Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk

Oxygen vacancy-mediated BiVO4/Bi3O4Br S-scheme heterojunction for enhanced photocatalytic degradation of antibiotics

Overuse of antibiotics has triggered severe water pollution issues. A novel S-scheme heterojunction nanocomposite, BiVO4/Bi3O4Br, was designed and successfully synthesized in this work, which exhibits superior performance in degrading fluoroquinolone antibiotics (gatifloxacin hydrochloride (GAT) and lomefloxacin hydrochloride (LOM)) and tetracycline antibiotics (tetracycline hydrochloride (TCH)). The construction of the S-scheme heterojunction structure and the incorporation of oxygen vacancies (OVs), which furnish vital channels at the interfaces for the efficient migration of photogenerated carriers, are primarily responsible for the enhanced photocatalytic efficiency of BiVO4/Bi3O4Br. Furthermore, the possible degradation routes of GAT were thoroughly explored, and the photocatalytic degradation mechanism of BiVO4/Bi3O4Br was comprehensively elucidated. This study highlights the combined action of S-scheme heterojunctions and OVs in boosting photocatalytic performance, thus providing a fresh perspective for developing OVs-rich S-scheme heterojunction photocatalysts for wastewater treatment.

Source orientation, environmental fate, and risks of antibiotics in the surface water of the largest sediment-laden river

Antibiotics present a more complex pollution profile in large rivers, particularly in suspended sediment-laden flows. This study quantified 25 antibiotics in surface water samples from the whole sediment-laden Yellow River. A new comprehensive prioritization index (CPI) was developed to identify priority risk control regions. The concentrations of the detected antibiotics ranged from 0.670 to 232 ng/L (mean: 9.62 ng/L), with the highest mean concentration observed for tetracyclines (TCs) at 20.2 ng/L. The most prominent antibiotic pollution was observed in the midstream region, with mean concentrations reaching 251 ng/L. Three SEMs were constructed for three antibiotic categories, with 75.6 % of the variation explained for SAs and CAs. Suspended particulate matter (SPM) significantly influences the environmental fate of antibiotics directly, negatively affecting TCs and QNs (λ = -0.302) but positively impacting SAs and CAs (λ = 0.475). Source apportionment precisely revealed that human sources in the midstream region and animal sources downstream contributed 80.75 % and 71.55 %, respectively. Although more than 85 % of the risk values were less than 0.1, the midstream region was identified as the priority control region (CPITOX >0.01). In particular, OFL, CTC, and ENO from human sources were the main contributors in the midstream region. This study elucidates antibiotic fate and risks in the whole sediment-laden Yellow River, providing a scientific basis for assessing pollution in other large rivers.

Evaluating degradation efficiency of pesticides by persulfate, Fenton, and ozonation oxidation processes with machine learning

Quantifying organic properties is pivotal for enhancing the precision and interpretability of degradation predictive machine learning (ML) models. This study used Binary Morgan Fingerprints (B-MF) and Count-Based Morgan Fingerprints (C-MF) to quantify pesticide structure, and built the ML model to forecast degradation rates of pesticides by persulfate (PS), Fenton (FT) and ozone oxidation (OZ). The result demonstrated that the C-MF-XGBoost model excelled, achieving R2 of 0.914, 0.934, and 0.971 on test-sets for the above three processes, respectively. The model accurately linked molecular structural variations to degradation rates, demonstrating that impact of molecular structure on the degradation rate was observed to be 12.4 %, 15.2 %, and 21.6 % respectively, across a broader range of SHAP values. Additionally, optimal pH ranges were identified for PS (3.5-5.5) and FT (2.5-4.0), while OZ showed a positive correlation with pH. The model identified electron gain/loss groups' promoting/inhibiting effects on degradation rates and highlighted the significance of N atomic structures in PS. Then, Tanimoto coefficient was used to evaluate the applicability of the model. This study lays a groundwork for quantifying organic compound structures and predicting their degradation impacts, presenting a novel framework to assess future organic pollutants' degradation performance.

