韶关市PM_(2.5)影响因素分析和预测研究  

The influencing factors' analysis and prediction of PM_(2.5) in Shaoguan

作　　者：谢壁君 王尚九[1] 黄冰娜 

机构地区：[1]韶关学院数学与统计学院,广东韶关512005

出　　处：《高师理科学刊》2017年第5期20-26,共7页Journal of Science of Teachers＇College and University

基　　金：国家自然科学基金资助项目(11501178);2016广东省省级大学生创新创业训练计划项目(201610576053);韶关学院校级科研项目(S201501016)

摘　　要：随着人们生活水平的不断提高,环境问题已成为备受关注的热点话题,其中最为突出的就是PM_(2.5)造成的雾霾天气.因此对环境中PM_(2.5)产生的影响因素进行分析和浓度的预测研究显得非常必要.从韶关市环境气象官方网站和天气网收集了2014-10-01至2015-05-31的相关数据,并对原始数据进行相关性分析、主成分分析和独立成分分析,建立多元回归模型,研究分析了PM_(2.5)与其他影响因素之间的关系.结合时间序列改进简单的回归模型,得到向量自回归平均模型.经过比较检验,认为向量自回归平均模型是最为理想的预测模型.With the continuous improvement of people＇s living standards, environmental issues have been paid more and more attention and become a popular topic, especially in the smog caused by PM2.5. Thus, it is extremely necessary to analyze the influence factors and predict the concentration of PM2.5. Mainly collects the related data from 2014-10-01 to 2015-05-31 from the Shaoguan environmental meteorology official website, applies the method of the correlation analysis, principal component analysis, independent component analysis to build multiple regression model. Analyzes the relationship between PM2.5 and other influencing factors. To obtain the vector autoregressive model, improves the simple regression model with time series. After analyzing and comparing, it＇s thought that vector autoregressive model is the best prediction model.

关 键 词：PM2.5 回归模型 向量自回归平均模型 韶关市 

分 类 号：O212.4[理学—概率论与数理统计] X821[理学—数学]