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刘珊,刘迪.基于GAM的北京PM2.5浓度变化的影响因素研究[J].河北工程大学自然版,2017,34(2):95-99
基于GAM的北京PM2.5浓度变化的影响因素研究
Exploring the factors influencing PM2.5 concentration change in Beijing based on Generalized Additive Model
投稿时间:2017-03-28  
DOI:10.3969/j.issn.1673-9469.2017.02.019
中文关键词:  PM2.5  北京  影响因素  GAM
英文关键词:PM2.5  Beijing  influencing factors  GAM
基金项目:国家自然科学基金资助项目(41201394)
作者单位
刘珊 河海大学 地球科学与工程学院, 江苏 南京 211100 
刘迪 河海大学 地球科学与工程学院, 江苏 南京 211100 
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中文摘要:
      为辨识与测度不同影响因子对PM2.5浓度变化的作用机理,以2015年北京PM2.5浓度时间演变模式为基础,建立PM2.5与各大气污染物(PM10、SO2、NO2、CO、O3)及气象因素(日均温度、风力、风向)的GAM模型,探索不同因素对PM2.5浓度变化的影响作用。结果显示:(1)北京PM2.5浓度具有夏秋季低、春冬季高的时间分布特点;(2)2015年北京PM2.5浓度变化与PM10、SO2、NO2、CO大体呈线性正相关,且正相关程度由强到弱为:CO > PM10> SO2> NO2,而与O3、温度和风因子的关系更为复杂;(3)GAM模型的拟合优度R2为0.725,线性回归模型的拟合优度R2为0.519,相比较,GAM模型对PM2.5浓度变化的解释度提高了20.6%。研究表明,GAM模型对于建立PM2.5浓度变化与影响因素间综合性复杂关系更灵活、更可靠,优于线性回归模型。
英文摘要:
      Based on the mechanism of PM2.5 concentration change in Beijing in 2015, the generalized additive model (GMA) of PM2.5 and atmospheric pollutants (PM10, SO2,NO2,CO,O3), as well as the meteorological factors (daily mean temperature, wind scale, wind direction) are established to explore the impact of different factors on PM2.5 concentration changes. The results showe that: (1) Beijing PM2.5 concentration has the characteristics of low distribution of summer and autumn, however higher in spring and winter; (2) The PM2.5 concentration in Beijing is linearly positively correlated with PM10, SO2, NO2, CO. The positive correlation is from strong to weak: CO> PM10> SO2> NO2, and the relationship with O3, temperature and wind factor is more complicated. (3) The goodness R2 of the GAM model is 0.725, and the goodness of the linear regression model is 0.519. Compared with the GAM model, the explanatory degree of the PM2.5 concentration increased by 20.6%. The results show that the GAM model is more flexible and reliable than the linear regression model in establishing the complex relationship between PM2.5 concentration and influencing factors.
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