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Atmosphere 2019, 10(8), 461; https://doi.org/10.3390/atmos10080461

Article
Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016
1
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing 100190, China
4
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 12 July 2019 / Accepted: 8 August 2019 / Published: 12 August 2019

Abstract

:
China is experiencing severe PM2.5 (fine particles with a diameter of 2.5 μg or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM2.5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM2.5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM2.5 pollution, we find that Tarim Basin endures a high PM2.5 concentration, and this should have some relationship with oil exploration. The relationship between PM2.5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.
Keywords:
PM2.5 pollution; spatiotemporal trend analysis; energy consumption; pixel-based linear trend analysis

1. Introduction

Fine particles with a diameter of 2.5 μg or smaller (PM 2.5 ) contain toxic substances which affect the respiratory and circulatory systems. A meta-analysis conducted by Huang et al. indicated that there was a strong association between exposure to PM2.5 and lung cancer incidence and mortality [1]. PM 2.5 also leads to a sharp decrease in visibility, which indirectly affects economic activities [2]. Moreover, by reflecting or absorbing incoming solar radiations, particulate matter can also influence the climate [3]. In addition to weather conditions, human activities can play the most important factors for PM 2.5 [4,5,6]. Increasing populations, local economic growth, and urban expansion are the three main driving forces impacting PM 2.5 concentrations [7,8,9], and major sources include road traffic, dust, industry, biomass burning, coal combustion and so on [3,10,11]. In China, economic development is prioritized to reduce poverty, one of the global tasks on sustainable development, and is heavily dependent on energy-intensive industries [12]. As a result, China is experiencing a severe PM 2.5 pollution problem [13].
Understanding the spatial and temporal patterns in PM 2.5 concentrations in China can provide a foundation for government decisions. A lot of PM 2.5 concentrations in China have been studied at regional and national scales. Studies examining the spatiotemporal patterns of PM 2.5 from global scales found that the highest threat of PM 2.5 concentrations was located in Eastern China [13]. By conducting studies on the spatio-temporal distribution features of PM 2.5 , Luo et al. found that the concentration of PM 2.5 in China had increased while the spatial patterns from 1998 to 2012 were very similar, showing an increasing trend from west to east [14]. Yan et al. applied network analysis on PM 2.5 emission data to study spatial and temporal characteristics [11]. At regional levels, many studies have focused on heavily polluted cities such as Beijing [15,16,17,18], Shanghai [19], Shandong [20] and Xi’an [21] etc. Moreover, the major focus has been given to the relation between PM 2.5 and emission sources. Sun et al. conducted a study about the relationship between air pollution and the economic boom in China, and gave suggestions of adjusting energy and industrial structures [12]. However, up till now, little is known by reseacrhers about how the trend of PM 2.5 change is spatially distributed.
Knowing the PM 2.5 concentration trend for all specific areas can fill the gap of understanding pollution control effects across the country. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Change detection approaches, especially long time-series analysis, are often used to obtain such results. Hansen et al. used an ordinary least squares slope of the regression of annual loss versus year to derive trends in annual forest loss [22]. Vogelmann et al. describe the use of linear regression relationships on a pixel by pixel basis between time (x variable) and the vegetation index value (y variable) for selected pixels [23]. Unlike bi-temporal change detection, long time-series analysis can identify a large disturbance occurring in a single year from regional to national areas over time.
Based on annual PM 2.5 time series, linear regression can make better use of the temporal depth of the data to reconstruct PM 2.5 disturbance histories with annual resolution and to map trends, such as PM 2.5 rise and fall. Here, for the first time, we perform a pixel-based linear trend analysis on the recently available satellite-derived annual PM 2.5 data [24] to characterize spatio-temporal patterns of PM 2.5 concentration changes. Not only the spatial changes of the PM 2.5 concentration are discussed, but also temporal context of changes are considered. Moreover, we have studied the relationship between PM2.5 concentration and energy consumption and new suggestions on controlling coal consumption are reported in the discussion.

