A comprehensive modelling approach to understanding the fate, transport and potential risks of emerging contaminants in a tropical reservoir

Pharmaceuticals and personal care product modelling: Unleashing artificial intelligence and machine learning capabilities and impact on one health and sustainable development goals

The presence of pharmaceutical and personal care products (PPCPs) in the environment poses a significant threat to environmental resources, given their potential risks to ecosystems and human health, even in trace amounts. While mathematical modelling offers a comprehensive approach to understanding the fate and transport of PPCPs in the environment, such studies have garnered less attention compared to field and laboratory investigations. This review examines the current state of modelling PPCPs, focusing on their sources, fate and transport mechanisms, and interactions within the whole ecosystem. Emphasis is placed on critically evaluating and discussing the underlying principles, ongoing advancements, and applications of diverse multimedia models across geographically distinct regions. Furthermore, the review underscores the imperative of ensuring data quality, strategically planning monitoring initiatives, and leveraging cutting-edge modelling techniques in the quest for a more holistic understanding of PPCP dynamics. It also ventures into prospective developments, particularly the integration of Artificial Intelligence (AI) and Machine Learning (ML) methodologies, to enhance the precision and predictive capabilities of PPCP models. In addition, the broader implications of PPCP modelling on sustainability development goals (SDG) and the One Health approach are also discussed. GIS-based modelling offers a cost-effective approach for incorporating time-variable parameters, enabling a spatially explicit analysis of contaminant fate. Swin-Transformer model enhanced with Normalization Attention Modules demonstrated strong groundwater level estimation with an R2 of 82 %. Meanwhile, integrating Interferometric Synthetic Aperture Radar (InSAR) time-series with gravity recovery and climate experiment (GRACE) data has been pivotal for assessing water-mass changes in the Indo-Gangetic basin, enhancing PPCP fate and transport modelling accuracy, though ongoing refinement is necessary for a comprehensive understanding of PPCP dynamics. The review aims to establish a framework for the future development of a comprehensive PPCP modelling approach, aiding researchers and policymakers in effectively managing water resources impacted by increasing PPCP levels.

Fugacity model covering abiotic and biotic matrices to investigate the transfer and fate of perfluoroalkyl acids in a large shallow lake of eastern China

Solving the challenges faced during the measurement of the cross-interface transfer of perfluoroalkyl acids (PFAAs) in lakes is crucial for clarifying environmental behaviours of these chemicals and their efficient governance. This study developed a multimedia fugacity model based on the quantitative water-air-sediment interaction (QWASI) covering abiotic/biotic matrices to investigate the cross-interface transfer and fate of PFAAs in Luoma Lake, a typical PFAA-contaminated shallow lake in eastern China. The accuracy and reliability of the established model were confirmed using Percent bias and Monte Carlo simulation, respectively. Using the QWASI model, the multimedia transfer of the PFAAs and their accumulation and persistence in different sub-compartments were described and measured, and the differences among individual PFAAs were explored. The simulation results showed that the sedimentation and resuspension of PFAAs were the most intense cross-interfacial transfers, and the sediments served as a chemical sink in the long term. A significant negative correlation of NC-F (the number of CF bonds) with the relative outflow flux (TW·out-ct) but a positive correlation with the relative net transfer across the interface between water and aquatic plants (Tp-ct) was detected, indicating that the PFAA migration capacity decreased but the bioaccumulation potential increased with the CF bond number. The persistence in water (Pw) of individual PFAAs ranged from 19.65d (PFOA) to 32.22d (PFOS), with an average of 26.15d; their persistence in sediment (Ps) ranged from 432d (PFBA) to 3216d (PFOS), with an average of 1524d, increasing linearly with an increase in NC-F. The water advection flows into and out of the lake (QW·in and QW·out), the PFAA concentration of water inflow (CW·in), and bioconcentration factor of aquatic plants (BCFp) were the primary parameters sensitive to PFAAs in all sub-compartments, which are essential indexes for exploring promising remediation pathways for lacustrine PFAA contamination based on the fugacity model simulation.

