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.

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.

Co-exposure levels of volatile organic compounds and metals/metalloids in children: Implications for E-waste recycling activity prediction.

Identifying informal e-waste recycling activity is crucial for preventing health hazards caused by e-waste pollution. This study attempted to build a prediction model for e-waste recycling activity based on the differential exposure biomarkers of the populations between the e-waste recycling area (ER) and non-ER. This study recruited children in ER and non-ER and conducted a quasi-experiment among the adult investigators to screen differential exposure or effect biomarkers by measuring urinary 25 volatile organic compound (VOC) metabolites, 18 metals/metalloids, and 8-hydroxy-2'-deoxyguanosine (8-OHdG). Compared with children of the non-ER, the ER children had higher metal/metalloid (e.g., manganese [Mn], lead [Pb], antimony [Sb], tin [Sn], and copper [Cu]) and VOC exposure (e.g., carbon-disulfide, acrolein, and 1-bromopropane) levels, oxidative DNA damage, and non-carcinogenic risks. Individually added 8-OHdG, VOC metabolites, and metals/metalloids to the support vector machine (SVM) classifier could obtain similar classification effects, with the area under curve (AUC) ranging from 0.741 to 0.819. The combined inclusion of 8-OHdG and differential VOC metabolites, metals/metalloids, and mixed indexes (e.g., product items or ratios of different metals/metalloids) in the SVM classifier showed the highest performance in predicting e-waste recycling activity, with an AUC of 0.914 and prediction accuracy of 83.3 %. "Sb × Mn", followed by "Sn × Pb/Cu", "Sb × Mn/Cu", and "Sn × Pb", were the top four important features in the models. Compared with non-ER children, the levels of urinary Mn, Pb, Sb, Sn, and Cu in ER children were 1.2 to 2.4 times higher, while the levels of "Sb × Mn", "Sn × Pb/Cu", "Sb × Mn/Cu", and "Sn × Pb" were 3.5 to 4.7 times higher, suggesting that these mixed indexes could amplify the differences between e-waste exposed and non-e-waste exposed populations. With the continued inclusion of new biomarkers of e-waste pollution in the future, our prediction model is promising for screening informal e-waste recycling sites.