2. Materials and Methods

2.1. PM 2.5 Concentration Data

Satellite-retrieved data are very suitable for studying spatiotemporally continuous distribution characteristics of PM 2.5 concentrations. The PM 2.5 datasets we use in this paper were inversed by van Donkelaar et al. based on multiple satellite data (MISR, MODIS and SeaWiFS), simulation model (GEOS-Chem) and ground-based sun photometer (AERONET) observations. The resultant global 10-km resolution PM 2.5 estimates have a long time span, from 1999 to 2016, and have been effectively applied on national and regional scales [8,12,14,25,26]. The dataset can be downloaded from Battelle Memorial Institute and the Center for International Earth Science Information Network (CIESIN)/Columbia University (http://beta.sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod). Data for China are extracted from the global dataset using the open-source Geospatial Data Abstraction Library (GDAL).

2.2. Pixel Based Trend Analysis

The PM 2.5 concentration data are in in a geospatial raster format. Trend analysis of PM 2.5 concentration in China from 1999 to 2016 would form a large amount of calculation for personal computers. To facilitate raster data processing, all raster files are segmented based on a grid tessellation. The grid tessellation has the same coordinate system as the PM 2.5 data, and segments in each grid are 1000 × 1300 raster arrays. Then, pixel-based trend analysis of segments of all years at the same grid location are processed in a three-step procedure.
First, extract pixel values of all years at the same location.
Then, linear least-squares regression is calculated using scipy. Among it, x is the vector of years in order, y is a vector of corresponding PM 2.5 values for each year. Slope, intercept, rvalue, pvalue and stderr are calculated for each pixel location.
At last, results for each pixel size location are spatially aggregated into an array, and are stored as a raster file with coordinate system information.
After linear regression was calculated for segments in all grids, all segments of slope, intercept, rvalue, pvalue and stderr were recombined, respectively, to form complete raster data for the Chinese region. All maps in this paper are generated in ArcGIS10.2, URL: http://www.esrichina-bj.cn/softwareproduct/ArcGIS/.

3. Results and Discussion

3.1. Trend Analysis of PM 2.5 Concentrations during 1999–2016

For the 18-year study period, the PM 2.5 levels in China have increased by 48%, and area percentage with PM 2.5 < 10 μg/m 3 decreased from 56.4% to 39.4% (Figure 1). The annual average concentrations of PM 2.5 had the highest of 18.68 μg/m 3 in 2007 and the lowest of 11.48 μg/m 3 in 2000, showing a general increase in recent years.
The whole time period can be divided into three phases in light of the distinctive characteristics in the PM 2.5 concentration trend. The years 2007 and 2012 are the turning points. In the first phase (from 1999 to 2007, designated as Phase I), the annual average concentrations increased at an annual change of 7.10% per year. The severe particulate matter (PM) pollution increase problem is probably closely linked to rapid economic growth in China. In contrast, the second phase (from 2007 to 2012, named as Phase II), displayed a distinct decreasing concentration trend with a rate of −2.75%. In this period, the 29th Olympic Games were held in Beijing in 2008, and this may have led to some air pollution control measures to be implemented [27]. During the third phase (from 2012 to 2016, named as Phase III), the annual concentrations showed a fluctuation trend (Figure 1). As air pollution in China has become particularly serious, it caused a great deal of attention of both the government and local residents. On 29 February 2012, China issued a newly revised “Environmental Air Quality Standard”, which increased the monitoring index of fine particulate matter (PM 2.5 ). In addition, a nationwide monitoring network was established in January 2013.