Characterizing PFASs in aquatic ecosystems with 3D hydrodynamic and water quality models

Understanding how per- and polyfluoroalkyl substances (PFASs) enter aquatic ecosystems is challenging due to the complex interplay of physical, chemical, and biological processes, as well as the influence of hydraulic and hydrological factors and pollution sources at the catchment scale. The spatiotemporal dynamics of PFASs across various media remain largely unknown. Here we show the fate and transport mechanisms of PFASs by integrating monitoring data from an estuarine reservoir in Singapore into a detailed 3D model. This model incorporates hydrological, hydrodynamic, and water quality processes to quantify the distributions of total PFASs, including the major components perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), across water, particulate matter, and sediments within the reservoir. Our results, validated against four years of field measurements with most relative average deviations below 40%, demonstrate that this integrated approach effectively characterizes the occurrence, sources, sinks, and trends of PFASs. The majority of PFASs are found in the dissolved phase (>95%), followed by fractions sorbed to organic particles like detritus (1.0-3.5%) and phytoplankton (1-2%). We also assess the potential risks in both the water column and sediments of the reservoir. The risk quotients for PFOS and PFOA are <0.32 and < 0.00016, respectively, indicating an acceptable risk level for PFASs in this water body. The reservoir also exhibits substantial buffering capacity, even with a tenfold increase in external loading, particularly in managing the risks associated with PFOA compared to PFOS. This study not only enhances our understanding of the mechanisms influencing the fate and transport of surfactant contaminants but also establishes a framework for future research to explore how dominant environmental factors and processes can mitigate emerging contaminants in aquatic ecosystems.

Spatiotemporal variations and priority ranking of emerging contaminants in nanwan reservoir: A case study from the agricultural region in huaihe river basin in China

The presence of emerging contaminants (ECs) in drinking water sources is an increasing concern, yet limited data exists on their occurrence and risk in the upper Huaihe River Basin, an important agricultural region in Central China. This study investigated 70 ECs, including pesticide and antibiotics in surface water from drinking water source areas in Nanwan Reservoir along the upper reaches of the Huaihe River Basin to prioritize the ECs based on ecological risk and health risk assessment. A total of 66 ECs were detected in the surface water at least once at the selected 38 sampling sites, with concentrations ranging from 0.04 to 2508 ng/L. Ecological risk assessment using the risk quotient (RQ) method revealed high risks (RQ > 1) from 7 ECs in the dry season and 15 ECs in the wet season, with triazine pesticides as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoid and carbamate pesticides and macrolide antibiotics were concerning for teenagers. Ciprofloxacin exhibited a high level of resistance risk during the wet season. A multi-indicator prioritization approach integrating occurrence, risk, and chemical property data ranked 6 pesticides and 3 antibiotics as priority pollutants. The results highlight EC contamination of drinking water sources in this agriculturally-intensive region and the need for targeted monitoring and management to protect water quality.

Full-chain analysis on emerging contaminants in soil: Source, migration and remediation

Emerging contaminants (ECs) are gaining attention due to their prevalence and potential negative impacts on the environment and human health. This paper provides a comprehensive review of the status and trends of soil pollution caused by ECs, focusing on their sources, migration pathways, and environmental implications. Significant ECs, including plastics, synthetic polymers, pharmaceuticals, personal care products, plasticizers, and flame retardants, are identified due to their widespread use and toxicity. Their presence in soil is attributed to agricultural activities, urban waste, and wastewater irrigation. The review explores both horizontal and vertical migration pathways, with factors such as soil type, organic matter content, and moisture levels influencing their distribution. Understanding the behavior of ECs in soil is critical to mitigating their long-term risks and developing effective soil remediation strategies. The paper also examines the advantages and disadvantages of in situ and ex situ treatment approaches for ECs, highlighting optimal physical, chemical, and biological treatment conditions. These findings provide a fundamental basis for addressing the challenges and governance of soil pollution induced by ECs.

Impact and migration behavior of triclosan on waste-activated sludge anaerobic digestion

Triclosan (TCS), a hydrophobic antibacterial agent, is extensive application in daily life. Despite a low biodegradability rate, its hydrophobicity results in its accumulation in waste-activated sludge (WAS) during domestic and industrial wastewater treatment. While anaerobic digestion is the foremost strategy for WAS treatment, limited research has explored the interphase migration behavior and impacts of TCS on WAS degradation during anaerobic digestion. This study revealed TCS migration between solid- and liquid-phase in WAS digestion. The solid-liquid distribution coefficients of TCS were negative for proteins and polysaccharides and positive for ammonium. High TCS levels promoted volatile-fatty-acid accumulation and reduced methane production. Enzyme activity tests and functional prediction indicated that TCS increased enzyme activity associated with acid production, in contrast to the inhibition of key methanogenic enzymes. The findings of the TCS migration behavior and its impacts on WAS anaerobic digestion provide an in-depth understanding of the evolution of enhanced TCS-removing technology.

