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.

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.

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.

A management framework for sudden water pollution: A systematic review output.

Numerous sudden water pollution (SWP) incidents have occurred frequently in recent years, constituting a potential risk to human, socio-economic, and ecological health. This paper systematically reviews the current literature, with the view to establishing a management framework for SWP incidents. Only 39 of the 327 downloaded articles were selected, and the ROSES protocol was utilized in this review. The results indicated industries, mining sites, and sewage treatment plants as key SWP contributors through accidental leakages, traffic accidents, illegal discharge, natural disasters, and terrorist attacks. These processes also presented five consequences, including the contamination of drinking water sources, disruption of drinking water supply, ecological damage, loss of human life, and agricultural water pollution. Meanwhile, five mitigation strategies included reservoir operation, real-time monitoring, early warning, and chemical and biological treatments. Although an advancement in mitigation strategies against SWP was observed in this review, previous studies reported only a few prevention strategies. Considering that this review provided an SWP-based management framework and a hydrodynamic model selection guideline, which provide a foundation for implementing proactive measures against the SWP. These guidelines and the SWP-based management framework require practical field trials for future studies. PRACTITIONER POINTS: Sudden water pollution increases with industrial growth but decrease with awareness. Human and ecosystem health and social economy are the endpoint receptacles. Mitigation strategies include reservoir dispatch, early warning, and treatments. DPSIR model forms the basis for proving proactive measures against sudden pollution. This review provides a guideline for the selection hydrodynamic models application.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects.

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

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.

Microplastic pollution calls for urgent investigations in stygobiont habitats: A case study from Classical karst.

Microplastic pollution in karst systems is still poorly studied, despite the presence of protected species and habitats, and important water reserves. Vulnerable key species hosted in these habitats could consume or assimilate microplastics, which can irreversibly damage management efforts, and thus ecosystems functionality. This can be particularly true for subterranean water habitats where microplastic pollution effects on wildlife management programs are not considered. The aim of this study is to provide a case study from the Classical Karst Region, which hosts peculiar habitats and key species protected at European level, such as the olm Proteus anguinus. As this area has been deeply exploited and modified over time, and is adjacent to highways, roads and railways, which could contribute to pollution within the karst system, threatening the ecosystems, it provides a perfect model system. In this study we collected and investigated water and sediment samples from aquatic environments of surface and subterranean habitats hosting several subterranean environment-adapted organisms. Examined particles were counted and characterized by size, color and shape via visual identification under a microscope, with and without UV light. Furthermore, spectroscopic analyses were carried out in order to identify microplastics typology. Microplastics were found in all examined habitats. In water, microplastics concentration ranged from 37 to 86 items/L, in sediments from 776 to 2064 items/kg. Fibre-shape was the main present, followed by fragments and beads, suggesting multiple sources of pollution, especially textile products. Most of the particles were fluorescent under UV light and were mainly transparent, while not-fluorescent ones were especially black, blue or brown. Samples contained especially polyesters and copolymers. These results highlight intense MP pollution in karst areas, with significant impacts on water quality, and potential effects on subterranean environment-dwelling species. We stress the importance of monitoring pollution in these critical environments for biodiversity and habitat conservation: monitoring in karst areas must become a priority for habitat and species protection, and water resources management, improving analyses on a larger number of aquatic surface and subterranean habitats.

Distribution and composition of floating marine debris in Shiretoko peninsula, Japan, using opportunistic sighting survey.

Floating marine debris (FMD) poses several threats to marine species, such as entanglement, ingestion, and the transport of pollutants. The Shiretoko Peninsula, located in northern Japan, is a registered World Natural Heritage Site and a biodiversity hotspot. However, FMD has not yet been thoroughly investigated in this region. In 2022, sighting surveys were conducted in Abashiri (west side of the peninsula) and Rausu (east side) to assess the abundance, composition, and distribution of FMD. The mean densities were notably higher in Abashiri, and there was more fishing-related debris in Rausu. Regarding local human activities, the population and number of tourists are higher in Abashiri, and fishing activities are higher in Rausu. While marine pollution is a global issue, our study suggests that addressing it should commence with community-based management at the local level.

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.

Identification of water pollution sources and analysis of pollution trigger conditions in Jiuqu River, Luxian County, China.

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics.

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Mitochondrial DNA marker: A PCR approach for tracking rat (Rattus rattus and Rattus norvegicus) fecal pollution in surface water systems.

