Article | Open | Published:

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Abstract

Fine particulate matter (PM2.5) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM2.5. To characterize these parameters and mechanisms, the 5-year PM2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m−3. Evident temporal variations showed high PM2.5 level in winter and low in summer. Spatially, PM2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.

Introduction

Air pollution is a worldwide environmental issue which can lead to significant ecological and environmental effects and threaten human health, especially the atmospheric fine particulate matters (PM2.5) in many Asian cities1,2,3,4. Typically in China, associated with the rapid industrialization and urbanization and massive energy consumption, air quality was deteriorated5,6 and haze pollution has frequently occurred7,8. In 2016, 75.1% cities in China exceeded the annual ambient air quality guidelines, and PM2.5 was the primary pollutant during most pollution days9. Specifically, the population-weighted mean PM2.5 in Chinese cities was 61 μg m−3, three times higher than the global mean10. Therefore, air pollution control polices and measures have also been gradually emphasized and strengthened, for which understanding the spatial and temporal distribution of air pollutants and related sources was the key step11,12,13. However, the PM2.5 distribution was significantly influenced by local terrain features, meteorological conditions, city characteristics and economic levels, local emission sources and regional pollution transport14,15,16,17,18. For instance, the PM2.5 concentrations in 20 monitoring sites of California, USA change daily and such change was cyclical and changed with the season in the corresponding cycle19. Therefore, it is desired to study the large-scale and long-term spatio-temporal distribution of PM2.5 and corresponding mechanisms. Nowadays, there were some high-resolution air pollution estimation methods, including interpolation method20 and the satellite top-down approach based on ground-based fixed-site air pollution monitoring networks21,22,23.

In this study, the 5-year high-resolution distributions of PM2.5 concentrations in a province with 13 cities in eastern China were investigated with air pollution sources and land uses by Geographic Information System (GIS) and backward trajectory clustering analysis. The main objectives were: (1) to explore the spatial and temporal distribution characteristics of PM2.5 in different geographical areas and under varied environmental managements; and (2) to illuminate the influence mechanisms of air pollution source, economic, topographic, and meteorological factors on PM2.5 pollution patterns.

Results and Discussions

Spatio-temporal distributions of the 5-year provincial air PM2.5 pollution

Intra-annual and inter-annual variations of PM2.5 in the overall province

The large-scale and long-term PM2.5 pollution was significant in Jiangsu. Compared with the Chinese Ambient Air Quality Standards (CAAQS) (Table A1), the average annual PM2.5 concentration of each city during 2013–2017 exceeded the Grade II guideline (Fig. A1). Intra-annually, the PM2.5 levels showed significant seasonal variations (Table 1), which was a U-shaped pattern of high in winter and low in summer24 (Fig. 1). According to the proportions of days with different PM2.5 levels (Fig. A2e), there were 57 days in 2017 exceeded the national Grade II standard, up to 42 days of which were in winter. The PM2.5 concentrations in winter and summer of 2017 were 72.5 and 32.3 μg m−3, respectively (Fig. 2). These seasonal phenomena were attributed to pollution sources and meteorological conditions. In winter, anthropogenic emissions related to heating demand were increased, and a combination of persistent temperature inversions and low mixed boundary layer was unfavorable to the atmospheric pollutant dispersion25,26,27,28. While in summer, influenced by the monsoon climate, the precipitation was high and could significantly reduce the particulates in atmosphere, and the prevailing east wind and the clean air from ocean has also dilution effects on the air pollution in Jiangsu province.

Table 1 Significant differences of PM2.5 levels among different seasons in 2013–2017.
Figure 1
figure1

Daily and annual variations of PM2.5 in Jiangsu province, China from 2013 to 2017.

Figure 2
figure2

Spatial distribution of PM2.5 concentrations in summer (a) and winter (b) of 2017 for Jiangsu province.

Inter-annually, the average annual concentration of the overall Jiangsu province has decreased significantly from 2013 to 2017 (Fig. 1), which was 71.8, 66.3, 57.7, 50.3, and 49.6 μg m−3, respectively. The proportion of pollution days has decreased simultaneously, that was 34.2%, 29.5%, 22%, 17.1%, and 15.6%, respectively. These air quality improvements were closely related to the environmental protection measures taken by the provincial government in recent years. It can be seen from Fig. 3 that the pollutants in various regions of Jiangsu Province have been reduced to varying degrees from 2013 to 2017, indicating that the government’s environmental protection measures were effective. As a key area for environmental governance, the pollution concentration of provincial capital Nanjing decreased significantly, while the air quality improvement in the northern Jiangsu was slight, which mainly due to the local heavy industry.