3.2. Spatial Distribution Analysis of PM 2.5 Concentration Trend

In order to understand the spatial distribution of the temporal trend of PM 2.5 concentration across China, pixel-based ordinary least squares regression was performed, where the dependent variable y was the value of annual PM 2.5 , and independent variable in the model was the time. The slope of the regression function is represented in Figure 2.
Areas with positive values of slope are considered as contributing to PM 2.5 increase and they account for about 87.9% of the entire country. Among them, marked upward PM 2.5 concentration trends are evidenced in the eastern parts of the country, including the southern part of the Beijing–Tianjin–Hebei region, the western part of the Shandong province, and the Three Northeastern Provinces, which includes the Heilongjiang, Jilin and Liaoning provinces.
For the Beijing–Tianjin–Hebei region, the PM 2.5 concentrations of more than 45% of the area are larger than 35 μg/m 3 since 2001. It had experienced a great increase up to 2006 (Figure 3a). Heavy industries are the main PM 2.5 sources, and the Taihang mountains in the west of the Hebei province inhibit pollutant dispersion, leading to pollutant accumulation in this area [28]. Coupled with unfavorable topographic conditions and prevailing wind directions [16], this region is also affected by air masses from the Gobi desert approximately 400 km to the northwest, particularly in the spring [3].
The Shandong province has a more serious situation. Almost the entire province endured annual PM 2.5 concentrations larger than 35 μg/m 3 from 2002 to 2016 (Figure 3b). On one hand, local pollution sources and imperfect urban pollution control mechanisms lead to heavy air pollution; on the other hand, external pollution sources also become a cause of serious pollution in the western part of the Shandong Province. Most of the cities with the worst air quality in China are located in the Hebei Province [29]. Since the northern part of the Shandong province is bordered by the Hebei Province, the Shandong province is affected by pollution source transmission and diffusion [30].
The Three Northeastern Provinces, including Heilongjiang, Jilin and Liaoning provinces, are primarily highly dependent on heavy industries, such as iron and steel factories and cement production, which are typical sources for emissions of PM 2.5 [3]. In 2003, almost the entire region had PM 2.5 values greater than 10 μg/m 3 . The mean value of PM 2.5 concentration in this region peaked in 2015, with more than 70% of the total area experiencing PM 2.5 larger than 25 μg/m 3 , the limit recommended by WHO (Figure 3c). Though heavy industries have dropped since the reform era in this area, the municipal coal fired heating system is still used [31]. Moreover, since China’s implementation of the exhaust gas purification equipment, investment in waste gas treatment and disposal capacity are not good enough, there is still a lot of dust generated [32].
As areas with negative values of slope had low values of t-statistics, annual PM 2.5 concentration values were plotted against time for the five main regions. Figure 4 illustrates the results, from which we can notice that all these regions had much lower annual PM 2.5 concentrations than national average values. In addition, there is a slightly decreasing pattern in the central region, which is mainly located in the southern Loess Plateau. This phenomenon may have a relationship with the effort known as the Loess Plateau Watershed Rehabilitation Project, which was launched in 1994.
Another phenomenon to be stressed is that the Tarim basin may experience dust events. Figure 5 illustrates the PM 2.5 concentration in 2016 for the Tarim region, where areas with PM 2.5 concentrations larger than 25 μg/m 3 are represented with shades of yellow. The high PM 2.5 concentration in the Tarim basin may be mainly caused by oil exploitation processes. All stages of oil management beginning from exploration and production and ending with the use of petroleum products are accompanied by strong air pollution problems, and a large volume of dust including PM 2.5 is produced in the oil exploration working site, shown in Figure 5. In addition, located at the heart of the Eurasian Continent, the Tarim Basin is one of the driest areas on earth [33]. As a consequence, it is easy to produce dust during transportation. To develop western provinces in a sustainable way, the government can use certain incentives to speed up technological breakthroughs and innovations to study how to reduce emissions.

3.3. Relationship with Energy Consumption

We all know that human activity can be a main reason for generating PM 2.5 . In general, high PM 2.5 concentration has been attributed to socio-demographic factors, such as population growth, economy and specific exploitation activities like commercial coal exploration [34].
Being a proxy for air pollution emission from anthropogenic sources, energy consumption, especially coal combustion, could be considered one of predictors that may be used to explain variations of PM 2.5 concentrations across Mainland China. So, in order to analyze the possible relationship of the ambient air pollution with the emission patterns, a short evaluation of the National Bureau of Statistics of China is presented here.
From Figure 6a, we can see that PM 2.5 concentration has a positive relationship with coal consumption (r = 0.78, p = 0.0001). Figure 6b shows the time variation of coal consumption and annual PM 2.5 concentration. From it, we can see that coal consumption had a stable increase during 1999 and 2013, and decreased slightly since 2013. While from 2007 there was no obvious increase in PM 2.5 concentration, but a fluctuation status was exhibited. This was accompanied by a huge energy structure reconstruction effort. As shown in Figure 6c, the percentage of natural gas and renewables accounting for the total energy consumption began to increase. From 2011, the increased speed became faster, up to 1.34%/year (during 2011–2016). However, only cutting down the proportion of coal in energy consumption is not enough. We should reduce energy consumption and improve the efficiency of coal utilization, such as developing coal to substitute the natural gas (coal-to-SNG) industry [35].

