大气与环境光学学报, 2016, 11 (2): 91, 网络出版: 2016-03-29
基于VAR模型的PM2.5与其它空气污染物的动态关系分析
Analysis of Dynamic Relationship Between PM2.5 and Other Air Pollutants Based on VAR Model
VAR模型 广义脉冲响应 空气污染治理 PM2.5 PM2.5 vector auto-regressive model generalized impulse response air pollution governance
摘要
通过建立向量 自回归(VAR)模型,综合运用格兰杰因果关系检验、广义脉冲响应函数和方差分解法,分析PM2.5与其它空气污染物的动态关系,探讨其它 空气污染物对PM2.5的影响,利用西安市2013年1月1日～2014年12月31日有关环境空气质量的数据进行了研究。结果表明: PM2.5与其它空气污染物所 构成的空气质量系统是稳定的, SO2 、NO2 、CO浓度值的增加会引起PM2.5浓度值持续较长时间的增加,其中SO2 对PM2.5影响作用最大; O3 浓度值的增加则会使PM2.5浓度值降低。
Abstract
The dynamic relationship between the PM2.5 and other kinds of air pollutants was analyzed based on vector auto-regressive(VAR) model. Methods of Granger causality test, generalized impulse response function and variance decomposition analysis were used, based on the daily data of the ambient air quality from January 1, 2013 to December 31, 2014 in Xi’an city to investigate the effect of other kinds of air pollutants on the PM2.5. The results show that the air quality system composed of PM2.5 and other kinds of air pollutants is stable, the increase of sulfur dioxide concentration, nitrogen dioxide and carbon monoxide will lead to the increase of PM2.5 concentration. Sulfur dioxide has the greatest effect on PM2.5 and the increase of ozone concentration will make PM2.5 concentration decrease.
汪官镇, 刘金培, 陈华友, 肖鹏, 蒋一聪, 杜博文. 基于VAR模型的PM2.5与其它空气污染物的动态关系分析[J]. 大气与环境光学学报, 2016, 11(2): 91. WANG Guanzhen, LIU Jinpei, CHEN Huayou, XIAO Peng, JIANG Yicong, DU Bowen. Analysis of Dynamic Relationship Between PM2.5 and Other Air Pollutants Based on VAR Model[J]. Journal of Atmospheric and Environmental Optics, 2016, 11(2): 91.
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