Figure 3
figure3

Differences of annual PM2.5 concentrations between 2013 (a) and 2017 (b) in the provincial distribution.

Spatial distribution of the provincial PM2.5

Using the average PM2.5 values at all 110 monitoring sites in 2013 and 2017, the Kriging interpolation analysis was performed to obtain the provincial simulated distribution map of PM2.5 concentrations (Fig. 3). In the whole studied area, the PM2.5 levels decreased gradually from west to east. According to the geographical location, urbanization level, population density, civilian car ownership, total GDP and per capita GDP, all cities of this province can be divided into 4 regions (Fig. 4), including western inland, eastern coastal, northern heavy industry and south developed regions. The industrial enterprises of XZ (Xuzhou) and HA (Huai’an) were dominated by heavy industries including mining, so they were divided into northern heavy industrial area. The specific features of each city with PM2.5 concentrations were showed in Table 2. The PM2.5 level was in the order of heavy industrial > inland > developed > coastal regions. It showed a pattern that the PM2.5 pollution in densely populated and developed areas with more emission sources was higher than those with better vegetation coverage and self-purification capacity28,29. Besides the effects of urbanization, the industry and geographic location also showed significant impacts. Figure 2 showed that the pollution level in developed areas was lower than that in the underdeveloped northern regions with heavy industrial pollution emissions. Impacted by the ocean atmospheric transmission, the pollution in coastal areas with stronger self-purification capabilities was lower than that in the inland regions.

Figure 4
figure4

Locations of Jiangsu province in China and the 110 scattered monitored sites (solid triangles) covering all 13 cities.

Table 2 Regional divisions of Jiangsu province and the corresponding PM2.5 concentrations in 2017.

Because the economic development between southern and northern regions of Jiangsu province was seriously unbalanced (GDP in Table 2), to balance the huge gap, developments of heavy industry in the northern region led by machinery, electricity, chemistry, building materials and energy were vigorously promoted by governments. For instance, of the 221 key enterprises with exhaust emissions in the 13 cities of Jiangsu Province in 2018, there were up to 30 in a north heavy industry city (XZ). Moreover, in order to accelerate the process of urbanization in the northern region, the municipal construction was in the peak period, corresponding dusts were substantial and aggravated air quality.

Of course, compared with the satellite-based top-down approach based ground fixed-site air pollution monitoring networks, Kriging interpolation method has limitations. Remote sensing provides the detailed information in space and time not only from accessible areas but also from inaccessible areas30. Satellite-retrieved aerosol optical depth has been increasingly utilized for the mapping of fine particulate matter concentrations23,31. Due to the lack of actual landform data, the spatial distribution of PM2.5 was mainly simulated by interpolation. The future further study about satellite mapping of fine particulates should be based on geographically weighted regression.

Effects of source emissions on spatial and temporal PM2.5 distributions

The sources of urban PM2.5 were complex, including exhaust of motor vehicles, emissions of power plants and industrial boilers, combustions of household coal and biomass, open waste incineration and the dusts32,33,34. Clarifying the contributions of main pollutant sources would be beneficial to the environmental managements and air pollution control measures.

Table 3 showed the detailed air pollutant emissions of Jiangsu from 2011 to 2015, that the emission of soot was increased by 127 thousand tons, while SO2 and NOx reduced by 219 thousand and 468 thousand tons. Because NOx and SO2 were the two key air pollutants for producing secondary PM2.5, they were of great significance to the decrease of PM2.5 pollution in this study area35. According to the structure of exhaust emissions, the percentage of industrial SO2 and NOx emissions were decreased due to vigorously promoting clean energy and renovating coal fired boilers. In 2015, the total emissions of soot, NOx, and SO2 in Jiangsu province were 654, 1068, and 835 thousand tons, of which industrial emissions contributed 612, 754, and 795 thousand tons and accounted for 93.6%, 70.6%, and 95.2% of corresponding total emissions, respectively. In addition to industrial emissions, vehicle exhaust contributed significantly to NOx emissions, accounting for 28.6%. As to the regional distribution, the anthropogenic emissions of soot, SO2 and NOx at the heavy industrial city were higher than coastal city. To effectively control the urban air pollution, reductions of local coal, vehicular and industrial emissions, and the regional joint pollution prevention and control policies are necessary.

