中国PM_(2.5)污染时空趋势研究——基于贝叶斯时空统计视角
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摘要
以遥感反演的PM_(2.5)年均浓度数据为基础,研究了2000-2014年中国PM_(2.5)污染的时空演化趋势与特征。采用了基于多尺度嵌套空间统计单元的贝叶斯时空层次模型,并对多尺度嵌套空间统计单元构建的剖分阈值范围进行了扩展。研究表明:第一,2000—2014年,中国PM_(2.5)重度污染区域已形成"两团一带"的稳态空间结构,主要位于华北平原、长三角地区、四川盆地和新疆塔里木盆地等区域;第二,PM_(2.5)污染程度高于全国总体水平的区域面积占比为63.3%,但对应的暴露人口比例却高达92.5%;第三,2000-2014年,PM_(2.5)轻度污染的西部地区出现了较强的局部加重趋势,同时湖北、河南、山东、长三角地区北部和京津冀地区等PM_(2.5)重度污染区域也出现了局部增加趋势,并形成了"X"型空间结构,但PM_(2.5)污染较重的四川盆地却呈现出下降的局部趋势。
Based on the remotely sensed PM_(2.5) annual average concentrations data,the spatio-temporal evolution trends and characteristics of PM_(2.5) pollution in China from 2000 to 2014 are researched.Bayesian space-time hierarchical model based on multi-scale nested spatial statistical units is employed,and the scope of the construction thresholds for the nested multi-scale spatial statistical units is extended.The results show that:firstly,the heavily polluted areas have formed a "two regiment and one belt" steady-state spatial structure,mainly locating in the North China Plain,the Yangtze River Delta region,the Sichuan Basin and the Xinjiang Tarim Basin.Secondly,the proportion of the areas with a higher pollution level than the national average is 63.3%,but the proportion of the corresponding exposed population is as high as 92.5%.Thirdly,a strong local aggravation trend occurred in the slightly polluted western regions from 2000 to 2014,while the areas with the severe PM_(2.5) pollution,i.e.,Hubei,Henan,Shandong,Northern Yangtze River Delta and Beijing-Tianjin-Hebei,also experienced an increasing local trends,forming an "X" type spatial structure,but a declining local trend emerged in the heavily PM_(2.5) polluted Sichuan Basin area.
Based on the remotely sensed PM_(2.5) annual average concentrations data,the spatio-temporal evolution trends and characteristics of PM_(2.5) pollution in China from 2000 to 2014 are researched.Bayesian space-time hierarchical model based on multi-scale nested spatial statistical units is employed,and the scope of the construction thresholds for the nested multi-scale spatial statistical units is extended.The results show that:firstly,the heavily polluted areas have formed a "two regiment and one belt" steady-state spatial structure,mainly locating in the North China Plain,the Yangtze River Delta region,the Sichuan Basin and the Xinjiang Tarim Basin.Secondly,the proportion of the areas with a higher pollution level than the national average is 63.3%,but the proportion of the corresponding exposed population is as high as 92.5%.Thirdly,a strong local aggravation trend occurred in the slightly polluted western regions from 2000 to 2014,while the areas with the severe PM_(2.5) pollution,i.e.,Hubei,Henan,Shandong,Northern Yangtze River Delta and Beijing-Tianjin-Hebei,also experienced an increasing local trends,forming an "X" type spatial structure,but a declining local trend emerged in the heavily PM_(2.5) polluted Sichuan Basin area.
引文
[1] Li G,Haining R,Richardson R,Best N.Space-time Variability in Burglary Risk:A Bayesian Spatio-Temporal Modelling Approach[J].Spatial Statistics,2014(9).
[2] Bennett E,Li G,Foreman K,et al.The Future of Life Expectancy and Life Expectancy Inequalities in England and Wales:Bayesian Spatiotemporal Forecasting[J].Lancet,2015,386(9989).
[3] Lawson B.Hierarchical Modeling in Spatial Epidemiology[J].Wiley Interdisciplinary Reviews Computational Statistics,2015(6).
[4] Kang Y,Cramb M,White M,Ball J,Mengersen L.Making the Most of Spatial Information in Health:A Tutorial in Bayesian Disease Mapping for Areal Data[J].Geospatial Health,2016,11(2).
