With rapid urbanization and energy consumption, environmental pollution and degradation have become increasingly serious problems in China. At the beginning of 2013, China implemented new ambient air quality standards (GB 3095-2012) in which the concentration of six pollutants including PM2.5, ozone, carbon monoxide, PM10, sulfur dioxide and nitrogen dioxide were monitored. This study gathered annual air pollutant concentration data for the six pollutants in 113 key environmental protection cites throughout China in 2014 and 2015 to explain spatial patterns of urban air pollution. Based on the Kernel density estimation method, spatial hotspots of air pollution were illustrated through which spatial cluster of each pollutants could be plotted. By employing an entropy evaluation system, urban air quality was assessed in terms of the six atmospheric pollutants. We conclude that, in general, CO and SO2 were two important pollutants in most Chinese cities, but this varied greatly among cities. The assessment results indicate that cities with the worst air quality were mainly located in northern and central provinces, dominantly in the Beijing-Tianjin-Hebei metropolitan area. Regression modeling showed that a combination of meteorological factors and human-related determinants, to say specifically, industrialization and urbanization factors, greatly influenced urban air quality variation in China. Results from spatial lag regression modeling confirmed that air pollution existed obvious spatial spillover effects among key cities. The spatial interdependence effects of urban air quality means that Chinese municipal governments should strengthen regional cooperation and deepen bilateral collaboration in terms of air regulation and pollution prevention.
1 Adams J W, Rodriguez D, Cox R A, 2005. The uptake of so2 on saharan dust: A flow tube study. Atmospheric Chemistry & Physics Discussions , 5(10): 2643 - 2676.
2 Anselin L, Griffith D A, 1988. Do spatial effecfs really matter in regression analysis? Papers in Regional Science , 65(1): 11 - 34.
3 Chen D, Liu X, Lang J, et al ., 2017. Estimating the contribution of regional transport to pm2.5 air pollution in a rural area on the north china plain. Science of The Total Environment , 583: 280 - 291.
4 Chen W, Tang H, Zhao H, 2015. Urban air quality evaluations under two versions of the national ambient air quality standards of china. Atmospheric Pollution Research , 7(1): 49-57.
5 Chen X, Zhang L-w, Huang J-j, et al ., 2016. Long-term exposure to urban air pollution and lung cancer mortality: A 12-year cohort study in northern china. Science of The Total Environment , 571: 855 - 861.
6 Chu H J, Liau C J, Lin C H, et al ., 2012. Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the taiwan region. Expert Systems with Applications , 39(10): 9451 - 9457.
7 Dehnad K, 1987. Density estimation for statistics and data analysis. Technometrics , 29(4): 296-297.
8 Fang C, Wang Z, Xu G, 2016. Spatial-temporal characteristics of pm 2.5 in china: A city-level perspective analysis. Journal of Geographical Sciences , 26(11): 1519 - 1532.
9 Gu C, Wu L, Lan C, 2012. Progress in research on chinese urbanization. Frontiers of Architectural Research , 1(2): 101 - 149.
10 Hu J, Ying Q, Wang Y, et al ., 2015. Characterizing multi-pollutant air pollution in china: Comparison of three air quality indices. Environment International , 84: 17 - 25.
11 Khanna N, 2000. Measuring environmental quality: An index of pollution. Ecological Economics , 35(2): 191 - 202.
12 Kyrkilis G, Chaloulakou A, Kassomenos P A, 2007. Development of an aggregate air quality index for an urban mediterranean agglomeration: Relation to potential health effects. Environment International , 33(5): 670 - 676.
13 Lin X, Wang D, 2016. Spatio-temporal variations and socio-economic driving forces of air quality in chinese cities. Acta Geographica Sinica , 71(8): 1357 - 1371. (in Chinese)
14 Liu C, Chen R, Zhao Y, et al ., 2017a. Associations between ambient fine particulate air pollution and hypertension: A nationwide cross-sectional study in china. Science of The Total Environment , 584-585: 869 - 874.
15 Liu M, Huang Y, Jin Z, et al ., 2017b. The nexus between urbanization and pm2.5 related mortality in china. Environmental Pollution , 227: 15 - 23.
