The research of river basin ecological compensation based on water emissions trading mechanism.

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

Using a combination of quantitative culture, molecular, and infrastructure data to rank potential sources of fecal contamination in Town Creek Estuary, North Carolina.

Estuarine water quality is declining worldwide due to increased tourism, coastal development, and a changing climate. Although well-established methods are in place to monitor water quality, municipalities struggle to use the data to prioritize infrastructure for monitoring and repair and to determine sources of contamination when they occur. The objective of this study was to assess water quality and prioritize sources of contamination within Town Creek Estuary (TCE), Beaufort, North Carolina, by combining culture, molecular, and geographic information systems (GIS) data into a novel contamination source ranking system. Water samples were collected from TCE at ten locations on eight sampling dates in Fall 2021 (n = 80). Microbiological water quality was assessed using US Environmental Protection Agency (U.S. EPA) approved culture-based methods for fecal indicator bacteria (FIB), including analysis of total coliforms (TC), Escherichia coli (EC), and Enterococcus spp. (ENT). The quantitative microbial source tracking (qMST) human-associated fecal marker, HF183, was quantified using droplet digital PCR (ddPCR). This information was combined with environmental data and GIS information detailing proximal sewer, septic, and stormwater infrastructure to determine potential sources of fecal contamination in the estuary. Results indicated FIB concentrations were significantly and positively correlated with precipitation and increased throughout the estuary following rainfall events (p < 0.01). Sampling sites with FIB concentrations above the U.S. EPA threshold also had the highest percentages of aged, less durable piping materials. Using a novel ranking system combining concentrations of FIB, HF183, and sewer infrastructure data at each site, we found that the two sites nearest the most aged sewage infrastructure and stormwater outflows were found to have the highest levels of measurable fecal contamination. This case study supports the inclusion of both traditional water quality measurements and local infrastructure data to support the current need for municipalities to identify, prioritize, and remediate failing infrastructure.

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Beachgoers' responses to beach health advisories.

Drawing on responses from 238 beachgoers who have visited a Georgia (U.S. state) beach in the past three years, this study asks respondents about their knowledge of beach water quality monitoring, awareness of beach health advisories, perception of water quality, and expected responses upon learning of a beach's water pollution advisory. Binomial logistic regression finds that the only demographic predictor of respondents who would completely stop visiting a beach with an advisory is whether the respondent is a visitor or resident (year-round or part-time). Nearly 40% of visitors would not come to a beach with an advisory compared to 13.4% of residents. Most respondents report they would continue to visit a beach but would stay out of the water and stop harvesting seafood from the beach's waters. More than a third (36.1%), however, are unaware Georgia regularly monitors beach water for water quality, and 41.2% have never read a beach sign warning of contaminated water or seafood. Alarmingly, just over half view aesthetic factors such as no litter, no odor, and clear water as criteria for defining whether beach water is safe.

Peril in the Pipeline: Unraveling the threads of PFAS contamination in U.S. drinking water systems.

We examined the spatial distribution of Per- and Polyfluoroalkyl Substances (PFAS) in the US drinking water and explored the relationship between PFAS contamination, public water systems (PWS) characteristics, and socioeconomic attributes of the affected communities. Using data from the EPA's third Unregulated Contaminant Rule, the Census Bureau, and the Bureau of Labor Statistics, we identified spatial contamination hot spots and found that PFAS contamination was correlated with PWSs size, non-surface raw water intake sources, population, and housing density. We also found that non-white communities had less PFAS in drinking water. Lastly, we observed that PFAS contamination varied depending on regional industrial composition. The results showed that drinking water PFAS contamination was an externality of not only some industrial activities but also household consumption.

Dynamics of antibiotic resistance genes in the sediments of a water-diversion lake and its human exposure risk behaviour.