Table 3 Annual detail emission information of air pollution sources in Jiangsu from 2011 to 2015.

Regional air pollutant transport

Besides local pollution sources, the typical monsoon climate plays an important role in the long distance transport of aerosols18,36. The typical life cycle of aerosols in the atmosphere was 3–10 days. To explore the impact of inter-regional air transport on pollutant concentrations, 72 h backward trajectory cluster and PSCF analysis were conducted for four typical regions in Jiangsu province.

Figure 5 compared the cluster analyses of them in summer and winter. Air pollution was found to be affected by the monsoon climate. In summer, the clustering percentage of air transport from nearby regions accounted for 65.9%, 59.1%, 73.9%, and 63.6%, respectively. Long-range air mass transport clusters account for a less proportion, and the directions were mainly from the southeast coast and the southern region. In winter, the air masses mainly come from the inland areas of the northwest, representing long-distance air mass transport, the transmission distance and vertical span were significantly larger than those in summer. During the process of winter air pollutants transmission, the study areas were impacted by the northwest polluted air masses originated from Inner Mongolia and along Shanxi, Hebei, Henan, Anhui and Shandong provinces37. The trajectory of the airflow was faster, indicated that the air mass rises to the free troposphere at the source, and moved to the downstream at a faster speed under the action of the wind speed. After reaching the research area, it entered the boundary layer after vertical mixing and sinking. In addition, pollution level was influenced by the monsoon climate and Siberia high pressure, the downdraft and stably stratified atmosphere help to increase PM2.5 pollution level in these cities38,39. These results of backward trajectory clustering analysis implied the significant effects of regional dispersion and meteorological conditions on regional air quality.

Figure 5
figure5

The 72-h backward trajectories clustering for four representative cities (XZ, northern heavy industrial city; NT, eastern coastal city; TZ, inland city; and NJ, developed city) during summer and winter.

PSCF analysis was applied to explore the potential source region distribution of PM2.5. A PSCF analysis of PM2.5 combined with atmospheric data based on the backward trajectory for four representative cities in 2017 was showed in Fig. 6. The spatial patterns were similar, and the source areas with higher contribution rate were mainly distributed in neighboring provinces such as Anhui, Shandong, Zhejiang provinces, etc., indicating the significance of pollution in the nearby area37. In addition, due to the long-range air mass transport, the contribution values of Inner Mongolia, Hebei, Gansu, Ningxia, Guangdong and Fujian provinces reached 0.5–0.8, which increased the pollutant concentrations in the study area40. It should be noted that the PSCF analysis did not estimate the spatial distribution of all sources. The high potential contribution source area may coincide with the regional emission sources, but the low contribution value does not necessarily indicate low emissions in the area.

Figure 6
figure6

The 72-h backward trajectories clustering and PSCF for four representative cities (XZ, northern heavy industrial city; NT, eastern coastal city; TZ, inland city; and NJ, developed city) during 2017 based on PM2.5 concentration.

Conclusions

Air PM2.5 pollution was still a significant environmental issue with varied spatial and temporal distributions. Typically in eastern China, 5-year data of 110 monitoring sites from all the 13 cities of a province were compared by statistics and illustrated by GIS. According to the intra-annual and inter-annual variations of PM2.5 in large-scale and long-term, the PM2.5 pollution contributed by industrial and traffic emissions was still obvious, but the level has reduced significantly in recent 5 years owing to the strengthened pollution control measures. PM2.5 concentrations were significantly higher in winter than summer, and were in an order of heavy industrial area > inland area > developed area > coastal area, due to different emission sources and meteorological conditions. Besides local primary emissions and secondary aerosol formation, the backward trajectory clustering analysis and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, it was mainly affected by the updraft from near region, while in winter, the long-distance transport from the northwest had a significant impact on air pollution. Moreover, the nearby provinces were important potential pollution sources.