Impact of low-dose free chlorine on the conjugative transfer of antibiotic resistance genes in wastewater effluents: Identifying key environmental factors for predictive modeling

Reclaimed water disinfection results in the coexistence of antibiotic resistance genes (ARGs) and low-dose free chlorine in receiving environments. However, the impact of low-dose free chlorine on ARGs conjugative transfer and the key factors influencing the transfer under complex environmental conditions remain unclear, hindering the establishment of an effective monitoring system for resistance pollution in reclaimed water. This study investigated ARGs conjugative transfer under the influence of free chlorine at environmentally relevant concentrations and key interactive factors using machine learning models. The results showed that low-dose free chlorine (0.05-0.3 mg/L) promoted ARGs conjugative transfer, with 0.15 mg/L having a greater promoting effect than free chlorine concentrations of 0.05 and 0.3 mg/L. Additionally, different exposure patterns of low-dose chlorine affected ARGs conjugative transfer, with intermittent exposure posing a higher risk of ARGs dissemination. SVM linear model performed best in predicting ARGs conjugative transfer (RMSE=0.012, R2=0.975), and the SHapley Additive Explanations (SHAP) method revealed that key factors such as HCO3-, SAA, NO3-, and HA had positive SHAP values, indicating a positive influence on ARGs transfer under low-dose chlorine, making them the key features for predicting the ARGs conjugative transfer under the low-dose chlorine exposure. This study also revealed potential mechanisms of ARGs transfer under continuous low-dose free chlorine exposure, including intracellular reactive oxygen species (ROS), enzyme activity, cell membrane permeability, and gene expression. The integration of the machine learning model and post-hoc interpretation methods clarified the key drivers of ARGs conjugative transfer in reclaimed water-replenished environments, providing new insights for the safe reuse of reclaimed water and the development of river monitoring indicators.

Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite

The sorption of radionuclides on granite has been extensively studied over the past few decades due to its significance in the safety assessment of geological disposal for high-level radioactive waste (HLW). The sorption properties of granite for radionuclides exhibit considerable variability under different experimental conditions. To reduce the time and cost associated with traditional experiments, this study developed a data-driven approach utilizing machine learning (ML) algorithms to predict the sorption distribution coefficients of cesium (Cs) on crushed granite efficiently. Four ML algorithms, namely AdaBoost, GBDT, LightGBM, and XGBoost, were employed to construct predictive models using a dataset of 384 data points. All models demonstrated strong performance, with R2 values exceeding 0.8 for both the training and test sets. Comparative analysis of evaluation metrics indicated that the XGBoost model exhibited the best predictive performance and generalization ability. An explanation analysis of the XGBoost model further revealed the importance and influence of each input feature in predicting the distribution coefficient of Cs on crushed granite. The features affecting radionuclide sorption on granite were ranked by importance as follows: solid/liquid ratio, ion strength, pH, contact time, initial concentration, and maximum particle size. The underlying sorption mechanisms by which different input features affect the sorption coefficient, as derived from shapley additive explanations (SHAP) analysis, correspond with experimental observations. The approach proposed in this study can serve as a supplement to resource-intensive experimental methods, providing new insights into predicting the sorption behavior of radionuclides on crushed granite for the safety assessment of HLW geological disposal.

Ferrocene-Based Metal-Organic Framework for Adsorption and Degradation of Antibiotics

Antibiotics have emerged as a significant class of organic pollutants, posing serious global challenges to both the environment and human health. To address the issue of water pollution by antibiotics, a ferrocene-based organic framework (FcMOF) with paramagnetism has been synthesized by hydrothermal complexation of ferrocene dicarboxylic acid with copper chloride and utilized for quick and efficient adsorption and degradation of antibiotics. The maximum adsorption capacity of TC was 736.59 mg g-1 and the degradation rate reached 94.24 % under the optimal conditions (40 °C, pH = 3, 3.0 % H2O2). This provides a new solution with high treatment capacity for antibiotic pollution in water without secondary pollution.

Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence

Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such, antimicrobial resistance is considered as one of the most significant and universal challenges to both health and society, as well as the environment. In our research, we employ the explainable artificial intelligence paradigm to identify the factors that most affect the onset of antimicrobial resistance in diversified territorial contexts, which can vary widely from each other in terms of climatic, economic and social conditions. Specifically, we employ a large set of indicators identified through the One Health framework to predict, at the country level, mortality resulting from antimicrobial resistance related to Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Streptococcus pneumoniae. The analysis reveals the outstanding importance of indicators related to water accessibility and quality in determining mortality due to antimicrobial resistance to the considered pathogens across countries, providing perspective as a potential tool for decision support and monitoring.

Distribution, transport and ecological risk prediction of organophosphate esters in China seas based on machine learning

Organophosphate esters (OPEs), widely used globally, have been detected in significant amounts in various environmental media, raising concerns about their persistence, bioaccumulation, and associated risks. Traditional sampling and detection methods are time-consuming and labor-intensive, limiting a comprehensive understanding. This study employs Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) models, using 12 feature variables and 463 OPEs concentration data points, to investigate the distribution and ecological risk of total OPEs (T-OPEs), chlorinated alkyl OPEs (Cl-OPEs), and aryl-OPEs in seawater of China Seas. The LGBM model proved optimal for predicting T-OPEs and Cl-OPEs concentrations, with RMSE of 0.48 and 0.46 and R2 values of 0.79 and 0.76. XGBoost was superior for aryl-OPEs, yielding an RMSE value of 0.82, and an R2 value of 0.87. Analysis revealed complex nonlinear relationships between features and OPEs concentrations. Maps showed higher OPEs pollution in urban agglomerations and estuaries, particularly in summer. The XGBoost model was the best predictor for ecological risks, with most sites categorized as low-risk, and a few as moderate-risk. This study offers valuable data and insights for managing OPEs pollution and ecological risks in the China Seas.

Based on T.E.S.T toxicity prediction and machine learning to forecast toxicity dynamics in the photocatalytic degradation of tetracycline

The integration of photocatalysis and biological treatment provides an effective strategy for controlling antibiotic contamination, which requires precise monitoring of toxicity changes during the photocatalytic process. In this study, nanoscale TiO2 (P25) was employed to degrade tetracycline (TC) under full-spectrum irradiation, with O2 identified as a crucial reactant for the generation reactive oxygen species (ROS). The toxicity simulation results of the degradation intermediates were closely correlated with the predictions of T.E.S.T software. By analyzing the content of intermediates under different experimental conditions, we developed a machine learning model utilizing the random forest algorithm with a correlation coefficient of R2 = 0.878 and a mean absolute error of MAE = 0.02. The model can track the changes of photocatalytic intermediates, in combination with toxicity simulation, which facilitates the prediction of toxicity at different degradation stages, thus allowing selection of the optimal timing of biological treatment interventions.

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g-1. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure-Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R2 = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area (Sdft) and pore size fractions of 1.1-1.2 nm (VN21.1-1.2) and 3.3-3.4 nm (VN23.3-3.4) for pores.

Impact of land use-induced soil heterogeneity on the adsorption of fluoroquinolone antibiotics, tested on organic matter pools

The effects on the adsorption of fluoroquinolone antibiotics of long-term soil heterogeneity induced by land-use were investigated. Three different land use areas with their two organic matter (OM) pools were tested for the adsorption of three antibiotics widely detected in the environment (ciprofloxacin, norfloxacin, ofloxacin). The soils were separated into two size fractions, > 63 µm fraction and < 63 µm fractions for the fast and slow OM pools, respectively. Any effect of land use on adsorption was only observed in the slow pool in the increasing order: arable land, grassland, and forest. The composition of the soil organic matter (SOM) did influence adsorption in the slow pool, but not in the bulk soilsThis was, because: 1) the ratio of the slow pool was low, as in forest, 2) the ratio of the slow pool was high but its adsorption capacity was low due to its SOM composition, as in arable land and grassland. Soils containing a large slow SOM pool fraction with aliphatic dominance were found to be more likely to adsorb micropollutants. It is our contention that the release of contaminated water, sludge, manure or compost into the environment should only be undertaken after taking this into consideration.

