Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Methods
2.2.1. Unary Linear Regression Model
2.2.2. The Univariate Spatial Autocorrelation Analysis
2.2.3. The Bivariate Spatial Correlation Analysis
2.2.4. The Spatial Regression Model
3. Results
3.1. The Spatial Distribution of Socioeconomic Factors and PM2.5 in China
3.2. The Temporal Variation of Socioeconomic Factors and PM2.5
3.2.1. The Temporal Variation of Socioeconomic Factors and PM2.5 in China
3.2.2. The Temporal Variation of Socioeconomic Factors and PM2.5 in the Seven Geographical Subareas
3.2.3. The Spatial Distribution of Temporal Trends for Socioeconomic Factors and PM2.5 in Different Provinces
3.3. The Traditional Statistical Relationship between Socioeconomic Factors and PM2.5
3.3.1. The Correlation between Socioeconomic Factors and PM2.5 in Mainland China
3.3.2. The Relationship between Socioeconomic Factors and PM2.5 in Provinces
3.3.3. The Relationship between Socioeconomic Factors and PM2.5 in the Geographical Subareas
3.4. The Spatial Statistical Relationship between Socioeconomic Factors and PM2.5
3.4.1. Global Spatial Autocorrelation of PM2.5
3.4.2. Spatial Correlations between PM2.5 and Socioeconomic Factors
3.5. Regression Results of the Spatial Regression Model
4. Discussion
4.1. Spatial Distribution and Temporal Variation of PM2.5 and Socioeconomic Factors
4.2. The Relationships between PM2.5 and Socioeconomic Factors
4.3. The Spatial Spillover Effect of PM2.5
4.4. Comparative Analysis of the Effects of GDPP, GDP per Area, IVA and IVA per Area on PM2.5
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
- Wang, S.; Hao, J. Air quality management in China: Issues, challenges, and options. J. Environ. Sci. 2012, 24, 2–13. [Google Scholar] [CrossRef]
- Shen, Y.; Yao, L. PM2.5, Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data. Int. J. Environ. Res. Public Health 2017, 14, 716. [Google Scholar] [CrossRef]
- Xu, L.; Jiao, L.; Hong, Z.; Zhang, Y.; Du, W.; Wu, X.; Chen, Y.; Deng, J.; Hong, Y.; Chen, J. Source identification of PM2.5 at a port and an adjacent urban site in a coastal city of China: Impact of ship emissions and port activities. Sci. Total Environ. 2018, 634, 1205–1213. [Google Scholar] [CrossRef] [PubMed]
- Yu, K.; Xing, Z.; Huang, X.; Deng, J.; Andersson, A.; Fang, W.; Gustafsson, Ö.; Zhou, J.; Du, K. Characterizing and sourcing ambient PM2.5 over key emission regions in China III: Carbon isotope based source apportionment of black carbon. Atmos. Environ. 2018, 177, 12–17. [Google Scholar] [CrossRef]
- Han, X.; Liu, Y.; Gao, H.; Ma, J.; Mao, X.; Wang, Y.; Ma, X. Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis. Sci. Total Environ. 2017, 607–608, 1009–1017. [Google Scholar] [CrossRef]
- Yan, B.; Liu, S.; Zhao, B.; Li, X.; Fu, Q.; Jiang, G. China’s Fight for Clean Air and Human Health. Environ. Sci. Technol. 2018, 52, 8063–8064. [Google Scholar] [CrossRef]
