A water quality assessment model involving novel fluorescence technology.

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Applications of engineered biochar in remediation of heavy metal(loid)s pollution from wastewater: Current perspectives toward sustainable development goals.

Environmental pollution of heavy metal(loid)s (HMs) caused adverse impacts, has become one of the emerging concerns and challenges worldwide. Metal(loid)s can pose significant threats to living organisms even when present in trace levels within environmental matrices. Extended exposure to these substances can lead to adverse health consequences in humans. Removing HM-contaminated water and moving toward sustainable development goals (SDGs) is critical. In this mission, biochar has recently gained attention in the environmental sector as a green and alternative material for wastewater removal. This work provides a comprehensive analysis of the remediation of typical HMs by biochars, associated with an understanding of remediation mechanisms, and gives practical solutions for ecologically sustainable. Applying engineered biochar in various fields, especially with nanoscale biochar-aided wastewater treatment approaches, can eliminate hazardous metal(loid) contaminants, highlighting an environmentally friendly and low-cost method. Surface modification of engineered biochar with nanomaterials is a potential strategy that positively influences its sorption capacity to remove contaminants. The research findings highlighted the biochars' ability to adsorb HM ions based on increased specific surface area (SSA), heightened porosity, and forming inner-sphere complexes with oxygen-rich groups. Utilizing biochar modification emerged as a viable approach for addressing lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), and chromium (Cr) pollution in aqueous environments. Most biochars investigated demonstrated a removal efficiency >90 % (Cd, As, Hg) and can reach an impressive 99 % (Pb and Cr). Furthermore, biochar and advanced engineered applications are also considered alternative solutions based on the circular economy.

Comprehensive assessment of exposure and environmental risk of potentially toxic elements in surface water and sediment across China: A synthesis study.

China faces a serious challenge with water pollution posed by potentially toxic elements (PTEs). Comprehensive and reliable environmental risk assessment is paramount for precise pollution prevention and control. Previous studies generally focused on a single environmental compartment within small regions, and the uncertainty in risk calculation is not fully considered. This study revealed the current exposure status of 11 PTEs in surface water and sediment across China using previously reported concentration data in 301 well-screened articles. Ecological and human health risks were evaluated and the uncertainty related to calculation parameters and exposure dataset were quantified. PTEs of high concern were further identified. Results showed Mn and Zn had the highest concentration levels, while Hg and Cd had the lowest concentrations in both surface water and sediment. Risk assessment of individual PTE showed that high-risk PTEs varied by risk receptors and environmental compartments. Nationwide, the probability of aquatic organisms being affected by Mn, Zn, Cu, and As in surface water exceeded 10 %. In sediment, Cd and Hg exhibited high and considerable risk, respectively. As was identified as the major PTE threatening human health as its carcinogenic risk was 1.45 × 10-4 through direct ingestion. Combined risk assessment showed the PTE mixture in surface water and sediment posed medium and high ecological risk with the risk quotient and potential ecological risk index of 1.76 and 558.36, respectively. Adverse health effects through incidental ingestion and dermal contact during swimming were negligible. This study provides a nationwide risk assessment of PTEs in China's aquatic environment and the robustness is verified, which can serve as a practical basis for policymakers to guide the early warning and precise management of water pollution.

A framework model to integrate sources and pathways in the assessment of river water pollution.

Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution are scarce, especially in the case of watersheds impacted by multiple hazardous activities. Therefore, a framework model for investigating the multiple sources of river water pollution was developed. The chosen study area was the Paraopeba River basin located in the Minas Gerais, Brazil. Besides multiple agriculture, industrial, and urban pollution sources, this region was profoundly affected by the rupture of the B1 tailings dam (in January 2019) at the Córrego do Feijão mine, resulting in the release of metal-rich waste. Considering this situation, thirty-nine physicochemical and hydromorphological parameters were examined in the Paraopeba River basin, in the 2019-2023 period. The analysis involved various statistical techniques, including bivariate and multivariate methods such as correlation analysis, principal component analysis, and clustering. The Paraopeba River was mainly impacted by metal contamination resulting from the dam collapse, whereas nutrient contamination, mainly from urban and industrial discharges, predominantly affected its tributaries. Additionally, the elevated concentrations of aluminum, iron, nitrate, and sulfate in both main river and tributaries can be attributed to diffuse and point source pollution. In terms of hydromorphology and soil type, the interaction between woody vegetation and erosion-resistant soils, especially latosols, contributes to the stability of riverbanks in the main river. Meanwhile, in the tributaries, the presence of neosols and sparse vegetation in urbanized areas promoted riverbank erosion potentially amplifying pollution. While the study was conducted in a particular watershed, the findings are based on a methodology that can be applied universally. Hence, the insights on surface water quality from this research can be a valuable resource for researchers studying watersheds with diverse pollution sources.

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern.

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

Assessment of wastewater derived pollution using viral monitoring in two estuaries.

Human wastewater-derived pollution of the environment is an emerging health risk that increases the number of waterborne and foodborne illnesses globally. To better understand and mitigate such health risks, we investigated the prevalence of faecal indicator bacteria, Escherichia coli, and indicator virus (crAssphage) along with human and animal enteric viruses (adenoviruses, noroviruses, sapoviruses, hepatitis E virus) in shellfish and water samples collected from two shellfish harvesting areas in the UK. Human noroviruses were detected at higher detection rates in oyster and water samples compared to mussels with peaks during the autumn-winter seasons. Human enteric viruses were sporadically detected during the warmer months, suggesting potential introduction by tourists following the relaxation of COVID-19 lockdown measures. Our results suggest that viral indicators are more suitable for risk assessment and source tracking than E. coli. The detection of emerging hepatitis and sapoviruses, support the need for comprehensive viral monitoring in shellfish harvesting areas.

A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis.

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

Eastern Mediterranean Health Journal [2024; Vol.30, Issue 2]

Eastern Mediterranean Health Journal is the official health journal published by the Eastern Mediterranean Regional Office of the World Health Organization. It is a forum for the presentation and promotion of new policies and initiatives in health services; and for the exchange of ideas concepts epidemiological data research findings and other information with special reference to the Eastern Mediterranean Region. It addresses all members of the health profession medical and other health educational institutes interested NGOs WHO Collaborating Centres and individuals within and outside the Region.

المجلة الصحية لشرق المتوسط هى المجلة الرسمية التى تصدرعن المكتب الاقليمى لشرق المتوسط بمنظمة الصحة العالمية. وهى منبر لتقديم السياسات والمبادرات الجديدة فى الصحة العامة والخدمات الصحية والترويج لها، و لتبادل الاراء و المفاهيم والمعطيات الوبائية ونتائج الابحاث وغير ذلك من المعلومات، و خاصة ما يتعلق منها باقليم شرق المتوسط. وهى موجهة الى كل اعضاء المهن الصحية، والكليات الطبية وسائر المعاهد التعليمية، و كذا المنظمات غير الحكومية المعنية، والمراكز المتعاونة مع منظمة الصحة العالمية والافراد المهتمين بالصحة فى الاقليم و خارجه

La Revue de Santé de la Méditerranée Orientale est une revue de santé officielle publiée par le Bureau régional de l’Organisation mondiale de la Santé pour la Méditerranée orientale. Elle offre une tribune pour la présentation et la promotion de nouvelles politiques et initiatives dans le domaine de la santé publique et des services de santé ainsi qu’à l’échange d’idées de concepts de données épidémiologiques de résultats de recherches et d’autres informations se rapportant plus particulièrement à la Région de la Méditerranée orientale. Elle s’adresse à tous les professionnels de la santé aux membres des instituts médicaux et autres instituts de formation médico-sanitaire aux ONG Centres collaborateurs de l’OMS et personnes concernés au sein et hors de la Région.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Economic risks hidden in local water pollution and global markets: A retrospective analysis (1995-2010) and future perspectives on sustainable development goal 6.