Data and Methods

Study areas

Jiangsu Province is an important part of the developed Yangtze River Delta located along the eastern coast of China (Fig. 4). It covers an area of 103 thousand km2 and has a population of 8.03 million. With Yellow Sea to its east, Jiangsu adjoins Anhui and Shandong provinces in the west and north respectively, with Zhejiang province and Shanghai city as neighbors in the southeast. It has a large area of plain as typical topography and dotted with two top-5 largest lakes in China. Situated in a transition belt from a subtropical to temperate zone, this province has a typical monsoon climate. Approximately demarcated by the Huai River, the south is subtropical monsoon climate and the north is warm moist monsoon climate. Generally, it is mild with moderate rainfall and four distinct seasons. Its economy is dominated by industrial activities with a GDP of 859 billion yuan (top 2 in China), and coal is the main energy source with an annual consumption of 258 million tons. Moreover, the number of motor vehicles reached 17.3 million in 2015, and 1232 polluting enterprises were monitored41. As an industry and transportation dense region, it was the highest PM2.5 emission province (0.28 million tons) in the year of 201442,43. Associated with the economic developments, the problem of air pollution has become a key environmental issue that regulatory policies and pollution control measures were implemented and strengthened by recent years44,45. Therefore, it provided an ideal area to study the profound characteristics of air pollution in a large-space scale and long-term scale.

To characterize the air PM2.5 pollution patterns and mechanisms, this study analyzed air pollution and related factor data from all the 13 cities of Jiangsu Province, including Nanjing (NJ), Suzhou (SZ), Wuxi (WX), Changzhou (CZ), Nantong (NT), Xuzhou (XZ), and Lianyungang (LYG), Zhenjiang (ZJ), Huai’an (HA), Yancheng (YC), Taizhou (TZ), Suqian (SQ) and Yangzhou (YZ). Figure 4 showed the location of study area and air monitoring sites.

Data sources

Based on the monitoring site information provided by the China Environmental Protection Monitoring Bureau, 110 national air quality monitoring stations in Jiangsu Province were used for spatial study (Table A2). Daily average of 24 h PM2.5 concentration and real-time concentration data (i.e., hourly average) from 18 January, 2013 to 31 December, 2017 were collected for intra-annual and inter-annual variation analyses. The meteorological data used for backward trajectory analysis was from the simultaneous global data assimilation system (GDAS) provided by the National Center for Environmental Prediction (NCEP) of USA, including temperature, relative humidity, surface precipitation, horizontal and vertical wind speeds. Data of pollution sources, emissions and economic development were obtained from the Statistical Yearbook of Jiangsu Province41. The numbers of companies with emissions were from the Self-Monitoring Information Release Platform of Focused Enterprises in Jiangsu Province (http://218.94.78.61:8080/newPub/web/home.htm).

The GIS spatial analysis by Kriging interpolation method

The GIS is a comprehensive subject combining geography and cartography, remote sensing and computer science. GIS technology integrates the unique visual effect and geo-analysis function of maps with general database operations46, and has been widely applied in environmental science47,48,49. Interpolation method is an important content of spatial statistical analysis in GIS. Among the various interpolation methods, the Kriging interpolation is flexible and can fully utilize the data exploratory analysis tools to improve the efficiency of spatial analysis effectively20. It can use the statistical characteristics of known samples to quantify the spatial autocorrelation between measurement points, highlighting the overall distribution trend, increasing the data fidelity, and has the highest prediction accuracy for normal data50. In this study, the Kriging interpolation method was applied to explore the spatial distribution of PM2.5 concentration data of 110 stations during 2013 and 2017, thus reveal the PM2.5 pollution patterns in the overall study area intuitively.

Long-range air mass transportation by trajectory calculation

As a long-range source analysis technique based on observational difference or simulated meteorological field51,52, the 72-h backward air trajectories clustering and PSCF analysis were calculated using the HYSPLIT model (http://ready.arl.noaa.gov/HYSPLIT.php). The meteorological data as 1° × 1°GDAS data, and the trajectory calculation points were NJ (Nanjing, 32.06N, 118.78E, 1000 m), NT (Nantong, 31.99N, 120.88E, 1000 m), XZ (Xuzhou, 34.25N, 117.21E, 1000 m) and TZ (Taizhou, 32.49N, 119.90E, 1000 m) were used in the trajectory calculation, and two trajectory were calculated every day at 0:00 and 12:00 (UTC), starting at: 2017.1.1 0:00 to 2018.1.1 0:00.