- Zheng, S.; Pozzer, A.; Cao, C.X.; Lelieveld, J. Long-term (2001–2012) fine particulate matter (PM2.5) and the impact on human health in Beijing, China. Atmos. Chem. Phys. Atmos. Chem. Phys. 2015, 15, 5715–5725. [Google Scholar] [CrossRef]
- Yu, S.; Zhang, Q.; Yan, R.; Wang, S.; Li, P.; Chen, B.; Liu, W.; Zhang, X. Origin of air pollution during a weekly heavy haze episode in Hangzhou, China. Environ. Chem. Lett. 2014, 12, 543–550. [Google Scholar] [CrossRef]
- Pui, D.Y.H.; Chen, S.-C.; Zuo, Z. PM2.5 in China: Measurements, sources, visibility and health effects, and mitigation. Particuology 2014, 13, 1–26. [Google Scholar] [CrossRef]
- Zhang, Y.; Lang, J.; Cheng, S.; Li, S.; Zhou, Y.; Chen, D.; Zhang, H.; Wang, H. Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn. Sci. Total Environ. 2018, 630, 72–82. [Google Scholar] [CrossRef] [PubMed]
- Lai, A.M.; Carter, E.; Shan, M.; Ni, K.; Clark, S.; Ezzati, M.; Wiedinmyer, C.; Yang, X.; Baumgartner, J.; Schauer, J.J. Chemical composition and source apportionment of ambient, household, and personal exposures to PM2.5 in communities using biomass stoves in rural China. Sci. Total Environ. 2019, 646, 309–319. [Google Scholar] [CrossRef] [PubMed]
- Tao, J.; Zhang, L.; Cao, J.; Zhong, L.; Chen, D.; Yang, Y.; Chen, D.; Chen, L.; Zhang, Z.; Wu, Y.; et al. Source apportionment of PM2.5 at urban and suburban areas of the Pearl River Delta region, south China—With emphasis on ship emissions. Sci. Total Environ. 2017, 574, 1559–1570. [Google Scholar] [CrossRef] [PubMed]
- Raaschounielsen, O.; Beelen, R.; Wang, M.; Hoek, G.; Andersen, Z.J.; Hoffmann, B.; Stafoggia, M.; Samoli, E.; Weinmayr, G.; Dimakopoulou, K. Particulate matter air pollution components and risk for lung cancer. Environ. Int. 2016, 87, 66–73. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Leclercq, B.; Kluza, J.; Antherieu, S.; Sotty, J.; Alleman, L.Y.; Perdrix, E.; Loyens, A.; Coddeville, P.; Lo Guidice, J.M.; Marchetti, P. Air pollution-derived PM2.5 impairs mitochondrial function in healthy and chronic obstructive pulmonary diseased human bronchial epithelial cells. Environ. Pollut. 2018, 243, 1434–1449. [Google Scholar] [CrossRef]
- Yin, H.; Pizzol, M.; Jacobsen, J.B.; Xu, L. Contingent valuation of health and mood impacts of PM2.5 in Beijing, China. Sci. Total Environ. 2018, 630, 1269–1282. [Google Scholar] [CrossRef]
- Wang, R.; Xue, D.; Liu, Y.; Liu, P.; Chen, H. The Relationship between Air Pollution and Depression in China: Is Neighbourhood Social Capital Protective? Int. J. Environ. Res. Public Health 2018, 15, 1160. [Google Scholar] [CrossRef]
- Hao, Y.; Liu, Y.M. The influential factors of urban PM2.5 concentrations in China: Aspatial econometric analysis. J. Clean. Prod. 2016, 112, 1443–1453. [Google Scholar] [CrossRef]
