长江经济带空气质量指数时空异质性及社会经济影响因素分析
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摘要
以长江经济带为研究对象,基于2015年城市空气质量监测数据,首先利用自然正交函数分析了126个城市空气质量指数(AQI)的时空演变特征,然后采用地理加权回归方法从空间异质性视角探讨了空气质量的社会经济影响因素。研究结果表明:(1) EOF第一模态特征向量的高值出现在长三角的江苏省,长江中游的武汉都市圈、长株潭地区以及成渝城市群。低值区则集中在云南的大部分城市、贵州的黔南州、四川的阿坝州以及浙江的舟山、台州和丽水。(2)月尺度的AQI时间系数变化大致呈U形分布,即1—5月呈现波动下降趋势,6—8月AQI总体处于"U"的谷底,9—12月逐渐波动上升。就年内季节变化来看,冬季空气质量最差,夏季空气质量最好,春秋两季居中。(3)人均地区生产总值、人口密度、PM_(2.5)浓度和SO_2浓度的增加均是导致空气污染加剧的重要因素,而FDI和建成区绿化覆盖率的增加有利于空气质量的改善,并且不同城市的影响因素对空气质量作用存在显著的空间异质性。
We employed a cross-sectional data of the Yangtze River Economic Belt at the city level to investigate spatiotemporal variation characteristics of air quality index(AQI) of 126 cities by means of the empirical orthogonal function(EOF) method, and then applied the geographically weighted regression method to analyze socio-economic factors of AQI from a perspective of spatial heterogeneity. The findings are the followings.(1) The first mode of EOF has high eigenvector values in Jiangsu Province, Wuhan metropolitan region, Chang-Zhu-Tan region and Chengdu-Chongqing urban cluster while low high eigenvector values are shown in most cities of Yunnan Province, Qiannan of Guizhou Province, Aba of Sichuan Province and Zhoushan, Taizhou and Lishui of Zhejiang Province.(2) On the monthly time scale, the time coefficient of AQI displays a U-shaped curve. Specifically, it first declines with fluctuations from January to May, and then keeps the lowest values from June to August, and finally increases from September to December. In other words, air quality is the worst in winter and the best in summer within the year of 2015.(3) The increase in per capita GDP, population density, PM_(2.5) concentration and SO_(2 )concentration are the important factors worsening air quality while FDI and green coverage ratio contribute to improving air quality. Finally, socio-economics factors may have impacts on air quality from city to city, presenting significant spatial heterogeneity.
We employed a cross-sectional data of the Yangtze River Economic Belt at the city level to investigate spatiotemporal variation characteristics of air quality index(AQI) of 126 cities by means of the empirical orthogonal function(EOF) method, and then applied the geographically weighted regression method to analyze socio-economic factors of AQI from a perspective of spatial heterogeneity. The findings are the followings.(1) The first mode of EOF has high eigenvector values in Jiangsu Province, Wuhan metropolitan region, Chang-Zhu-Tan region and Chengdu-Chongqing urban cluster while low high eigenvector values are shown in most cities of Yunnan Province, Qiannan of Guizhou Province, Aba of Sichuan Province and Zhoushan, Taizhou and Lishui of Zhejiang Province.(2) On the monthly time scale, the time coefficient of AQI displays a U-shaped curve. Specifically, it first declines with fluctuations from January to May, and then keeps the lowest values from June to August, and finally increases from September to December. In other words, air quality is the worst in winter and the best in summer within the year of 2015.(3) The increase in per capita GDP, population density, PM_(2.5) concentration and SO_(2 )concentration are the important factors worsening air quality while FDI and green coverage ratio contribute to improving air quality. Finally, socio-economics factors may have impacts on air quality from city to city, presenting significant spatial heterogeneity.
引文
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[23]杨冕,王银.长江经济带PM2.5时空特征及影响因素研究[J].中国人口·资源与环境,2017,27(1):91-100.
[24]Wang M, Cao C, Li G, et al. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China[J]. Atmospheric Environment, 2015,102:112-121.
[25]刘华军,杜广杰.中国城市大气污染的空间格局与分布动态演进:基于161个城市AQI及6种分项污染物的实证[J].经济地理,2016,36(10):33-38.
[26]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报,2016,71(8):1357-1371.
[27]刘满凤,谢晗进.基于空气质量指数AQI的污染集聚空间异质性分析[J].经济地理,2016,36(8):166-175.
[28]Pearson K. On lines and planes of closest fit to systems of points in space[J]. the London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901,2(11):559-572.
[29]Lorenz E N. Statistical forecasting program: empirical orthogonal functions and statistical weather prediction [J]. Scientific Report, 1956,409(2):997-999.
[30]姜磊,周海峰,柏玲,等.空气质量指数(AQI)的社会经济影响因素分析:基于指数衰减效应视角[J].环境科学学报,2018,38(1):390-398.
[31]白景锋,张海军.基于EOF和GWR模型的中原经济区经济增长的时空分析[J].地理研究,2014,33(7):1230-1238.
[32]李静,彭飞.城市空气污染与收入关系的EKC再检验[J].统计与决策,2013,29(20):86-89.
[33]白俊红,吕晓红. FDI质量与中国环境污染的改善[J].国际贸易问题,2015,41(8):72-83.
