A new modelling framework for assessing the relative burden of antimicrobial resistance in aquatic environments

Potential of nature-based solutions to reduce antibiotics, antimicrobial resistance, and pathogens in aquatic ecosystems. a critical review

This comprehensive scientific review evaluates the effectiveness of nature-based solutions (NBS) in reducing antibiotics (ABs), combating antimicrobial resistance (AMR), and controlling pathogens in various aquatic environments at different river catchment levels. It covers conventional and innovative treatment wetland configurations for wastewater treatment to reduce pollutant discharge into the aquatic ecosystems as well as exploring how river restoration and saltmarshes can enhance pollutant removal. Through the analysis of experimental studies and case examples, the review shows NBS's potential for providing sustainable and cost-effective solutions to improve the health of aquatic ecosystems. It also evaluates the use of diagnostic indicators to predict NBS effectiveness in removing specific pollutants such as ABs and AMR. The review concludes that NBS are feasible for addressing the new challenges stemming from human activities such as the presence of ABs, AMR and pathogens, contributing to a better understanding of NBS, highlighting success stories, addressing knowledge gaps, and providing recommendations for future research and implementation.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Food waste compost and digestate as novel fertilizers: Impacts on antibiotic resistome and potential risks in a soil-vegetable system

As a novel agricultural practice, the reuse of food waste compost and digestate as fertilizers leads to a circular economy, but inevitably introduces bio-contaminants such as antibiotic resistance genes (ARGs) into the agroecosystem. Moreover, heavy metal and antibiotic contamination in farmland soil may exert selective pressures on the evolution of ARGs, posing threats to human health. This study investigated the fate, influencing mechanisms and potential risks of ARGs in a soil-vegetable system under different food waste fertilization and remediation treatments and soil contamination conditions. Application of food waste fertilizers significantly promoted the pakchoi growth, but resulted in the spread of ARGs from fertilizers to pakchoi. A total of 56, 80, 84, 41, and 73 ARGs, mobile genetic elements (MGEs) and metal resistance genes (MRGs) were detected in the rhizosphere soil (RS), bulk soil (BS), control soil (CS), root endophytes (RE), and leaf endophytes (LE), respectively. Notably, 7 genes were shared in the above five subgroups, indicating a specific soil-root-endophytes transmission pathway. 36 genes were uniquely detected in the LE, which may originate from airborne ARGs. The combined application of biochar and fertilizers reduced the occurrence of ARGs and MGEs to some extent, showing the remediation effect of biochar. The average abundance of ARGs in the RS, BS and CS was 3.15 × 10-2, 1.31 × 10-2 and 2.35 × 10-1, respectively. Rhizosphere effects may reduce the abundance of ARGs in soil. The distribution pattern of ARGs was influenced by the types of soil, endophyte and contaminant. MGEs is the key driver shaping ARGs dynamics. Soil properties and pakchoi growth status may affect the bacterial composition, and consequently regulate ARGs fate, while endophytic ARGs were more impacted by biotic factors. Moreover, the average daily doses of ARGs from pakchoi consumption is 107-109 copies/d/kg, and its potential health risks should be emphasized.

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.

Current examining methods and mathematical models of horizontal transfer of antibiotic resistance genes in the environment

The increasing prevalence of antibiotic resistance genes (ARGs) in the environment has garnered significant attention due to their health risk to human beings. Horizontal gene transfer (HGT) is considered as an important way for ARG dissemination. There are four general routes of HGT, including conjugation, transformation, transduction and vesiduction. Selection of appropriate examining methods is crucial for comprehensively understanding characteristics and mechanisms of different HGT ways. Moreover, combined with the results obtained from different experimental methods, mathematical models could be established and serve as a powerful tool for predicting ARG transfer dynamics and frequencies. However, current reviews of HGT for ARG spread mainly focus on its influencing factors and mechanisms, overlooking the important roles of examining methods and models. This review, therefore, delineated four pathways of HGT, summarized the strengths and limitations of current examining methods, and provided a comprehensive summing-up of mathematical models pertaining to three main HGT ways of conjugation, transformation and transduction. Finally, deficiencies in current studies were discussed, and proposed the future perspectives to better understand and assess the risks of ARG dissemination through HGT.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

Risk control of antibiotics, antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) during sewage sludge treatment and disposal: A review

Sewage sludge is an important reservoir of antibiotics, antibiotic resistance genes (ARGs), and antibiotic resistant bacteria (ARB) in wastewater treatment plants (WWTPs), and the reclamation of sewage sludge potentially threats human health and environmental safety. Sludge treatment and disposal are expected to control these risks, and this review summarizes the fate and controlling efficiency of antibiotics, ARGs, and ARB in sludge involved in different processes, i.e., disintegration, anaerobic digestion, aerobic composting, drying, pyrolysis, constructed wetland, and land application. Additionally, the analysis and characterization methods of antibiotics, ARGs, and ARB in complicate sludge are reviewed, and the quantitative risk assessment approaches involved in land application are comprehensively discussed. This review benefits process optimization of sludge treatment and disposal, with regard to environmental risks control of antibiotics, ARGs, and ARB in sludge. Furthermore, current research limitations and gaps, e.g., the antibiotic resistance risk assessment in sludge-amended soil, are proposed to advance the future studies.