ABSTRACT Mathematical models provide a powerful tool for estimating emerging contaminants (ECs) concentrations and enabling continuous observation of contamination levels in aquatic environments, serving as a practical tool for studying the transport, transformation, and fate of ECs within water systems. A systematic review integrating urban drainage systems and receiving water bodies is lacking. Furthermore, existing research on EC modelling is predominantly focused on rivers or watersheds, representing a relatively narrow scope of application. This paper reviews EC modelling studies across different water systems – including urban water systems (water supply networks, sewer networks, wastewater treatment plants) and surface water bodies (rivers, reservoirs, lakes, floodplains, wetlands, and tidal zones)—from the perspective of modelling methodologies, namely mechanistic models, data-driven models, and hybrid models. It provides insights on the aspect of model structural simplification, parameter uncertainty, dynamic variability representation, multi-pollutant interactions and current case study abundance. It also identifies emerging research hotpots in EC simulation and provides information on relevant EC databases. This study aims to identify gaps in current research and provide guidance for users in selecting appropriate study areas and models for practical water quality assessment and management.
ABSTRACT A comprehensive analysis of new technologies, challenges and trends in wastewater treatment plant modeling and control Wastewater treatment is a critical process for protecting water resources and ensuring environmental sustainability. The modeling of key parameters such as dissolved oxygen (DO), nitrogen (in various forms like ammonia, nitrite, and nitrate), plays a fundamental role in understanding and optimizing the performance of wastewater treatment plants (WWTPs). The present document offers a thorough review of modeling strategies for oxygen and nitrogen compounds, emphasizing the importance of predictive accuracy and process control. A comprehensive review of the extant literature reveals the current state of the field, while also analyzing recent research findings to identify gaps and limitations in existing models. This review employed a systematic approach to analyze 64 peer-reviewed studies (2021–2025) using PRISMA criteria to identify emerging trends in AI-driven and hybrid modeling. The paper's conclusions offer a series of recommendations for the refinement of the model and its subsequent implementation in real-time monitoring systems. The review underscores recent advancements in data-driven, classical, and hybrid modeling strategies, with a particular emphasis on the increasing integration of artificial intelligence and machine learning techniques into conventional process models. Key trends, limitations, and research gaps are identified.
ABSTRACT Schematic illustration of transport and transformation pathways of pharmaceuticals, endocrine-disrupting compounds, and PFAS in aquatic systems, showing their release into surface water, movement through wastewater and groundwater compartments, interaction with sediments and biofilms, partial degradation or persistence under natural and engineered processes, and eventual accumulation or transport to downstream ecosystems, highlighting key fate processes including adsorption, dilution, biodegradation, photolysis, and long-range environmental dispersion. Pharmaceuticals, endocrine-disrupting compounds (EDCs), and per- and polyfluoroalkyl substances (PFAS) are widely detected contaminants of emerging concern that pose significant risks to aquatic ecosystems and human health due to their persistence, bioaccumulation potential, and resistance to conventional treatment processes. Originating from anthropogenic sources including municipal wastewater effluents, industrial discharges, and agricultural runoff, these contaminants are increasingly reported in surface water, groundwater, and wastewater systems worldwide. Their physicochemical properties, such as hydrophobicity, molecular structure, ionization state, and environmental stability, govern their mobility, sorption behavior, degradation pathways, and transformation processes. Beyond environmental persistence, these contaminants exert ecotoxicological effects, including endocrine disruption, altered reproductive function, behavioral changes in aquatic organisms, antimicrobial resistance development, and trophic transfer within food webs. Chronic human exposure through drinking water and dietary intake has been associated with endocrine disorders, immunotoxicity, liver dysfunction, reproductive impairment, and elevated cancer risk. This review provides a comparative synthesis of the environmental fate, transport mechanisms, transformation pathways, and bioaccumulation dynamics of pharmaceuticals, EDCs, and PFAS across aquatic matrices. Advances and limitations in analytical detection methods and environmental fate modeling are critically evaluated. The review also discusses regulatory challenges, risk assessment frameworks, and emerging mitigation strategies.
