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Open AccessArticle

Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Department of Public Health, China Medical University, Taichung 40402, Taiwan
Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan
Department of Geomatics, National Cheng Kung University, Tainan 70403, Taiwan
National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(16), 5906;
Received: 7 July 2020 / Revised: 4 August 2020 / Accepted: 12 August 2020 / Published: 14 August 2020
(This article belongs to the Section Environmental Analysis and Methods)
Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations. View Full-Text
Keywords: indoor air; multiple linear regression model; PM2.5 indoor air; multiple linear regression model; PM2.5
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Jung, C.-C.; Lin, W.-Y.; Hsu, N.-Y.; Wu, C.-D.; Chang, H.-T.; Su, H.-J. Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity. Int. J. Environ. Res. Public Health 2020, 17, 5906.

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