首页|A practical framework for predicting residential indoor PM_(2.5) concentration using land-use regression and machine learning methods

A practical framework for predicting residential indoor PM_(2.5) concentration using land-use regression and machine learning methods

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  • NETL
  • NSTL
  • Elsevier
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 R-2 and cross-validation R-2 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 R-2 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. (C) 2020 Elsevier Ltd. All rights reserved.

Indoor airPM2.5HouseholdsPrediction modelRandom forest

Li, Zhiyuan、Tong, Xinning、Ho, Jason Man Wai、Kwok, Timothy C. Y.、Dong, Guanghui、Ho, Kin-Fai、Yim, Steve Hung Lam

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Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Shatin, Hong Kong, Peoples R China

Chinese Univ Hong Kong, Jockey Club Sch Publ Hlth & Primary Care, Shatin, Hong Kong, Peoples R China

Chinese Univ Hong Kong, Stanley Ho Big Data Decis Analyt Res Ctr, Shatin, Hong Kong, Peoples R China

Chinese Univ Hong Kong, Fac Med, Dept Med & Therapeut, Shatin, Hong Kong, Peoples R China

Sun Yat Sen Univ, Guangdong Prov Engn Technol Res Ctr Environm Poll, Sch Publ Hlth, Dept Prevent Med,Guangzhou Key Lab Environm Pollu, Guangzhou 510080, Peoples R China

Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Shatin, Hong Kong, Peoples R China|Chinese Univ Hong Kong, Jockey Club Sch Publ Hlth & Primary Care, Shatin, Hong Kong, Peoples R China

Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Shatin, Hong Kong, Peoples R China|Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China|Chinese Univ Hong Kong, Stanley Ho Big Data Decis Analyt Res Ctr, Shatin, Hong Kong, Peoples R China

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2021

10.1016/j.chemosphere.2020.129140

Chemosphere

Chemosphere

ISTP
ISSN：0045-6535
年,卷(期)：2021.265(Feb.)
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