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.

This mini review explores the potential of visible light–driven bismuth vanadate (BiVO₄)-based photoanodes for removing trace organic pollutants from water. It highlights the advantages of using BiVO₄-based photoanodes over conventional UV-driven photoanodes in water treatment. The mechanism of reactive species generation through water oxidation is discussed. The review also highlights the role of sulfate and sulfite radicals in enhancing pollutant degradation. Furthermore, it evaluates how heterojunction formation improves the removal efficiency of BiVO₄-based photoanodes by reducing charge carrier recombination. Limited research on BiVO₄-based photoanodes for the simultaneous removal of multiple organic pollutants at low concentrations (<1 mg L⁻¹) from real wastewater is identified as a key knowledge gap. Addressing this gap could advance the application of BiVO₄-based photoanodes in photoelectrocatalytic-based advanced oxidation processes.

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.

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.

Purpose of Review: Liquid crystal monomers (LCMs), used extensively in liquid crystal displays (LCDs), have emerged as persistent, bioaccumulative, and toxic organic pollutants. A network analysis of SCOPUS data revealed significant knowledge gaps, especially concerning the fate of LCMs in WWTPs. The available literature highlights that influent LCM concentrations vary widely, with elevated levels linked to industrial and e-waste recycling activities. This review examines the occurrence, fate, and treatment of LCMs, particularly fluorinated LCMs (F-LCMs), in wastewater treatment plants (WWTPs). Recent Findings: Conventional WWTP processes achieve moderate removal efficiencies (~ 84%) for LCMs, but F-LCMs often persist. Advanced treatment techniques such as UV/peroxydisulfate (UV/PDS) showed removal rates of 77–84% for LCMs with biphenyl and ethoxy groups. These groups alter electron distribution, making the molecules more susceptible to oxidative attack by reactive species such as hydroxyl and sulfate radicals. Degradation pathways include cleavage of biphenyl, ethoxy, and C-F bonds, producing less toxic by-products such as oxalic acid and cyclohexane. However, some degradation intermediates formed are toxic, necessitating further research of the treatment processes. Summary: This review underscores the need for systematic monitoring of LCMs in wastewater and their transformation products in treated wastewater and sludge, alongside advancements in treatment technologies to mitigate environmental and health risks. This review highlights the urgency of improving wastewater management strategies for LCMs and the need for future research to address the critical knowledge gaps.

Fluorinated liquid crystal monomers (FLCMs) are emerging aquatic pollutants that co-occur with microplastics (MPs); however, their combined ecological impacts remain poorly understood. This study investigated the interactions between a representative FLCM, 4-Ethoxy-2,3-difluoro-4′-(trans-4-propylcyclohexyl) biphenyl (EDPB), and four major types of MPs: polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). The results revealed plastic-type-dependent adsorption capacities (PE > PS > PP > PVC; 380–850 μg/g) through distinct mechanisms: hydrophobic interactions predominantly influenced PE and PP adsorption, while π-π coordination enhanced PS binding. Microcosm experiments demonstrated that MP-EDPB composites significantly altered sediment microbiomes, showing consistent declines in Proteobacteria abundance (27–29 % vs 36.8 % in controls), pathogen enrichment in marine sediments (Acinetobacter 1.2 → 3.5 %; Vibrio 0.8 → 2.1 %), and ecosystem-specific functional disruptions. Notably, marine systems exhibited greater biodiversity shifts, while freshwater environments showed stronger nitrogen cycle inhibition. These findings provide mechanistic insights into FLCM-MP co-pollution effects on aquatic ecosystems. Environmental implication: Liquid crystal monomers (LCMs) and microplastics (MPs) are emerging contaminants that increasingly co-occur in aquatic ecosystems, yet their interactions and co-exposure risks remain poorly understood. This study demonstrated that the adsorption of LCMs onto microplastics was significantly influenced by plastic type, with equilibrium capacities (Q_max) following the order: PE (849.5 ± 1.2 μg/g) > PS (825.3 ± 0.8 μg/g) > PP (629.1 ± 0.3 μg/g) > PVC (380.2 ± 0.2 μg/g). The MPs-LCMs composites affected microbial composition and functions in sediments across both freshwater and seawater environments. These findings provide a quantitative basis for assessing the environmental partitioning and potential ecological risks associated with MPs-LCMs composite pollutants.

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.