Pollution from untreated wastewater discharges depletes clean water supply for humans and the environment. It poses adverse economic impacts by determining agricultural yields, manufacturing productivity, and ecosystem functionality. Current studies mainly focus on quantity-related water scarcity assessment. It is unknown how low water quality amplifies local water stress and induces cascading economic risks globally. In this study, we estimated both quality and quantity-related water scarcity index (WSI), local economic water scarcity risk (WSR), and cascading virtual WSR evident in global trade markets across 40 major economies from 1995 to 2010. We find developing countries, e.g., India and China, witnessed fast growth in both quantity and quality-related WSI. Major developed economies, e.g., the US and Germany, experienced a modest increase in water stress but alleviated quality-related risks. Local economic risk (WSR) grew from $116B to $380B, with quality-related risks rising from 20 % to 30 %. Virtual economic WSR in global supply chains increased from $39B to $160B, with quality-related risks increasing from 19 % to 27 %. China became the top exporter of economic WSR, ranked above the US, France, and Japan, and the second-largest position as an importer, trailing only the US. We finally conducted scenario modeling by 2030, assuming different progresses on SDG 6 targets. The findings suggest that only the most ambitious progress in both water quality enhancement and efficiency improvement helps to alleviate ∼20 % economic WSR globally. Our findings underscore the necessity for strategies that integrate management of untreated wastewater flows, improved water use efficiency, and diversification of supply chain networks to enhance global economic resilience to water challenges in the future.

In-situ assessment of the performance of oil-water separation by superhydrophobic coated cotton under extreme conditions.

The present study aims to address the issue of oil in water pollution by application of a superhydrophobic cotton fabric. The superhydrophobic cotton fabric with a water contact angle of 158 ± 2°, is developed by a solution immersion technique using zirconium dioxide (ZrO2) nanoparticles and hexadecyltrimethoxysilane. The synthesis parameters such as concentration, curing temperature, and immersion time were optimized using Box-Behnken design method. With mechanical durability, chemical resilience and thermal stability, the coated fabric can separate different oil-water mixtures with an efficiency of 99.9 %. The coated fabric can also be reused for 50 separation cycles in acidic and neutral medium. Besides, droplet dynamic behavior of oil-water mixture has also been studied to ascertain the effect of mixture impact velocities on separation performance. Additionally, coated fabric possesses self-cleaning feature, which makes it viable for muddy oil-water separation. Prepared coated fabric holds tremendous potential for industrial use and oil-water separation in extreme conditions.

Responsibility-sharing and compensation scheme of transboundary water pollution embodied in China's inter-provincial trade: a perspective from economic value capturing.

Transboundary water pollution induced by inter-regional trade is a complex and challenging issue due to the multiple jurisdictions involved. This study combined water pollution discharge inventory, multi-regional input-output analysis, discharge responsibility-sharing, and ecological compensation model to advance the collaborative control of water pollution embodied in China's inter-provincial trade. Over a fifth of China's water pollution discharges in 2017, equivalent to 1376 Kt, were a result of inter-provincial trade, which primarily flowed from wealthier coastal provinces to less developed ones. Moreover, the analysis demonstrates a mismatch between economic gains and environmental costs. In particular, Jiangxi and Guangxi bear the greatest environmental impact (64 and 58 Kt, respectively) while only receiving meager economic returns (131 and 80 billion Yuan). The economic benefit shared responsibility results for the great majority of provinces fell between production- and consumption-based discharges, and this compromise-based allocation of responsibility is more likely to gain acceptance across various regions. Provinces such as Shanghai, Jiangsu, and Beijing necessitate the highest compensation volumes to others, with 31 Kt, 25 Kt, and 20 Kt, respectively, while provinces including Guangxi, Jiangxi, and Ningxia require the largest inflows of compensation, totaling 26 Kt, 23 Kt, and 18 Kt, respectively. The compensation outcomes ensure that less developed regions, bearing a greater pollution burden, receive compensation from more developed regions with lower pollution burdens. The compensation values aligned with compensation volumes, with a few exceptions driven by variations in shadow prices of water pollution. Our study sheds light on the inter-provincial water pollution burdens and benefits and provides a quantitative basis for optimizing the responsibility-sharing and compensation strategies in China, thereby promoting regional cooperation on water pollution control.

