An evaluation model for PM2.5 in China

Particulate matter 2.5 (PM
_2.5
) pollution has become one of the most urgent issues to be addressed in China. The anthropogenic reasons of PM
_2.5
formation have considered to be highly related to local economic development model, industrial structure, energy structure and fast urbanization. The aim of this study is to explore the quantitative relationship between PM
_2.5
and anthropogenic factors in China during the period February to December in 2013. In this paper, techniques of Geographic Information System (GIS) and spatial statistics are applied to form a novel evaluation model for studying the PM
_2.5
formation of China. We first design a Forward-selection stepwise regression model for tracing major factors of PM
_2.5
formation. Based on the estimated G statistic index, Lagrange Multiplier and goodness-of-fit tests, the Spatial Lag Model (SLM) was selected to estimate PM
_2.5
. The results show that: (1) the SLM has better goodness-of-fit (R2=0.71) than classical linear regression model (R2=0.62). (2) The ranking of contribution to PM
_2.5
are the square of per capita motor vehicle, spatial factors, population, the square of forest coverage, and the square of the proportion of second industry. (3) Positive correlation coefficients were observed between the annual mean concentrations of PM
_2.5
and the square of per capita motor vehicle, population, the square of the proportion of second industry. However, the correlation coefficient of the square of forest coverage was found to be negative.