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.

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.

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.

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.

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.

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

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.

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.

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).

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.

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.

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.

Explore the challenges faced by developing nations in dealing with PFAS contamination, a class of persistent chemicals affecting drinking water safety. How can these countries navigate these complex issues to ensure clean water for all?

Photochemical transformation of pharmaceuticals plays an important role in their natural attenuation, especially in lagoon-based wastewater treatment plants and surface waters receiving substantial sunlight. In this study, the photodegradation of five important pharmaceuticals was studied in samples obtained from a wastewater treatment plant and surface water sources. Batch photodegradation studies for a mixture of pharmaceuticals (diclofenac, sulfamethoxazole, acetaminophen, carbamazepine and gemfibrozil) were carried out in a photochemical reactor. Multiple aliquots of samples removed from the reactor during the experiment were analyzed through high-performance liquid chromatography (HPLC) coupled to a photodiode array (PDA) detector. Intermediate products formed due to photodegradation were identified by ultra-high-performance liquid chromatography coupled with a time-of-flight mass spectrometry (UHPLC-MS/MS). Diclofenac and sulfamethoxazole were found to undergo direct photodegradation due to strong light absorption, whereas the indirect route of photosensitized degradation in the presence of dissolved organic matter (DOM) and model humic acid was significant for acetaminophen, carbamazepine, and gemfibrozil. The reactive radicals such as hydroxyl (OH•), singlet oxygen (1O2) and excited states of DOM (*DOM) were predominantly responsible for the indirect photodegradation of acetaminophen, gemfibrozil and carbamazepine, respectively. Computational analysis revealed that chlorine and carbon atoms belonging to the benzene ring of diclofenac were more reactive to radical attack. Sulfamethoxazole photodegradation occurred through oxidation of the NH2 group. Acetaminophen was more susceptible to electrophilic radical attack at the O-11, and N-7 positions and carbon atoms ortho to the phenolic oxygen and the amine group. The double bonds between C-7, C-8 and C-13 were the most reactive sites for carbamazepine that participated in the phototransformation pathway. Organic matter plays a critical role in the photodegradation of emerging contaminants. The coupling of DFT calculations with UHPLC-MS/MS analysis provided insights on key functional groups participating in the phototransformation pathway. Thus, both parent pharmaceuticals and the photodegradation intermediates should be considered during wastewater treatment.

In this study, we report the implementation of a comprehensive wastewater surveillance testing program at a university campus in Singapore to identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected individuals and the usage of pharmaceuticals and personal care products (PPCPs) as well as other emerging contaminants (ECs). This unique co-monitoring program simultaneously measured SARS-CoV-2 with chemical markers/contaminants as the COVID-19 situation evolved from pandemic to endemic stages, following a nationwide mass vaccination drive. SARS-CoV-2 RNA concentrations in wastewater from campus dormitories were measured using real-time reverse transcription-polymerase chain reaction (RT-qPCR) and corroborated with the number of symptomatic COVID-19 cases confirmed with the antigen rapid test (ART). Consistent results were observed where the concentrations of SARS-CoV-2 RNA detected in wastewater increased proportionately with the number of COVID-19 infected individuals residing on campus. Similarly, a wide range of ECs, including disinfectants and antibiotics, were detected through sensitive liquid chromatography with tandem mass spectrometry (LC-MS/MS) techniques to establish PPCPs consumption patterns during various stages of the COVID-19 pandemic in Singapore. Statistical correlation of SARS-CoV-2 RNA was observed with few ECs belonging to disinfectants, PCPs and antibiotics. A high concentration of disinfectants and subsequent positive correlation with the number of reported cases on the university campus indicates that disinfectants could serve as a chemical marker during such unprecedented times.

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.