Global occurrence characteristics, drivers, and environmental risk assessment of microplastics in lakes: A meta-analysis.

Microplastic (MP) pollution in lakes has received much attention as an increasing amount of plastic waste enters aquatic ecosystems. However, there is still a lack of comprehensive understanding of the global distribution patterns, environmental hazards, factors driving their presence, and the relationships between sources and sinks of MPs. In this study, we conducted a meta-analysis of drivers of lake MP pollution based on 42 articles on MP pollution from three different aspects: geographical distribution, driving factors and environmental risks. The results revealed differences in the MP pollution levels across the different sampling sites in the global lakes. Moreover, there is significant heterogeneity in the abundance of MPs among various lakes, whose distribution pattern is affected by geographical location, sampling method and extraction method. The size of the MPs differed significantly between water and sediment, and the proportion of small (<1 mm) MPs in sediment was significantly greater than that in water (72% > 46%). Environmental risk assessment reveals that the risk level of MP pollution in most lakes worldwide is low, and the environmental risk of pollution in lake water is higher than that in sediment. Based on the risk assessment and geographical location of the lake, the risk of MP pollution is related not only to human activities and economic development but also to local waste management practices, which directly impact the accumulation of MPs. Therefore, we suggest that the production of biodegradable low-risk polymer plastics instead of high-risk materials, and plastic solid waste recycling management should be strengthened to effectively mitigate the presence of MPs in the environment.

Advances in identifying and managing emerging contaminants in aquatic ecosystems: Analytical approaches, toxicity assessment, transformation pathways, environmental fate, and remediation strategies.

Emerging contaminants (ECs) are increasingly recognized as threats to human health and ecosystems. This review evaluates advanced analytical methods, particularly mass spectrometry, for detecting ECs and understanding their toxicity, transformation pathways, and environmental distribution. Our findings underscore the reliability of current techniques and the potential of upcoming methods. The adverse effects of ECs on aquatic life necessitate both in vitro and in vivo toxicity assessments. Evaluating the distribution and degradation of ECs reveals that they undergo physical, chemical, and biological transformations. Remediation strategies such as advanced oxidation, adsorption, and membrane bioreactors effectively treat EC-contaminated waters, with combinations of these techniques showing the highest efficacy. To minimize the impact of ECs, a proactive approach involving monitoring, regulations, and public education is vital. Future research should prioritize the refining of detection methods and formulation of robust policies for EC management.

A fuzzy logic-based approach for groundwater vulnerability assessment.

Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating and weighting parameters is the DRASTIC model's major weakness, which increases uncertainty. This study developed a Mamdani fuzzy logic (MFL) in combination with data mining to handle this uncertainty and predict the specific vulnerability. To highlight this approach, the susceptibility of the Qorveh-Dehgolan plain (QDP) and the Ardabil plain aquifers was investigated. The DRASTIC index was calculated between 63 and 160 for the Ardabil plain and between 39 and 146 for the QDP. Despite some similarities between vulnerability maps and nitrate concentration maps, the results of the DRASTIC model based on nitrate concentration cannot be verified according to Heidke skill score (HSS) and total accuracy (TA) criteria. Then the MFL was developed in two scenarios; the first included all seven parameters, whereas the second used only four parameters of the DRASTIC model. The results showed that, in the first scenario of the MFL modeling, TA and HSS values were respectively 0.75 and 0.51 for the Ardabil plain and 0.45 and 0.33 for the QDP. In addition, according to the TA and HSS values, the proposed model was more reliable and practical in groundwater vulnerability assessment than the traditional method, even using four input data.

Assessment of crAssphage as a human fecal source tracking marker in the lower Great Lakes.

