Pharmaceuticals have received extensive scientific and socio-economic attention worldwide due to their acute and chronic toxic effects on plants, animals, and human health. However, the geographical distribution and seasonal variability of pharmaceutical mixtures in aquatic environments remain underexplored, especially in resource-deficient countries. The present review provides an in-depth analysis of the seasonal occurrence of pharmaceuticals, particularly antibiotics detected over the last five years in surface water, groundwater, and drinking water. The effectiveness of the conventional and advanced drinking water treatment processes is discussed with a focus on the adsorption and ozonation processes, commonly employed at drinking water treatment plants (DWTPs). Findings reveal median concentrations of antibiotics and other pharmaceuticals in drinking water worldwide, often exceeding their levels in groundwater. This underscores the urgent need for global-scale, long-term monitoring of antibiotics in aquatic systems, especially in DWTPs. Beyond targeted analysis, non-targeted analysis (NTA) should be integrated into routine water quality monitoring at DWTPs to identify novel contaminants, including fluorinated pharmaceuticals. Finally, this review provides an overview concerning the process-based and data-driven modelling of pharmaceutical occurrence, fate, and transport as a complementary approach to sampling and lab-scaled experiments, especially in resource-limited settings. Strengthening long-term monitoring, expanding treatment solutions, integrating modelling tools, and promoting green chemistry innovations are crucial to mitigating risks and safeguarding water quality.

Antimicrobial resistance (AMR) in aquatic environments poses a critical threat to both environmental and human health. This study presents a novel hybrid modeling framework that integrates a process-based hydrodynamic-environmental model with a data-driven approach to predict the spatiotemporal dynamics of AMR in coastal waters. Macrolide-related antimicrobial resistance genes (ARGs_Macro) were selected as representative markers. The model results were validated using data from a monthly sampling campaign conducted across Singapore’s coastal waters, yielding a mean coefficient of determination (R²) of 0.693, a Nash-Sutcliffe efficiency (NSE) of 0.589, and a root-mean-square deviation (RMSE) of 0.0257 GC/16S rRNA across 12 sampling points. Lincomycin, pH, dissolved oxygen, zinc and temperature were identified as significant influencers of ARGs_Macro. Although Lincomycin is not classified as a macrolide, it ranks as the most important driver of ARGs_Macro due to its shared resistance mechanisms with macrolides, potentially facilitating cross-resistance. The spatiotemporal model results revealed that coastal areas, particularly in the northern part of Singapore, are vulnerable to significant ARG accumulation, with monsoon seasons amplifying the spread of AMR due to hydrodynamic conditions. This study highlights the development of a robust modeling framework that provides valuable insights into the environmental drivers of AMR in coastal waters, offering a foundation for regulatory strategies and future research aimed at mitigating the risks of antimicrobial resistance in aquatic environments.

Lake ecosystems are critical slow-flow environments where silver nanoparticles (AgNPs) and bacterio-plankton interact. AgNPs, known for their strong antimicrobial activity and unique physicochemical properties, are widely used across industries but raise environmental concerns due to their size-depen dent distinct biochemical effects. Dissolved organic matter (DOM), primarily shaped by microbial activity, constitutes a key organic carbon component in lakes. Understanding DOM turnover under the influence of AgNPs is essential for gaining deeper insights into carbon cycling within lake ecosystems. This study investigated the effects of AgNPs on DOM properties using advanced spectroscopic techniques, highlighting the size-dependent impacts on bacterial community structures and DOM characteristics. Smaller AgNPs exhibited greater microbial toxicity, leading to higher concentrations of protein-associated C1 components within DOM. Furthermore, DOM influenced the transformation of silver between ionic and nanoparticle forms, modulating the toxicity of silver species. AgNPs also enhanced associations between specific bacterial taxa and environmental indicators. Size-dependent effects of AgNPs substantially altered microbial functions related to carbon and nitrogen cycling, affecting bacterial metabolism and the environmental behavior of functional genes. These findings underscore the pivotal role of nanomaterial size in shaping DOM turnover, bacterial community interactions, and biogeochemical processes. Overall, this study provides a foundational understanding of the ecological implications of AgNPs in lake ecosystems and informs future environmental risk assessments.

Purpose of the Review: Climate change is intensifying the pressures on aquatic ecosystems by altering the dynamics of contaminants, with cascading effects on ecological and human health. This review synthesizes recent evidence on how rising temperatures, altered precipitation patterns, and extreme weather events influence chemical and microbial contaminant dynamics in aquatic environments. Recent Findings: Key findings reveal that elevated temperatures enhance phosphorus pollution and algal blooms, increase heavy metal release from sediments, and promote the mobilization of organic pollutants. Concurrently, climate change exacerbates microbial contamination by facilitating the spread of waterborne microbial contaminants, especially posing more pressure to antimicrobial resistance-related contaminants through temperature-driven horizontal gene transfer and extreme precipitation events. Complex interactions between chemical and microbial contaminants like heavy metals co-selecting for antibiotic resistance further amplify risks. The compounded effects of climate change and contaminants threaten water quality, ecosystem resilience, and public health, particularly through increased toxicant exposure via seafood and waterborne disease outbreaks. Despite growing recognition of these interactions, critical gaps remain in understanding their synergistic mechanisms, especially in data-scarce regions. Summary: This review highlights the urgent need for integrated monitoring, predictive modeling, and adaptive policies under a One Health framework to mitigate the multifaceted impacts of climate-driven contamination. Future research should prioritize real-world assessments of temperature effects, urban overflow dynamics during extreme weather, and the socio-behavioral dimensions of contaminant spread to inform effective mitigation strategies.

Antimicrobial resistance (AMR) is a global challenge that has impacted aquaculture and surrounding marine environments. In this study, a year-long monitoring program was implemented to evaluate AMR in two different aquaculture settings (i.e., open cage farming, recirculating aquaculture system (RAS)) and surrounding marine environment within a tropical coastal region. The objectives of this study are to (i) investigate the prevalence and co-occurrence of antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARGs), antibiotics (AB) and various associated chemical compounds at these study sites; (ii) explore the contributing factors to development and propagation of AMR in the coastal environment; and (iii) assess the AMR risks from different perspectives based on the three AMR determinants (i.e., ARB, ARGs and AB). Key findings revealed a distinct pattern of AMR across the different aquaculture settings, notably a higher prevalence of antibiotic-resistant Vibrio at RAS outfalls, suggesting a potential accumulation of microorganisms within the treatment system. Despite the relative uniform distribution of ARGs across marine sites, specific genes such as qepA, blaCTX−M and bacA, were found to be abundant in fish samples, especially from the RAS. Variations in chemical contaminant prevalence across sites highlighted possible anthropogenic impacts. Moreover, environmental and seasonal variations were found to significantly influence the distribution of ARGs and chemical compounds in the coastal waters. Hierarchical cluster analysis that was based on ARGs, chemical compounds and environmental data, categorized the sites into three distinct clusters which reflected strong association with location, seasonality and aquaculture activities. The observed weak correlations between ARGs and chemical compounds imply that low environmental concentrations may be insufficient for resistance selection. A comprehensive risk assessment using methodologies such as the multiple antibiotic resistance (MAR) index, comparative AMR risk index (CAMRI) and Risk quotient (RQ) underscored the complexity of AMR risks. This research significantly contributes to the understanding of AMR dynamics in natural aquatic systems and provides valuable insights for managing and mitigating AMR risks in coastal environments.

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.

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.

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.

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.

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.