A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods
2021
Li, Zhiyuan | Tong, Xinning | Ho, Jason Man Wai | Kwok, Timothy C.Y. | Dong, Guanghui | Ho, Kin-Fai | Yim, Steve Hung Lam
People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM₂.₅ concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM₂.₅ concentration in residential households. PM₂.₅ concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM₂.₅ concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM₂.₅ prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R² and cross-validation R² values of 0.93 and 0.65, respectively. Outdoor PM₂.₅ concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R² value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM₂.₅ prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.
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