- Jiang, P.; Yang, J.; Huang, C.; Liu, H. The contribution of socioeconomic factors to PM2.5 pollution in urban China. Environ. Pollut. 2018, 233, 977–985. [Google Scholar] [CrossRef] [PubMed]
- Peng, J.; Chen, S.; Lü, H.; Liu, Y.; Wu, J. Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011. Remote Sens. Environ. 2016, 174, 109–121. [Google Scholar] [CrossRef]
- Zhang, H.; Wang, Z.; Zhang, W. Exploring spatiotemporal patterns of PM2.5 in China based on ground-level observations for 190 cities. Environ. Pollut. 2016, 216, 559–567. [Google Scholar] [CrossRef] [PubMed]
- Hao, Y.; Peng, H.; Temulun, T.; Liu, L.Q.; Mao, J.; Lu, Z.N.; Chen, H. How harmful is air pollution to economic development? New evidence from PM2.5 concentrations of Chinese cities. J. Clean. Prod. 2017, 172, 743–757. [Google Scholar] [CrossRef]
- Zhou, C.; Chen, J.; Wang, S. Examining the effects of socioeconomic development on fine particulate matter (PM2.5) in China’s cities using spatial regression and the geographical detector technique. Sci. Total Environ. 2018, 619–620, 436–445. [Google Scholar] [CrossRef]
- Qiao, X.; Ying, Q.; Li, X.; Zhang, H.; Hu, J.; Tang, Y.; Chen, X. Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model. Sci. Total Environ. 2018, 612, 462–471. [Google Scholar] [CrossRef]
- Liu, Q.; Wang, S.; Zhang, W.; Li, J.; Dong, G. The effect of natural and anthropogenic factors on PM2.5: Empirical evidence from Chinese cities with different income levels. Sci. Total Environ. 2019, 653, 157–167. [Google Scholar] [CrossRef]
- Gao, J.; Wang, K.; Wang, Y.; Liu, S.; Zhu, C.; Hao, J.; Liu, H.; Hua, S.; Tian, H. Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China. Environ. Pollut. 2018, 233, 714–724. [Google Scholar] [CrossRef]
- Wang, H.; Tian, M.; Chen, Y.; Shi, G.; Liu, Y.; Yang, F.; Zhang, L.; Deng, L.; Yu, J.; Peng, C. Seasonal characteristics, formation mechanisms and source origins of PM2.5 in two megacities in Sichuan Basin, China. Atmos. Chem. Phys. 2018, 18, 865–881. [Google Scholar] [CrossRef]
- Liu, Y.; Xing, J.; Wang, S.; Fu, X.; Zheng, H. Source-specific speciation profiles of PM2.5 for heavy metals and their anthropogenic emissions in China. Environ. Pollut. 2018, 239, 544–553. [Google Scholar] [CrossRef]
- Mota, B.; Wooster, M.J. A new top-down approach for directly estimating biomass burning emissions and fuel consumption rates and totals from geostationary satellite fire radiative power (FRP). Remote Sens. Environ. 2018, 206, 45–62. [Google Scholar] [CrossRef]
- Huang, R.-J.; Zhang, Y.; Bozzetti, C.; Ho, K.-F.; Cao, J.-J.; Han, Y.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F.; et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 2014, 514, 218–222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lou, C.R.; Liu, H.Y.; Li, Y.F.; Li, Y.L. Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China. Int. J. Environ. Res. Public Health 2016, 13, 928. [Google Scholar] [CrossRef]