Groundwater is the primary drinking water source for peri-urban communities surrounding Islamabad, yet its quality remains insufficiently characterized. This study assessed the physicochemical and microbiological quality of groundwater from Mera Abadi, Saidpur Village, and Kalajar Village between March and June 2023. A total of 30 samples were analyzed using standard laboratory protocols for physical, chemical, and bacteriological parameters. Mean electrical conductivity exceeded the WHO guideline of 400 mu S/cm at all sites, reaching 402.9 +/- 30.7 mu S/cm (378-480) in Mera Abadi, 399.0 +/- 52.0 mu S/cm (336-499) in Saidpur, and 513.7 +/- 170.7 mu S/cm (368-884) in Kalajar. Total dissolved solids ranged from 262 to 630 mg/L, while measured total salinity (as dissolved salts) ranged from 173 to 427 mg/L. Alkalinity averaged 147.8-185.6 mg/L but reached 300 mg/L in some samples, exceeding recommended limits. Microbiological analysis revealed total bacterial counts ranging from 5 to >300 CFU/mL, with several samples, particularly from Kalajar, exceeding the acceptable limit of 100 CFU/mL; limited coliform presence was detected in selected samples. These findings indicate potential risks of waterborne diseases such as diarrhea, typhoid, and gastrointestinal infections. This study provides a comparative assessment of groundwater quality across three peri-urban settlements and identifies spatial variability in contamination patterns. The results highlight the need for household-level filtration, improved sanitation management, community awareness programs, and routine seasonal monitoring to reduce health risks and protect drinking water safety.
This article examines how various stakeholders have engaged with emerging scientific findings that challenge established assumptions about the sources of glyphosate in European surface waters. We focus on two scientific publications which suggest that municipal wastewater, rather than agricultural runoff, may be the primary source of glyphosate in European rivers, with common laundry detergents hypothesised as a likely contributor through the transformation of aminopolyphosphonates during wastewater treatment. We argue that reliance on emerging scientific evidence represents a high-risk, high-gain scenario for stakeholders: those who engage early may gain a first-mover advantage in agenda-setting, but they risk losing credibility if the findings are subsequently disconfirmed. Drawing on a dedicated analytical framework, we analyse how different actors strategically responded to this emerging evidence. Agricultural actors were among the earliest and most active engagers, given the potential of the findings to partially exculpate the sector. Actors whose interests would be negatively affected by a reorientation of glyphosate policy responded differently: they challenged the findings while public attention was rising and largely withdrew as interest declined.HIGHLIGHTSEmerging evidence of glyphosate in wastewater led to a controversial public debate. Competing actor groups behave strategically in debate: those positively affected by emerging evidence aim to attract public attention; those negatively affected question the evidence and aim to minimise attention. The process of formulating policies to protect water quality is influenced by politics rather than being purely evidence based.
Plastic is extensively used in various sectors, and its breakdown to microplastics (MPs) poses significant ecological and health concerns. In Ethiopia, the Akaki River plays a crucial role in socioeconomic use in urban and peri-urban agricultural regions, but it is vulnerable to natural and human-made pollution. This study aims to assess the abundance, morphology, and polymer types of MPs in the Akaki River during the wet season. A laboratory-based cross-sectional study was conducted using a purposive sampling technique, and 30 water samples were collected from 10 sites. The laboratory analysis is performed by sieving, density separation, organic digestion, filtration, and detection of MPs under a stereomicroscope and Fourier-transform infrared spectroscopy. MPs were found in all water samples, ranging from 331 to 1,361 particles/L. The most prevalent MPs were transparent particles, fibers, and polyethylene. Kruskal-Wallis testing found statistically significant differences in the median microplastic abundance of sampling points. The abundance of fibers and transparent particles was found to be strongly correlated. The microplastic concentrations after rescaling are higher than reported for river waters in other areas, highlighting that the river is highly polluted. The study emphasizes the need for improved waste management and stricter regulations on wastewater treatment in Addis Ababa.
This study assessed Cryptosporidium parvum infection risks associated with protected and unprotected drinking-water sources in three rural kebeles of Oromia, Ethiopia. Using Escherichia coli data collected in rainy and dry seasons, we developed a Monte Carlo-based quantitative microbial risk assessment (QMRA) to estimate Cryptosporidium infection risk indirectly from E. coli indicators and compute household-level annual probabilities for children under two. We then applied generalized estimating equations, clustered by kebele and adjusted for season, to derive epidemiologically interpretable odds ratios (OR) and risk ratios (RR). Annual infection probabilities substantially exceeded the WHO benchmark of 10(-4) infections per person-year for all sources. While unprotected water sources exhibited moderately higher QMRA-derived risks, protection status was not a significant predictor in adjusted models (OR = 1.34 (95% CI: 0.62-3.44) and RR = 1.07 (95% CI: 0.66-1.72); both p > 0.3). In contrast, rainy season samples showed markedly higher risks (OR = 6.07; RR = 3.10; both p < 0.001), indicating strong hydrological amplification during rainfall. These findings highlight the need for targeted, season-responsive interventions and integrated behavioral strategies to reduce waterborne disease risk among vulnerable children.
