| Title: | Construction of Models To Predict the Effectiveness of E-Waste Control through Capture of Volatile Organic Compounds and Metals/Metalloids Exposure Fingerprints: A Six-Year Longitudinal Study |
| Author(s): | Yu YJ; Li MY; Li LZ; Liao ZQ; Zhu XH; Li ZC; Xiang MD; Kuang HX; |
| Address: | "State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, P.R. China. School of Life Sciences, South China Normal University, Guangzhou 510631, P.R. China" |
| ISSN/ISBN: | 1520-5851 (Electronic) 0013-936X (Linking) |
| Abstract: | "The significant health implications of e-waste toxicants have triggered the global tightening of regulation on informal e-waste recycling sites (ER) but with disparate governance that requires effective monitoring. Taking advantage of the opportunity to implement e-waste control in the Guiyu ER since 2015, we investigated the temporal variations in levels of oxidative DNA damage, 25 volatile organic compound metabolites (VOCs), and 16 metals/metalloids (MeTs) in urine in 918 children between 2016 and 2021 to demonstrate the effectiveness of e-waste control in reducing population exposure risks. The hazard quotients of most MeTs and levels of 8-hydroxy-2'-deoxyguanosine in children decreased significantly during this time, indicating that e-waste control effectively reduces the noncarcinogenic risks of MeT exposure and levels of oxidative DNA damage. Using mVOC-derived indexes as a feature, a bagging-support vector machine algorithm-based machine learning model was constructed to predict the extent of e-waste pollution (EWP). The model exhibited excellent performance with accuracies >97.0% in differentiating between slight and severe EWP. Five simple functions established using mVOC-derived indexes also had high accuracy in predicting the presence of EWP. These models and functions provide a novel human exposure monitoring-based approach for assessing e-waste governance or the presence of EWP in other ERs" |
| Keywords: | Child Humans *Volatile Organic Compounds *Metalloids/analysis *Electronic Waste Longitudinal Studies Metals Recycling China children e-waste recycling machine learning regulation support vector machine; |
| Notes: | "MedlineYu, Yun-Jiang Li, Meng-Yang Li, Lei-Zi Liao, Zeng-Quan Zhu, Xiao-Hui Li, Zhen-Chi Xiang, Ming-Deng Kuang, Hong-Xuan eng Research Support, Non-U.S. Gov't 2023/06/15 Environ Sci Technol. 2023 Jun 27; 57(25):9150-9162. doi: 10.1021/acs.est.3c01550. Epub 2023 Jun 15" |
