Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors
Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors
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DOI：
10.1016/j.scitotenv.2021.145304
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发表时间：
2021-01-26
影响因子：
9.8
通讯作者：
Tao, Shu
中科院分区：
文献类型：
--
作者：
Shen, Huizhong;Hou, Weiying;Tao, Shu
室内空气污染对健康有显著的不利影响，但其时空变化和来源贡献尚未得到很好的量化。在这项研究中，我们使用低成本的传感器来测量北京一个典型公寓的PM2.5浓度。测量于2020年3月14日至24日在15个室内站点和1个室外站点进行，时间分辨率为1分钟（转换为10分钟平均值进行数据分析）。基于这些高度时空分辨率的数据，我们表征了该公寓室内PM2.5的时空变化和来源贡献。研究发现，室内颗粒物主要来源于室外入渗和烹饪排放物，室外的细颗粒物贡献更多。研究发现，室内PM2.5浓度与环境水平相关，但总体上低于室外，平均I/O为0.85。主要的室内来源是烹饪，导致偶尔的高峰值。在大多数房间里观察到的变化都落后于在室外和厨房里测量到的变化。房间之间的差异取决于与源的路径距离。平均而言，室外污染源贡献了室内PM2.5的36%，随着时间和房间的不同变化很大。基于室内PM2.5浓度、源强度和路径距离，建立了预测PM2.5时空变化的多元回归模型。该模型解释了79%的观测变化，并可用于动态模拟室内任何地点的PM2.5浓度。该模型的简单性表明了在区域尺度上应用室内空气质量模型的潜力。(C) 2021 Elsevier B.V.版权所有
Indoor air pollution has significant adverse health impacts, but its spatiotemporal variations and source contributions are not well quantified. In this study, we used low-cost sensors to measure PM2.5 concentrations in a typical apartment in Beijing. The measurements were conducted at 15 indoor sites and one outdoor site on 1-minute temporal resolution (convert to 10-minute averages for data analysis) from March 14 to 24, 2020. Based on these highly spatially-and temporally-resolved data, we characterized spatiotemporal variations and source contributions of indoor PM2.5 in this apartment. It was found that indoor particulate matter predominantly originates from outdoor infiltration and cooking emissions with the latter contributing more fine particles. Indoor PM2.5 concentrations were found to be correlated with ambient levels but were generally lower than those outdoors with an average I/O of 0.85. The predominant indoor source was cooking, leading to occasional high spikes. The variations observed in most rooms lagged behind those measured outdoors and in the studied kitchen. Differences between rooms were found to depend on pathway distances from sources. On average, outdoor sources contributed 36% of indoor PM2.5, varying extensively over time and among rooms. From observed PM2.5 concentrations at the indoor sites, source strengths, and pathway distances, a multivariate regression model was developed to predict spatiotemporal variations of PM2.5. The model explains 79% of the observed variation and can be used to dynamically simulate PM2.5 concentrations at any site indoors. The model's simplicity suggests the potential for regional-scale application for indoor air quality modeling. (C) 2021 Elsevier B.V. All rights reserved.