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Prediction of phthalate in dust in children's bedroom based on gradient boosting regression tree

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成果类型:
期刊论文
作者:
Sun, Chanjuan;Wang, Qinghao;Zhang, Jialing;Liu, Wei;Zhang, Yinping;...
通讯作者:
Chen, H
作者机构:
[Zou, Zhijun; Chen, Huang; Chen, H; Sun, Chanjuan; Wang, Qinghao] Univ Shanghai Sci & Technol, Sch Environm & Architecture, Shanghai, Peoples R China.
[Zhang, Jialing] Henan Polytech Univ, Sch Civil Engn, Henan 454000, Peoples R China.
[Liu, Wei] Chongqing Univ Sci & Technol, Inst Hlth & Environm, Chongqing, Peoples R China.
[Zhang, Yinping] Tsinghua Univ, Sch Architecture, Beijing, Peoples R China.
[Li, Baizhan] Chongqing Univ, Key Lab Three Gorges Reservoir Reg Ecoenvironm, Chongqing, Peoples R China.
通讯机构:
[Chen, H ] U
Univ Shanghai Sci & Technol, Sch Environm & Architecture, Shanghai, Peoples R China.
语种:
英文
关键词:
Concentration prediction;GBRT model;Indoor dust;Phthalate;Residential buildings
期刊:
Building and Environment
ISSN:
0360-1323
年:
2024
卷:
251
页码:
111216
基金类别:
Natural Science Foundation of Shanghai Municipality#&#&#21ZR1444800 National Natural Science Foundation of China#&#&#51708347
机构署名:
本校为其他机构
院系归属:
生命科学学院
摘要:
This study developed a prediction method to determine the distribution of phthalate esters (PAEs) in indoor dust. A gradient boosting decision tree model (GBRT) was trained by using 267 samples in Shanghai, including PAEs concentrations in indoor dust and data obtained from continuous monitoring, as well as the survey of indoor environment. Environmental exposure, residents' lifestyle, and building characteristics data were collected from 8 cities in China. Based on this, the well -trained GBRT model accurately predicted PAEs concentrations, with goodness of fit (R-2) > 0.94, mean absolute err...

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