使用低成本传感器进行室内空气质量监测和污染源分配

Christina Higgins, Prashant Kumar, Lidia Morawska
{"title":"使用低成本传感器进行室内空气质量监测和污染源分配","authors":"Christina Higgins, Prashant Kumar, Lidia Morawska","doi":"10.1088/2515-7620/ad1cad","DOIUrl":null,"url":null,"abstract":"\n Understanding of the various sources of indoor air pollution requires indoor air quality (IAQ) data that is usually lacking. Such data can be obtained using unobtrusive, low-cost sensors (LCS). The aim of this review is to examine the recent literature published on LCS for IAQ measurements and to determine whether these studies employed any methods to identify or quantify sources of indoor air pollution. Studies were reviewed in terms of whether any methods of source apportionment were employed, as well as the microenvironment type, geographical location, and several metrics relating to the contribution of outdoor pollutant ingress versus potential indoor pollutant sources. We found that out of 60 relevant studies, just four employed methods for source apportionment, all of which utilised receptor models. Most studies were undertaken in residential or educational environments. There is a lack of data on IAQ in other types of microenvironments and in locations outside of Europe and North America. There are inherent limitations with LCS in terms of producing data which can be utilised in source apportionment models. This applies to external pollution data, however IAQ can be even more challenging to measure due to its characteristics. The indoor environment is heterogeneous, with significant variability within the space as well as between different microenvironments and locations. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can ingress into the space via the building envelope, however measurement of external pollution and environmental conditions, as well as recording details on the building fabric and ventilation conditions, can help apportion external contributions. Whether or not source apportionment models are employed on indoor data from LCS, there are parameters which, if carefully considered during measurement campaigns, can aid in source identification of pollutants.","PeriodicalId":505267,"journal":{"name":"Environmental Research Communications","volume":"16 13","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Indoor Air Quality Monitoring and Source Apportionment Using Low-Cost Sensors\",\"authors\":\"Christina Higgins, Prashant Kumar, Lidia Morawska\",\"doi\":\"10.1088/2515-7620/ad1cad\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Understanding of the various sources of indoor air pollution requires indoor air quality (IAQ) data that is usually lacking. Such data can be obtained using unobtrusive, low-cost sensors (LCS). The aim of this review is to examine the recent literature published on LCS for IAQ measurements and to determine whether these studies employed any methods to identify or quantify sources of indoor air pollution. Studies were reviewed in terms of whether any methods of source apportionment were employed, as well as the microenvironment type, geographical location, and several metrics relating to the contribution of outdoor pollutant ingress versus potential indoor pollutant sources. We found that out of 60 relevant studies, just four employed methods for source apportionment, all of which utilised receptor models. Most studies were undertaken in residential or educational environments. There is a lack of data on IAQ in other types of microenvironments and in locations outside of Europe and North America. There are inherent limitations with LCS in terms of producing data which can be utilised in source apportionment models. This applies to external pollution data, however IAQ can be even more challenging to measure due to its characteristics. The indoor environment is heterogeneous, with significant variability within the space as well as between different microenvironments and locations. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can ingress into the space via the building envelope, however measurement of external pollution and environmental conditions, as well as recording details on the building fabric and ventilation conditions, can help apportion external contributions. Whether or not source apportionment models are employed on indoor data from LCS, there are parameters which, if carefully considered during measurement campaigns, can aid in source identification of pollutants.\",\"PeriodicalId\":505267,\"journal\":{\"name\":\"Environmental Research Communications\",\"volume\":\"16 13\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Research Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2515-7620/ad1cad\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Research Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2515-7620/ad1cad","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

要了解室内空气的各种污染源,就必须获得通常缺乏的室内空气质量(IAQ)数据。这些数据可以通过不显眼的低成本传感器(LCS)获得。本综述旨在研究近期发表的有关用于室内空气质量测量的 LCS 的文献,并确定这些研究是否采用了任何方法来识别或量化室内空气污染源。我们从是否采用了污染源分配方法、微环境类型、地理位置以及与室外污染物进入和潜在室内污染源的贡献相关的几个指标等方面对这些研究进行了审查。我们发现,在 60 项相关研究中,仅有 4 项采用了污染源分配方法,所有研究都使用了受体模型。大多数研究都是在住宅或教育环境中进行的。其他类型的微环境以及欧洲和北美以外地区的室内空气质量数据还很缺乏。LCS 在生成可用于污染源分配模型的数据方面存在固有的局限性。这适用于外部污染数据,但由于室内空气质量的特点,对其进行测量可能更具挑战性。室内环境是异构的,空间内部以及不同微环境和位置之间都存在显著差异。传感器位置、占用率和活动报告,以及不同微环境和位置的测量结果,都有助于了解这种变化。室外污染物可以通过建筑围护结构进入空间,然而,对外部污染和环境条件的测量,以及对建筑结构和通风条件的详细记录,可以帮助分摊外部污染。无论是否对来自 LCS 的室内数据采用污染源分配模型,如果在测量过程中仔细考虑一些参数,都有助于污染物的来源识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indoor Air Quality Monitoring and Source Apportionment Using Low-Cost Sensors
Understanding of the various sources of indoor air pollution requires indoor air quality (IAQ) data that is usually lacking. Such data can be obtained using unobtrusive, low-cost sensors (LCS). The aim of this review is to examine the recent literature published on LCS for IAQ measurements and to determine whether these studies employed any methods to identify or quantify sources of indoor air pollution. Studies were reviewed in terms of whether any methods of source apportionment were employed, as well as the microenvironment type, geographical location, and several metrics relating to the contribution of outdoor pollutant ingress versus potential indoor pollutant sources. We found that out of 60 relevant studies, just four employed methods for source apportionment, all of which utilised receptor models. Most studies were undertaken in residential or educational environments. There is a lack of data on IAQ in other types of microenvironments and in locations outside of Europe and North America. There are inherent limitations with LCS in terms of producing data which can be utilised in source apportionment models. This applies to external pollution data, however IAQ can be even more challenging to measure due to its characteristics. The indoor environment is heterogeneous, with significant variability within the space as well as between different microenvironments and locations. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can ingress into the space via the building envelope, however measurement of external pollution and environmental conditions, as well as recording details on the building fabric and ventilation conditions, can help apportion external contributions. Whether or not source apportionment models are employed on indoor data from LCS, there are parameters which, if carefully considered during measurement campaigns, can aid in source identification of pollutants.
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