Integration of hydrodynamic and water quality modeling to mitigate the effects of spill pollution into the Nile River, Egypt

Mitigating spill pollution in the Nile River is crucial to protecting aquatic life, water quality, and public health. Extensive studies focused on the assessment of water quality and hydrodynamics of the Nile River, but there have been relatively few studies that have applied integrated hydrodynamic and water quality modeling approaches to simulate actual accidents in the Nile Fourth Reach. The goal of this study is to develop advanced computational models to simulate accidental spills in the Nile River and track the resulting impacts on water quality. Hydrodynamic and water quality simulations were performed using Delft3D software for 144 km of the Nile River, Egypt, from El-Menia to Assuit. Once the hydrodynamic and water quality models were calibrated, two phosphate spill scenarios were modeled under maximum and minimum flow conditions. The spatial distribution of the spill plume along the studied river section was visualized every 12 h following the spill occurrence for both scenarios. The results of the research were calibrated and validated against measured field data. In addition, various error and performance indicators were calculated to thoroughly assess the rigor and reliability of the results. The results demonstrated that flow velocity was the main factor influencing the spill plume characteristics and behavior. Initially, advection force plays a significant role after a spill occurs. After that, phosphate becomes mixed and diluted through dispersion. The spill plume took less time to reach downstream areas during the period of maximum flow compared to minimum flow. Additionally, the concentration of phosphate decreased as the water flowed downstream. The spatial distribution of the spill over time can assist water treatment facilities in developing mitigation strategies to address the spill impacts. However, complex Nile River dynamics demand extensive computational power. Therefore, the model was simplified for spill events, using the modeling capabilities to analyze hypothetical spills and contaminant spread in the absence of real data.

Implementation of temporal moments to elucidate the reactive transport of metformin and erythromycin in the saturated porous media

This study investigates the fate and transport dynamics of metformin (MTN) and erythromycin (ETM), both classified as pharmaceutical and personal care products (PPCPs), in a saturated sandy soil column using temporal moment analysis (TMA). The key flow and transport parameters, including Darcy velocity, longitudinal dispersivity, adsorption, and degradation coefficients, were analyzed. The results reveal that MTN, a highly mobile contaminant, is eliminated from the column in approximately 40 days, while ETM shows significant adsorption due to its hydrophobic and adsorptive nature. Darcy velocity significantly affects PPCP transport; a one-order magnitude change alters contaminant mass recovery at the column outlet by 88% for MTN and 39-fold for ETM. Longitudinal dispersivity has minimal impact on the transport of PPCPs. However adsorption primarily governs the fate of PPCPs with high adsorption coefficients (Kd), and degradation rates control the fate of low-sorbing PPCPs. A one-order magnitude change in Kd results in a 55% change in the zeroth temporal moment (ZTM) of MTN and a 30-fold change in the case of ETM. Additionally, a one-order magnitude change in the degradation coefficient leads to a 60% variation in MTN's ZTM and a 5% variation in ETM's ZTM. Thus, TMA is a valuable tool for understanding PPCP dynamics in subsurface environments, providing critical insights for managing their increasing concentrations.

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

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.

Oxidation of emerging contaminants by S(IV) activated ferrate: Identification of reactive species

Studies on the Fe(VI)/S(IV) process have focused on improving the efficiency of emerging contaminants (ECs) degradation under alkaline conditions. However, the performance and mechanisms under varying pH levels remain insufficiently investigated. This tudy delved into the efficiency and mechanism of Fe(VI)/S(IV) process using sulfamethoxazole (SMX) and ibuprofen (IBU) as model contaminants. We found that pH was crucial in governing the generation of reactive species, and both Fe(V/IV) and SO4•- were identified in the reaction system. Specifically, an increase in pH favored the formation of SO4•-, while the formation of Fe(VI) to Fe(V/IV) became more significant at lower pH. At pH 3.2, Fe(III) resulting from the Fe(VI) self-decay reactedwith HSO3-to produce SO4•-and •OH. Under near-neutral conditions, the coexistance of Fe(V/IV) and SO4•- in abundance contributed to the optimal oxidation of both pollutants in the Fe(VI)/S(IV) process, with the removal exceeding 74% in 5 min. Competitive quenching experiments showed that the contributions of Fe(V/IV) to SMX and IBU destruction dimished, while the contributions of radicals increased with an increase in pH. However, this evolution was slower during SMX degradation compared to IBU degradation. A comprehensive understnding of pH as the key factor is essential for the optimization of the sulfite-activated Fe(VI) oxidation process in water treatment.