16 Liu W, Cai J, Huang C, et al ., 2016. Associations of gestational and early life exposures to ambient air pollution with childhood atopic eczema in shanghai, china. Science of The Total Environment , 572: 34 - 42.
17 Liu Z, Jing-Shun D, Wang Q, et al ., 2005. An evaluation method of scheme based on entropy weight multi-objection decision-making. Mathematics in Practice & Theory , 35(10): 114 - 119. (in Chinese）
18 Lu X, Han L, 2014. Research on meteorological features of pbl during heavy haze episodes in the city of chengdu, sichuan basin, china. AGU Fall Meeting .
19 Ni J P, Li P, Wei C F, et al ., 2009. Potentialities evaluation of regional land consolidation based on ahp and entropy weight method. Transactions of the Chinese Society of Agricultural Engineering , 25(5): 202 - 209(208).
20 Place S E, Mitloehner F M, 2010. Invited review: Contemporary environmental issues: A review of the dairy industry's role in climate change and air quality and the potential of mitigation through improved production efficiency. Journal of Dairy Science , 93(8): 3407 - 3416.
21 Qin R X, Xiao C, Zhu Y, et al ., 2017. The interactive effects between high temperature and air pollution on mortality: A time-series analysis in hefei, china. Science of The Total Environment , 575: 1530 - 1537.
22 Rubinstein R Y, Kroese D P, 2008. The cross-entropy method. Technometrics , 50(1): 92 - 92.
23 Shannon C E, 1948. A mathematical theory of communiation. Bell System Technical Journal , 27(3): 3 - 55.
24 Swamee P K, Tyagi A, 1999. Formation of an air pollution index. Journal of the Air & Waste Management Association , 49(1): 88.
25 Tai A P K, Mickley L J, Jacob D J, 2010. Correlations between fine particulate matter (PM 2.5 ) and meteorological variables in the united states: Implications for the sensitivity of pm2.5 to climate change. Atmospheric Environment , 44(32): 3976 - 3984.
26 Wang D, Liu Y, 2016. Spatio-temporal differences and driving forces of air quality in chinese cities. Journal of Resources and Ecology , 7(2): 77 - 84.
27 Wang F, Peng Y, Jiang C, 2017a. Influence of road patterns on pm2.5 concentrations and the available solutions: The case of beijing city, china. Sustainability , 9(2): 217.
28 Wang N, Lyu X P, Deng X J, et al ., 2016a. Assessment of regional air quality resulting from emission control in the pearl river delta region, southern china. Science of The Total Environment , 573: 1554 - 1565.
29 WANG X, GUO X, LANG J, 2016b. Population weighted distribution of PM 2.5 and PM 10 in 31 provinces in china. Ecological Economy , 32(9): 24 - 26. (in Chinese)
30 Wang Z, Ke L, Cui X, et al ., 2017b. Monitoring environmental quality by sniffing social media. Sustainability , 9: 85.
31 Wen-Jie X U, Shi-Guo X U, 2008. Entropy weight comprehensive health index method for assessment of lake ecosystem health. Research of Soil & Water Conservation , 15(1): 126 - 128. (in Chinese)
32 WHO, 2012. Mortality and global burden of disease (gbd). World Health Organisation .
33 Xie Z, Yan J, 2008. Kernel density estimation of traffic accidents in a network space. Computers Environment & Urban Systems , 32(5): 396 - 406.
34 Yang J, Ou C-Q, Song Y-F, et al ., 2016. Estimating years of life lost from cardiovascular mortality related to air pollution in guangzhou, china. Science of The Total Environment , 573: 1566 - 1572.
35 Zhang X, Huang P, Sun L, et al ., 2013. Spatial evolution and locational determinants of high-tech industries in beijing. Chinese Geographical Science , 23(2): 249 - 260.
36 Zhao D, Zhang Y, Jianfeng M A, 2004. Fuzzy risk assessment of entropy-weight coefficient method applied in network security. Computer Engineering , 30(18): 21 - 23.
37 Zhou L, Wu J, Jia R, et al ., 2016. Investigation of temporal-spatial characteristics and underlying risk factors of pmpollution in beijing-tianjin-hebei area. Research of Environmental Sciences , 29(4): 483 - 493. (in Chinese)
