上海市大气PM2.5时空分布特征

陈杨欢; 王杨君; 张苗云; 许振影; 鲍胜威; 吴明红; 张钢锋

doi:10.12030/j.cjee.201610092

环境工程学报

上海市大气PM2.5时空分布特征

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陈杨欢, 王杨君, 张苗云, 许振影, 鲍胜威, 吴明红, 张钢锋. 上海市大气PM2.5时空分布特征[J]. 环境工程学报, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

引用本文:

陈杨欢, 王杨君, 张苗云, 许振影, 鲍胜威, 吴明红, 张钢锋. 上海市大气PM2.5时空分布特征[J]. 环境工程学报, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

CHEN Yanghuan, WANG Yangjun, ZHANG Miaoyun, XU Zhenying, BAO Shengwei, WU Minghong, ZHANG Gangfeng. Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis[J]. Chinese Journal of Environmental Engineering, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

Citation:

CHEN Yanghuan, WANG Yangjun, ZHANG Miaoyun, XU Zhenying, BAO Shengwei, WU Minghong, ZHANG Gangfeng. Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis[J]. Chinese Journal of Environmental Engineering, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

上海市大气PM2.5时空分布特征

1.
上海大学环境与化学工程学院, 上海 200444
2.
金华市环境监测中心站, 金华 321013
3.
上海市环境科学研究院, 上海 200444
基金项目:
浙江省金华市环保局项目（YG2014-FW673-ZFCG046）

浙江省金华市科技局项目（2013-3-001）
中图分类号: X513

Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis

1.
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
2.
Jinhua Environmental Monitoring Station, Jinhua 321013, China
3.
Shanghai Academy of Environmental Sciences, Shanghai 200444, China
Fund Project:

摘要: 对2014年上海市大气监测国控点的PM2.5浓度数据进行统计分析和聚类分析。统计分析结果表明，上海市PM2.5浓度冬春季高，夏秋季低，按月呈U形分布，且上海市大气PM2.5浓度在空间上总体趋势呈西高东低。利用MATLAB的聚类分析结果表明上海市的10个监测站可分为4类：1）跨省传输影响显著的青浦淀山湖站；2）受海洋大气影响显著的浦东川沙站；3）不稳定的过渡类，其包括杨浦四漂和浦东张江监测站；4）受本地排放影响显著的中心城区类，其包括普陀、十五厂（卢湾师专附小）、徐汇上师大、虹口凉城、静安和浦东新区监测站。本文聚类分析结果揭示了上海不同地理位置的大气PM2.5浓度的相互关系。
- PM2.5 /
- 聚类分析 /
- 监测站 /
- MATLAB /
- 上海
Abstract: Statistical analysis and clustering analysis of observed daily PM2.5 concentrations in 2014 were conducted for Shanghai using Matlab software. The results indicated that PM2.5 concentrations were higher in spring and winter, than those in summer and autumn, The annual distribution of PM2.5 concentrations showed U-shape. Spatial distribution showed that PM2.5 concentrations were higher in the west while they were lower in the east. According to results of the clustering analysis,Ten monitoring sites in shanghai could be grouped into 4 group scategories:1)The Dianshan Lake site in Qingpu whose PM2.5 concentrations wereis significantly influenced by the cross-provinciale transportation;2)The Chuansha site in Pudong whose PM2.5 concentrations wereis easily diluted by clean wind from ocean;3)Unstable and interim sites,including Sipiao site in Yangpu and Zhangjiang sites in Pudong;4)The central urban area group whose PM2.5 concentrations were significantly impacted by local emission, including Putuo site, Shiwu factory(School of Luwan Normal College) site, Shanghai Normal University site in Xuhui, Liangcheng site in Hongkou, Jing'an site and site of Pudong New Area. The clustering analysis results revealed the inter-relationship of the PM2.5 concentrations among these monitoring sites in shanghai.
- PM2.5 /
- clustering analysis /
- monitoring site /
- MATLAB /
- shanghai