- Jeong, C.-H.; Wang, J.M.; Hilker, N.; Debosz, J.; Sofowote, U.; Su, Y.; Noble, M.; Healy, R.M.; Munoz, T.; Dabek-Zlotorzynska, E.; et al. Temporal and spatial variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust emissions. Atmos. Environ. 2019, 198, 55–69. [Google Scholar] [CrossRef]
- Wang, S.; Zhou, C.; Wang, Z.; Feng, K.; Hubacek, K. The characteristics and drivers of fine particulate matter (PM2.5) distribution in China. J. Clean. Prod. 2017, 142, 1800–1809. [Google Scholar] [CrossRef]
- Yang, D.; Wang, X.; Xu, J.; Xu, C.; Lu, D.; Ye, C.; Wang, Z.; Bai, L. Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China. Environ. Pollut. 2018, 241, 475–483. [Google Scholar] [CrossRef] [PubMed]
- Yang, G.; Huang, J.; Li, X. Mining Sequential Patterns of PM2.5 Pollution in Three Zones in China. J. Clean. Prod. 2018, 170, 388–398. [Google Scholar] [CrossRef]
- Luo, K.; Li, G.; Fang, C.; Sun, S. PM2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies. J. Environ. Manag. 2018, 213, 47–55. [Google Scholar] [CrossRef] [PubMed]
- Wu, J.; Zheng, H.; Zhe, F.; Xie, W.; Song, J. Study on the relationship between urbanization and Fine Particulate Matter (PM2.5) concentration and its implication in China. J. Clean. Prod. 2018, 182, 872–882. [Google Scholar] [CrossRef]
- Ma, Y.R.; Ji, Q.; Fan, Y. Spatial linkage analysis of the impact of regional economic activities on PM2.5 pollution in China. J. Clean. Prod. 2016, 139, 1157–1167. [Google Scholar] [CrossRef]
- Lu, D.; Xu, J.; Yang, D.; Zhao, J. Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014. Atmos. Pollut. Res. 2017, 8, 1151–1159. [Google Scholar] [CrossRef]
- Yang, D.; Ye, C.; Wang, X.; Lu, D.; Xu, J.; Yang, H. Global distribution and evolvement of urbanization and PM 2.5 (1998–2015). Atmos. Environ. 2018, 182, 171–178. [Google Scholar] [CrossRef]
- Boys, B.L.; Martin, R.V.; van Donkelaar, A.; MacDonell, R.J.; Hsu, N.C.; Cooper, M.J.; Yantosca, R.M.; Lu, Z.; Streets, D.G.; Zhang, Q.; et al. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter. Environ. Sci. Technol. 2014, 48, 11109–11118. [Google Scholar] [CrossRef]
- 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 Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 2016, 50, 3762–3772. [Google Scholar] [CrossRef] [PubMed]
- Moran, P.A. Notes on continuous stochastic phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef]
- Anselin, L. Local Indicators of Spatial Association - Lisa. Geogr. Anal. 1995, 27, 93–115. [Google Scholar] [CrossRef]
- Anselin, L.; Rey, S.J. Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace, and PySAL; GeoDa Press LLC: Chicago, IL, USA, 2014. [Google Scholar]
- Lee, S.I. Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I. J. Geogr. Syst. 2001, 3, 369–385. [Google Scholar] [CrossRef]
- Anselin, L. Exploring Spatial Data with GeoDa: A Workbook; Spatial Analysis Laboratory: Urbana, IL, USA, 2005. [Google Scholar]