Quaternary ammonium compounds (QACs) are commonly used in many products, such as disinfectants, detergents and personal care products. However, their widespread use has led to their ubiquitous presence in the environment, posing a potential risk to human and environmental health. Several methods, including direct and indirect photodegradation, have been explored to remove QACs such as benzylalkyldimethyl ammonium compounds (BACs) and alkyltrimethyl ammonium compounds (ATMACs) from the environment. Hence, in this research, a systematic review of the literature was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to understand the fate of these QACs during direct and indirect photodegradation in UV/H2O2, UV/PS, UV/PS/Cu2+, UV/chlorine, VUV/UV/chlorine, O3/UV and UV/O3/TiO2 systems which produce highly reactive radicals that rapidly react with the QACs, leading to their degradation. As a result of photodegradation, several transformation products (TPs) of QACs are formed, which can pose a greater risk to the environment and human health than the parent QACs. Only limited research in this area has been conducted with fewer QACs. Hence, quantum mechanical calculations such as density functional theory (DFT)-based computational calculations using Gaussian09 software package were used here to explain better the photo-resistant nature of a specific type of QACs, such as BACs C12-18 and ATMACs C12-C18, and their transformation pathways, providing insights into active sites participating in the phototransformation. Recognizing that different advanced oxidation processes (AOPs) come with pros and cons in the elimination of QACs, this review also highlighted the importance of implementing each AOP concerning the formation of toxic transformation products and electrical energy per order (EEO), especially when QACs coexist with other emerging contaminants (ECs).

Liquid crystal monomers pose rising environmental risks. Discover their impact, health concerns, and global efforts to manage and mitigate these effects.

Wastewater surveillance has been successfully employed to monitor pharmaceutical usage on a university campus. In this study, the usage patterns of antibiotics and disinfectants during the COVID-19 pandemic were evaluated. Firstly, the results showed that during COVID-19 waves, different pharmaceuticals correlated with normalized SARS-CoV-2 concentrations, suggesting that distinct medications may have been necessary to address the unique symptoms associated with each COVID-19 variant. Secondly, it was observed that both the total concentrations of antibiotics and disinfectants were higher during the peak of the COVID-19 pandemic compared to before the Delta Wave. Overall, this indicates an increased chemical usage associated with COVID-19, which raises concerns about its impact on the emergence of Antibiotic Resistance Genes (ARGs) during this period.

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.

Waste-to-Energy: Sustainable Approaches for Emerging Economies presents the latest developments and applications for the conversion of waste into biofuels and other energy products. Divided into two parts, Section I reviews the major sources of solid waste and their management strategies in developing countries, and includes the collection, composition, segregation, and dispersal of various waste streams, as well as the generation of biogas and other value-added products. Section II examines the transformation of waste into biofuels and the management strategies required to efficiently implement waste-to-energy processes. Methods for the production of hydrogen, biomethane, biofuels, and bioenergy, as well as resource recovery are discussed in depth, and mathematical models are provided for anaerobic digestion techniques. The benefits and challenges of waste-to-energy as a waste management strategy are explored through dedicated chapters on the techno-economics, environmental and social regulation, and the operation of WtE plants. The final chapter of the book presents a lifecycle assessment and environmental impact analysis of the technologies and strategies discussed.

Plastic pollution has grown into an epidemic. Among them, plastics smaller than 5mm, defined as microplastics (MPs), have caused severe environmental pollution and emerged as the second major global environmental and ecological issue in the 21st century. As the major receptacles for land-based MPs, the aquatic systems in the urbanized region are considered to be one of the most widely polluted environments, where waterborne MPs can be transported and distributed through water flow; coherent strategies are required to segregate and control MPs pollution. This chapter reviews and describes MPs pollution in urbanized aquatic environments, where wastewater treatment plants and estuarine fronts (for coastal cities) can act as control points for the retention of MPs during transportation. Furthermore, waterborne MPs also undergo complex processes, including sedimentation, fragmentation, aging, and aggregation, further increasing the challenge of MPs pollution assessment and control. This chapter highlights that the Asiatic region displays greater MPs pollution in the aquatic environment. Hence, taking Asia as an example, we further explored the causes of increased MPs pollution and provided an outlook on plausible management strategies for pollution control.

Solid Waste Management for Resource-Efficient Systems: Circularity in Action promotes innovation and shares best practices based on the principles of the circular economy and resource conservation on different aspects of sustainable solid waste management. The book also explains sources, impacts, and recycling potential of emerging wastes. It presents management strategies, including emerging green infrastructure and digitalization for recycling and gainful application of waste. In addition, sections highlight various environmental and health hazards while providing different management strategies based on the principle of resource recovery and circular economy that can help to minimize the environmental impacts.

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.