摘要
采用马尔可夫链和空间马尔可夫链分析方法,对2003-2013年长三角城市群PM10年均浓度的时空演化特征进行了分析。根据PM10年均浓度的平均水平,将长三角城市群大气污染划分为低浓度、较低浓度、较高浓度和高浓度4种类型,分别计算了其马尔可夫转移概率矩阵和空间马尔可夫转移概率矩阵。结果表明:近年来长三角城市群大气污染空间分布的不平衡性有扩大趋势,并存在一定的趋同现象;低浓度类型和高浓度类型趋同的稳定性相对较大;短期内各地区大气污染类型发生跨级转移的可能性极低;长三角城市群大气污染格局演化在空间上并不独立,受邻域条件的影响较为明显;邻域污染水平等级越低,大气污染类型向下转变的概率越大,反之越小。
Based on the annual average concentration of PM10 dataset at the city level in the Yangtze River Delta from 2003 to 2013, this essay analyzes the spatial and temporal characteristics of air pollution pattern of urban agglomeration in the Yangtze River Delta, by a comprehensive application of Markov framework. All the annual average concentration of PM10 data in the Yangtze River Delta are classified into four different classes by annual regional average, and Markov transition probability matrices and spatial Markov transition probability matrices are estimated. The conclusions show that, air pollution pattern of urban agglomeration in the Yangtze River Delta becomes more unbalanced in recent years, and the process of regional convergence, in terms of air pollution, has been globally characterized by "convergence clubs" , the lowest and the highest air pollution types do not seem to change their relative position over time. The possibility of regional air pollution types cross - level transfer is very low in short term. Urban agglomerations in the Yangtze River Delta are greatly affected by their spatial neighbors, and the higherlevel neighbors tend to prevent cities from fall- ing down in the annual average concentration of PMIO distribution.
出处
《城市问题》
CSSCI
北大核心
2016年第1期23-27,共5页
Urban Problems
基金
国家社会科学基金重大项目(12&ZD081)
关键词
空间马尔可夫链
大气污染
PM10
时空演变
长三角
spatial Markov chains
air pollution
PM10
spatial- temporal evolution
the Yangtze River Delta