- Anselin, L. Spatial Econometrics: Methods and Models. Econ. Geogr. 1988, 65, 160–162. [Google Scholar]
- Chi, G.; Zhu, J. Spatial Regression Models for Demographic Analysis. Popul. Res. Policy Rev. 2008, 27, 17–42. [Google Scholar] [CrossRef]
- An, L.; Che, H.; Xue, M.; Zhang, T.; Wang, H.; Wang, Y.; Zhou, C.; Zhao, H.; Gui, K.; Zheng, Y.; et al. Temporal and spatial variations in sand and dust storm events in East Asia from 2007 to 2016: Relationships with surface conditions and climate change. Sci. Total Environ. 2018, 633, 452–462. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Liu, J.; Che, H.; Ji, F.; Liu, J. Spatial and temporal evolution of natural and anthropogenic dust events over northern China. Sci. Rep. 2018, 8, 2141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, J.; Ding, S.; Liu, D. Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach. Sci. Total Environ. 2019, 659, 1513–1525. [Google Scholar] [CrossRef]
- Li, J.; Lin, B. Green economy performance and green productivity growth in China’s cities: Measures and policy implication. Sustainability 2016, 8, 947. [Google Scholar] [CrossRef]
- Wang, G.; Zhang, R.; Gomez, M.E.; Yang, L.; Levy Zamora, M.; Hu, M.; Lin, Y.; Peng, J.; Guo, S.; Meng, J.; et al. Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. USA 2016, 113, 13630–13635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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] [PubMed]
- Zhang, Y.-L.; Cao, F. Fine particulate matter (PM2.5) in China at a city level. Sci. Rep. 2015, 5, 14884. [Google Scholar] [CrossRef]
- Tai, A.P.K.; Mickley, L.J.; Jacob, D.J. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmos. Environ. 2010, 44, 3976–3984. [Google Scholar] [CrossRef]
- Yang, Q.; Yuan, Q.; Li, T.; Shen, H.; Zhang, L. The Relationships between PM(2.5) and Meteorological Factors in China: Seasonal and Regional Variations. Int. J. Environ. Res. Public Health 2017, 14, 1510. [Google Scholar] [CrossRef] [PubMed]
- Zhang, R.; Jing, J.; Tao, J.; Hsu, S.C.; Wang, G.; Cao, J.; Lee, C.S.L.; Zhu, L.; Chen, Z.; Zhao, Y.; et al. Chemical characterization and source apportionment of PM2.5 in Beijing: Seasonal perspective. Atmos. Chem. Phys. 2013, 13, 7053–7074. [Google Scholar] [CrossRef]
- Sun, J.; Shen, Z.; Zhang, L.; Lei, Y.; Gong, X.; Zhang, Q.; Zhang, T.; Xu, H.; Cui, S.; Wang, Q.; et al. Chemical source profiles of urban fugitive dust PM2.5 samples from 21 cities across China. Sci. Total Environ. 2019, 649, 1045–1053. [Google Scholar] [CrossRef] [PubMed]
- Shen, Z.; Sun, J.; Cao, J.; Zhang, L.; Zhang, Q.; Lei, Y.; Gao, J.; Huang, R.-J.; Liu, S.; Huang, Y.; et al. Chemical profiles of urban fugitive dust PM2.5 samples in Northern Chinese cities. Sci. Total Environ. 2016, 569–570, 619–626. [Google Scholar]