The dynamics and exposure risk behaviours of antibiotic resistance genes (ARGs) in the sediments of water-diversion lakes remain poorly understood. In this study, spatiotemporal investigations of ARG profiles in sediments targeting non-water (NWDP) and water diversion periods (WDP) were conducted in Luoma Lake, a typical water-diversion lake, and an innovative dynamics-based risk assessment framework was constructed to evaluate ARG exposure risks to local residents. ARGs in sediments were significantly more abundant in the WDP than in the NWDP, but there was no significant variation in their spatial distribution in either period. Moreover, the pattern of ARG dissemination in sediments was unchanged between the WDP and NWDP, with horizontal gene transfer (HGT) and vertical gene transfer (VGT) contributing to ARG dissemination in both periods. However, water diversion altered the pattern in lake water, with HGT and VGT in the NWDP but only HGT in the WDP, which were critical pathways for the dissemination of ARGs. The significantly lower ARG sediment-water partition coefficient in the WDP indicated that water diversion could shift the fate of ARGs and facilitate their aqueous partitioning. Risk assessment showed that all age groups faced a higher human exposure risk of ARGs (HERA) in the WDP than in the NWDP, with the 45-59 age group having the highest risk. Furthermore, HERA increased overall with the bacterial carrying capacity in the local environment and peaked when the carrying capacity reached three (NWDP) or four (WDP) orders of magnitude higher than the observed bacterial population. HGT and VGT promoted, whereas ODF covering gene mutation and loss mainly reduced HERA in the lake. As the carrying capacity increased, the relative contribution of ODF to HERA remained relatively stable, whereas the dominant mechanism of HERA development shifted from HGT to VGT.

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.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method.

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Geographical distribution of radon and associated health risks in drinking water samples collected from the Mulazai area of Peshawar, Pakistan.

Geospatial methods, such as GIS and remote sensing, map radon levels, pinpoint high-risk areas and connect geological traits to radon presence. These findings direct health planning, focusing tests, mitigation, and policies where radon levels are high. Overall, geospatial analyses offer vital insights, shaping interventions and policies to reduce health risks from radon exposure. There is a formidable threat to human well-being posed by the naturally occurring carcinogenic radon (222Rn) gas due to high solubility in water. Under the current scenario, it is crucial to assess the extent of 222Rn pollution in our drinking water sources across various regions and thoroughly investigate the potential health hazards it poses. In this regard, the present study was conducted to investigate the concentration of 222Rn in groundwater samples collected from handpumps and wells and to estimate health risks associated with the consumption of 222Rn-contaminated water. For this purpose, groundwater samples (n = 30) were collected from handpumps, and wells located in the Mulazai area, District Peshawar. The RAD7 radon detector was used as per international standards to assess the concentration of 222Rn in the collected water samples. The results unveiled that the levels of 222Rn in the collected samples exceeded the acceptable thresholds set by the US Environmental Protection Agency (US-EPA) of 11.1 Bq L-1. Nevertheless, it was determined that the average annual dose was below the recommended limit of 0.1 mSv per year, as advised by both the European Union Council and the World Health Organization. In order to avoid the harmful effects of such excessive 222Rn concentrations on human health, proper ventilation and storage of water in storage reservoirs for a long time before use is recommended to lower the 222Rn concentration.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring.

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Organic cocatalysts improved Fenton and Fenton-like processes for water pollution control: A review.

In recent times, organic compounds have been extensively utilized to mitigate the limitations associated with Fe(Ⅲ) reduction and the narrow pH range in Fenton and Fenton-like processes, which have garnered considerable attention in relevant studies. This review presents the latest advancements in the comprehensive analysis and applications of organic agents as assistant/cocatalysts during Fenton/Fenton-like reactions for water pollution control. The primary focus includes the following: Firstly, the mechanism of organic co-catalytic reactions is introduced, encompassing both complexation and reduction aspects. Secondly, these organic compounds are classified into distinct categories based on their functional group structures and applications, namely polycarboxylates, aminopolycarboxylic acids, quinones, phenolic acids, humic substances, and sulfhydryl compounds, and their co-catalytic functions and mechanisms of each category are discussed in meticulous detail. Thirdly, a comprehensive comparison is conducted among various types of organic cocatalysts, considering their relative merits, cost implications, toxicity, and other pertinent factors. Finally, the review concludes by addressing the universal challenges and development prospects associated with organic co-catalytic systems. The overarching objective of this review is to provide insights into potential avenues for the future advancement of organic co-catalytic Fenton/Fenton-like reactions in the context of water purification.

Using Inundation Extents to Predict Microbial Contamination in Private Wells after Flooding Events.

