期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 770, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2021.145304
关键词
Indoor air quality; Particulate matter; Spatial distribution; Temporal trend; Low-cost sensor; Cooking
资金
- National Natural Science Foundation of China [41991312, 41830641, 41922057, 41821005]
- Chinese Academy of Science [XDA23010100]
- Ministry of Science and Technology of the People's Republic of China [2019QZKK0605]
- Undergraduate Student Research Training Program
- High-performance Computing Platform of Peking University
- U.S. Environmental Protection Agency (EPA) [R835880]
- National Science Foundation (NSF SRN) [1444745]
- EPA [R835880, 909403] Funding Source: Federal RePORTER
This study investigated spatiotemporal variations and source contributions of indoor PM2.5 in a typical apartment in Beijing using low-cost sensors. 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 correlated with ambient levels but generally lower, with the predominant indoor source being cooking.
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.
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