- Cao, J.J.; Chow, J.C.; Watson, J.G.; Wu, F.; Han, Y.M.; Jin, Z.D.; Shen, Z.X.; An, Z.S. Size-differentiated source profiles for fugitive dust in the Chinese Loess Plateau. Atmos. Environ. 2008, 42, 2261–2275. [Google Scholar] [CrossRef]
- Zhang, R.; Cao, J.; Tang, Y.; Arimoto, R.; Shen, Z.; Wu, F.; Han, Y.; Wang, G.; Zhang, J.; Li, G. Elemental profiles and signatures of fugitive dusts from Chinese deserts. Sci. Total Environ. 2014, 472, 1121–1129. [Google Scholar] [CrossRef] [PubMed]
- Han, L.; Zhou, W.; Li, W.; Qian, Y. Urbanization strategy and environmental changes: An insight with relationship between population change and fine particulate pollution. Sci. Total Environ. 2018, 642, 789–799. [Google Scholar] [CrossRef] [PubMed]
Region | 1998 | 2016 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
PM2.5 | GDPP | IVA | UPD | PCO | PM2.5 | GDPP | IVA | UPD | PCO | |
East China | 27.38 | 10,269.47 | 1269.72 | 1501.86 | 58.90 | 43.91 | 74,496.00 | 11,324.87 | 2756.00 | 5439.19 |
South China | 16.32 | 7025.50 | 418.47 | 999.00 | 34.23 | 23.86 | 52,130.00 | 3995.00 | 2378.00 | 1943.85 |
Central China | 30.62 | 4865.51 | 369.43 | 867.67 | 90.69 | 38.57 | 48,207.33 | 4091.64 | 3684.67 | 2056.22 |
Northwest China | 35.26 | 4563.55 | 125.08 | 890.80 | 29.69 | 33.84 | 41,989.40 | 1398.92 | 2965.00 | 1090.65 |
Southwest China | 16.19 | 3957.29 | 253.26 | 443.40 | 43.24 | 17.04 | 39,605.60 | 2493.59 | 2531.00 | 1904.52 |
North China | 31.22 | 10,074.37 | 415.56 | 1338.40 | 125.75 | 45.28 | 76,781.80 | 3560.12 | 2634.60 | 2683.10 |
Northeast China | 20.87 | 7591.06 | 350.02 | 722.00 | 41.11 | 32.38 | 48,363.67 | 1653.55 | 2986.67 | 1212.68 |
Mainland China | 23.97 | 6860.00 | 3413.49 | 459.00 | 423.65 | 29.68 | 53,935.00 | 24,787.78 | 2408.00 | 16,330.22 |
Region | z-slope | ||||
---|---|---|---|---|---|
PM2.5 | GDPP | IVA | UPD | PCO | |
East China | 0.129 | 0.174 | 0.175 | 0.162 | 0.165 |
South China | 0.110 | 0.173 | 0.175 | 0.148 | 0.168 |
Central China | 0.108 | 0.172 | 0.172 | 0.158 | 0.159 |
Northwest China | −0.015 | 0.172 | 0.171 | 0.136 | 0.160 |
Southwest China | 0.066 | 0.170 | 0.170 | 0.156 | 0.163 |
North China | 0.128 | 0.175 | 0.172 | 0.166 | 0.169 |
Northeast China | 0.145 | 0.172 | 0.165 | 0.165 | 0.165 |
Mainland China | 0.138 | 0.173 | 0.174 | 0.164 | 0.165 |
Year | Variables | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GDPP | IVA | UPD | PCO | R2 | Log-L | SLM-LM | SLM-RLM | SEM-LM | SEM-RLM | |
1998 | 0.165 | −0.0180 | 0.026 | 0.116 | 0.048 | −42.717 | 5.571 ** | 1.583 | 4.826 ** | 0.838 |
1999 | 0.202 | 0.216 | −0.012 | −0.199 | 0.092 | −41.981 | 2.778 * | 0.115 | 2.667 | 0.005 |
2000 | 0.166 | 0.216 | 0.027 | −0.076 | 0.085 | −42.103 | 7.717 *** | 0.587 | 7.222 *** | 0.093 |
2001 | 0.361 | 0.168 | 0.068 | −0.119 | 0.192 | −40.179 | 7.661 *** | 2.569 | 6.106 ** | 1.014 |
2002 | 0.438 ** | 0.422 * | −0.063 | −0.292 | 0.302 | −37.908 | 7.884 *** | 0.850 | 7.036 *** | 0.003 |
