCs:

Volatile organic compounds

WHO:

World Health Organization

WPSCF:

Weighted PSCF

YRD:

Yangtze River Delta

References

  1. Ji H, Wang J, Meng B, Cao Z, Yang T, Zhi G, Chen S, Wang S, Zhang J (2022) Research on adaption to air pollution in Chinese cities: evidence from social media-based health sensing. Environ Res. https://doi.org/10.1016/j.envres.2022.112762

    Article  Google Scholar 

  2. Li C, Managi S (2022) Spatial variability of the relationship between air pollution and well-being. Sustain Cities Soc 76:103447. https://doi.org/10.1016/j.scs.2021.103447

    Article  Google Scholar 

  3. Wang Y, Duan X, Liang T, Wang L, Wang L (2022) Analysis of spatiotemporal distribution characteristics and socioeconomic drivers of urban air quality in China. Chemosphere 291:132799. https://doi.org/10.1016/j.chemosphere.2021.132799

    CAS  Article  Google Scholar 

  4. Wang L, Li P, Yu S, Mehmood K, Li Z, Chang S, Liu W, Rosenfeld D, Flagan RC, Seinfeld JH (2018) Predicted impact of thermal power generation emission control measures in the Beijing-Tianjin-Hebei region on air pollution over Beijing China. Sci Rep 8(1):1–10. https://doi.org/10.1038/s41598-018-19481-0

    CAS  Article  Google Scholar 

  5. Seinfeld JH, Pandis SN (2016) Chapter 2: atmospheric trace constituents. Atmospheric chemistry and physics: from air pollution to climate change. John Wiley & Sons, Hoboken

    Google Scholar 

  6. Cummings LE, Stewart JD, Kremer P, Shakya KM (2022) Predicting citywide distribution of air pollution using mobile monitoring and three-dimensional urban structure. Sustain Cities Soc 76:103510. https://doi.org/10.1016/j.scs.2021.103510

    Article  Google Scholar 

  7. Liu Y, Xu X, Yang X, He J, Zhang W, Liu X, Ji D, Wang Y (2022) Significant contribution of secondary particulate matter to recurrent air pollution: evidence from in situ observation in the most polluted city of Fen-Wei Plain of China. J Environ Sci. https://doi.org/10.1016/j.jes.2021.09.030

    Article  Google Scholar 

  8. Mannucci PM, Franchini M (2017) Health effects of ambient air pollution in developing countries. Int J Environ Res Public Health 14(9):1048. https://doi.org/10.3390/ijerph14091048

    CAS  Article  Google Scholar 

  9. Li M, Zhang Q, Kurokawa JI, Woo JH, He K, Lu Z, Ohara T, Song Y, Streets DG, Carmichael GR, Cheng Y (2017) MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos Chem Phys 17(2):935–963. https://doi.org/10.5194/acp-17-935-2017

    CAS  Article  Google Scholar 

  10. Sulaymon ID, Zhang Y, Hopke PK, Hu J, Rupakheti D, Xie X, Zhang Y, Ajibade FO, Hua J, She Y (2021) Influence of transboundary air pollution and meteorology on air quality in three major cities of Anhui Province China. J Clean Prod 329:129641. https://doi.org/10.1016/j.jclepro.2021.129641

    CAS  Article  Google Scholar 

  11. Li B, Shi XF, Liu YP, Lu L, Wang GL, Thapa S, Sun XZ, Fu DL, Wang K, Qi H (2020) Long-term characteristics of criteria air pollutants in megacities of Harbin-Changchun megalopolis, Northeast China: Spatiotemporal variations, source analysis, and meteorological effects. Environ Pollut 267:115441. https://doi.org/10.1016/j.envpol.2020.115441

    CAS  Article  Google Scholar 

  12. Rupakheti D, Yin X, Rupakheti M, Zhang Q, Li P, Rai M, Kang S (2021) Spatio-temporal characteristics of air pollutants over Xinjiang Northwestern China. Environ Pollut 268:115907. https://doi.org/10.1016/j.envpol.2020.115907

    CAS  Article  Google Scholar 

  13. Zhou X, Strezov V, Jiang Y, Kan T, Evans T (2022) Temporal and spatial variations of air pollution across China from 2015 to 2018. J Environ Sci 112:161–169. https://doi.org/10.1016/j.jes.2021.04.025

