地理学报 ›› 2016, Vol. 71 ›› Issue (7): 1119-1129.doi: 10.11821/dlxb201607003

• 模拟研究 • 上一篇    下一篇

基于灰色关联模型对江苏省PM2.5浓度影响因素的分析

贺祥1,2,3(), 林振山1,3(), 刘会玉1,3, 齐相贞1,3   

  1. 1. 南京师范大学地理科学学院,南京 210023
    2. 凯里学院,凯里 556011
    3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 收稿日期:2016-02-25 修回日期:2016-03-21 出版日期:2016-07-25 发布日期:2016-07-27
  • 作者简介:

    作者简介:贺祥(1978-), 男, 贵州水城人, 博士, 副教授, 主要从事生态与环境研究。E-mail:hexiang1997403@163.com

  • 基金资助:
    国家自然科学基金项目(31470519);2015年江苏省高校自然科学研究重大项目(15KJA17002);江苏省自然科学基金项目(BK20131399);江苏省高校优势学科建设工程资助项目;贵州省科技厅基金项目([2014]7237);贵州省教育厅人文社科项目(13GH004)

Analysis of the driving factors of PM2.5 in Jiangsu province based on grey correlation model

Xiang HE1,2,3(), Zhenshan LIN1,3(), Huiyu LIU1,3, Xiangzhen QI1,3   

  1. 1. College of Geography Science, Nanjing Normal University, Nanjing 210023, China
    2. Kaili University, Kaili 556011, Guizhou, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2016-02-25 Revised:2016-03-21 Online:2016-07-25 Published:2016-07-27
  • Supported by:
    National Natural Science Foundation of China, No.31470519;University Natural Science Major Project of Jiangsu Province in 2015;Natural Science Foundation of Jiangsu Province, No.BK20131399;The Priority Academic Program Development of Jiangsu Higher Institutions;Science and Technology Department Project of Guizhou Province, No.[2014]7237;Education Department Project of Guizhou Province, No.13GH004]

摘要:

采用克里金插值法分析2014年江苏省PM2.5浓度空间分布特征,运用灰色关联模型计算PM2.5浓度与影响因素间关联度,分析主要影响指标因子与PM2.5浓度空间分布的相互关系。结果显示:① 江苏省PM2.5浓度具有沿海低、内陆高,南部高、北部低的空间分布特征;② PM2.5污染来源指标层的权重值最大(wi = 0.4691),空气质量与气象要素指标层的权重稍大(wi = 0.2866),城市化与产业结构层的权重值最小(wi = 0.2453);③ 在27个指标因子中,与PM2.5浓度关联度为中度的仅有公路客运量、房屋建筑施工面积、园林绿地面积、人口密度等4个指标因子,PM2.5与其余指标因子均呈强度相关联,其中与PM10、O3、降雨量、公路货运总量、地区工业总产值和第二产业占地区生产总值比重等指标的关联度较高;④ PM2.5污染源指标层与PM2.5浓度关联度值较大的城市分别是南京、无锡、常州、南通、泰州市;城市化与产业结构指标层与PM2.5浓度关联度值较大的城市分别是徐州、苏州、盐城、常州市;空气质量与气象要素指标层与PM2.5浓度关联度值较大的城市分别是盐城、扬州、常州、南通市。综合分析可知,影响指标因子关联度值与PM2.5浓度空间分布有较好相关性。研究表明,灰色关联模型可有效分析影响PM2.5浓度的主要因素,能对PM2.5浓度影响指标进行定量分析与评价。

关键词: 灰色关联模型, PM2.5浓度空间分布, 影响指标因子, 江苏省

Abstract:

In this paper, the Kriging interpolation method was introduced to analyze the spatial distribution characteristics of PM2.5 in Jiangsu province in 2014, and then the evaluation index system for the PM2.5 was constructed, which consists of three index layers and 27 indexes. The grey correlation analysis method was used to explore the correlation between PM2.5 and its influencing factors. Finally, the relationship between the spatial distribution of PM2.5 and the main influencing factors was analyzed. The conclusions can be drawn as follows: (1) The PM2.5 in the coastal areas and the north is lower, while it is higher in the inland areas and the south. (2) The weight of PM2.5 pollution sources index layer is the largest (wi = 0.4691), the weight of the air quality index and meteorological elements layer is larger (wi = 0.2866), and the weight value of urbanization and industrial structure index layer is the minimum (wi = 0.2453). (3) In the 27 indexes, the volume of highway freight, housing construction area, garden green space area and population density have moderate correlation degrees. The other indexes have strong correlation degrees, among which, the correlation degree of the PM10, O3, total road freight volume and gross industrial output value are relatively high. (4) The synthetic correlation degree values between the PM2.5 pollution sources index layer and PM2.5 are much higher in cities of Nanjing, Wuxi, Changzhou, Nantong and Taizhou. The synthetic correlation degree values between urbanization and industrial structure index layer and PM2.5 are much higher in cities of Xuzhou, Suzhou, Yancheng and Changzhou. The synthetic correlation degree values between the air quality index and meteorological elements layer and PM2.5 are much higher in cities of Yancheng, Yangzhou, Changzhou and Nantong. Our results demonstrate that the grey correlation degrees of the evaluation indexes system are closely related with spatial distribution of PM2.5 in Jiangsu province. Therefore, the grey correlation analysis model can be employed to analyze and evaluate the spatial distribution of PM2.5.

Key words: grey correlation model, the spatial distribution of PM2.5, influencing index, Jiangsu province