2003 | 0.449 ** | 0.460 * | 0.195 | −0.280 | 0.406 | −35.417 | 8.874 *** | 3.486 * | 5.816 ** | 0.429 |
2004 | 0.466 ** | 0.470 * | 0.144 | −0.317 | 0.358 | −36.611 | 6.037 ** | 2.729 * | 3.933 ** | 0.625 |
2005 | 0.406 ** | 0.613 ** | 0.113 | −0.414 | 0.363 | −36.485 | 7.922 *** | 1.938 | 6.067 ** | 0.082 |
2006 | 0.504 *** | 0.384 | 0.265 | −0.199 | 0.374 | −36.216 | 13.218 *** | 3.156 * | 10.158 *** | 0.096 |
2007 | 0.442 ** | 0.596 * | 0.224 | −0.354 | 0.390 | −35.819 | 13.435 *** | 3.741 * | 9.842 *** | 0.148 |
2008 | 0.546 *** | 0.620 * | 0.120 | −0.429 | 0.429 | −34.796 | 8.044 *** | 2.218 | 5.844 ** | 0.018 |
2009 | 0.564 *** | 0.494 | 0.139 | −0.310 | 0.416 | −35.141 | 7.249 *** | 2.393 | 4.954 ** | 0.098 |
2010 | 0.439 ** | 0.515 | 0.515 | −0.249 | 0.356 | −36.665 | 6.853 *** | 1.221 | 5.647 ** | 0.014 |
2011 | 0.501 *** | 0.264 | 0.175 | −0.012 | 0.379 | −36.096 | 9.957 *** | 2.826 * | 7.183 *** | 0.052 |
2012 | 0.424 ** | 0.236 | 0.144 | 0.082 | 0.336 | −37.138 | 7.965 *** | 3.252 * | 5.070 ** | 0.356 |
2013 | 0.515 *** | 0.013 | 0.201 | 0.229 | 0.371 | −36.297 | 8.174 *** | 4.133 ** | 4.726 ** | 0.685 |
2014 | 0.462 ** | 0.128 | 0.159 | 0.169 | 0.366 | −36.422 | 6.566 ** | 4.074 ** | 3.335 * | 0.842 |
2015 | 0.575 *** | −0.019 | 0.226 | 0.221 | 0.419 | −35.051 | 6.294 ** | 3.891 ** | 3.196 * | 0.793 |
2016 | 0.578 *** | −0.286 | 0.122 | 0.494 | 0.385 | −35.939 | 5.762 ** | 5.050 ** | 2.465 | 1.752 |
Year | Variables | ||||||||
---|---|---|---|---|---|---|---|---|---|
W*PM25 | GDPP | IVA | UPD | PCO | R2 | Log-L | AIC | SC | |
1998 | 0.646 *** | 0.151 | 0.005 | −0.114 | 0.064 | 0.328 | −39.165 | 90.330 | 98.934 |
1999 | 0.488 *** | 0.196 | 0.149 | −0.110 | −0.156 | 0.237 | −40.237 | 92.474 | 101.078 |
2000 | 0.677 *** | 0.087 | 0.170 | −0.121 | −0.057 | 0.401 | −37.598 | 87.197 | 95.800 |
2001 | 0.694 *** | 0.245 | 0.095 | −0.128 | −0.098 | 0.483 | −35.457 | 82.914 | 91.518 |
2002 | 0.674 *** | 0.279 ** | 0.258 | −0.191 | −0.233 | 0.549 | −33.196 | 78.391 | 86.995 |
2003 | 0.647 *** | 0.225 * | 0.345 * | 0.082 | −0.262 | 0.618 | −30.406 | 72.812 | 81.416 |
2004 | 0.595 *** | 0.274 * | 0.345 * | 0.046 | −0.315 | 0.536 | −33.075 | 78.150 | 86.754 |
2005 | 0.623 *** | 0.206 | 0.476 ** | 0.028 | −0.422 * | 0.570 | −32.075 | 76.151 | 84.755 |
2006 | 0.766 *** | 0.227 ** | 0.378 * | 0.274 *** | −0.311 | 0.690 | −28.214 | 68.428 | 77.032 |
2007 | 0.754 *** | 0.207 * | 0.471 ** | 0.217 ** | −0.407 * | 0.695 | −27.832 | 67.664 | 76.268 |
2008 | 0.646 *** | 0.311 ** | 0.535 ** | 0.121 | −0.483 ** | 0.630 | −29.898 | 71.796 | 80.400 |
2009 | 0.645 *** | 0.321 ** | 0.502 ** | 0.144 | −0.447 * | 0.614 | −30.556 | 73.112 | 81.716 |
2010 | 0.626 *** | 0.242 * | 0.515 * | 0.153 | 0.383 | 0.561 | −32.421 | 76.842 | 85.446 |
2011 | 0.729 *** | 0.238 ** | 0.369 | 0.186 * | −0.268 | 0.653 | −29.580 | 71.160 | 79.764 |
2012 | 0.674 *** | 0.206 | 0.290 | 0.176 | −0.128 | 0.580 | −32.073 | 76.146 | 84.750 |
