Development of Hourly Indoor PM 2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

Chien-Cheng Jung, Wan-Yi Lin, Nai-Yun Hsu, Chih-Da Wu, Hao-Ting Chang and Huey-Jen Su
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Chien-Cheng Jung: Department of Public Health, China Medical University, Taichung 40402, Taiwan
Wan-Yi Lin: Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan
Nai-Yun Hsu: Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan
Chih-Da Wu: Department of Geomatics, National Cheng Kung University, Tainan 70403, Taiwan
Hao-Ting Chang: Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan
Huey-Jen Su: Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan

IJERPH, 2020, vol. 17, issue 16, 1-17

Abstract: Exposure to indoor particulate matter less than 2.5 µm in diameter (PM 2.5 ) is a critical health risk factor. Therefore, measuring indoor PM 2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM 2.5 data is difficult and expensive. Therefore, several indoor PM 2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM 2.5 concentrations in cold or temperate regions. The factors that influence PM 2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM 2.5 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 PM 2.5 concentrations, the difference between indoor and outdoor CO 2 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 PM 2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM 2.5 concentrations and suggested that outdoor PM 2.5 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 PM 2.5 concentrations.

Keywords: indoor air; multiple linear regression model; PM 2.5 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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