Modelling based study on the occurrence characteristics and influencing factors of the typical antibiotics in Bohai Bay

Previous studies have demonstrated that antibiotics have the potential impacts to ecosystems and human health. However, due to their various classes and distinct characteristics, creating comprehensive, integrated and dynamic simulations has proven to be a challenging task. In this study, a 3D hydrodynamic-contaminant model was developed to gain a better understanding of the transportation and prevalence of antibiotics in the Bohai Bay. Specifically, we focused on four types of antibiotics as examples. To accurately capture the dynamic distribution of antibiotics, both transport and biochemical processes were taken into account. Based on this model, the antibiotics' spatial and temporal distribution was examined, the potential impact of the future antibiotics consumption and climate change was also analyzed. The study found that human activity has a greater impact on the presence of antibiotics in Bohai Bay than temperature rise. Based on the current consumption rate, the total amount of antibiotics in Bohai Bay may increase by 10 ng/L and affect nearly one third of the study area within the next 20-30 years. The significant impact of human activity on water contamination in coastal areas may also have implications for other coastal regions. This finding can provide a valuable framework for pollution prevention and control.

Global development and future trends of artificial sweetener research based on bibliometrics

Artificial sweeteners have sparked a heated debate worldwide due to their ambiguous impacts on public and environmental health and food safety and quality. Many studies on artificial sweeteners have been conducted; however, none scientometric studies exist in the field. This study aimed to elaborate on the knowledge creation and development of the field of artificial sweeteners and predict the frontiers of knowledge based on bibliometrics. In particular, this study combined VOSviewer, CiteSpace, and Bibliometrix to visualize the mapping of knowledge production, covered 2389 relevant scientific publications (1945-2022), and systematically analyzed articles and reviews (n = 2101). Scientific publications on artificial sweeteners have been growing at an annual rate of 6.28% and globally attracting 7979 contributors. Susan J. Brown with total publications (TP) of 17, average citation per article (AC) of 36.59, and Hirsch (h)-index of 12 and Robert F. Margolskee (TP = 12; AC = 2046; h-index = 11) were the most influential scholars. This field was clustered into four groups: eco-environment and toxicology, physicochemical mechanisms, public health and risks, and nutrition metabolism. The publications about environmental issues, in particular, "surface water," were most intensive during the last five years (2018-2022). Artificial sweeteners are gaining importance in the monitoring and assessment of environmental and public health. Results of the dual-map overlay showed that the future research frontiers tilt toward molecular biology, immunology, veterinary and animal sciences, and medicine. Findings of this study are conducive to identifying knowledge gaps and future research directions for scholars.

Emerging contaminants in the northwest area of the Tai Lake Basin, China: Spatial autocorrelation analysis for source apportionment and wastewater-based epidemiological analysis

In the present study, 60 emerging contaminants (ECs) were detected from 88 target compounds in the district of Wujin, which is the northwest area of Tai Lake Basin, China. Among them, CF (caffeine), a type of PhAC (pharmaceutically active compound), was detected as the pollutant with the highest concentration. It was observed that the removal efficiencies of PFASs (per-/polyfluoroalkyl substances) in wastewater treatment plants were lower than those of pesticides; further, those of pesticides were lower than those of PhACs. Based on the spatial agglomeration estimated by the spatial autocorrelation model, the probable sources of 28 contaminants were identified. This model provided a new perspective that would help to clarify the location of sources with high accuracy. The point sources of 6 PFASs and 14 PhACs were successfully found; in contrast, the main source of pesticides was identified as an agricultural nonpoint source. Finally, the potential risks of the ECs were also assessed in this study, including their aquatic ecological risks and human exposure risks. It was concluded that pesticides generally had the highest ecological risk, followed by PFASs and PhACs. To evaluate the population risk of pesticides, the wastewater-based epidemiological model was extended to back-calculate the per capita pesticide consumption, which was 0.22 g d^-1 (10³capita)^-1.