2013 | 0.679 *** | 0.244 * | 0.223 | 0.204 * | −0.098 | 0.607 | −31.099 | 74.198 | 82.802 |
2014 | 0.637 *** | 0.203 | 0.302 | 0.166 | −0.137 | 0.571 | −32.146 | 76.292 | 84.896 |
2015 | 0.639 *** | 0.266 ** | 0.249 | 0.175 | −0.148 | 0.606 | −30.845 | 73.689 | 82.293 |
2016 | 0.628 *** | 0.247 * | 0.081 | 0.107 | 0.06 | 0.570 | −32.100 | 76.201 | 84.805 |
Region | Province | Correlation Coefficient (r) | |||
---|---|---|---|---|---|
r-GDPP | r-GDPP per Area | r-IVA | r-IVA per Area | ||
North China | Beijing | 0.785 ** | 0.777 ** | 0.779 ** | 0.779 ** |
Tianjin | 0.701 ** | 0.701 ** | 0.695 ** | 0.695 ** | |
Hebei | 0.756 ** | 0.756 ** | 0.759 ** | 0.759 ** | |
Shanxi | 0.525 * | 0.525 * | 0.548 * | 0.453 | |
Inner Mongolia | 0.323 | 0.342 | 0.311 | 0.443 | |
Northeast China | Liaoning | 0.822 ** | 0.812 ** | 0.783 ** | 0.783 ** |
Jilin | 0.829 ** | 0.829 ** | 0.822 ** | 0.822 ** | |
Heilongjiang | 0.742 ** | 0.740 ** | 0.600 ** | 0.600 ** | |
East China | Shanghai | 0.604 ** | 0.604 ** | 0.556 * | 0.556 * |
Jiangsu | 0.754 ** | 0.754 ** | 0.754 ** | 0.754 ** | |
Zhejiang | 0.560 * | 0.558 * | 0.560 * | 0.548 * | |
Anhui | 0.798 ** | 0.804 ** | 0.798 ** | 0.798 ** | |
Fujian | 0.474 * | 0.474 * | 0.474 * | 0.474 * | |
Jiangxi | 0.552 * | 0.548 * | 0.554 * | 0.439 | |
Shandong | 0.752 ** | 0.752 ** | 0.752 ** | 0.750 ** | |
Central China | Henan | 0.756 ** | 0.756 ** | 0.773 ** | 0.763 ** |
Hubei | 0.641 ** | 0.641 ** | 0.628 ** | 0.628 ** | |
Hunan | 0.585 * | 0.585 * | 0.585 * | 0.585 * | |
South China | Guangdong | 0.552 * | 0.552 * | 0.552 * | 0.552 * |
Guangxi | 0.649 ** | 0.626 ** | 0.649 ** | 0.484 * | |
Hainan | 0.498 * | 0.643 ** | 0.513 * | 0.628 ** | |
Southwest China | Chongqing | 0.331 | 0.331 | 0.340 | 0.340 |
Sichuan | 0.418 | 0.418 | 0.449 | 0.480 * | |
Guizhou | 0.467 | 0.467 | 0.467 | 0.488 * | |
Yunnan | 0.457 | 0.515 * | 0.449 | 0.368 | |
Northwest China | Shaanxi | −0.030 | −0.028 | −0.003 | −0.096 |
Gansu | –0.152 | 0.038 | −0.160 | 0.189 | |
Qinghai | 0.567 * | 0.451 | 0.579 * | 0.480 * | |
Ningxia | −0.562 * | −0.562 * | −0.562 * | −0.470 * | |
Xinjiang | 0.240 | 0.240 | 0.230 | 0.232 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yang, Y.; Li, J.; Zhu, G.; Yuan, Q. Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016. Int. J. Environ. Res. Public Health 2019, 16, 1149. https://doi.org/10.3390/ijerph16071149
Yang Y, Li J, Zhu G, Yuan Q. Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016. International Journal of Environmental Research and Public Health. 2019; 16(7):1149. https://doi.org/10.3390/ijerph16071149
Chicago/Turabian StyleYang, Yi, Jie Li, Guobin Zhu, and Qiangqiang Yuan. 2019. "Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016" International Journal of Environmental Research and Public Health 16, no. 7: 1149. https://doi.org/10.3390/ijerph16071149