    Article  Google Scholar 

  14. Qin Y, Li J, Gong K, Wu Z, Chen M, Qin M, Huang L, Hu J (2021) Double high pollution events in the Yangtze River Delta from 2015 to 2019: characteristics, trends, and meteorological situations. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.148349

    Article  Google Scholar 

  15. Ding A, Huang X, Nie W, Chi X, Xu Z, Zheng L, Xu Z, Xie Y, Qi X, Shen Y, Sun P (2019) Significant reduction of PM2.5 in eastern China due to regional-scale emission control: evidence from SORPES in 2011–2018. Atmos Chem Phys 19(18):11791–11801. https://doi.org/10.5194/acp-19-11791-2019

    CAS  Article  Google Scholar 

  16. Gong S, Liu Y, He J, Zhang L, Lu S, Zhang X (2022) Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 20201: synoptic circulation patterns and pollution. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.152770

    Article  Google Scholar 

  17. Lu H, Xie M, Liu X, Liu B, Liu C, Zhao X, Du Q, Wu Z, Gao Y, Xu L (2022) Spatial-temporal characteristics of particulate matters and different formation mechanisms of four typical haze cases in a mountain city. Atmos Environ 269:118868. https://doi.org/10.1016/j.atmosenv.2021.118868

    CAS  Article  Google Scholar 

  18. Zhang J, Yuan Q, Liu L, Wang Y, Zhang Y, Xu L, Pang Y, Zhu Y, Niu H, Shao L, Yang S (2021) Trans-regional transport of haze particles from the North China Plain to Yangtze River Delta during winter. J Geophys Res Atmos 126(8):e2020JD03778. https://doi.org/10.1029/2020JD033778

    Article  Google Scholar 

  19. Duan L, Xiu G, Feng L, Cheng N, Wang C (2016) The mercury species and their association with carbonaceous compositions, bromine and iodine in PM2. 5 in Shanghai. Chemosphere 146:263–271. https://doi.org/10.1016/j.chemosphere.2015.11.058

    CAS  Article  Google Scholar 

  20. Hong Q, Xie Z, Liu C, Wang F, Xie P, Kang H, Xu J, Wang J, Wu F, He P, Mou F (2016) Speciated atmospheric mercury on haze and non-haze days in an inland city in China. Atmos Chem Phys 16(21):13807–13821. https://doi.org/10.5194/acp-16-13807-2016

    CAS  Article  Google Scholar 

  21. Javed Z, Tanvir A, Bilal M, Su W, Xia C, Rehman A, Zhang Y, Sandhu O, Xing C, Ji X, Xie M (2021) Recommendations for HCHO and SO2 retrieval settings from MAX-DOAS observations under different meteorological conditions. Remote Sens 13(12):2244. https://doi.org/10.3390/rs13122244

    Article  Google Scholar 

  22. Dai H, Zhu J, Liao H, Li J, Liang M, Yang Y, Yue X (2021) Co-occurrence of ozone and PM2.5 pollution in the Yangtze River Delta over 2013–2019: Spatiotemporal distribution and meteorological conditions. Atmos Res 249:5363. https://doi.org/10.1016/j.atmosres.2020.105363

    CAS  Article  Google Scholar 

  23. Wang Y, Gao W, Wang S, Song T, Gong Z, Ji D, Wang L, Liu Z, Tang G, Huo Y, Tian S (2020) Contrasting trends of PM25 and surface-ozone concentrations in China from 2013 to 2017. Nat Sci Rev 7(8):1331–1339. https://doi.org/10.1093/nsr/nwaa032

    CAS  Article  Google Scholar 

  24. Gao D, Xie M, Chen X, Wang T, Liu J, Xu Q, Mu X, Chen F, Li S, Zhuang B, Li M (2020) Systematic classification of circulation patterns and integrated analysis of their effects on different ozone pollution levels in the Yangtze River Delta Region. China Atmos Environ 242:117760. https://doi.org/10.1016/j.atmosenv.2020.117760

    CAS  Article  Google Scholar 

  25. Hu J, Wang Y, Ying Q, Zhang H (2014) Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta. China Atmos Environ 95:598–609. https://doi.org/10.1016/j.atmosenv.2014.07.019

    CAS  Article  Google Scholar 

  26. Liu X, Zhu B, Kang H, Hou X, Gao J, Kuang X, Yan S, Shi S, Fang C, Pan C, Meng K (2021) Stable and transport indices applied to winter air pollution over the Yangtze River Delta China. Env Pollut 272:115954. https://doi.org/10.1016/j.envpol.2020.115954