Explication on distribution patterns of volatile organic compounds in petro-chemistry and oil refineries of China using a species-transport model and health risk assessment

Volatile organic compounds (VOCs) from industrial emissions have attracted great attention due to their negative effects on human, but there is lack of deterministic air quality model for VOC emissions. In this study, airborne VOCs from a typical petrochemical and oil refinery region, Lanzhou, Gansu province of China, were on-site measured. The regional pollution patterns were investigated using a species transport model and the health risks were evaluated. The spatial distribution of VOCs showed that 87.5 % of the airborne VOCs were benzene, toluene, ethylbenzene, and xylene having higher concentration (146 μg/m³) in the north direction oil refinery industrial areas. The concentrations of toluene and benzene were as high as 41.5 and 33.3 μg/m³ in the 4 km² area away from the petrochemical emission source, respectively, and the concentration of o-/m + p-xylene was up to 79.7 μg/m³. Based on the measured concentration data, the numerical results showed that the accumulation of high concentration of VOC species by mass transfer in the region is related to the atmospheric diffusion driven by downward-moving air over the valley areas. Non-carcinogenic risk assessments showed that airborne benzene exposure had acceptable hazard quotient of 0.185 for adults, which was 1.8 times of children's (0.102), whereas it was found that a high carcinogenic risk (>10^-4) from benzene in several sampling sites and diffuse distance become significant for carcinogenic risk. This study verified the effectiveness of VOC atmospheric diffusion model through a large number of on-site monitoring data, providing data support for model-based risk assessment.

A comprehensive study of the source, occurrence, and spatio-seasonal dynamics of 12 target antibiotics and their potential risks in a cold semi-arid catchment

Antibiotics are widely consumed and are ubiquitous in aquatic ecosystems, such as in agricultural and fishery lake catchments, for prophylactic treatment. However, there are very few comprehensive studies reporting all seasonal occurrences, spatiotemporal dynamics, and risk assessments of antibiotics in agricultural lake catchments, especially in cold regions during the winter season. This study measured seasonality in the concentrations of 12 antibiotics belonging to seven different classes in the surface waters (tributary rivers and lakes) of the Chagan lake catchment in northeast China. All antibiotics were detected in most of the water samples across most seasons, with concentrations varying for different compounds, locations, and seasons. These levels were discussed in terms of the main sources at different sampling sites, including agriculture, fish farming, municipal wastewater, and others. In general, the highest concentrations of most compounds were observed during the freeze-thaw periods. The number of antibiotic resistance genes (ARGs) correlated with compound lipophilicity and half-life. Based on the ecological risks of antibiotics and the relative abundance of ARGs, a hierarchical control priority list (HCPL) of antibiotics was determined, considering four levels (critical, high, medium, and low). To further strengthen the control and effectively manage antibiotics, we highly recommend the reduction and selective use of veterinary antibiotics in winter and spring during the freeze-thaw periods in the Chagan lake catchment.

Combining knowledge graph with deep adversarial network for water quality prediction

Water quality prediction is an important research focus in smart water and can provide the support to control and reduce water pollution. However, existing water quality prediction models are mainly data-driven and only rely on various sensor data. This paper proposes a new water quality prediction modeling approach integrating data and knowledge. We develop a water quality prediction framework that combines knowledge graph and deep adversarial networks. The knowledge extraction and management compound extracts the water quality knowledge graph from different knowledge sources by using the deep adversarial joint model. The fusing parameter importance learning compound calculates the contribution of parameters in water quality prediction by taking into account both knowledge and data levels of correlation. Finally, a water quality prediction model combining weighted CNN-LSTM with adversarial learning predicts the values of total nitrogen based on real-time monitoring data. The experimental results on monitoring data from the Juhe River of China show that the proposed model can greatly improve the effect of water quality prediction.

Understanding the effects of sludge characteristics on the biosorption of triclosan

On account of hydrophobic nature, the adsorption process on solids is considered as the major pathway for triclosan (TCS) removal in wastewater treatment plants. In this work, four sludge sources (primary sludge, thickened sludge, dewatered sludge, and anaerobic digested sludge) were collected to evaluate the adsorption performance of TCS. The solid-liquid distribution coefficients of TCS were increased with total solids increasing of primary sludge, thickened sludge, and dewatered sludge, whereas decreased in anaerobic digested sludge. Results further revealed differences in sludge floc sub-structures of TCS adsorption. The residues contained most of adsorbed TCS in all sub-structures, while distinguished in various extracellular polymeric substances (EPS). The major contributor of EPS sub-fractions to TCS adsorption was identified as tightly bound EPS in thickened sludge and soluble EPS in anaerobic digested sludge. Based on the excitation-emission matrix spectra and Fourier infrared spectrum results, the protein-like and humic acid-like substances were closely related to the TCS adsorption, and hydrogen bond, hydrophobic interaction, and electrostatic interaction were considered as the dominant mechanisms. This study comprehensively reveals the effects of sludge sources and sub-structures on TCS adsorption, which improves the understanding of interaction and migration processes between TCS and sludge.