This prospective cohort study assessed the relationship between recreational activities and gastrointestinal (GI) illness at a beach where ocean swimming in poor water quality conditions was the suspected primary exposure route, following US EPA NEEAR study methodologies. A 4.7% incidence of self-reported GI illness within 14 days after visiting was observed. Using logistic regression and backward selection, minimizing the model's Akaike Information Criterion, a statistically significant elevated GI illness risk among beachgoers with a health condition and for visits in the dry season was identified; food consumption and playing in the sand also explained increased GI illness. While ocean swimming exposure was not selected in the models, an increased incidence of GI illness was observed for ocean swimmers with underlying health conditions, and data suggested poor water quality conditions may contribute to increased risks. This study is the first of its kind in Central America. Since the GI illness rate was relatively low, it is possible that this study lacked a large enough sample size to fully understand which activities increased GI illness. Additional research on the disease burden from recreation on tropical beaches is needed.
Micro- and nanoplastics (MNPs) are emerging contaminants whose physico-chemical characteristics govern their environmental fate, bioavailability, degradation, and transport into biota, positioning them as potential vectors for other environmental pollutants. Advanced analytical techniques, including matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, Raman spectroscopy, and micro-Fourier transform infrared spectroscopy, have significantly improved MNP identification and quantification. Increasing evidence links MNP exposure to oxidative stress, inflammation, and immune dysregulation in critical organs, including the liver, kidney, brain, and gastrointestinal tract. However, most ecotoxicological studies remain laboratory-based, limiting their environmental relevance. Critical gaps persist regarding co-contaminant interactions, bioaccumulation, trophic transfer, and long-term field-scale impacts. Conventional wastewater treatment systems can remove a substantial fraction of microplastics, although smaller particles, particularly nanoplastics, often evade treatment. Advanced remediation approaches, including membrane filtration, adsorption, and oxidation processes, provide enhanced removal efficiency but are constrained by fouling, energy demand, and operational costs. Consequently, integrated and hybrid treatment systems combining physical, chemical, and biological mechanisms are increasingly recognized as promising strategies for effective MNP management. Continued interdisciplinary research and coordinated policy interventions are essential to mitigate the ecological and human health risks associated with MNPs contamination.
Sentinel-2 satellite imagery has been widely used for water quality retrieval applications and provides higher spatial and temporal resolution than most similar Earth observation satellites (e.g., Landsat). However, accurately retrieving water quality parameters from Sentinel-2 can be challenging in smaller lakes with high variability of topographic features, bottom reflectance, fringing submerged vegetation, and interference by optically active, non-phytoplankton particles. The aim of this study was to compare chlorophyll a (Chl-a) retrievals from Sentinel-2 satellites, a DJI Phantom 4 Multispectral (P4M) unmanned aerial vehicle (UAV), and in-situ estimates of Chl-a from four small lakes in South-East Queensland, Australia, with diverse optical signatures. In-situ sampling was conducted at four lakes concurrently with P4M flights and simple spectral band combinations such as red and green ratios and the normalised difference chlorophyll index (NDCI) were applied to the reflectance data, and calibrated to Chl-a concentrations. The Sentinel-2 NDCI models showed good agreement (R2 = 0.85-0.88) with in-situ Chl-a at three lakes, that were more eutrophic. However, the P4M red/green ratio performed better in the remaining lake, which contained high concentrations of suspended sediment. Our findings highlight the advantages of UAV-based remote sensing that include distinguishing non-water pixels and facilitating integration with satellite observations.
Infographic showing a 30% increase in stream chloride loads in Etobicoke Creek from 2011 to 2020, driven by a 33.87% rise in impervious surfaces, illustrated through a conceptual model and subcatchment map. Freshwater salinization (FS) has led stream chloride (Cl) to be a ubiquitous threat in urban watersheds with cold climates. Currently, there are no urban watershed models that integrate long-term hydrological processes with Cl transport for response to changes in climate, land use, and watershed management. We created such a model for a heavily urbanized watershed. We compared the role of sampling frequency on the model's outcomes, and tested its ability to capture exceedances over chronic and acute toxicity thresholds. Using data from daily streamflow and monthly Cl, we calibrated for 2018-2020, then validated with the different climate and land use conditions over 2011-2013. The hydrologic model performed satisfactorily throughout calibration and validation periods. The water quality model using monthly samples was able to consistently capture monthly averages in stream Cl. The model showed a 30% increase (comparable to the observed 31%) in Cl loads corresponding to an increase of total impervious surfaces by 34% from 2011-2013 to 2018-2020. Using hourly Cl data from 2021 to 2022, it captured chronic exceedances within 3% of observations, but failed to model acute events. We discussed the implications of such models for future scenarios in climate, land use, and management, current challenges in urban FS modeling, and recommendations.