CrAssphage or crAss-like phage ranks as the most abundant phage in the human gut and is present in human feces-contaminated environments. Due to its high human specificity and sensitivity, crAssphage is a potentially robust source tracking indicator that can distinguish human fecal contamination from agricultural or wildlife sources. Its suitability in the Great Lakes area, one of the world's most important water systems, has not been well tested. In this study, we tested a qPCR-based quantification method using two crAssphage marker genes (ORF18-mod and CPQ_064) at Toronto recreational beaches along with their adjacent river mouths. Our results showed a 71.4 % (CPQ_064) and 100 % (ORF18-mod) human sensitivity for CPQ_064 and ORF18-mod, and a 100 % human specificity for both marker genes. CrAssphage was present in 57.7 % or 71.2 % of environmental water samples, with concentrations ranging from 1.45 to 5.14 log10 gene copies per 100 mL water. Though concentrations of the two marker genes were strongly correlated, ORF18-mod features a higher human sensitivity and higher positive detection rates in environmental samples. Quantifiable crAssphage was mostly present in samples collected in June and July 2021 associated with higher rainfall. In addition, rivers had more frequent crAssphage presence and higher concentrations than their associated beaches, indicating more frequent and greater human fecal contamination in the rivers. However, crAssphage was more correlated with E. coli and Enterococcus at the beaches than in the rivers, suggesting human fecal sources may be more predominant in driving the increases in E. coli and Enterococcus at the beaches when impacted by river plumes.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

The driving factors and spatial evolution of industrial water pollution governance efficiency in the Yangtze River Delta urban agglomeration in China.

In order to explore the effective path to improve the efficiency of industrial water pollution governance efficiency (WGE), this study takes 27 prefecture-level cities in the Yangtze River Delta urban agglomeration in China as the research object, measures WGE through the improved SBM model, and tests the impact of WGE drivers using the spatial Durbin model (SDM). The study found that environmental pollution governance investment (EPGI) is positively correlated with WGE, and industrial agglomeration status (IAS) has inhibitory effects on the improvement of WGE. By testing IAS2, it was determined that the impact of IAS on WGE has a "U"-shaped relationship. The direct impact of EPGI on WGE is 0.5016, and the indirect impact on WGE is 0.6428; the direct impact of IAS on WGE is -0.3036, and the indirect impact on WGE is -0.5158. Among the other tested impact drivers, per capita GDP (PCG), industrial structure (IS), and level of technological innovation (TIL) are positively correlated with the dependent variable WGE, while energy consumption intensity (ECI), environmental regulation intensity (ERI), and degree of openness to foreign investment (FIR) are negatively correlated with the dependent variable WGE. In addition to the impact of the aforementioned main drivers, IAS and EPGI, these six drivers also largely influence and determine the final impact on WGE.

A critical review on organophosphate esters in drinking water: Analysis, occurrence, sources, and human health risk assessment.

Organophosphate esters (OPEs) are ubiquitous in the environment. Copious studies assessed OPEs in various environmental media. However, there is limited summative information about OPEs in drinking water. This review provides comprehensive data for the analytical methods, occurrence, sources, and risk assessment of OPEs in drinking water. In general, liquid-liquid extraction and solid-phase extraction are the most common methods in the extraction of OPEs from drinking water, while gas chromatography and liquid chromatography are the most commonly used instrumental methods for detecting OPEs in drinking water. On the basis of these techniques, a variety of methods on OPEs pretreatment and determination have been developed to know the pollution situation of OPEs. Studies on the occurrence of OPEs in drinking water show that the total concentrations of OPEs vary seasonally and regionally, with tris(1-chloro-2-isopropyl) phosphate and tris(2-chloroethyl) phosphate dominant among different kinds of drinking water. Source identification studies show that there are three main sources of OPEs in drinking water: 1) source water contamination; 2) residual in drinking water treatment process; 3) leakage from device or pipeline. Besides, risk assessments indicate that individual and total OPEs pose no or negligible health risk to human, but this result may be significantly underestimated. Finally, the current knowledge gaps on the research of OPEs in drinking water are discussed and some suggestions are provided for future environmental research.

Alleviating sample imbalance in water quality assessment using the VAE-WGAN-GP model.

Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE-WGAN-GP model. The VAE-WGAN-GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE-WGAN-GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.