Impact of agricultural runoff and domestic sewage discharge on the spatial-temporal occurrence of emerging contaminants in an urban stream in São Paulo, Brazil

Ribeirão das Pedras, a 10-km-long stream from the source to mouth, is part of a predominantly urban catchment located in Campinas metropolitan area in the state of São Paulo, Brazil, and it is also surrounded by sugarcane farms. Monthly sampling of 31 selected emerging contaminants (ECs) was conducted for 1 year (October 2018 to October 2019) in five points, including the spring, agricultural, and urban areas, to assess the dynamics and impact of ECs on the stream. The ECs were quantified using LC-MS/MS analysis. Out of the 31 ECs monitored in this study, 13 were detected in the Ribeirão das Pedras catchment, which were mainly pesticides and caffeine. Eight ECs (hexazinone, malathion, desethylatrazine (DEA), desisopropylatrazine (DIA), fipronil, ametryn, 2-hidroxyatrazine, and diuron) were detected with risk quotients higher than 1, indicating some level of environmental concern. Statistical analyses showed that caffeine, hexazinone, atrazine, DEA, and DIA were the most statistically important contaminants in temporal analysis, with caffeine concentrations varying randomly. Hexazinone, atrazine, DIA, and DEA concentrations increased from November 2018 to January 2019, and atrazine, hexazinone, and DEA concentrations increased from June 2019 to September 2019. Spatial analysis indicates that the spring of Ribeirão das Pedras is the only statistically different sampling point, with lower concentrations of EC. Points 3 and 5, both located in urban areas next to the stream's mouth, differ from each other due to the possible dilution of caffeine downstream of point 3 and domestic sewage discharge upstream of point 5.

Spatial and Temporal Distribution Characteristics and Potential Risks of Sulfonamides in the Shaanxi Section of the Weihe River

The hazards of antibiotics as emerging contaminants to aquatic ecosystems and human health have received global attention. This study investigates the presence, concentration levels, spatial and temporal distribution patterns, and their potential risks to aquatic organisms and human health of sulfonamides (SAs) in the Shaanxi section of the Weihe River. The SA pollution in the Weihe River was relatively less than that in other rivers in China and abroad. The spatial and temporal distribution showed that the total concentrations of SAs in the Weihe River were highest in the main stream (ND−35.296 ng/L), followed by the south tributary (3.718−34.354 ng/L) and north tributary (5.476−9.302 ng/L) during the wet water period. Similarly, the order of concentration from highest to lowest during the flat water period was main stream (ND−3 ng/L), north tributary (ND−2.095 ng/L), and south tributary (ND−1.3 ng/L). In addition, the ecological risk assessment showed that the SAs other than sulfadiazine (SDZ) and sulfamethoxazole (SMZ) posed no significant risk (RQS < 0.01) to the corresponding sensitive species during both periods, with no significant risk to human health for different age groups, as suggested by the health risk assessment. The risk of the six SAs to both aquatic organisms and human health decreased significantly from 2016 to 2021.

Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives

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.

Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach

Monitoring and predicting the occurrence and dynamic distributions of emerging contaminants (ECs) in the aquatic environment has always been a great challenge. This study aims to explore the potential of fully utilizing the advantages of combining traditional process-based models (PBMs) and data-driven models (DDMs) with general water quality indicators in terms of improving the accuracy and efficiency of predicting ECs in aquatic ecosystems. Two representative ECs, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), in a tropical reservoir were chosen for this study. A total of 36 DDMs based on different input datasets using Artificial Neural Networks (ANN) and Random Forests (RF) were examined in three case studies. The models were applied in prognosis validation based on easily accessible data on water quality indicators. Our results revealed that all the models yielded good fits when compared to the observed data. These new insights into the advantages using the combination of traditional PBMs and DDMs with general water quality datasets help to overcome the constraints in terms of model accuracy and efficiency as well as technical and budget limitations due to monitoring surveys and laboratory experiments in the study of fate and transport of ECs in aquatic environments.