Attenuation effects of ZVI/PDS pretreatment on propagation of antibiotic resistance genes in bioreactors: Driven by antibiotic residues and sulfate assimilation.

Sulfate radical-based advanced oxidation processes (AOPs) combined biological system was a promising technology for treating antibiotic wastewater. However, how pretreatment influence antibiotic resistance genes (ARGs) propagation remains largely elusive, especially the produced by-products (antibiotic residues and sulfate) are often ignored. Herein, we investigated the effects of zero valent iron/persulfate pretreatment on ARGs in bioreactors treating sulfadiazine wastewater. Results showed absolute and relative abundance of ARGs reduced by 59.8%- 81.9% and 9.1%- 52.9% after pretreatments. The effect of 90-min pretreatment was better than that of the 30-min. The ARGs reduction was due to decreased antibiotic residues and stimulated sulfate assimilation. Reduced antibiotic residues was a major factor in ARGs attenuation, which could suppress oxidative stress, inhibit mobile genetic elements emergence and resistant strains proliferation. The presence of sulfate in influent supplemented microbial sulfur sources and facilitated the in-situ synthesis of antioxidant cysteine through sulfate assimilation, which drove ARGs attenuation by alleviating oxidative stress. This is the first detailed analysis about the regulatory mechanism of how sulfate radical-based AOPs mediate in ARGs attenuation, which is expected to provide theoretical basis for solving concerns about by-products and developing practical methods to hinder ARGs propagation.

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate.

The apparent second-order rate constant with hexavalent ferrate (Fe(VI)) (kFe(VI)) is a key indicator to evaluate the removal efficiency of a molecule by Fe(VI) oxidation. kFe(VI) is often determined by experiment, but such measurements can hardly catch up with the rapid growth of organic compounds (OCs). To address this issue, in this study, a total of 437 experimental second-order kFe(VI) rate constants at a range of conditions (pH and temperature) were used to train four machine learning (ML) algorithms (lasso regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM)). Using the Morgan fingerprint (MF)) of a range of organic compounds (OCs) as the input, the performance of the four algorithms was comprehensively compared with respect to the coefficient of determination (R2) and root-mean-square error (RMSE). It is shown that the RR, XGBoost, and LightGBM models displayed generally acceptable performance kFe(VI) (R2test > 0.7). In addition, the shapely additive explanation (SHAP) and feature importance methods were employed to interpret the XGBoost/LightGBM and RR models, respectively. The results showed that the XGBoost/LightGBM and RR models suggestd pH as the most important predictor and the tree-based models elucidate how electron-donating and electron-withdrawing groups influence the reactivity of the Fe(VI) species. In addition, the RR model share eight common features, including pH, with the two tree-based models. This work provides a fast and acceptable method for predicting kFe(VI) values and can help researchers better understand the degradation behavior of OCs by Fe(VI) oxidation from the perspective of molecular structure.