基于VAR模型的石家庄市PM2.5的影响因素研究
Study on the Impact Factors of PM2.5 in Shijiazhuang Based on VAR Model
李梦涵 1杨进 2周建山 2李金洁1
作者信息
- 1. 河北经贸大学,石家庄 050061
- 2. 湖北省恩施州气象局,湖北恩施 445000
- 折叠
摘要
根据石家庄市2013年12月2日至2021年2月1日的空气污染物PM2.5、PM10、SO2、CO、NO2和O3浓度数据,以及日平均气温、日平均气压、日平均相对湿度、2 min平均风速和10 min平均风速气象数据,利用向量 自回归模型(Vector autoregression,VAR),从各空气污染物变量和气象变量中选取对时间序列影响较大的重要变量,研究PM2.5与重要变量之间的动态关系及它们对PM2.5浓度的影响.结果表明:SO2浓度、NO2浓度、O3浓度、日平均气温、日平均气压、2 min平均风速相较其他变量对PM2.5浓度有显著影响;从长期关系来看,SO2浓度和 日平均气压变量对PM2.5浓度有正向影响,NO2浓度、O3浓度、日平均气温,以及2 min平均风速变量对PM2.5浓度的增加有抑制作用.
Abstract
In this paper,the concentration data of air pollutants PM2.5,PM10,SO2,CO,NO2 and O3 as well as meteorological data such as temperature,pressure,humidity and wind speed in Shijiazhuang from December 2,2013 to February 1,2021 are collated and analyzed.By using Vector autoregression(VAR)model,we select the important variables with greater influence on time series of PM2.5 from each air pollutant variable and meteorological variable to probe the dynamic relationship between PM2.5 and im-portant variables and their influence on PM2.5.The results show that the concentrations of SO2,NO2 and O3,the daily mean temperature,the daily mean air pressure and the 2 min average wind speed have sig-nificant effects on the concentration of PM2.5 compared to other variables.In terms of long-term relation-ships,SO2 concentration and mean air pressure have positive effects on PM2.5 concentration,while NO2 concentration,O3 concentration,daily mean temperature and 2 min average wind speed have inhibitory effects on the increase of PM2.5 concentration.
关键词
PM2.5/VAR模型/脉冲响应函数/方差分解Key words
PM2.5/VAR model/impulse response function/variance decomposition引用本文复制引用
基金项目
河北省自然科学基金面上项目(A2020207006)
出版年
2024
NETL
NSTL
万方数据