Prediction model for air particulate matter levels in the households of elderly individuals in Hong Kong

• Published in: The Science of the total environment 2020-05, Vol.717, p.135323-135323, Article 135323
• Main Authors: Tong, Xinning, Ho, Jason Man Wai, Li, Zhiyuan, Lui, Ka-Hei, Kwok, Timothy C.Y., Tsoi, Kelvin K.F., Ho, K.F.
• Format: Article
• Language: English
• Subjects: Aged; Air Pollutants; Air Pollution; Air Pollution, Indoor; Environmental Monitoring; Hong Kong; Humans; Indoor air; Linear mixed-effects regression; Particulate Matter; PM2.5; Prediction model
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.135323

[Display omitted] •Linear mixed regression was applied to develop a prediction model for indoor PM2.5.•Model was moderately predictive for indoor PM2.5 (R2 = 0.61 by cross-validation).•Indoor PM2.5 is positively related to ambient PM2.5 levels.•Window opening, cooking and crowded living area are not beneficial to indoor PM2.5. 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