标题:
基于广义加性模型的北京市PM2.5浓度影响因素分析Analysis of Beijing PM2.5 Concentration Effect Factors Based on Generalized Additive Models
作者:
李晓童, 张默
关键字:
PM2.5, 广义加性模型, 线性回归模型PM2.5 Concentration, Generalized Additive Model, Linear Regression Model
期刊名称:
《Hans Journal of Data Mining》, Vol.6 No.4, 2016-10-27
摘要:
近年来，北京的空气污染日趋严重，PM2.5也引起了社会各界的广泛关注。目前针对北京市PM2.5浓度影响因素的研究中，在影响因素种类和模型选择方面有明显的局限性。文章基于上述两点，建立了以PM2.5浓度为响应变量、影响因素为预测变量的广义加性模型，结果发现PM2.5浓度的影响因素包括NO2浓度、风速、温度、月份、CO浓度、O3浓度和湿度。文章还建立了线性回归模型进行对比，结果发现加性模型的拟合效果明显优于线性模型。
In recent years, air pollution in Beijing is increasingly serious; PM2.5 has caused widespread con-cern in the community. There are obvious limitations about influencing factors’ type and model selection in current studies on the influence factors of PM2.5 concentration in Beijing. Based on the above two points, this paper conducted a generalized additive model which regarded PM2.5 concentration as response variable and influence factors as predictor variable, and the results showed that: the factors influencing PM2.5 concentration include NO2 concentration, wind speed, temperature, month, CO concentration, O3 concentration and humidity. This paper also conducted a linear regression model in order to make contrast, and the results showed that the goodness of fit of the additive model is much better than the linear model.