Air pollution has shown to cause adverse health effects on mankind. Aging causes functional decline and leaves elderly people more susceptible to health threats associated with air pollution exposure. Elderly spend approximately 80% of their lifetime at home every day. To understand air pollution exposure, indoor air pollutants are the targets for consideration especially for the elderly population. However, indoor air monitoring for epidemiological studies requires a large population, is labor intensive and time consuming. As a result, a prediction model is necessary. For 3 consecutive days in summer and winter, 24-h average of mass concentrations of fine particulate matter (aerodynamic diameter <2.5 μm: PM2.5) were measured in indoors for 116 households. A PM2.5 prediction model for elderly households in Hong Kong has been developed by combining ambient PM2.5 concentrations obtained from land use regression model and questionnaire-elicited information related to the indoor PM2.5 sources. The fitted linear mixed-effects model is moderately predictive for the observed indoor PM2.5, with R2 = 0.67 (or R2 = 0.61 by cross-validation). The model shows indoor PM2.5 was positively influenced by outdoor PM2.5 levels. Meteorological factors (e.g. temperature and relative humidity) were related to the indoor PM2.5 in a relatively complex manner. Congested living areas, opening windows for extended periods for ventilation and use of liquefied petroleum gas for cooking were the factors determining the ultimate indoor air quality. This study aims to provide information about controlling household air quality and can be used for future epidemiological studies associated with indoor air pollution in large population.
Keywords: Indoor air; Linear mixed-effects regression; PM(2.5); Prediction model.
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