The graphical abstract depicts a workflow where water quality data is collected and preprocessed, processed through a Water Quality Index (WQI), and analyzed using a QLSTM model to classify dissolved oxygen levels into hypoxia, anoxia, and normoxia categories.Dissolved oxygen (DO) monitoring in real-time is an important parameter to maintain aquatic health and promote the sustainability of aquaculture systems. Conventional deep learning methods are promising, but tend to possess major drawbacks, such as poor performance with time-series data of a long-range dependency, data imbalance sensitivity, and insufficient stability of the method in the noised and incomplete Internet of Things (IoT) setting. To overcome these challenges, this article develops a framework for continuous monitoring of DO with the help of IoT and an advanced Quantum Long Short-Term Memory (QLSTM) model to classify DO in aqua ponds. The time-series data collected from fishponds are labelled using the Water Quality Index (WQI), namely hypoxia, anoxia, and normoxia. The proposed QLSTM model classifies the water quality data into different DO conditions for fish. The proposed model is better in handling long-range time dependencies and well suited for imbalanced and time-sensitive applications. Performance results demonstrated that the performance of proposed QLSTM has attained classification accuracy of 92.4%, precision, recall, and f1-score of 90.5, 91.8, and 91.1%, respectively, with a less-computation time compared to the state-of-the-art models. The outcomes underscore the model efficiency and its applicability in real-time decision-making processes in aquaculture management.HIGHLIGHTSSmart IoT system monitors dissolved oxygen in aqua ponds in real time. The Water Quality Index classifies oxygen levels into hypoxia, anoxia, and normoxia. The quantum LSTM model improves prediction accuracy and processing speed. It achieves 92.4% accuracy, outperforming traditional deep learning models. The proposed model supports effective aquaculture management.
Eutrophication, driven by human-induced nutrient enrichment, primarily nitrogen and phosphorus promote algal blooms, reduces dissolved oxygen and adversely impacts aquatic biota across diverse systems, including lotic, lentic, and coastal waters. Traditional assessment methods rely on laboratory analyses of chlorophyll, nitrogen, and phosphorus, with costs in CONAGUA-accredited laboratories ranging from approximately USD $75 to $200 per sample, plus transport expenses of USD $15-40 per shipment. These approaches are expensive, time-consuming, and often vary by water body typology. Moreover, heterogeneous datasets encompassing diverse aquatic environments introduce computational challenges that require advanced processing. This study proposes a deterministic artificial intelligence model to estimate total nitrogen and phosphorus from low-cost, in situ measurable parameters, offering generalizability across multiple surface water bodies without temporal constraints. Data preprocessing included augmentation, class balancing, normalization, and stratification to address unbalanced categories and ensure robust performance. Several supervised machine learning algorithms were implemented within a Monte Carlo simulation, and hyperparameters were optimized via Bayesian methods. The models achieved high predictive accuracy (R-2 > 0.97) in validation tests across four Pacific states (Sinaloa, Jalisco, Michoac & aacute;n, Nayarit). Independent datasets from two additional states (Sonora and Oaxaca) confirmed the model's generalization capacity, maintaining robust performance despite slightly lower accuracy.
Legacy mercury from historic atmospheric deposition poses a continued threat to aquatic ecosystems. A change in water level management plans for the Moses-Saunders hydropower dam on the Upper St. Lawrence River (USLR) resulted in water level fluctuations that more closely resemble natural fluctuations. Prior stable water levels (1958-2016) created conditions for overgrowth of cattail (Typha spp.) in riparian wetlands. This study of 81 wetlands during a flood year (2017) built upon a previous assessment (2016, non-flood year; 16 wetlands), quantified methylmercury content, and hgcA, a microbial gene responsible for mercury methylation. Here, total mercury, methylmercury, and hgcA copy number per gram of hydric soil did not differ significantly among geomorphological types of riparian cattail wetlands (p > 0.05). Total mercury, total carbon, and longitude (distance downstream) were the top three predictors of methylmercury content in hydric soils. After adjusting for organic matter, total and methylmercury concentrations increased with distance downstream (proximity to the dam), where younger wetlands were created by the impoundment. Historic flooding may have created sites of legacy mercury accumulation in wetlands close to the hydropower dam, while ongoing flooding generates a continued risk of mobilizing wetland mercury into adjacent food chains.