PSCF was a trajectory-based gridded statistical analysis method that can obtain the spatial distribution of pollution sources in a semi-quantitative manner40. The basic concept of PSCF was to combine the air mass trajectory with the atmospheric component data to generate a conditional probability in a given region which was divided into i*j small grids. The conditional probability was combined with the air mass trajectory to describe the possible spatial distribution of geographic source locations. The PSCFij value of the ij grid point was

$$PSC{F}_{ij=\frac{{m}_{ij}}{{n}_{ij}}=\frac{thenumberoftrajectoryendpoints{\rm{reaching}}{\rm{the}}{\rm{pollution}}{\rm{threshold}}{\rm{in}}{\rm{the}}{\rm{ij}}{\rm{grid}}{\rm{points}}}{thenumberofendpointsintheijgridpoints}}$$

In order to reduce the uncertainty caused by the small nij value, the weight function wij was introduced, and when the number of trajectory endpoints in the ij grid points was less than about three times the average number of trajectory endpoints, wij decreased the PSCFij value. The PSCF method was widely used in atmospheric chemistry research, such as analysis of particulate matter in the atmosphere and potential source distribution of inorganic components in the particles. It was generally considered that when PSCF ≤ 0.5, it was a low contribution source region, and conversely, a high contribution source region. The area analyzed in this paper was located in 85–135°E, 15–60°N, and the grid resolution was 0.5° × 0.5°, Weight function

$${\rm{wij}}=\{\begin{array}{cc}1.00\, & 12 < {n}_{ij}\\ 0.70\, & 6 < {n}_{ij}\le 12\\ 0.42\, & 4 < {n}_{ij}\le \,6\\ 0.17\, & {n}_{ij}\le 4,\end{array}$$

the pollution threshold was set to the primary standard of PM2.5 concentration (35 μg.m−3).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Goto, D. et al. Estimation of excess mortality due to long-term exposure to PM2.5 in Japan using a high-resolution model for present and future scenarios. Atmos. Environ. 140, 320–332 (2016).

  2. 2.

    Katanoda, K. et al. An association between long-term exposure to ambient air pollution and mortality from lung cancer and respiratory diseases in Japan. J. Epidemiol. 21, 132–143 (2011).

  3. 3.

    Yorifuji, T. et al. Health impact assessment of PM10 and PM2.5 in 27 southeast and east Asian cities. J. Occup. Environ. Med. 57, 751 (2015).

  4. 4.

    Wong, C. M. et al. Satellite-based estimates of long-term exposure to fine particles and association with mortality in elderly Hong Kong residents. Environ. Health. Persp. 123, 1167–1172 (2015).

  5. 5.

    Cox, P. M. et al. Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature. 553, 319–322 (2018).

  6. 6.

    Chen, Y. et al. Summer-winter differences of PM2.5 toxicity to human alveolar epithelial cells (A549) and the roles of transition metals. Ecotox. Environ. Safe. 165, 505–509 (2018).

  7. 7.

    Luo, X. S. et al. Effects of emission control and meteorological parameters on urban air quality showed by the 2014 Youth Olympic Games in China. Fresen. Environ. Bull. 26, 4798–4807 (2017).

  8. 8.

    Sun, L. et al. Impact of Land-Use and Land-Cover Change on urban air quality in representative cities of China. J. Atmos. Sol-terr. Phy. 142, 43–54 (2016).

  9. 9.

    MEEPRC (Ministry of Ecology and Environment of the People’s Republic of China), China environmental status bulletin, http://www.zhb.gov.cn/ (2016).

  10. 10.

    Zhang, Y. L. & Cao, F. Fine partic.ulate matter (PM 2.5) in China at a city level. Sci. Rep. 5, 14884 (2015).

  11. 11.

    Baudic, A. et al. Seasonal variability and source apportionment of volatile organic compounds (VOCs) in the Paris megacity (France). Atmos. Chem. Phys. 16, 11961–11989 (2016).

