Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA.
Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA.
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DOI：
10.1016/j.scitotenv.2022.156625
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发表时间：
2022-09-20
影响因子：
9.8
通讯作者：
Fabian, M. Patricia
中科院分区：
文献类型：
--
作者：
Milando, Chad W.;Carnes, Fei;Vermeer, Kimberly;Levy, Jonathan I.;Fabian, M. Patricia
关键词：
许多估计暴露在空气中的污染物的技术没有考虑到建筑物的特征，这些特征可以放大室内和室外来源的污染物贡献。影响暴露的建筑特征可能难以大规模获取，但其中一些可能会被纳入使用公共数据集的暴露评估中。我们提出了一种使用公共数据集为测试队列生成住房模型的方法，并检查了预测的细颗粒物(PM2.5)暴露对选定建筑和源特征的敏感性。我们使用了一组哮喘儿童的地址和公共纳税评估员的数据来指导从公共数据库中选择美国住宅的平面图。这反过来又指导了耦合多区域模型(CONTAM和EnergyPlus)的生成，这些模型估计了室内PM2.5暴露曲线。为了检查对模型参数的敏感性，我们改变了建筑楼层和楼层平面图、暖气、通风和空调(HVAC)类型、房间或楼层的模型分辨率以及室内源强度和时间表(针对假设的燃气炉烹饪和吸烟)。乘员时间-活动和环境污染物水平保持不变。我们的地址匹配方法确定了两个多户住宅模板和一个单户住宅模板，它们的特征与60%的测试地址相似。在选定的建筑特征、暖通空调类型和模型分辨率(在其他条件相同的情况下)，暴露在渗透的环境PM2.5中的情况相似。相比之下，两个多户住宅对室内来源的PM2.5的暴露比单户住宅高(例如，烹饪PM2.5的暴露分别高出26%和47%)，并且对暖通空调类型和模型分辨率敏感。我们利用公共数据源和耦合的多区域模型推导了建筑特性和暖通空调类型对室内PM2.5暴露的影响。随着个性化居民行为数据的重要纳入，类似的住房建模可以用于将暴露变量纳入室内居住环境的健康研究。
Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM2.5) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor’s data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60% of test addresses. Exposure to infiltrated ambient PM2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM2.5 exposure, by 26% and 47% respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment.
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影响因子：
6.7
作者：
Crawley, DB;Lawrie, LK;Glazer, J
通讯作者：
Glazer, J
DOI：
10.1038/sj.jea.7500188
发表时间：
2001-11-01
期刊：
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY
影响因子：
--
作者：
Burke, JM;Zufall, MJ;Özkaynak, H
通讯作者：
Özkaynak, H
影响因子：
11.4
作者：
Fazli, Torkan;Dong, Xinyi;Stephens, Brent
通讯作者：
Stephens, Brent
实时室内PM（2.5）在城市队列中监视：对暴露差异和来源控制的影响。
影响因子：
8.3
作者：
Chu MT;Gillooly SE;Levy JI;Vallarino J;Reyna LN;Cedeño Laurent JG;Coull BA;Adamkiewicz G
通讯作者：
Adamkiewicz G
影响因子：
2.5
作者：
Dogan, Timur;Reinhart, Christoph;Michalatos, Panagiotis
通讯作者：
Michalatos, Panagiotis