Manganese oxides have been recently investigated as excellent catalysts for peroxymonosulfate (PMS) activation, and the reported mechanisms are mostly forming reactive oxygen species (ROSs). This study investigated the use of iron-doped manganese oxide, synthesized via air oxidation under strong alkaline conditions. The oxidation of three substrates was affected by their adsorption at the catalyst surface, solution pH, and co-solutes. Common ROS scavengers inhibited the oxidation of bisphenol A (BPA), suggesting the possible involvement of ROSs; however, the PMS decomposition tests with and without BPA and the comparison with a 1O2-generation system ruled out the formation of ROSs and pointed to direct electron transfer between the adsorbed BPA and complexed PMS as the mechanism. To prove this mechanism, the catalyst was coated to graphite sheets and a galvanic oxidation process (GOP) was developed to separate BPA and PMS into two half cells. Upon PMS addition into one cell, BPA was quickly oxidized in the other cell, confirming the occurrence of electron transfer. The GOP system successfully degraded BPA in both surface water and hypersaline shale gas-produced water. Overall, this study developed a new catalyst for PMS activation and unveiled the advantages and potential applications of electron shuttling catalysts.

Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.

No abstract

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R 2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C�O > O�C−O > OH > CH 3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pK a and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

No abstract