This systematic review examines the pathways and risks associated with pesticides, microplastics, and emerging contaminants in drinking water sources in Ghana. The current review employed the PRISMA approach to synthesize peer-reviewed literature published between 2020 and 2025. It identifies the routes, levels, and sources of contaminants, as well as their related public health and environmental impacts. A total of 1,160 articles were initially identified, and 450 were shortlisted after a careful screening process. Of these, 39 articles were selected for final inclusion. The key findings showed contaminants enter water sources through runoff from agricultural activities, industrial effluent, and poor waste disposal, presenting a significant health risk. The quantitative data showed widespread contamination by pesticides (0.5–18 μg/L), microplastics (12–547 particles/L), pharmaceuticals, and per- and polyfluoroalkyl substances (PFAS), with significant health implications, such as hormonal disruptions, antibiotic resistance, endocrine disorders, neurological problems, carcinogenic effects, and developmental impairment in children. The presence of endocrine-disrupting chemicals and PFAS in 30% of tap water samples further indicates contamination after treatment. These findings highlight for an urgent comprehensive risk assessment, enforced policy action, and area-specific interventions to provide clean drinking water and protect public health in Ghana.
Wastewater stabilisation ponds (WSPs) provide favourable conditions for cyanobacterial proliferation. Cyanobacterial toxins (cyanotoxins) pose a risk to human health, environmental flows, and recycled water schemes if not managed appropriately. Cyanotoxins are released predominantly during cyanobacterial cell lysis and under cell stress, but also during normal cell death. A variety of algaecides and oxidants are proven to be effective against cyanobacteria, yet each has a range of limitations, which include low effectiveness towards cyanotoxins, increased cyanotoxin gene synthesis, impact on non-target microorganisms and the microbiological assemblage composition, toxic residuals that can accumulate in sediment, high cost, and impracticality to implement at a large scale. This paper critically reviews the options for chemical control of microcystin-producing bloom-forming cyanobacteria, covering published literature describing laboratory, field experiments, and full-scale trials. This is the first study to assess the scalability of chemical treatment options in WSPs against toxic cyanobacterial blooms. Based on the reviewed case studies, multiple-criteria decision analysis (MCDM) of chemical remediation options was developed and showed that hydrogen peroxide and ozone nanobubbles are the most feasible and scalable treatment methods for future implementation in large-scale WSPs.
The water quality of drinking water reservoirs is critical for human and ecosystem health. In this study, we examined the drivers of three metals, aluminum (Al), barium (Ba), and copper (Cu), across two drinking water reservoirs in southwestern Virginia, USA, over 4 years. One reservoir has a hypolimnetic oxygenation system; the other does not. We used time series modeling and multivariate analysis of water column chemistry, suspended sediment, inflow, and precipitation data to assess the relative roles of hydrologic and geochemical drivers of metal behaviors in the two reservoirs. Results suggest that Al concentrations were primarily influenced by high-flow events, consistent with the mobilization of clays from physical weathering. In contrast, Ba showed stronger sensitivity to geochemical drivers, specifically redox conditions. Drivers of Cu behavior were obscured by low Cu concentrations. For all metals, patterns varied among years. Our findings highlight the importance of long-term monitoring and integrated approaches to evaluate the drivers of metal dynamics in reservoir ecosystems and inform strategies for maintaining safe drinking water supplies.
Nitrification is limited in horizontal flow (HF) wetlands due to prevailing anaerobic conditions in these systems. This study examined how high interstitial velocity affects nitrification performance in HF systems used for tertiary wastewater treatment. Experiments were conducted using planted and unplanted HF wetlands operated in batch recycle mode, functioning as continuous stirred tank reactors across interstitial velocities of 15, 36, 56, and 72 m/d. From the Reynolds (Re) number, these velocities fall within a transition hydraulic flow range. In planted and unplanted cells, the levels of DO (2.7-3.3 mgO(2)/L and 1.9-2.4 mgO(2)/L, respectively) and COD (15-27 mg/L and 21-35 mg/L, respectively) differed significantly at different velocities. However, the rate constants for NH4-N in planted (0.24-0.33 d(-1)) and unplanted cells (0.18-0.31 d(-1)) differed insignificantly at varying velocities due to enhanced aeration caused by high velocities. Similarly, NO3-N concentrations did not differ significantly between systems, although each system showed notable changes with velocity. Up to interstitial velocities of 36 m/d and 1
