基于GIS和RS的城市空气可吸入颗粒物时空变化及其影响因素分析

Wen-hui Zhao, H. Gong, Wen-ji Zhao, Lin Zhu, T. Tang
{"title":"基于GIS和RS的城市空气可吸入颗粒物时空变化及其影响因素分析","authors":"Wen-hui Zhao, H. Gong, Wen-ji Zhao, Lin Zhu, T. Tang","doi":"10.1109/GEOINFORMATICS.2009.5293540","DOIUrl":null,"url":null,"abstract":"To identify the inhalable particle matter(IPM) sources and to estimate the variability in their contributions to inhalable particle concentrations across the Beijing city, the spatial distribution of PM0.3, PM1.0 and PM3.0 concentration are simulated by monitoring data obtained from 93 air sampling stations in Beijing urban city and Kriging techniques. Inhalable particles in this study had aerodynamic size between 0.3 and 3.0µm. By taking streets and towns as the basic spatial analysis unit, some factors are mapped influencing urban airborne inhalable particulates pollutions such as urban ground surface types based on GIS and RS. The correlation between PM0.3, PM1.0 and PM3.0 and their influencing factors are quantitatively evaluated by using GIS multifactor integrated analysis and GIS overlay of ranked data layers. The results show that spherical models with nuggets could fit the variograms of PM0.3, PM1.0 and PM3.0. The IPM concentration had significant decreasing trend from 2007 to 2008. Meanwhile, the pollution center has transferred from north and northeast district to southwest and northwest. The spatial relativity between three air particles and their impact factors have spatial heterogeneity in the north, southwest and downtown. Among the three pollutions, the spatial distribution of PM1.0 is firstly influenced by the influence factors; PM3 is secondly, PM0.3 is thirdly.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"14 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Temporal and spatial variation of urban airborne inhalable particle and it's influence factor analysis using GIS & RS\",\"authors\":\"Wen-hui Zhao, H. Gong, Wen-ji Zhao, Lin Zhu, T. Tang\",\"doi\":\"10.1109/GEOINFORMATICS.2009.5293540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To identify the inhalable particle matter(IPM) sources and to estimate the variability in their contributions to inhalable particle concentrations across the Beijing city, the spatial distribution of PM0.3, PM1.0 and PM3.0 concentration are simulated by monitoring data obtained from 93 air sampling stations in Beijing urban city and Kriging techniques. Inhalable particles in this study had aerodynamic size between 0.3 and 3.0µm. By taking streets and towns as the basic spatial analysis unit, some factors are mapped influencing urban airborne inhalable particulates pollutions such as urban ground surface types based on GIS and RS. The correlation between PM0.3, PM1.0 and PM3.0 and their influencing factors are quantitatively evaluated by using GIS multifactor integrated analysis and GIS overlay of ranked data layers. The results show that spherical models with nuggets could fit the variograms of PM0.3, PM1.0 and PM3.0. The IPM concentration had significant decreasing trend from 2007 to 2008. Meanwhile, the pollution center has transferred from north and northeast district to southwest and northwest. The spatial relativity between three air particles and their impact factors have spatial heterogeneity in the north, southwest and downtown. Among the three pollutions, the spatial distribution of PM1.0 is firstly influenced by the influence factors; PM3 is secondly, PM0.3 is thirdly.\",\"PeriodicalId\":121212,\"journal\":{\"name\":\"2009 17th International Conference on Geoinformatics\",\"volume\":\"14 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 17th International Conference on Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GEOINFORMATICS.2009.5293540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 17th International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

为了确定可吸入颗粒物(IPM)来源,并估算其对北京市可吸入颗粒物浓度贡献的变异性,利用北京市区93个空气监测站的监测数据,利用Kriging技术模拟了PM0.3、PM1.0和PM3.0浓度的空间分布。本研究中可吸入颗粒的气动尺寸在0.3 ~ 3.0µm之间。以街道和城镇为基本空间分析单元,在GIS和RS的基础上,绘制城市地表类型等影响城市大气可吸入颗粒物污染的因素,采用GIS多因素综合分析和GIS分层叠加的方法,定量评价PM0.3、PM1.0和PM3.0及其影响因素的相关性。结果表明,含掘金的球形模型能较好地拟合PM0.3、PM1.0和PM3.0的方差。2007 - 2008年IPM浓度呈显著下降趋势。同时,污染中心由北部和东北部向西南和西北部转移。三种空气颗粒物及其影响因子的空间相关性在北部、西南部和市区具有空间异质性。三种污染中,PM1.0的空间分布首先受到影响因子的影响;PM3排名第二,PM0.3排名第三。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporal and spatial variation of urban airborne inhalable particle and it's influence factor analysis using GIS & RS
To identify the inhalable particle matter(IPM) sources and to estimate the variability in their contributions to inhalable particle concentrations across the Beijing city, the spatial distribution of PM0.3, PM1.0 and PM3.0 concentration are simulated by monitoring data obtained from 93 air sampling stations in Beijing urban city and Kriging techniques. Inhalable particles in this study had aerodynamic size between 0.3 and 3.0µm. By taking streets and towns as the basic spatial analysis unit, some factors are mapped influencing urban airborne inhalable particulates pollutions such as urban ground surface types based on GIS and RS. The correlation between PM0.3, PM1.0 and PM3.0 and their influencing factors are quantitatively evaluated by using GIS multifactor integrated analysis and GIS overlay of ranked data layers. The results show that spherical models with nuggets could fit the variograms of PM0.3, PM1.0 and PM3.0. The IPM concentration had significant decreasing trend from 2007 to 2008. Meanwhile, the pollution center has transferred from north and northeast district to southwest and northwest. The spatial relativity between three air particles and their impact factors have spatial heterogeneity in the north, southwest and downtown. Among the three pollutions, the spatial distribution of PM1.0 is firstly influenced by the influence factors; PM3 is secondly, PM0.3 is thirdly.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信