武汉市PM2.5影响因素多元回归分析
摘要
本文对武汉市2013 年1 月—2013 年8 月PM2.5 质量浓度和影响因素数据资料
进行整理统计，对影响大气污染的各个污染指标进行综合分析，分别建立了PM2.5 质量浓度与其他污染指标存在不同的相关性，这类模型具有一定的实际应用价值，分
别采用“强行进入法”“逐步进入法”建立了PM2.5 指标的多元线性回归模型，比较了
对缺失值进行不同的方法处理时，差异不是特别大；还有就是共线性的问题，多重共
线性并没有影响到整个模型的拟合，因而不会对模型得到因变量的点估计值有影响。
通过定性分析，定量计算以及对各物理量之间的相互作用过程研究，得出PM2.5 质
量浓度变化特征和各影响因素之间的关系。结果表明，对于5 个基本指标，两两之
间，出了臭氧与二氧化氮以及臭氧与可吸入颗粒物，其余的指标在0.01 的水平上都
是显著相关的；通过F 假设检验得出在0.01 的水平上，PM2.5 与其余指标均是显著
相关的，其中，PM2.5 与臭氧呈现负相关，与其余量呈现正相关，与一氧化碳的相关
系数最高；在最后进行了检验分析。在进行验证时，我们利用线性模拟，二次模拟分
别与原始数据进行比较，得到的拟合效果比较好，是我们所要的结果；在最后面，根
据线性多元回归模型得到的结果，做出相应的预测并且判断出一氧化碳对PM2.5 的
影响是最大的，提出了一些相应的措施，能够有效地控制大气污染。
关键词：PM2.5 污染特征相关性回归分析
万方数据
华中科技大学硕士学位论文
II
Abstract
In this paper, Wuhan January 2013 2013 August PM2.5 mass concentrations and
factors influencing collate statistical data , effects of atmospheric pollution on various
pollution indicators comprehensive analysis of PM2.5 mass concentrations were established
with other pollution indicators have different correlations such models have some practical
value respectively, using "enter" " stepwise" to establish a multiple
linear regression model
PM2.5 indicators compared to the missing values different approach , the difference is not
particularly large ; there is collinearity problem of multicollinearity does not affect the fit
of the entire model , and thus will not get the model variables because the point estimate
values affected. Through qualitative analysis , quantitative calculation as well as the
physical interaction between the various studies , the relationship between PM2.5 mass
concentration derived characteristics and the influencing factors. The results show that for
the five basic indicators between any two out of ozone and nitrogen dioxide , and ozone and
particulate matter , the rest of the index at the 0.01 level were significantly associated ;
through F hypothesis test results in 0.01 on the level , PM2.5 and other indicators were
significantly correlated , in which , PM2.5 and ozone negatively correlated positively related
to its margin , the highest correlation coefficient with carbon monoxide ; were tested in the
final analysis . Upon verification, we use linear analog, two simulations were compared
with the original data were fitted get better results, we want results; in the final surface,
according to the results obtained by the linear regression model, make the appropriate
predictions and determine the impact of carbon monoxide on PM2.5 is the largest, made a
number of appropriate measures to effectively control air pollution. Key words: PM2.5 Pollution characteristics correlation Regression analysis
万方数据
华中科技大学硕士学位论文
III
目录
摘要................................................................... .......................................... I
Abstract ............................................................ ......................................... II
1 引言