4. Conclusions

We used recently updated annual average PM 2.5 gridded data to study the spatial and temporal trend at 1 km 2 resolution from 1999 to 2016. PM 2.5 concentrations throughout most areas of Mainland China have increased during the period 1999–2016, but there are still some areas where PM 2.5 concentration levels are stable or showing a downward trend. Such trends are analyzed to inform air quality management and policies. Mitigation efforts need to be strengthened in areas where PM 2.5 concentrations are strongly increasing, including the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces. Oil exploitation work should take emission control and dust prevention measures in Tarim. For the energy control measures, not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned also should be strictly controlled.

Author Contributions

Conceptualization, X.W. and H.S.; Formal analysis, J.Z.; Methodology, J.Z.; Supervision, Y.D. and Y.Z.; Writing—original draft, J.Z.; Writing—review & editing, W.C.

Funding

This research was funded by the National Key Research and Development Plan under Grant No. 2017YFC1601504 and the Natural Science Foundation of China under Grant No. 61836013.

Acknowledgments

The authors would like to thank Socioeconomic Data and Applications Center for sharing data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Huang, F.; Pan, B.; Wu, J.; Chen, E.; Chen, L. Relationship between exposure to PM2.5 and lung cancer incidence and mortality: A meta-analysis. Oncotarget 2017, 8, 43322–43331. [Google Scholar] [CrossRef] [PubMed]
  2. Cai, W.; Ke, L.; Hong, L.; Wang, H.; Wu, L. Weather conditions conducive to Beijing severe haze more frequent under climate change. Nat. Clim. Chang. 2017, 7, 257–262. [Google Scholar] [CrossRef]
  3. Lv, B.; Zhang, B.; Bai, Y. A Systematic Analysis of PM2.5 in Beijing and its Sources from 2000 to 2012. Atmos. Environ. 2015, 124, 98–108. [Google Scholar] [CrossRef]
  4. Chen, Z.; Cai, J.; Gao, B.; Xu, B.; Dai, S.; He, B.; Xie, X. Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region. Sci. Rep. 2017, 7, 40735. [Google Scholar] [CrossRef] [PubMed]
  5. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef]
  6. Petäjä, T.; Järvi, L.; Kerminen, V.M.; Ding, A.J.; Sun, J.N.; Nie, W.; Kujansuu, J.; Virkkula, A.; Yang, X.; Fu, C.B. Enhanced air pollution via aerosol-boundary layer feedback in China. Sci. Rep. 2016, 6, 18998. [Google Scholar] [CrossRef] [PubMed]
  7. Lin, G.; Fu, J.; Jiang, D.; Hu, W.; Dong, D.; Huang, Y.; Zhao, M. Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China. Int. J. Environ. Res. Public Health 2014, 11, 173–186. [Google Scholar] [CrossRef]
  8. Han, L.; Zhou, W.; Li, W.; Li, L. Impact of urbanization level on urban air quality: A case of fine particles (PM2.5) in Chinese cities. Environ. Pollut. 2014, 194, 163–170. [Google Scholar] [CrossRef]
  9. Han, L.; Zhou, W.; Li, W. Fine particulate (PM2.5) dynamics during rapid urbanization in Beijing, 1973–2013. Sci. Rep. 2016, 6, 23604. [Google Scholar] [CrossRef]
  10. King, M.; Kaufman, Y.J.; Tanre, D.; Nakajima, T. Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future. Bull. Am. Meteorol. Soc. 1998, 80, 2229–2260. [Google Scholar] [CrossRef]
  11. Yan, S.; Wu, G. Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China. Sci. Rep. 2016, 6, 33227. [Google Scholar] [CrossRef]
  12. Jian, S.; Wang, J.; Wei, Y.; Li, Y.; Miao, L. The Haze Nightmare Following the Economic Boom in China: Dilemma and Tradeoffs. Int. J. Environ. Res. Public Health 2016, 13, 402. [Google Scholar] [CrossRef]
  13. Zhang, R. Atmospheric science: Warming boosts air pollution. Nat. Clim. Chang. 2017, 7, 238–239. [Google Scholar] [CrossRef]
  14. Luo, J.; Du, P.; Samat, A.; Xia, J.; Che, M.; Xue, Z. Spatiotemporal Pattern of PM2.5Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression. Sci. Rep. 2017, 7, 40607. [Google Scholar] [CrossRef]