    CAS  Article  Google Scholar 

  27. Wang HL, Qiao LP, Lou SR, Zhou M, Ding AJ, Huang HY, Chen JM, Wang Q, Tao SK, Chen CH, Li L (2016) Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai China. J Clean Prod 112:1302–1311. https://doi.org/10.1016/j.jclepro.2015.04.099

    CAS  Article  Google Scholar 

  28. National Bureau of Statistics of China (NBSC). http://www.stats.gov.cn/tjsj/pcsj/rkpc/decrkpc/. Accessed 26 Dec 2021.

  29. Sulaymon ID, Zhang Y, Hopke PK, Hu J, Zhang Y, Li L, Mei X, Gong K, Shi Z, Zhao B, Zhao F (2021) Persistent high PM25 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region China. Environ Res 198:1186. https://doi.org/10.1016/j.envres.2021.111186

    CAS  Article  Google Scholar 

  30. He J, Gong S, Yu Y, Yu L, Wu L, Mao H, Song C, Zhao S, Liu H, Li X, Li R (2017) Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities. Environ Pollut 223:484–496. https://doi.org/10.1016/j.envpol.2017.01.050

    CAS  Article  Google Scholar 

  31. Javed Z, Liu C, Khokhar MF, Xing C, Tan W, Subhani MA, Rehman A, Tanvir A (2019) Investigating the impact of Glyoxal retrieval from MAX-DOAS observations during haze and non-haze conditions in Beijing. J Environ Sci 80:296–305. https://doi.org/10.1016/j.jes.2019.01.008

    CAS  Article  Google Scholar 

  32. Lewis-Beck MS (1994) Factor analysis and related techniques. Sage

    Google Scholar 

  33. Filonchyk M, Yan H (2018) The characteristics of air pollutants during different seasons in the urban area of Lanzhou Northwest China. Environ Earth Sci 77(22):1–17. https://doi.org/10.1007/s12665-018-7925-1

    CAS  Article  Google Scholar 

  34. Stein AF, Draxler RR, Rolph GD, Stunder BJ, Cohen MD, Ngan F (2015) NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Am Meteor Soc 96(12):2059–2077. https://doi.org/10.1175/BAMS-D-14-00110.1

    Article  Google Scholar 

  35. Fleming ZL, Monks PS, Manning AJ (2012) Untangling the influence of air-mass history in interpreting observed atmospheric composition. Atmos Res 104:1–39. https://doi.org/10.1016/j.atmosres.2011.09.009

    CAS  Article  Google Scholar 

  36. Bilal M, Mhawish A, Nichol JE, Qiu Z, Nazeer M, Ali MA, de Leeuw G, Levy RC, Wang Y, Chen Y, Wang L (2021) Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases. Remote Sens Environ 264:112617. https://doi.org/10.1016/j.rse.2021.112617

    Article  Google Scholar 

  37. Feng Z, Zheng F, Liu Y, Fan X, Yan C, Zhang Y, Daellenbach KR, Bianchi F, Petäjä T, Kulmala M, Bao X (2022) Evolution of organic carbon during COVID-19 lockdown period: possible contribution of nocturnal chemistry. Sci Total Environ 808:152191. https://doi.org/10.1016/j.scitotenv.2021.152191

    CAS  Article  Google Scholar 

  38. Cheng M, Jiang H, Guo Z (2012) Evaluation of long-term tropospheric NO2 columns and the effect of different ecosystem in Yangtze River Delta. Procedia Environ Sci 13:1045–1056. https://doi.org/10.1016/j.proenv.2012.01.098

    CAS  Article  Google Scholar 

  39. Liu F, Zhang Q, Zheng B, Tong D, Yan L, Zheng Y, He K (2016) Recent reduction in NOx emissions over China: synthesis of satellite observations and emission inventories. Environ Res Lett 11(11):114002. https://doi.org/10.1088/1748-9326/11/11/114002

    CAS  Article  Google Scholar 

  40. Wang Y, Ali M, Bilal M, Qiu Z, Mhawish A, Almazroui M, Shahid S, Islam MN, Zhang Y, Haque M (2021) Identification of NO2 and SO2 pollution hotspots and sources in Jiangsu Province of China. Remote Sens 13(18):3742. https://doi.org/10.3390/rs13183742

