Shiva Nourani, Ana María Villalobos, Héctor Jorquera
{"title":"估算导致 PM2.5 的室内来源的方法。","authors":"Shiva Nourani, Ana María Villalobos, Héctor Jorquera","doi":"10.1039/d4em00538d","DOIUrl":null,"url":null,"abstract":"<p><p>Quantifying source contributions to indoor PM<sub>2.5</sub> levels by indoor PM<sub>2.5</sub> sources has been limited by the costs associated with chemical speciation analyses of indoor PM<sub>2.5</sub> samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM<sub>2.5</sub> concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM<sub>2.5</sub>. We found four dominant STPs in outdoor PM<sub>2.5</sub>, and we denoted them as regional, overnight mix, traffic, and secondary PM<sub>2.5</sub>. For indoor PM<sub>2.5,</sub> we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM<sub>2.5</sub>. Concentration peaks were evident for this indoor STP due to children's activities and classroom housekeeping, and there were minimum contributions on sundays when schools were closed. The average indoor-generated estimated contribution to PM<sub>2.5</sub> was 5.7 μg m<sup>-3</sup>, which contributed to 17% of the total PM<sub>2.5</sub>, and if we consider only school hours, the respective figures are 8.1 μg m<sup>-3</sup> and 22%. A cluster-wise indoor-outdoor PM<sub>2.5</sub> regression was applied to estimate STP-specific infiltration factors (<i>F</i><sub>inf</sub>) per school. The median and interquartile range (IQR) values for <i>F</i><sub>inf</sub> are 0.83 [0.7-0.89], 0.76 [0.68-0.84], 0.72 [0.64-0.81], and 0.7 [0.62-0.9], for overnight mix, secondary, traffic, and regional sources, respectively. This cost-effective methodology can identify the indoor-generated contributions to indoor PM<sub>2.5</sub>, including their temporal variability.</p>","PeriodicalId":74,"journal":{"name":"Environmental Science: Processes & Impacts","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A methodology for estimating indoor sources contributing to PM<sub>2.5</sub>.\",\"authors\":\"Shiva Nourani, Ana María Villalobos, Héctor Jorquera\",\"doi\":\"10.1039/d4em00538d\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Quantifying source contributions to indoor PM<sub>2.5</sub> levels by indoor PM<sub>2.5</sub> sources has been limited by the costs associated with chemical speciation analyses of indoor PM<sub>2.5</sub> samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM<sub>2.5</sub> concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM<sub>2.5</sub>. We found four dominant STPs in outdoor PM<sub>2.5</sub>, and we denoted them as regional, overnight mix, traffic, and secondary PM<sub>2.5</sub>. For indoor PM<sub>2.5,</sub> we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM<sub>2.5</sub>. Concentration peaks were evident for this indoor STP due to children's activities and classroom housekeeping, and there were minimum contributions on sundays when schools were closed. The average indoor-generated estimated contribution to PM<sub>2.5</sub> was 5.7 μg m<sup>-3</sup>, which contributed to 17% of the total PM<sub>2.5</sub>, and if we consider only school hours, the respective figures are 8.1 μg m<sup>-3</sup> and 22%. A cluster-wise indoor-outdoor PM<sub>2.5</sub> regression was applied to estimate STP-specific infiltration factors (<i>F</i><sub>inf</sub>) per school. The median and interquartile range (IQR) values for <i>F</i><sub>inf</sub> are 0.83 [0.7-0.89], 0.76 [0.68-0.84], 0.72 [0.64-0.81], and 0.7 [0.62-0.9], for overnight mix, secondary, traffic, and regional sources, respectively. This cost-effective methodology can identify the indoor-generated contributions to indoor PM<sub>2.5</sub>, including their temporal variability.</p>\",\"PeriodicalId\":74,\"journal\":{\"name\":\"Environmental Science: Processes & Impacts\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Science: Processes & Impacts\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1039/d4em00538d\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Science: Processes & Impacts","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1039/d4em00538d","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
A methodology for estimating indoor sources contributing to PM2.5.
Quantifying source contributions to indoor PM2.5 levels by indoor PM2.5 sources has been limited by the costs associated with chemical speciation analyses of indoor PM2.5 samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM2.5 concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM2.5. We found four dominant STPs in outdoor PM2.5, and we denoted them as regional, overnight mix, traffic, and secondary PM2.5. For indoor PM2.5, we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM2.5. Concentration peaks were evident for this indoor STP due to children's activities and classroom housekeeping, and there were minimum contributions on sundays when schools were closed. The average indoor-generated estimated contribution to PM2.5 was 5.7 μg m-3, which contributed to 17% of the total PM2.5, and if we consider only school hours, the respective figures are 8.1 μg m-3 and 22%. A cluster-wise indoor-outdoor PM2.5 regression was applied to estimate STP-specific infiltration factors (Finf) per school. The median and interquartile range (IQR) values for Finf are 0.83 [0.7-0.89], 0.76 [0.68-0.84], 0.72 [0.64-0.81], and 0.7 [0.62-0.9], for overnight mix, secondary, traffic, and regional sources, respectively. This cost-effective methodology can identify the indoor-generated contributions to indoor PM2.5, including their temporal variability.
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Environmental Science: Processes & Impacts publishes high quality papers in all areas of the environmental chemical sciences, including chemistry of the air, water, soil and sediment. We welcome studies on the environmental fate and effects of anthropogenic and naturally occurring contaminants, both chemical and microbiological, as well as related natural element cycling processes.
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