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.
Environ Sci Technol
; 57(25): 9150-9162, 2023 06 27.
Article
em En
| MEDLINE
| ID: mdl-37319360
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Compostos Orgânicos Voláteis
/
Metaloides
/
Resíduo Eletrônico
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Child
/
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
Environ Sci Technol
Ano de publicação:
2023
Tipo de documento:
Article