    CAS  Article  Google Scholar 

  41. Zhang L, Lee CS, Zhang R, Chen L (2017) Spatial and temporal evaluation of long-term trend (2005–2014) of OMI retrieved NO2 and SO2 concentrations in Henan Province, China. Atmos Environ 154:151–166. https://doi.org/10.1016/j.atmosenv.2016.11.067

    CAS  Article  Google Scholar 

  42. Zhang NN, Ma F, Qin CB, Li YF (2018) Spatiotemporal trends in PM2. 5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China. Chemosphere 210:1176–1184. https://doi.org/10.1016/j.chemosphere.2018.07.142

    CAS  Article  Google Scholar 

  43. Li K, Jacob DJ, Liao H, Shen L, Zhang Q, Bates KH (2019) Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc Natl Acad Sci 116(2):422–427. https://doi.org/10.1073/pnas.1812168116

    CAS  Article  Google Scholar 

  44. Yin H, Lu X, Sun Y, Li K, Gao M, Zheng B, Liu C (2021) Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology. Environ Res Lett 16(12):124069. https://doi.org/10.1088/1748-9326/ac3e22

    CAS  Article  Google Scholar 

  45. Wang W, der Vander AR, Ding J, van Weele M, Cheng T (2021) Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations. Atmos Chem Phys 21(9):7253–7269. https://doi.org/10.5194/acp-21-7253-2021

    CAS  Article  Google Scholar 

  46. The Chinese ministry of environmental and ecology, the volatile organic compound management attack program in 2020. www.mee.gov.cn/xxgk2018/xxgk/xxgk03/202006/t20200624_785827.html)

  47. Javed Z, Wang Y, Xie M, Tanvir A, Rehman A, Ji X, Xing C, Shakoor A, Liu C (2020) Investigating the impacts of the COVID-19 lockdown on trace gases using ground-based MAX-DOAS observations in Nanjing China. Remote Sens 12(23):3939

    Article  Google Scholar 

  48. Javed Z, Tanvir A, Wang Y, Waqas A, Xie M, Abbas A, Sandhu O, Liu C (2021) Quantifying the impacts of COVID-19 lockdown and spring festival on air quality over Yangtze river delta region. Atmosphere 12(6):735. https://doi.org/10.3390/atmos12060735

    CAS  Article  Google Scholar 

  49. Li L, Li Q, Huang L, Wang Q, Zhu A, Xu J, Liu Z, Li H, Shi L, Li R, Azari M (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. https://doi.org/10.1016/j.scitotenv.2020.139282

    CAS  Article  Google Scholar 

  50. Sun Y, Zhuang G, Tang A, Wang Y, An Z (2006) Chemical characteristics of PM2.5 and PM10 in haze-fog episodes in Beijing. Environ Sci Technol 40(10):3148–3155. https://doi.org/10.1021/es051533g

    CAS  Article  Google Scholar 

  51. Xiao ZM, Zhang YF, Hong SM, Bi XH, Jiao L, Feng YC, Wang YQ (2011) Estimation of the main factors influencing haze, based on a long-term monitoring campaign in Hangzhou China. Aerosol Air Qual Res 11(7):873–882. https://doi.org/10.4209/aaqr.2011.04.0052

    CAS  Article  Google Scholar 

  52. Lv M, Li Z, Jiang Q, Chen T, Wang Y, Hu A, Cribb M, Cai A (2021) Contrasting trends of surface PM25, O3, and NO2 and their relationships with meteorological parameters in typical coastal and inland cities in the Yangtze River Delta. Int J Environ Res Pub Health 18(23):12471. https://doi.org/10.3390/ijerph182312471

    CAS  Article  Google Scholar 

  53. Tanvir A, Javed Z, Jian Z, Zhang S, Bilal M, Xue R, Wang S, Bin Z (2021) Ground-based MAX-DOAS observations of tropospheric NO2 and HCHO during COVID-19 lockdown and spring festival over Shanghai China. Remote Sens 13(3):488. https://doi.org/10.3390/rs13030488

    Article  Google Scholar 

  54. Yue X, Unger N, Harper K, Xia X, Liao H, Zhu T, Xiao J, Feng Z, Li J (2017) Ozone and haze pollution weakens net primary productivity in China. Atmos Chem Phys 17(9):6073–6089. https://doi.org/10.5194/acp-17-6073-2017

