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

Abstract

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.