: The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of chemicals which are discharged to wastewater. In a recent study, non-target screening analysis was applied to quantify the percentage removal efficiency (RE%) of more than 300 polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Based on subsets extracted from these data, we developed quantitative structure-property relationships (QSPRs) for the prediction of WWTP breakthrough (BT) to the effluent water. QSPRs were developed by means of multiple linear regression (MLR) and were selected after checking for overfitting and chance relationships by means of bootstrap and randomization procedures. A first model provided good fitting performance, showing that the proposed approach for the development of QSPRs for the prediction of BT is reasonable. By further populating the dataset with similar chemicals using a Tanimoto index approach based on substructure count fingerprints, a second QSPR indicated that the prediction of BT is also applicable to new chemicals sufficiently similar to the training set. Finally, a class-specific QSPR for PEGs and PPGs showed BT prediction trends consistent with known degradation pathways.
In silico approaches for the prediction of the breakthrough of organic contaminants in wastewater treatment plants
Chirico N.
;Papa E.
2024-01-01
Abstract
: The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of chemicals which are discharged to wastewater. In a recent study, non-target screening analysis was applied to quantify the percentage removal efficiency (RE%) of more than 300 polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Based on subsets extracted from these data, we developed quantitative structure-property relationships (QSPRs) for the prediction of WWTP breakthrough (BT) to the effluent water. QSPRs were developed by means of multiple linear regression (MLR) and were selected after checking for overfitting and chance relationships by means of bootstrap and randomization procedures. A first model provided good fitting performance, showing that the proposed approach for the development of QSPRs for the prediction of BT is reasonable. By further populating the dataset with similar chemicals using a Tanimoto index approach based on substructure count fingerprints, a second QSPR indicated that the prediction of BT is also applicable to new chemicals sufficiently similar to the training set. Finally, a class-specific QSPR for PEGs and PPGs showed BT prediction trends consistent with known degradation pathways.
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