[34]姜磊,周海峰,柏玲.外商直接投资对空气污染影响的空间异质性分析:以中国150个城市空气质量指数(AQI)为例[J].地理科学,2018,38(3):351-360.
[2]Wang H, Xu J, Zhang M, et al. A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China[J]. Atmospheric Environment, 2014,98:146-157.
[3]汪克亮,孟祥瑞,杨宝臣,等.基于环境压力的长江经济带工业生态效率研究[J].资源科学,2015,37(7):1491-1501.
[4]Song M, Wang S, Yu H, et al. To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model[J]. Renewable and Sustainable Energy Reviews, 2011,15(9):5129-5134.
[5]Shaltout A A, Boman J, Al-Malawi D R, et al. Elemental composition of PM2.5 particles sampled in industrial and residential areas of Taif, Saudi Arabia[J]. Aerosol and Air Quality Research, 2013,13(4):1356-1364.
[6]郑玫,张延君,闫才青,等.上海PM2.5工业源谱的建立[J].中国环境科学,2013,33(8):1354-1359.
[7]郑晓霞,李令军,赵文吉,等.京津冀地区大气NO2污染特征研究[J].生态环境学报,2014,23(12):1938-1945.
[8]王振波,方创琳,许光,等.2014年中国城市PM2.5浓度的时空变化规律[J].地理学报,2015,70(11):1720-1734.
[9]李名升,任晓霞,于洋,等.中国大陆城市PM2.5污染时空分布规律[J].中国环境科学,2016,36(3):641-650.
[10]柏玲,姜磊,刘耀彬.长江中游城市群环境压力的时空特征:以工业SO2排放为例[J].经济地理,2017,37(3):174-181.
[11]Fang W, Yang Y, Xu Z. PM10 and PM2.5 and health risk assessment for heavy metals in a typical factory for cathode ray tube television recycling[J]. Environmental Science & Technology, 2013,47(21):12469-12476.
[12]Shen X J, Sun J Y, Zhang X Y, et al. Characterization of submicron aerosols and effect on visibility during a severe haze-fog episode in Yangtze River Delta, China [J]. Atmospheric Environment, 2015,120:307-316.
[13]Kulmala M. China′s choking cocktail[J]. Nature, 2015,526(7574):497-499.
[14]Lin G, Fu J, Jiang D, et al. Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China[J]. International Journal of Environmental Research and Public Health, 2013,11(1):173-186.
[15]贺祥,林振山,刘会玉,等.基于灰色关联模型对江苏省PM2.5浓度影响因素的分析[J].地理学报,2016,71(7):1119-1129.
[16]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报,2016,71(8):1357-1371.
[17]岑晓喻,周寅康,单薇,等.长江经济带资源环境格局与可持续发展[J].中国发展,2015,15(3):1-9.
[18]方创琳,周成虎,王振波.长江经济带城市群可持续发展战略问题与分级梯度发展重点[J].地理科学进展,2015,34(11):1398-1408.
[19]宓泽锋,曾刚,尚勇敏,等.长江经济带市域生态文明建设现状及发展潜力初探[J].长江流域资源与环境,2016,25(9):1438-1447.
[20]吴传清,陈文艳.长江经济带经济增长与环境质量关系的实证研究[J].生态经济,2016,32(5):34-37.
[21]刘登娟,吕一清.长江经济带成渝城市群环境与经济协调发展评价[J].经济体制改革,2017,35(2):36-42.
[22]卢丽文,宋德勇,李小帆.长江经济带城市发展绿色效率研究[J].中国人口·资源与环境,2016,26(6):35-42.
[23]杨冕,王银.长江经济带PM2.5时空特征及影响因素研究[J].中国人口·资源与环境,2017,27(1):91-100.
[24]Wang M, Cao C, Li G, et al. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China[J]. Atmospheric Environment, 2015,102:112-121.
[25]刘华军,杜广杰.中国城市大气污染的空间格局与分布动态演进:基于161个城市AQI及6种分项污染物的实证[J].经济地理,2016,36(10):33-38.
[26]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报,2016,71(8):1357-1371.
[27]刘满凤,谢晗进.基于空气质量指数AQI的污染集聚空间异质性分析[J].经济地理,2016,36(8):166-175.
[28]Pearson K. On lines and planes of closest fit to systems of points in space[J]. the London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901,2(11):559-572.
[29]Lorenz E N. Statistical forecasting program: empirical orthogonal functions and statistical weather prediction [J]. Scientific Report, 1956,409(2):997-999.
[30]姜磊,周海峰,柏玲,等.空气质量指数(AQI)的社会经济影响因素分析:基于指数衰减效应视角[J].环境科学学报,2018,38(1):390-398.
[31]白景锋,张海军.基于EOF和GWR模型的中原经济区经济增长的时空分析[J].地理研究,2014,33(7):1230-1238.
[32]李静,彭飞.城市空气污染与收入关系的EKC再检验[J].统计与决策,2013,29(20):86-89.
[33]白俊红,吕晓红. FDI质量与中国环境污染的改善[J].国际贸易问题,2015,41(8):72-83.
[34]姜磊,周海峰,柏玲.外商直接投资对空气污染影响的空间异质性分析:以中国150个城市空气质量指数(AQI)为例[J].地理科学,2018,38(3):351-360.
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