Rats act as reservoirs for a wide range of zoonotic pathogens and can negatively impact human health. In this study, we developed a novel dye base mitochondrial DNA (mtDNA) PCR-assay (RatMt) specifically targeting a 180 bp fragment of the NADH dehydrogenase subunit 2 gene for detecting fecal pollution from two species of rats (Rattus rattus and Rattus norvegicus) in environmental samples. Estimation of Escherichia coli concentrations in Rattus norvegicus fecal pellets suggested that there were approximately 2.24 × 10 4 ± 4.86 × 103 MPN/g of fecal pellet. The RatMt PCR assay was robust, had a detection limit of rat feces in water of 0.274 ± 0.14 mg/100 mL and was 100 % specific for detecting Rattus rattus and Rattus norvegicus fecal mtDNA. Fecal Indicator Bacteria (FIB) along an urbanized gradient in Pensacola-Bay was assessed by the IDEXX Colilert™ - 18 and indicated that the majority of the fifteen sampling sites in the Pensacola-Bay area had E. coli concentrations >410 MPN/100 mL. Rattus rattus and Rattus norvegicus mtDNA were detected in all the urban marine sites, three of the urban freshwater sampling areas, and three of the forested sampling sites. The RatMt PCR assay is a useful tool for rapidly detecting Rattus rattus and Rattus norvegicus fecal pollution in environmental samples.

Spatio-temporal dynamics of ecological, bacteriological, and overall water quality of the Damodar River, India.

Assessing river water quality is crucial for human and ecological needs because of the deterioration of the river and escalated water pollution under the threats of anthropogenic activities. In order to assess river water quality, the Damodar River water was evaluated from the perspectives of spatio-temporal dynamics of ecological (organic pollution index or OPI and eutrophication index or EI), bacteriological (coliform count and comprehensive bathing water quality index or CBWQI), and overall water quality assessments (water quality index or WQI and comprehensive pollution index or CPI). The OPI reveals that 44.66% of water samples have heavy organic pollution; however, EI depicts that almost all water samples of Damodar River have severe eutrophication, especially in the pre- and post-monsoon seasons. Moreover, the fecal coliform count and CBWQI indicate the unsuitability of river water for bathing. The overall WQI portrays that 21.56%, 33.59%, and 22.47% of water samples have heavy pollution in pre-monsoon, monsoon, and post-monsoon, respectively. Moreover, 73.39% of water samples have low CPI indicating slight comprehensive pollution. This study also reveals that the pollution level in the Damodar River downstream of the Durgapur barrage is high among the other stations. The major reasons behind the severe contamination of Damodar River water are urban-industrial and agricultural effluents mixing into the river that lead to higher concentrations of BOD, DO, fecal coliform, COD, fluoride TSS, and turbidity in the river water. Thus, this study carries appreciated information on policy recommendations for the different stakeholders of the Damodar River basin including regional planners, agri-engineers, and ecological river engineers for sustainable river management.

Microplastics in aquatic ecosystems of Africa: A comprehensive review and meta-analysis.

Microplastic pollution is a global issue of great public concern. Africa is flagged to host some of the most polluted water bodies globally, but there is no enough information on the extent of microplastic contamination and the potential risks of microplastic pollution in African aquatic ecosystems. This meta-analysis has integrated data from published articles about microplastic pollution in African aquatic ecosystems. The data on the microplastic distribution and morphological characteristics in water, sediments and biota from African rivers, lakes, oceans and seas were extracted from 75 selected studies. Multivariate statistics were used to critically analyze the effects of sampling and detection methods, ecological risks, spatial distribution and similarity of microplastics in relation to the geographical distance between sampling sites. This study found that sampling methods have significant effect on abundance and morphological characteristics of microplastics and that African aquatic ecosystems are highly contaminated with microplastics compared to global data. The most prevalent colors were white, transparent and black, the most prevalent shapes were fibres and fragments, and the most available polymers were polypropylene (PP), polystyrene (PS) and polyethene terephthalate (PET). Microplastic polymers similarity decreased with an increase in geographical distance between sites. Risk levels of microplastics in African aquatic ecosystems were comparatively high, and more than 40 % of water and sediments showed highest level of ecological risk. This review provides recent information on the prevalence, distribution and risks of microplastics in African aquatic ecosystems.

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.

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.

Monitoring grey water footprint and associated environmental controls in agricultural watershed.

The grey water footprint (GWF) is an advanced index linking pollution load and water resources. However, the existing agriculture-related GWF was developed based on hydrological processes, which limits its role in watershed water pollution level (WPL) measurements. The main scope of this study is to calculate GWF and WPL based on runoff, total nitrogen (TN), and total phosphorus (TP) observations in the Hujiashan Watershed of China's Yangtze River Basin. Partial least squares structural equation modeling (PLS-SEM) was utilized to explore the impact pathways of environmental features on GWF and WPL. On this basis, propose measures for the management of this agricultural watershed. The results showed that the TN concentration had a V-shaped trend in 2008-2015, while the TP gradually decreased. The GWF calculations for the TN and TP were compatible with the temporal trends for the concentrations, which were higher in the wet season (0.45 m3/m2 for TN, 0.10 m3/m2 for TP) than in the dry season (0.11 m3/m2 for TN, 0.02 m3/m2 for TP) and increased from upstream to downstream. The WPLs of TN exceeded 2.0 in the midstream and downstream areas, whereas those for TP were inconspicuous. According to PLS-SEM, the GWF is primarily influenced by topographical variables and hydrological features, whereas the WPL is mainly controlled by hydrological features and landscape composition. Fertilizer reduction and efficiency measures should be implemented on farmland and appropriately reducing farming activities on slopes to relieve the GWF and WPL in the watershed.

Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

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.