  15. Wang, S.; Gao, J.; Zhang, Y.; Zhang, J.; Cha, F.; Tao, W.; Ren, C.; Wang, W. Impact of emission control on regional air quality: An observational study of air pollutants before, during and after the Beijing Olympic Games. J. Environ. Sci. 2014, 26, 175–180. [Google Scholar] [CrossRef]
  16. Zhao, X.; Zhang, X.; Xiaofeng, X.U.; Jing, X.U.; Wei, M.; Weiwei, P.U. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing. Atmos. Environ. 2009, 43, 2893–2900. [Google Scholar] [CrossRef]
  17. Shen, X.; Yao, Z.; Hong, H.; Kebin, H.E.; Zhang, Y.; Liu, H.; Ye, Y.U. PM2.5 emissions from light-duty gasoline vehicles in Beijing, China. Sci. Total Environ. 2014, 487, 521–527. [Google Scholar] [CrossRef]
  18. He, K.; Yang, F.; Ma, Y.; Zhang, Q.; Yao, X.; Chan, C.K.; Cadle, S.; Chan, T.; Mulawa, P. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 2001, 35, 4959–4970. [Google Scholar] [CrossRef]
  19. Liu, C.; Henderson, B.H.; Wang, D.; Yang, X.; Peng, Z.R. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China. Sci. Total Environ. 2016, 565, 607–615. [Google Scholar] [CrossRef]
  20. Yong, Y.; George, C. Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China). Environ. Sci. Technol. 2015, 49, 13431–13438. [Google Scholar] [CrossRef]
  21. Wei, Y.; Zengliang, Z.; Xiaobin, P.; Lifeng, Z.; Dan, C. Estimating PM2.5 in Xi’an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models. Sci. Total Environ. 2015, 505, 1156–1165. [Google Scholar]
  22. Hansen, M.; Potapov, P.; Margono, B.; Stehman, S.; Turubanova, S.; Tyukavina, A. Response to comment on “High-resolution global maps of 21st-century forest cover change”. Science 2014, 342, 850–853. [Google Scholar] [CrossRef]
  23. Vogelmann, J.E.; Xian, G.; Homer, C.; Tolk, B. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sens. Environ. 2012, 122, 92–105. [Google Scholar] [CrossRef]
  24. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Hsu, N.C.; Kahn, R.A.; Levy, R.C.; Lyapustin, A.; Sayer, A.M.; Winker, D.M. Global Annual PM2.5 Grids from MODIS MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR 1998–2016; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA, 2018. [Google Scholar]
  25. Sherbinin, A.D.; Levy, M.A.; Zell, E.; Weber, S.; Jaiteh, M. Using satellite data to develop environmental indicators. Environ. Res. Lett. 2014, 9, 084013. [Google Scholar] [CrossRef]
  26. Zhang, Q.; Jiang, X.; Tong, D.; Davis, S.J.; Zhao, H.; Geng, G.; Feng, T.; Zheng, B.; Lu, Z.; Streets, D.G. Transboundary health impacts of transported global air pollution and international trade. Nature 2017, 543, 705–709. [Google Scholar] [CrossRef]
  27. Li, S.; Williams, G.; Guo, Y. Health benefits from improved outdoor air quality and intervention in China. Environ. Pollut. 2016, 214, 17–25. [Google Scholar] [CrossRef]
  28. Lin, C.; Ying, L.; Yuan, Z.; Lau, A.K.H.; Li, C.; Fung, J.C.H. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sens. Environ. 2015, 156, 117–128. [Google Scholar] [CrossRef]
  29. Zhang, Y.; Li, Z. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sens. Environ. 2015, 160, 252–262. [Google Scholar] [CrossRef]
  30. He, Q.; Geng, F.; Li, C.; Yang, S.; Wu, Z. Long-term characteristics of satellite-based PM2.5 over East China. Sci. Total Environ. 2017, 612, 1417. [Google Scholar] [CrossRef]
  31. Zhou, T.; Jian, S.; Huan, Y. Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015. Atmosphere 2017, 8, 137. [Google Scholar] [CrossRef]
  32. Wu, R.; Bo, Y.; Li, J.; Li, L.; Li, Y.; Xie, S. Method to establish the emission inventory of anthropogenic volatile organic compounds in China and its application in the period 2008–2012. Atmos. Environ. 2016, 127, 244–254. [Google Scholar] [CrossRef]
  33. Zhang, S.J.; Chaudhry, A.S.; Ramdani, D.; Osman, A.; Cheng, L. Chemical composition and in vitro fermentation characteristics of high sugar forage sorghum as an alternative to forage maize for silage making in Tarim Basin, China. J. Integr. Agric. 2016, 15, 175–182. [Google Scholar] [CrossRef]
  34. Guan, D.; Su, X.; Zhang, Q.; Peters, G.; Liu, Z.; Lei, Y.; He, K. The socioeconomic drivers of China’s primary PM2.5 emissions. Environ. Res. Lett. 2014, 9, 024010. [Google Scholar] [CrossRef]
