Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.

Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar. However, the influence of a specific property on As immobilization varies among different studies, and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge. To enhance immobilization efficiency and reduce labor and time costs, a machine learning (ML) model was employed to predict As immobilization efficiency before biochar application. In this study, we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models. The results demonstrated that the random forest (RF) model outperformed gradient boost regression tree and support vector regression models in predictive performance. Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization. These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils. Furthermore, the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization. These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.

Artificial Sweetener Enhances the Spread of Antibiotic Resistance Genes During Anaerobic Digestion.

Artificial sweeteners have been frequently detected in the feedstocks of anaerobic digestion. As these sweeteners can lead to the shift of anaerobic microbiota in the gut similar to that caused by antibiotics, we hypothesize that they may have an antibiotic-like impact on antibiotic resistance genes (ARGs) in anaerobic digestion. However, current understanding on this topic is scarce. This investigation aimed to examine the potential impact of acesulfame, a typical artificial sweetener, on ARGs in anaerobic digestion by using metagenomics sequencing and qPCR. It was found that acesulfame increased the number of detected ARG classes and the abundance of ARGs during anaerobic digestion. The abundance of typical mobile genetic elements (MGEs) and the number of potential hosts of ARGs also increased under acesulfame exposure, suggesting the enhanced potential of horizontal gene transfer of ARGs, which was further confirmed by the correlation analysis between absolute abundances of the targeted ARGs and MGEs. The increased horizontal dissemination of ARGs may be associated with the SOS response induced by the increased ROS production, and the increased cellular membrane permeability. These findings indicate that artificial sweeteners may accelerate ARG spread through digestate disposal, thus corresponding strategies should be considered to prevent potential risks in practice.