摘要
以R语言为数据分析的工具,基于相关分析和回归分析方法,对太原市PM2.5的影响因素进行分析。研究PM2.5与其他气态污染物之间的关系,探讨各气态污染物在PM2.5二次合成中的贡献;建立PM2.5和PM10的回归模型,方便通过PM10对PM2.5进行预测。结果显示:(1)太原市区空气污染物中,PM2.5和PM10相关性最强;(2)PM2.5和PM10回归分析得到回归模型为PM2.5=0.63PM10-11.76(R2=0.8427),回归方程拟合度较好;(3)PM2.5和其他气态污染物多元线性回归模型为PM2.5=0.24SO_2+20.08CO+11.54(R2=0.4844),拟合度检验效果一般,考虑三者之间还有其他因素的影响。
Using R language as a tool for data analysis,based on correlation analysis and multiple linear regression analysis methods,the paper analyzes the influencing factors of PM2.5 in Taiyuan City,studies the relationship between PM2.5 and other gaseous pollutants,and explores the contribution of gaseous pollutants in secondary synthesis.The regression model of PM2.5 and PM10 is established to facilitate the prediction of PM2.5 through PM10.The results show:the correlation between PM2.5 and PM10 is the strongest among the air pollutants in Taiyuan;the regression model achieved through the regression analysis on PM2.5 and PM10 is PM2.5=0.63PM10-11.76(R2=0.8427),with high fitting degree;the PM2.5 and other gaseous pollutants multivariate linear regression model is PM2.5=0.24SO 2+20.08CO+11.54(R2=0.4844).The test results are general,considering the influence of other factors.
作者
解蕾
狄光智
XIE Lei;DI Guangzhi(Department of Mathematics and Computer Science,Yuncheng Advanced College,Yuncheng 044000,China;School of Computer and Information Technology,Southwest Forestry University,Kunming 650224,China)
出处
《软件工程》
2019年第5期15-17,8,共4页
Software Engineering
基金
全国高等院校计算机基础教育研究会项目(编号:2018-AFCEC-377)
关键词
R语言
数据分析
相关分析
回归分析
R language
data analysis
correlation analysis
regression analysis