    CAS  Article  Google Scholar 

  55. Ma S, Xiao Z, Zhang Y, Wang L, Shao M (2020) Assessment of meteorological impact and emergency plan for a heavy haze pollution episode in a core city of the North China Plain. Aerosol Air Qual Res 20(1):26–42. https://doi.org/10.4209/aaqr.2019.08.0392

    Article  Google Scholar 

  56. Zhang H, Wang Y, Hu J, Ying Q, Hu XM (2015) Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ Res 140:242–254. https://doi.org/10.1016/j.envres.2015.04.004

    CAS  Article  Google Scholar 

  57. Chen H, Zhuang B, Liu J, Wang T, Li S, Xie M, Li M, Chen P, Zhao M (2019) Characteristics of ozone and particles in the near-surface atmosphere in the urban area of the Yangtze River Delta China. Atmos Chem Phys 19(7):4153–4175. https://doi.org/10.5194/acp-19-4153-2019

    CAS  Article  Google Scholar 

  58. Hama SM, Kumar P, Harrison RM, Bloss WJ, Khare M, Mishra S, Namdeo A, Sokhi R, Goodman P, Sharma C (2020) Four-year assessment of ambient particulate matter and trace gases in the Delhi-NCR region of India. Sustain Cities Soc 54:102003. https://doi.org/10.1016/j.scs.2019.102003

    Article  Google Scholar 

  59. Zhao C, Wang Y, Shi X, Zhang D, Wang C, Jiang JH, Zhang Q, Fan H (2019) Estimating the contribution of local primary emissions to particulate pollution using high-density station observations. J Geophys Res Atmos 124(3):1648–1661. https://doi.org/10.1029/2018JD028888

    CAS  Article  Google Scholar 

  60. Zhang YL, Cao F (2015) Fine particulate matter (PM 25) in China at a city level. Sci Rep 5(1):1–12. https://doi.org/10.1038/srep14884

    CAS  Article  Google Scholar 

  61. Yang G, Liu Y, Li X (2020) Spatiotemporal distribution of ground-level ozone in China at a city level. Sci Rep 10(1):1–12. https://doi.org/10.1038/s41598-020-64111-3

    CAS  Article  Google Scholar 

  62. Zhao H, Chen K, Liu Z, Zhang Y, Shao T, Zhang H (2021) Coordinated control of PM2.5 and O3 is urgently needed in China after implementation of the “Air pollution prevention and control action plan.” Chemosphere 270:9441. https://doi.org/10.1016/j.chemosphere.2020.129441

    CAS  Article  Google Scholar 

  63. Chen Z, Zhuang Y, Xie X, Chen D, Cheng N, Yang L, Li R (2019) Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing During 2006–2016. Environ Pollut 245:29–37. https://doi.org/10.1016/j.envpol.2018.10.117

    CAS  Article  Google Scholar 

  64. Zhang G, Xu H, Qi B, Du R, Gui K, Wang H, Jiang W, Liang L, Xu W (2018) Characterization of atmospheric trace gases and particulate matter in Hangzhou China. Atmos Chem Phys 18(3):1705–1728. https://doi.org/10.5194/acp-17-6073-2017

    CAS  Article  Google Scholar 

  65. Niu Z, Hu T, Kong L, Zhang W, Rao P, Ge D, Zhou M, Duan Y (2021) Air-pollutant mass concentration changes during COVID-19 pandemic in Shanghai, China. Air Qual Atmos Health 14(4):523–532

    CAS  Article  Google Scholar 

  66. Li K, Ni R, Jiang T, Tian Y, Zhang X, Li C, Xie C (2022) The regional impact of the COVID-19 lockdown on the air quality in Ji’nan China. Scient Rep 12(1):1–12

    Article  Google Scholar 

  67. Zhang L, An J, Liu M, Li Z, Liu Y, Tao L, Liu X, Zhang F, Zheng D, Gao Q, Guo X (2020) Spatiotemporal variations and influencing factors of PM25 concentrations in Beijing China. Environ Pollut 262:4276. https://doi.org/10.1016/j.envpol.2020.114276

    CAS  Article  Google Scholar 

  68. Mandal J, Chanda A, Samanta S (2022) Air pollution in three megacities of India during the Diwali festival amidst COVID-19 pandemic. Sustain Cities Soc 76:103504. https://doi.org/10.1016/j.scs.2021.103504

