GEOGRAPHICAL RESEARCH ›› 2016, Vol. 35 ›› Issue (6): 1051-1060.doi: 10.11821/dlyj201606005

• Orginal Article • Previous Articles     Next Articles

Social and economic drivers of PM2.5 and their spatial relationship in China

Kun YANG1,2(), Yulian YANG1,2, Yanhui ZHU1,2, Cen LI1,2, Chao MENG1,2   

  1. 1. School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
    2. GIS Technology Engineering Research Centre forWest-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
  • Received:2016-01-09 Revised:2016-04-20 Online:2016-06-20 Published:2016-06-30

Abstract:

The anthropogenic reasons of particulate matter 2.5 (PM2.5) formations are considered to be highly related to local economic development model and fast urbanization. The aim of this study is to explore the social and economic factors of PM2.5 and their spatial and quantitative relationship in China during the period of February to December in 2013. In this paper, Geographic Information System (GIS) technology combined with spatial statistics to form a novel evaluation model for studying the PM2.5 formation of China. The surface of PM2.5 is obtained by using inverse distance weighted (IDW). We first design a forward-selection stepwise regression model for tracing major factors of PM2.5 formation, and then study spatial distribution of PM2.5 and the major factors of PM2.5. Their G statistic indexes are calculated to detect the spatial dependence. Based on the estimated G statistic index, and Lagrange Multiplier and goodness-of-fit tests, the Spatial Lag Model (SLM) is selected to estimate PM2.5. The results show that: (1) PM2.5 presents an obviously uneven spatial distribution. East and most parts of northeast China have severe pollution. Especially, the provinces of Beijing, Hebei, Henan, Anhui, Jiangsu, Sichuan and Shanghai are seriously severe areas. (2) The square of per capita motor vehicle, population, the square of forest coverage, and the square of the proportion of secondary industry are the major factors of PM2.5. (3) Because G statistic indexes of PM2.5 and major factors confirm the presence of their spatial autocorrelation, the data of spatial autocorrelation need to be considered in the modeling process. The Spatial Lag Model has better goodness-of- fit test (R2=0.71) than classical linear regression model (R2=0.62). (4) The ranking of contribution to PM2.5 is the square of per capita motor vehicle, spatial factors, population, the square of forest coverage, the square of the proportion of secondary industry. Positive correlation coefficients were observed between the annual mean concentrations of PM2.5 and the square of per capita motor vehicle, population, and the square of the proportion of secondary industry. However, the correlation coefficient of the square of forest coverage was found to be negative. (5) PM2.5 governance is an extremely urgent task. In order to reduce PM2.5, we need to take measures in regional collaborative governance, and should terminate industrial production with high energy consumption. Optimization of the industrial structure and energy structure, as well as reforestation and afforestation can also reduce PM2.5.

Key words: PM2.5, socioeconomic factors, spatial distribution, spatial statistics, GIS