函数型数据视角下基于伪分位数聚类的空气质量治理区域划分
Regional Division of Air Quality Governance Based on Pseudo-Quantile Clustering with the View of Functional Data
梁永玉 1曹苏周 2周梦雨 3田茂再4
作者信息
- 1. 临夏县统计局,甘肃临夏 731800
- 2. 西安思源学院基础部,陕西西安 710038
- 3. 新疆财经大学统计与数据科学学院,新疆乌鲁木齐 830012
- 4. 中国人民大学应用统计科学研究中心,北京 100872;中国人民大学统计学院,北京 100872
- 折叠
摘要
近几年,空气污染与质量问题受到广泛关注.由于我国各大城市空气质量差异较大,区域性特征明显,所以划分空气质量区域,实施针对性空气质量防治措施,对改善和提高我国各地区空气质量具有重要的现实意义.本文以我国312个地级市2015年至2019年空气质量AQI逐日数据,使用伪分位数聚类,即Expectlie曲线聚类和M分位数函数型数据聚类方法,对各地级市空气质量进行研究和治理区域划分.根据两种聚类结果最终划分为9个不同的空气质量治理区域,并针对各区域特点提出污染防治措施.
Abstract
In recent years,air pollution and quality problems have been widely concerned.Due to the large difference of air quality in China's major cities and obvious regional characteristics,it is of great practical significance to divide air quality regions and implement targeted air quality prevention and control measures to improve air quality in China's various regions.In this paper,based on the daily AIR quality AQI data of 312 prefecture-level cities from 2015 to 2019,pseudo-quantile clustering(Expectlie curve clustering and M-quantile functional data clustering)was used to study the air quality of each prefecture-level city and divide the air quality control regions.According to the two kinds of clustering results,9 different air quality control regions were finally divided,and pollution prevention measures were proposed according to the regional characteristics of each region.
关键词
Expectile曲线/M分位数/伪分位数/函数型数据聚类/空气质量Key words
expectile curves/M-quantile/pseudo-quantile/functional data clustering/air quality引用本文复制引用
出版年
2024
NETL
NSTL
万方数据