  12. 12.

    Zhao, S. et al. Decadal variability in the occurrence of wintertime haze in central eastern China tied to the Pacific Decadal Oscillation. Sci. Rep. 6, 27424 (2016).

  13. 13.

    Wang, H. J. & Chen, H. P. Understanding the recent trend of haze pollution in eastern China: roles of climate change. Atmos. Chem. Phys. 16, 4205–4211 (2016).

  14. 14.

    Huang, R. J. et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature. 514, 218–222 (2014).

  15. 15.

    Křůmal, K. et al. Seasonal variations of monosaccharide anhydrides in PM1 and PM2.5 aerosol in urban areas. Atmos. Environ. 44, 5148–5155 (2010).

  16. 16.

    Li, L. et al. Characteristics and source distribution of air pollution in winter in Qingdao, eastern China. Environ. Pollut. 224, 44–53 (2017).

  17. 17.

    Rinehart, L. R. et al. Spatial distribution of PM2.5 associated organic compounds in central California. Atmos. Environ. 40, 290–303 (2006).

  18. 18.

    He, J. et al. Air pollution characteristics and their relation to meteorological conditions during 2014-2015 in major Chinese cities. Environ. Pollut. 223, 484–496 (2017).

  19. 19.

    Friberg, M. D. et al. Method for fusing observational data and chemical transport model simulations to estimate spatiotemporally resolved ambient air pollution. Environ. Sci. Technol. 50, 3695–3705 (2016).

  20. 20.

    Rullière, D. et al. Nested Kriging predictions for datasets with a large number of observations. Stat. Comput. 4, 849–867 (2017).

  21. 21.

    Zou, B. et al. High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression. IEEE. Geosci. Remote. S. 13, 495–499 (2016).

  22. 22.

    Xu, S. et al. A hybrid Grey-Markov/LUR model for PM10 concentration prediction under future urban scenarios. Atmos. Environ 187, 401–409 (2018).

  23. 23.

    Zou, B. et al. Air pollution intervention and life-saving effect in China. Environ. Int, 125, 529–541 (2019). 

  24. 24.

    Sun, X. et al. Spatio-temporal characteristics of air pollution in Nanjing during 2013 to 2016 under the pollution control and meteorological factors. Journal. of. Earth. Environment. 8, 506–515 (2017).

  25. 25.

    Chai, F. H. et al. Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China. Environ. Sci. 26, 75–82 (2014).

  26. 26.

    Li, Y. et al. Ambient temperature enhanced acute cardiovascular-respiratory mortality effects of PM2.5, in Beijing, China. Int. J. Biometeorol. 59, 1761–1770 (2015).

  27. 27.

    Liu, Y. et al. A statistical model to evaluate the effectiveness of PM2.5 emissions control during the Beijing 2008 Olympic Games. Environ. Int. 44, 100–105 (2012).

  28. 28.

    Sun, Y. L. et al. Aerosol composition, sources and processes during wintertime in Beijing, China. Atmos. Chem. Phy. Discuss. 13, 4577–4592 (2013).

  29. 29.

    Reizer, M. & Juda-Rezler, K. Explaining the high PM10 concentrations observed in Polish urban areas. Air. Qual. Atmos. Heal. 9, 517–531 (2016).

  30. 30.

    Sharma, B. et al. Application of Remote Sensing and GIS in Hydrological Studies in India: An Overview. Natl. Acad. Sci. Lett 38, 1–8 (2015).

  31. 31.

    Fang, X. et al. Satellite-based ground PM2.5 estimation using timely structure adaptive modeling. Remote. Sens. Environ 186, 152–163 (2016).

  32. 32.

    Xie, J. W. et al. Seasonal disparities in airborne bacteria and associated antibiotic resistance genes in PM2.5 between urban and rural sites. Environ. Sci. Technol. Let. 5, 74–79 (2018).

  33. 33.

    Juneng, L. et al. Spatio-temporal characteristics of PM10 concentration across Malaysia. Atmos. Environ. 43, 4584–4594 (2009).

  34. 34.

    Liu, Z. et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature. 524, 335–338 (2015).

  35. 35.

    He, H. et al. Precipitable silver compound catalysts for the selective catalytic reduction of NOx by ethanol. Appl. Catal. A-Gen. 375, 258–264 (2010).

