Xiaozhen Liu, H Christopher Frey, Ye Cao, Bela Deshpande
{"title":"利用随机人体暴露和剂量模拟模型建立车内 PM(2.5) 暴露模型。","authors":"Xiaozhen Liu, H Christopher Frey, Ye Cao, Bela Deshpande","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Factors that influence in-vehicle PM(2.5) exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM(2.5) concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM(2.5) concentration based on ambient PM(2.5) concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to location, vehicle type and other factors. SHEDS-PM was used to estimate PM(2.5) exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM(2.5) exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM(2.5) concentration based on FSM data and a mass balance model for estimating in-vehicle concentration.Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.</p>","PeriodicalId":89075,"journal":{"name":"Annual meeting & exhibition proceedings CD-ROM. Air & Waste Management Association. Meeting","volume":"2 102","pages":"1087-1100"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013375/pdf/nihms146383.pdf","citationCount":"0","resultStr":"{\"title\":\"Modeling of In-vehicle PM(2.5) Exposure Using the Stochastic Human Exposure and Dose Simulation Model.\",\"authors\":\"Xiaozhen Liu, H Christopher Frey, Ye Cao, Bela Deshpande\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Factors that influence in-vehicle PM(2.5) exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM(2.5) concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM(2.5) concentration based on ambient PM(2.5) concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to location, vehicle type and other factors. SHEDS-PM was used to estimate PM(2.5) exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM(2.5) exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM(2.5) concentration based on FSM data and a mass balance model for estimating in-vehicle concentration.Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.</p>\",\"PeriodicalId\":89075,\"journal\":{\"name\":\"Annual meeting & exhibition proceedings CD-ROM. Air & Waste Management Association. Meeting\",\"volume\":\"2 102\",\"pages\":\"1087-1100\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013375/pdf/nihms146383.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual meeting & exhibition proceedings CD-ROM. Air & Waste Management Association. Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual meeting & exhibition proceedings CD-ROM. Air & Waste Management Association. Meeting","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling of In-vehicle PM(2.5) Exposure Using the Stochastic Human Exposure and Dose Simulation Model.
Factors that influence in-vehicle PM(2.5) exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM(2.5) concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM(2.5) concentration based on ambient PM(2.5) concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to location, vehicle type and other factors. SHEDS-PM was used to estimate PM(2.5) exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM(2.5) exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM(2.5) concentration based on FSM data and a mass balance model for estimating in-vehicle concentration.Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.