Sustainable Remediation of Soil and Water Utilizing Arbuscular Mycorrhizal Fungi: A Review

Phytoremediation is recognized as an environmentally friendly technique. However, the low biomass production, high time consumption, and exposure to combined toxic stress from contaminated media weaken the potential of phytoremediation. As a class of plant-beneficial microorganisms, arbuscular mycorrhizal fungi (AMF) can promote plant nutrient uptake, improve plant habitats, and regulate abiotic stresses, and the utilization of AMF to enhance phytoremediation is considered to be an effective way to enhance the remediation efficiency. In this paper, we searched 520 papers published during the period 2000-2023 on the topic of AMF-assisted phytoremediation from the Web of Science core collection database. We analyzed the author co-authorship, country, and keyword co-occurrence clustering by VOSviewer. We summarized the advances in research and proposed prospective studies on AMF-assisted phytoremediation. The bibliometric analyses showed that heavy metal, soil, stress tolerance, and growth promotion were the research hotspots. AMF-plant symbiosis has been used in water and soil in different scenarios for the remediation of heavy metal pollution and organic pollution, among others. The potential mechanisms of pollutant removal in which AMF are directly involved through hyphal exudate binding and stabilization, accumulation in their structures, and nutrient exchange with the host plant are highlighted. In addition, the tolerance strategies of AMF through influencing the subcellular distribution of contaminants as well as chemical form shifts, activation of plant defenses, and induction of differential gene expression in plants are presented. We proposed that future research should screen anaerobic-tolerant AMF strains, examine bacterial interactions with AMF, and utilize AMF for combined pollutant removal to accelerate practical applications.

Research on environmental aspects of retention/release of pollutants in soils and sorbents. What should be next?

Although studies dealing with adsorption/desorption (and/or retention/release) of pollutants present in environmental compartments is a classical field of research, recent papers are focusing on some weak points of investigations and publications within the area. In addition, an increasing number of works are being published related to new possibilities and alternatives in this kind of research works, many of them in relation to the use of artificial intelligence (AI). Considering the existence of eventual controversies, eventual mistakes, and the convenience of suggesting alternatives to go ahead in the future, in this work, after taking into account some relevant publications in the previous literature, a simple workflow is proposed as a kind of protocol to revise successive steps that could guide the direction to follow when programing research dealing with the retention/release of pollutants in soils and sorbent materials.

Deciphering silver nanoparticles perturbation effects and risks for soil enzymes worldwide: Insights from machine learning and soil property integration

Globally extensive research into how silver nanoparticles (AgNPs) affect enzyme activity in soils with differing properties has been limited by cost-prohibitive sampling. In this study, customized machine learning (ML) was used to extract data patterns from complex research, with a hit rate of Random Forest > Multiple Imputation by Chained Equations > Decision Tree > K-Nearest Neighbors. Results showed that soil properties played a pivotal role in determining AgNPs' effect on soil enzymes, with the order being pH > organic matter (OM) > soil texture ≈ cation exchange capacity (CEC). Notably, soil enzyme activity was more sensitive to AgNPs in acidic soil (pH < 5.5), while elevated OM content (>1.9 %) attenuated AgNPs toxicity. Compared to soil acidification, reducing soil OM content is more detrimental in exacerbating AgNPs' toxicity and it emerged that clay particles were deemed effective in curbing their toxicity. Meanwhile sand particles played a very different role, and a sandy soil sample at > 40 % of the water holding capacity (WHC), amplified the toxicity of AgNPs. Perturbation mapping of how soil texture alters enzyme activity under AgNPs exposure was generated, where soils with sand (45-65 %), silt (< 22 %), and clay (35-55 %) exhibited even higher probability of positive effects of AgNPs. The average calculation results indicate the sandy clay loam (75.6 %), clay (74.8 %), silt clay (65.8 %), and sandy clay (55.9 %) texture soil demonstrate less AgNPs inhibition effect. The results herein advance the prediction of the effect of AgNPs on soil enzymes globally and determine the soil types that are more sensitive to AgNPs worldwide.