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}
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.