[5] 张俊辉,冯子健,杨超,等.基于层次贝叶斯时空模型的空间多尺度联合分析模型的构建及应用研究[J].中国卫生统计,2013,30(2).
[6] 吴北平,杨典,王劲峰,等.利用贝叶斯时空模型分析山东省手足口病时空变化及影响因素[J].地球信息科学学报,2016,18(12).
[7] 夏勇,胡雅蓓.经济增长与环境污染脱钩的因果链分解及内外部成因研究——来自中国 30个省份的工业SO2排放数据[J].产业经济研究,2017(9).
[8] 江佳,邹滨,陈璟雯.中国大陆1998年以来PM2.5浓度时空分异规律[J].遥感信息,2017,32(1).
[9] 周亮,周成虎,杨帆,等.2000—2011年中国PM2.5时空演化特征及驱动因素解析[J].地理学报,2017,72(11).
[10] Peng J,Chen S,Lü H,Liu Y,Wu J.Spatiotemporal Patterns of Remotely Sensed PM2.5 Concentration in China from 1999 to 2011[J].Remote Sensing of Environment,2016(174).
[11] 吴振信,闫洪举,张雪峰.基于SpSVAR模型的区域产业结构与大气污染互动研究[J].商业研究,2016(3).
[12] Fortin J,Drapeau P,Legendre P.Spatial Autocorrelation and Sampling Design in Plant Ecology[J].Vegetatio,1989,83(1/2).
[13] 李俊明.基于Bayesian层次时空模型的我国老龄化分析与预测[J].统计研究,2016,33(8).
[14] Van Donkelaar A,Martin V,Brauer M,et al.Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites,Models,and Monitors[J].Environmental Science & Technology,2016,50(7).
[15] Li J,Jin M,Xu Z.Spatiotemporal Variability of Remotely Sensed PM2.5 Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model[J].International Journal of Environmental Research & Public Health,2016,13(8).
[2] Bennett E,Li G,Foreman K,et al.The Future of Life Expectancy and Life Expectancy Inequalities in England and Wales:Bayesian Spatiotemporal Forecasting[J].Lancet,2015,386(9989).
[3] Lawson B.Hierarchical Modeling in Spatial Epidemiology[J].Wiley Interdisciplinary Reviews Computational Statistics,2015(6).
[4] Kang Y,Cramb M,White M,Ball J,Mengersen L.Making the Most of Spatial Information in Health:A Tutorial in Bayesian Disease Mapping for Areal Data[J].Geospatial Health,2016,11(2).
[5] 张俊辉,冯子健,杨超,等.基于层次贝叶斯时空模型的空间多尺度联合分析模型的构建及应用研究[J].中国卫生统计,2013,30(2).
[6] 吴北平,杨典,王劲峰,等.利用贝叶斯时空模型分析山东省手足口病时空变化及影响因素[J].地球信息科学学报,2016,18(12).
[7] 夏勇,胡雅蓓.经济增长与环境污染脱钩的因果链分解及内外部成因研究——来自中国 30个省份的工业SO2排放数据[J].产业经济研究,2017(9).
[8] 江佳,邹滨,陈璟雯.中国大陆1998年以来PM2.5浓度时空分异规律[J].遥感信息,2017,32(1).
[9] 周亮,周成虎,杨帆,等.2000—2011年中国PM2.5时空演化特征及驱动因素解析[J].地理学报,2017,72(11).
[10] Peng J,Chen S,Lü H,Liu Y,Wu J.Spatiotemporal Patterns of Remotely Sensed PM2.5 Concentration in China from 1999 to 2011[J].Remote Sensing of Environment,2016(174).
[11] 吴振信,闫洪举,张雪峰.基于SpSVAR模型的区域产业结构与大气污染互动研究[J].商业研究,2016(3).
[12] Fortin J,Drapeau P,Legendre P.Spatial Autocorrelation and Sampling Design in Plant Ecology[J].Vegetatio,1989,83(1/2).
[13] 李俊明.基于Bayesian层次时空模型的我国老龄化分析与预测[J].统计研究,2016,33(8).
[14] Van Donkelaar A,Martin V,Brauer M,et al.Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites,Models,and Monitors[J].Environmental Science & Technology,2016,50(7).
[15] Li J,Jin M,Xu Z.Spatiotemporal Variability of Remotely Sensed PM2.5 Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model[J].International Journal of Environmental Research & Public Health,2016,13(8).