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.@en

The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and endocrine-disrupting chemicals (EDCs) in aquatic environments represent a major threat to water resources due to their potential risks to the ecosystem and humans even at trace levels. Mathematical modelling can be a useful tool as a comprehensive approach to study their fate and transport in natural waters. However, modelling studies of the occurrence, fate and transport of ECs in aquatic environments have generally received far less attention than the more widespread field and laboratory studies. In this study, we reviewed the current status of modelling ECs based on selected representative ECs, including their sources, fate and various mechanisms as well as their interactions with the surrounding environments in aquatic ecosystems, and explore future development and perspectives in this area. Most importantly, the principles, mathematical derivations, ongoing development and applications of various ECs models in different geographical regions are critically reviewed and discussed. The recommendations for improving data quality, monitoring planning, model development and applications were also suggested. The outcomes of this review can lay down a future framework in developing a comprehensive ECs modelling approach to help researchers and policymakers effectively manage water resources impacted by rising levels of ECs.@en

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.@en

Turbulence generated within the vegetated confluence system is important for water quality and river management. In this study, we conducted a series of experiments to explore the extent to which emergent rigid vegetation in the confluence channel influences hydrodynamic characteristics and contaminant transport. First, a series of tests with increasing discharge ratios (from 0.35, 0.5, and 1) was conducted to quantify the effects of the discharge ratio on hydrodynamic conditions within the vegetated confluence. Then, tests with different discharge ratios were also set up to explore how contaminants released locations and modes (line and point source) influence the transport and mixing of contaminants. The results showed that increasing the discharge ratio induced larger momentum in the confluence area. The increase in discharge ratio rendered the circulation stronger, and its position came earlier in the non-vegetative area. In addition, the dimensionless turbulent kinetic energy peaked near the interface of the non/vegetated zone. With the increase in the discharge ratio, the dimensionless turbulent kinetic energy was found to be smaller. In the contaminants transport tests, the results revealed larger discharge ratio could speed up contaminants transport and mixing. The applications from this study would be helpful to pollutant transport management in natural confluences.@en

Vegetation greatly affects the flow characteristics and contaminant transport in river confluences. In this study, the flow characteristics and contaminant transport in the non-vegetated/vegetated Y-shaped confluence were explored systematically through a series of experiments. A total of 10 scenarios were designed to answer the three main research questions: what is the difference between the flow characteristics and contaminant transport in (1) asymmetrical and Y-shaped confluences; (2) non-vegetated/vegetated Y-shaped confluences; (3) vegetated Y-shaped confluences with different confluence ratios? The experimental results revealed that vegetation remarkably changes the internal flow structure in Y-shaped confluences. Briefly, the velocity profile can be divided into three vertical layers within the vegetated system, but it remains nearly constant in the non-vegetated channel. Vegetation changes the circulation location and reduces the intensity of the secondary current, weakening the strength of contaminant mixing. However, the turbulent kinetic energy within the vegetated system is larger than that in the non-vegetated case, and it peaks at the top of the vegetation canopy. Under different confluence ratio cases, the overall fluctuation of the longitudinal dispersion coefficients along the cross-sections in the mainstream was similar but increasing the confluence ratio causes the circulation to appear to advance and enhances its intensity. In addition, the vegetation density (200 item/m2) in this study render the manning roughness coefficient at 0.068, which is larger than that under lower vegetation density cases. The outcomes from this study are helpful for both environmental and river management applications.@en