    Article  Google Scholar 

  69. Zhou B, Yu L, Zhong S, Bian X (2018) The spatiotemporal inhomogeneity of pollutant concentrations and its dependence on regional weather conditions in a coastal city of China. Environ Monit Assess 190(5):1–17. https://doi.org/10.1007/s10661-018-6623-5

    CAS  Article  Google Scholar 

  70. Mhawish A, Banerjee T, Sorek-Hamer M, Bilal M, Lyapustin AI, Chatfield R, Broday DM (2020) Estimation of high-resolution PM25 over the Indo-Gangetic plain by fusion of satellite data, meteorology, and land use variables. Environ Sci Technol 54(13):7891–7900. https://doi.org/10.1021/acs.est.0c01769

    CAS  Article  Google Scholar 

Download references

Acknowledgements

All authors are highly obliged towards China National Environmental Monitoring Center for Providing the datasets used in this study. The authors also acknowledge the the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT air parcel back trajectories (https://www.ready.noaa.gov) used in this publication.

Funding

This study was supported by the Jiangsu Funding Program for Excellent Postdoctoral Talent (Grant No. 2022ZB651), National Natural Science Foundation of China [Grant No. 32071521], the Scientific Research Foundation for Senior Talent of Jiangsu University, China [Grant No. 20JDG067], the Jiangsu Province “Double Innovation PhD” Grant, Special Project of Jiangsu Distinguished Professor [R2018T22], and the Startup Foundation for Introduction Talent of NUIST [2017r107].

Author information

Authors and Affiliations

Authors

Contributions

ZJ: conceptualization, methodology, formal analysis, data curation, visualization, writing—original draft, writing—review and editing. XZ: conceptualization, supervision, funding acquisition, writing—review and editing. MB: writing—review and editing, supervision, data curation, conceptualization, funding acquisition. ZQ: methodology, writing—review and editing. GL: data curation, visualization. OS: formal analysis, data curation. KM: formal analysis, writing—review and editing. YW and Md. AA: data curation. CL and YW: writing—review and editing. DD: writing—review and editing, project administration. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Daolin Du or Xiaojun Zheng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Availability of data and material

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. The spatial position of National monitoring stations over the studied cities of YRD region. Table S2. Criteria air pollutants in Hangzhou during the study period (2018-2021). Table S3. Criteria air pollutants in Nanjing during the study period (2018-2021). Table S4. Criteria air pollutants in Hefei during the study period (2018–2021). Table S5. Criteria air pollutants in Shanghai during the study period (2018–2021). Table S6. The standards for annual mean concentration (µg/m3) of different pollutants. Table S7. The relative (%) change in concentration of trace gases and particulate matter from 2019-2020. Table S8. Spring Festival during different Years in China. Table S9. Total variance and rotated component matrix of PCA of pollutants concentrations over Nanjing. Table S10. Total variance and rotated component matrix of PCA of pollutants concentrations over Hangzhou. Table S11. Total variance and rotated component matrix of PCA of pollutants concentrations over Shanghai. Table S12. Total variance and rotated component matrix of PCA of pollutants concentrations over Hefei. Figure S1. The inter- annual variation of NO2, O3, SO2, PM2.5 and PM10 in Hangzhou, Nanjing, Hefei and Shanghai during 2018-2021excluding COVID-19 lockdown days (24th of January–31st of March) from all years. Figure S2. PSCF Analysis based on NO2 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S3. PSCF Analysis based on SO2 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S4. PSCF Analysis based on O3 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S5. PSCF Analysis based on PM2.5 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S6. PSCF Analysis based on PM10 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S7. PSCF Analysis based on NO2 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S8. PSCF Analysis based on SO2 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S9. PSCF Analysis based on O3 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S10. PSCF Analysis based on PM2.5 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S11. PSCF Analysis based on PM10 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S12. PSCF Analysis based on NO2 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S13. PSCF Analysis based on SO2 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S14. PSCF Analysis based on O3 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S15. PSCF Analysis based on PM2.5 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S16. PSCF Analysis based on PM10 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S17. Different regions of Peoples republic of China.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Javed, Z., Bilal, M., Qiu, Z. et al. Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology. Environ Sci Eur 34, 86 (2022). https://doi.org/10.1186/s12302-022-00668-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12302-022-00668-2

Keywords

  • Yangtze River Delta region
  • Ozone
  • Particulate matter
  • China’s clean air action plan
  • TROPOMI
  • National Ambient Air Quality Standards