  36. 36.

    Khanum, F. et al. Characterization of five-year observation data of fine particulate matter in the metropolitan area of Lahore. Air. Qual. Atmos. Heal. 10, 725–736 (2017).

  37. 37.

    Wang, L. T. et al. The2013 severe haze over southern Hebei, China: model evaluation, source apportionment, and policy implications. Atmos. Chem. Phy. Discuss. 13, 28395–28451 (2014).

  38. 38.

    Xu, H. et al. Particulate matter mass and chemical component concentrations over four Chinese cities along the western Pacific coast. Environ. Sci. Pollut. Res. Int. 22, 1940–53 (2015).

  39. 39.

    Zhu, S. et al. Impact of the air mass trajectories on PM2.5 concentrations and distribution in the Yangtze River Delta in December 2015. Acta. Scientiae. Circumstantiae. 36, 4285–4294 (2016).

  40. 40.

    Brattich, E. et al. Influence of stratospheric air masses on radiotracers and ozone over the central Mediterranean. J Geophys Res. 13, 7164–7182 (2017).

  41. 41.

    BSJP (Bureau of Statistics of Jiangsu Province) Publication of the statistical bulletin on economic and social development of Jiangsu Province in 2017, http://tj.jiangsu.gov.cn/ (2018).

  42. 42.

    Jin, Q. et al. Spatio-temporal variations of PM2.5 emission in China from 2005 to 2014. Chemosphere. 183, 429–436 (2017).

  43. 43.

    Yang, Y. & Christakos, G. Spatiotemporal characterization of ambient PM2.5 concentrations in Shandong province (China). Environ. Sci. Technol. 49, 13431–13438 (2015).

  44. 44.

    Luo, X. et al. Pulmonary bioaccessibility of trace metals in PM2.5 from different megacities simulated by lung fluid extraction and DGT method. Chemosphere. 218, 915–921 (2019).

  45. 45.

    Luo, X. S. et al. Spatial-temporal variations, sources, and transport of airborne inhalable metals (PM10) in urban and rural areas of northern China. Atmos. Chem. Phy. Discuss. 14, 13133–13165 (2014).

  46. 46.

    Pearce, J. L. & Naeher, L. P. Characterizing the spatiotemporal variability of PM2.5 in Cusco, Peru using Kriging with external drift. Atmos. Environ. 43, 2060–2069 (2009).

  47. 47.

    Nowak, M. & Pędziwiatr, K. Modeling potential tree belt functions in rural landscapes using a new GIS tool. J. Environ. Manage. 217, 315–326 (2018).

  48. 48.

    Liu, S. et al. Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis. Sci. Total. Environ. 624, 396–406 (2017).

  49. 49.

    Anlauf, R. et al. Coupling HYDRUS-1D with ArcGIS to estimate pesticide accumulation and leaching risk on a regional basis. J. Environ. Manage. 217, 980–990 (2018).

  50. 50.

    Xiao, M. et al. Extended Co-Kriging interpolation method based on multi-fidelity data. Appl. Math. Comput. 323, 120–131 (2018).

  51. 51.

    Jeong, U. et al. Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: a PSCF model approach. J. Environ. Monit. 13, 1905–1918 (2011).

  52. 52.

    Xu, W. Y. et al. A new approach to estimate pollutant emissions based on trajectory modeling and its application in the North China Plain. Atmos. Environ. 11, 31175–31183 (2011).

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of China (NSFC 41471418, 91543205), the Distinguished Talents of Six Domains in Jiangsu Province (2014-NY-016), and the Startup Foundation for Introducing Talent of NUIST (2017r001).

Author information

The data were analyzed by X.S.; X.S. conducted the literature survey, drafted the main manuscript text, and prepared the Tables and Figures. X.L. designed this study and made major revisions to the manuscript text. J.X. and X.L. reviewed and edited the draft manuscript for scientific content. In addition, X.S. and X.L. performed the overall internal review (with assistance from the other authors, Z.Z., Y.C., L.W., Q.C. and D.Z.). All the authors read and approved the final manuscript.

Competing Interests

The authors declare no competing interests.

Correspondence to Xiao-San Luo.

Supplementary information

Supplementary

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.