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运筹学学报 ›› 2018, Vol. 22 ›› Issue (2): 105-114.doi: 10.15960/j.cnki.issn.1007-6093.2018.02.009

所属专题： 庆贺邓乃扬教授80华诞专辑

• 运筹学 • 上一篇    下一篇

京津冀地区PM2.5污染特征的研究-----基于函数型数据分析的视角

梁银双1 刘黎明2,*   

  1. 1. 郑州工程技术学院信息工程学院, 郑州 450044;
    2. 首都经济贸易大学统计学院, 北京 100070
  • 收稿日期:2017-11-16 出版日期:2018-06-15 发布日期:2018-06-15
  • 通讯作者: 刘黎明 E-mail: llm5609@163.com
  • 基金资助:

    特大城市经济社会发展研究协同创新中心课题(No. TDJD201502), 北京市自然科学基金(No. 9172003), 郑州工程技术学院国家级科研项目培育基金(No. GJJKTPY2018K4)

PM2.5 pollution characteristic in Beijing-Tianjin-Hebei region based on the perspective of functional data analysis

LIANG Yinshuang1 LIU Liming2,*   

  1. 1. Information Engineering School, Zhengzhou Institute of Technology, Zhengzhou 450044, China; 2. Statistic School, Capital University of Economics and Business, Beijing 100070, China
  • Received:2017-11-16 Online:2018-06-15 Published:2018-06-15

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摘要：

近年来京津冀地区的雾霾重度污染事件频发, 引起国家和社会的普遍关注. 以京津冀地区68个监测站的数据为基础, 研究了京津冀地区PM2.5小时间隔的年度数据主要变异模式、时空变化类型等污染特征. 还研究了二氧化硫、氮氧化物年度累计排放量对PM2.5浓度变化的影响. 结果表明, 氮氧化物的排放对PM2.5浓度的贡献更显著, 削减氮氧化物等污染物的排放可有效降低PM2.5浓度, 改善空气质量. 采用函数型数据分析方法, 相对于传统的统计均值方法, 能够更有效的使用所采集到的不同的数据类型, 进行更细致的分析, 从而得到更可靠的结论.

关键词: 函数型数据分析, 京津冀地区, PM2.5

Abstract:

In recent years, there have been frequent smog and heavy pollution incidents in the Beijing-Tianjin-Hebei (Jing-Jin-Ji) area, which have caused widespread concern in the country and society. Based on the data of 68 monitoring stations in the Jing-Jin-Ji area , this paper studied the main variation patterns of the annual data of PM2.5 hours in the Jing-Jin-Ji area, and the characteristics of the temporal and spatial changes. The effect of cumulative annual emissions of sulfur dioxide and nitrogen oxides on changes in PM2.5 concentrations has also been studied. The results show that the emission of nitrogen oxides contributes more to the concentration of PM2.5. The reduction of nitrogen oxides and other pollutants can effectively reduce the concentration of PM2.5 and improve air quality. In this paper, a functional data analysis method is used. Compared with the traditional statistical mean method, it can more effectively use the different data types collected to perform more detailed analysis and thus obtain more reliable conclusions.

Key words: functional data analysis, Beijing-Tianjin-Hebei area, PM2.5