Disaster recovery poses unique challenges for residents reliant on private wells. Flooding events are drivers of microbial contamination in well water, but the relationship observed between flooding and contamination varies substantially. Here, we investigate the performance of different flood boundaries─the FEMA 100 year flood hazard boundary, height above nearest drainage-derived inundation extents, and satellite-derived extents from the Dartmouth Flood Observatory─in their ability to identify well water contamination following Hurricane Florence. Using these flood boundaries, we estimated about 2600 wells to 108,400 private wells may have been inundated─over 2 orders of magnitude difference based on boundary used. Using state-generated routine and post-Florence testing data, we observed that microbial contamination rates were 7.1-10.5 times higher within the three flood boundaries compared to routine conditions. However, the ability of the flood boundaries to identify contaminated samples varied spatially depending on the type of flooding (e.g., riverine, overbank, coastal). While participation in testing increased after Florence, rates were overall still low. With <1% of wells tested, there is a critical need for enhanced well water testing efforts. This work provides an understanding of the strengths and limitations of inundation mapping techniques, which are critical for guiding postdisaster well water response and recovery.

Toxicity removal from contaminated water by constructed wetlands assessed using multiple biomarkers in human stem cell assays.

Constructed wetlands (CWs) have been developed rapidly as a sustainable water treatment technique. However, the capability of CWs for remediating the contaminated water based on toxicity assessment remains largely unknown. Four surface flow CWs and two integrated surface-subsurface flow CWs, from five cities in central and eastern region of China were evaluated, concerning the adverse effects of effluents and the toxicity reduction efficiency. Human bone marrow mesenchymal stem cells (hBMSCs) were employed as a human relevant in vitro model. The influent extractions caused cytotoxicity in a dose-dependent manner. The non-cytotoxic dilutions of the influents enhanced the genotoxicity marker γ-H2AX and reactive oxygen species levels. In addition, the influent repressed the osteogenic and neurogenic differentiation, and stimulated the adipogenic differentiation. Cytotoxicity of the contaminated water was reduced by 54 %-86 % after treatment with CWs. CWs were effective to remove part of the sub-lethal effects, with lower reduction than cytotoxicity. The integrated biomarker response (IBR) value of the effluents from the six CWs is lower than that of four secondary and one tertiary wastewater treatment plants. The IBR of the six CWs influents were in the range of 8.6-10.6, with a reduction of 15-50 % after the pollution restoration in CWs. The two integrated surface-subsurface flow CWs achieved higher IBR removal than the four surface flow CWs, possibly due to improved treatment effects by the combined systems. Cytotoxic and genotoxic effects of polar fractions in the CW effluents were stronger than the medium-polar and the non-polar fractions. Besides, PPARγ agonists present in the effluents played crucial roles and ERα agonists may make modest contributions. The present study enhances understanding of the role of CWs in achieving safe wastewater reclamation and provides evidence for further improving toxicity reduction in CWs performance.

River water pollution in Lebanon: the country's most underestimated public health challenge.

Background: Due to the several interconnected crises that Lebanon has been facing for the past 4 years, many important social and environmental issues have been overlooked until more "pressing" ones are dealt with. Consequently, water pollution in Lebanon continues to worsen. Aim: This study aimed to describe the microbiological and chemical properties of the 10 main rivers in Lebanon and to assess their suitability for irrigation, while exploring some of the solutions to the problem. Methods: This cross-sectional study evaluated the pollution level of water from 10 rivers in Lebanon in June 2023 and their suitability for irrigation. Samples were collected at 3°C and their quality parameters were measured. Statistical analysis was conducted using R statistical software version 4.0.2. Results: Compared to the Food and Agriculture Organization (FAO) guidelines for safe irrigation water use, 4 out of the 10 samples had pH levels exceeding the permissible threshold, resulting in severe limitations on their usability. Three rivers had nitrate concentrations that exceeded the approved range, thus constraining their severe usage. Among the rivers, 60% had Escherichia coli levels higher than the permissible spectrum and 40% had faecal coliform counts exceeding FAO's upper limit recommendation. All water sources, however, had total dissolved solid levels that were within the recommended range. Conclusions: Polluted water can have a negative impact on human, wildlife and ecosystem health. Most of the assessed rivers in our study contained bacterial colonies, above the maximum recommended internationally. There is therefore an urgent need to address pollution issues in Lebanese waters to make them suitable for irrigation and other uses.

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.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model.

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM.

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.