[1]	王振波, 方创琳, 许光,等. 2014年中国城市PM_2.5浓度的时空变化规律[J]. 地理学报, 2015(11):1720-1734
[2]	WANG Y, LI L, CHEN C, et al. Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China[J]. Journal of Geophysical Research, 2014, 119(4):1903-1914
[3]	张殷俊, 陈曦, 谢高地, 等.中国细颗粒物(PM_2.5)污染状况和空间分布[J]. 资源科学,2015,37(7):1339-1346
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[5]	殷永文, 程金平, 段玉森, 等. 上海市霾期间PM_2.5, PM₁₀ 污染与呼吸科, 儿呼吸科门诊人数的相关分析[J].环境科学,2014,32(7):1894-1898
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[8]	徐建辉, 江洪. 长江三角洲PM_2.5 质量浓度遥感估算与时空分布特征[J].环境科学,2015,36(9):3119-3127
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[13]	孙冉, 王成都, 刘国东. 2014年成都市PM_2.5污染及其与气象要素的关系[J]. 环境工程, 2015,(S1):472-475

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陈杨欢, 王杨君, 张苗云, 许振影, 鲍胜威, 吴明红, 张钢锋. 上海市大气PM2.5时空分布特征[J]. 环境工程学报, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

引用本文:

陈杨欢, 王杨君, 张苗云, 许振影, 鲍胜威, 吴明红, 张钢锋. 上海市大气PM2.5时空分布特征[J]. 环境工程学报, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

Citation:

CHEN Yanghuan, WANG Yangjun, ZHANG Miaoyun, XU Zhenying, BAO Shengwei, WU Minghong, ZHANG Gangfeng. Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis[J]. Chinese Journal of Environmental Engineering, 2017, 11(6): 3671-3677. doi: 10.12030/j.cjee.201610092

上海市大气PM2.5时空分布特征

1. 上海大学环境与化学工程学院, 上海 200444
2. 金华市环境监测中心站, 金华 321013
3. 上海市环境科学研究院, 上海 200444

收稿日期: 2016-11-25

基金项目:

浙江省金华市环保局项目（YG2014-FW673-ZFCG046）

浙江省金华市科技局项目（2013-3-001）

关键词:

PM2.5 /
聚类分析 /
监测站 /
MATLAB /
上海

摘要: 对2014年上海市大气监测国控点的PM2.5浓度数据进行统计分析和聚类分析。统计分析结果表明，上海市PM2.5浓度冬春季高，夏秋季低，按月呈U形分布，且上海市大气PM2.5浓度在空间上总体趋势呈西高东低。利用MATLAB的聚类分析结果表明上海市的10个监测站可分为4类：1）跨省传输影响显著的青浦淀山湖站；2）受海洋大气影响显著的浦东川沙站；3）不稳定的过渡类，其包括杨浦四漂和浦东张江监测站；4）受本地排放影响显著的中心城区类，其包括普陀、十五厂（卢湾师专附小）、徐汇上师大、虹口凉城、静安和浦东新区监测站。本文聚类分析结果揭示了上海不同地理位置的大气PM2.5浓度的相互关系。

English Abstract

Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis

1. School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
2. Jinhua Environmental Monitoring Station, Jinhua 321013, China
3. Shanghai Academy of Environmental Sciences, Shanghai 200444, China

Received Date: 2016-11-25

Fund Project:

Keywords:

Abstract: Statistical analysis and clustering analysis of observed daily PM2.5 concentrations in 2014 were conducted for Shanghai using Matlab software. The results indicated that PM2.5 concentrations were higher in spring and winter, than those in summer and autumn, The annual distribution of PM2.5 concentrations showed U-shape. Spatial distribution showed that PM2.5 concentrations were higher in the west while they were lower in the east. According to results of the clustering analysis,Ten monitoring sites in shanghai could be grouped into 4 group scategories:1)The Dianshan Lake site in Qingpu whose PM2.5 concentrations wereis significantly influenced by the cross-provinciale transportation;2)The Chuansha site in Pudong whose PM2.5 concentrations wereis easily diluted by clean wind from ocean;3)Unstable and interim sites,including Sipiao site in Yangpu and Zhangjiang sites in Pudong;4)The central urban area group whose PM2.5 concentrations were significantly impacted by local emission, including Putuo site, Shiwu factory(School of Luwan Normal College) site, Shanghai Normal University site in Xuhui, Liangcheng site in Hongkou, Jing'an site and site of Pudong New Area. The clustering analysis results revealed the inter-relationship of the PM2.5 concentrations among these monitoring sites in shanghai.

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上海市大气PM2.5时空分布特征

Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis

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上海市大气PM2.5时空分布特征

English Abstract

Temporal and spatial distribution of PM2.5 in Shanghai based on clustering analysis

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