  35. Wang, D.; Li, S.; He, S.; Gao, L. Coal to substitute natural gas based on combined coal-steam gasification and one-step methanation. Appl. Energy 2019, 240, 851–859. [Google Scholar] [CrossRef]
  36. China Statistics Press. National Bureau of Statistics of China (NBSC); China Statistics Press: Beijing, China, 2017. [Google Scholar]
Figure 1. Trend analysis of annual average PM 2.5 concentrations during 1999–2016 for China. In order to make the analysis straightforward, annual mean PM 2.5 concentrations were categorized sequentially into five grades according to WHO air quality guidelines. WHO air quality guidelines have four standards, including one air quality guideline (<10 μg/m 3 ) and three interim targets, which are 10–15 μg/m 3 , 15–25 μg/m 3 , 25–35 μg/m 3 respectively. In this paper, we add another guideline, of which PM 2.5 concentration values are larger than 35 μg/m 3 . The cumulative proportion of areas of each concentration range were calculated).
Figure 1. Trend analysis of annual average PM 2.5 concentrations during 1999–2016 for China. In order to make the analysis straightforward, annual mean PM 2.5 concentrations were categorized sequentially into five grades according to WHO air quality guidelines. WHO air quality guidelines have four standards, including one air quality guideline (<10 μg/m 3 ) and three interim targets, which are 10–15 μg/m 3 , 15–25 μg/m 3 , 25–35 μg/m 3 respectively. In this paper, we add another guideline, of which PM 2.5 concentration values are larger than 35 μg/m 3 . The cumulative proportion of areas of each concentration range were calculated).
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Figure 2. Geographic distribution of trend analysis of PM 2.5 concentration in China during 1999–2016 (areas of increasing trend in red, and decreasing trend in blue).
Figure 2. Geographic distribution of trend analysis of PM 2.5 concentration in China during 1999–2016 (areas of increasing trend in red, and decreasing trend in blue).
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Figure 3. Trend analysis of annual average PM2.5 concentrations during 1999–2016 for regions ((a) Beijing–Tianjin–Hebei region. (b) Shandong province. (c) Three Northeastern Provinces.)
Figure 3. Trend analysis of annual average PM2.5 concentrations during 1999–2016 for regions ((a) Beijing–Tianjin–Hebei region. (b) Shandong province. (c) Three Northeastern Provinces.)
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Figure 4. Trajectories of annual PM 2.5 concentration values over regions of the negative trend (the gray dashed lines are national annual average PM 2.5 concentration values, and the blue lines are annual average PM 2.5 concentration values for the corresponding regions).
Figure 4. Trajectories of annual PM 2.5 concentration values over regions of the negative trend (the gray dashed lines are national annual average PM 2.5 concentration values, and the blue lines are annual average PM 2.5 concentration values for the corresponding regions).
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Figure 5. PM 2.5 concentration in 2016 and oil exploration in Tarim regions.
Figure 5. PM 2.5 concentration in 2016 and oil exploration in Tarim regions.
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Figure 6. Relationship of annual average PM2.5 concentration to coal consumption. ((a) Scatterplot of annual average PM2.5 concentration versus annual coal consumption with linear regression lines. (b) Time series of average PM2.5 concentration and coal consumption for each year. (c) Total energy consumption and composition of China from 1999 to 2016 [36]).
Figure 6. Relationship of annual average PM2.5 concentration to coal consumption. ((a) Scatterplot of annual average PM2.5 concentration versus annual coal consumption with linear regression lines. (b) Time series of average PM2.5 concentration and coal consumption for each year. (c) Total energy consumption and composition of China from 1999 to 2016 [36]).
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