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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.