Search Results (230)

This study examined the effects of pollution from the Shituru hydrometallurgic complex on the Upper Lufira Basin, Democratic Republic of the Congo, between September 2015 and September 2017. Physico-chemical water variables and trace metal elements in water and sediment, as well as diversity and infection parameters of monopisthocotylan parasites infesting Clarias ngamensis, were assessed at three sites: the Lufira River, Panda River, and Lake Tshangalele. We hypothesised that low pollution would correlate with greater ectoparasite species richness and higher infection parameters. Results indicated severe ecological degradation in the highly polluted Panda River (with high concentrations of TMEs; e.g., 510.830 ± 0.86; 82.470 ± 0.200 µg/L for Co²⁺ and Cu²⁺ in water; 15,771 ± 7068 and 1585 ± 1450 µg/g for Cu²⁺ and Zn²⁺ in the sediment), where neither fish nor parasites were present. Across the other sites, eight parasite species were identified. Seven species occurred on fish from the slightly polluted Lufira River (mean intensity (MI) of 31.28 ± 28.95 parasites per infested fish), while five were found in Lake Tshangalele (MI: 3.23 ± 2.89 parasites per infested fish), confirming the hypothesis. Three species, Quadriacanthus halajiani, Q. domatanai, and Macrogyrodactylus clarii, demonstrated potential as sensitive bioindicators of aquatic pollution in the region. Full article

With rapid socioeconomic development and intensified human activities, nitrogen (N) loads have continued to rise, exerting significant impacts on the environment. Most existing studies focus on single cities or short time periods, which limits their ability to capture nitrogen dynamics under rapid urbanization. Based on statistical data from multiple cities in Zhejiang Province from 2011 to 2021, this study applied nitrogen balance accounting and statistical analysis to systematically evaluate the spatiotemporal variations in nitrogen inputs, outputs, and surpluses, as well as their driving factors. The results indicate that although nitrogen inputs and outputs fluctuated over the past decade, the overall nitrogen surplus showed an increasing trend, with the nitrogen surplus per unit area rising from 49.89 kg/(ha·a) in 2011 to 62.59 kg/(ha·a) in 2021. Zhejiang’s nitrogen load was higher than the national average but remained below the levels of highly urbanized regions such as the Yangtze River Delta and Pearl River Delta. Accelerated urbanization and increasing anthropogenic pressures were identified as major contributors to the rising nitrogen surplus, with significant inter-city disparities. Cities like Hangzhou, Ningbo, Wenzhou, and Jinhua were found to face higher risks of nitrogen pollution. Redundancy analysis and Pearson correlation analysis revealed that nitrogen surplus was positively correlated with cropland area, livestock population, total population, precipitation, GDP, and industrial output, further highlighting the dominant role of human activities in nitrogen cycling. This study provides the long-term quantitative assessment of nitrogen balance under multi-city coupling at the provincial scale and identifies key influencing factors. These findings provide scientific support for integrated nitrogen management across multiple environmental compartments in Zhejiang Province, including surface water, groundwater, agricultural systems, and urban wastewater, under conditions of rapid urbanization. Full article

Water functional zoning plays a crucial role in water resource allocation, pollution prevention, and ecological protection. With the increasing intensity of human activities, there is a significant mismatch between current water functional zoning and the economic, social development needs and ecological protection goals. Existing water functional zoning methods mainly rely on expert experience for qualitative judgment, which is highly subjective and inefficient. In response, this paper presents a transferable quantitative feature system and introduces a machine learning-based progressive zoning framework for water functions, validated through a case study of the Yangtze River Basin. The results show that the overall accuracy of the framework is 0.78, which is 4–7% higher compared to traditional single models. In terms of spatial distribution, the transformation of protection and reserved zones in 2020 mainly occurred in the middle and lower reaches, where human activities are frequent, particularly in Sichuan and Jiangxi provinces. The development zones are highly concentrated in the downstream areas, with some regions transitioning into protection or reserved zones, mainly in Hubei and Chongqing provinces. Adjustments to buffer zones are primarily concentrated along inter-provincial boundary areas, such as the junction between Hubei and Anhui provinces. This framework helps managers quickly identify key areas for optimizing water functional zones, providing valuable reference for the precise management of water resources and the formulation of ecological protection strategies in the basin. Full article

Highly accurate quantitative detection of heavy metals is crucial for preventing environmental pollution and safeguarding public health. To address the demand for sensitive and specific detection of Cu²⁺ ions, we have developed carbon dots using a simple hydrothermal process. The synthesized carbon dots are highly stable in aqueous media, environmentally friendly, and exhibit strong blue photoluminescence at 440 nm when excited at 352 nm, with a quantum yield of 5.73%. Additionally, the size distribution of the carbon dots ranges from 2.0 to 20 nm, and they feature excitation-dependent emission. They retain consistent optical properties across a wide pH range and under high ionic strength. The photoluminescent probes are selectively quenched by Cu²⁺ ions, with no interference observed from other metal cations such as Ag⁺, Ca²⁺, Cr³⁺, Fe²⁺, Fe³⁺, Hg²⁺, K⁺, Mg²⁺, Sn²⁺, Pb²⁺, Sr²⁺, and Zn²⁺. The emission of carbon dots exhibits a strong linear correlation with Cu²⁺ concentration in the range of 0–14 μM via a static quenching mechanism, with a detection limit (LOD) of 4.77 μM in water. The proposed carbon dot sensor is low cost and has been successfully tested for detecting Cu²⁺ ions in general water samples collected from rivers in Taiwan. Full article

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

Rapid urbanization has intensified nitrate pollution in megacity rivers, posing severe challenges to urban water governance and sustainable nitrate management. This study presents nitrate dual-isotope signatures (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) from surface water samples collected during the wet season from the Yongding River (YDR) and Chaobai River (CBR) in the Beijing–Tianjin–Hebei megacity region of North China. Average concentrations of nitrate (as NO₃⁻) were 8.5 mg/L in YDR and 12.7 mg/L in CBR. The δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ values varied from 6.1‰ to 19.1‰ and −1.1‰ to 10.6‰, respectively. The spatial distribution of NO₃⁻/Cl⁻ ratios and isotopic data indicated mixed sources, primarily sewage and manure in downstream sections and agricultural inputs in upstream areas. Isotopic evidence revealed widespread nitrification processes and could have potentially localized denitrification under low-oxygen conditions in the lower YDR. Bayesian mixing model (MixSIAR) results indicated that sewage and manure constituted the main nitrate sources (49.4%), followed by soil nitrogen (23.7%), chemical fertilizers (19.2%), and atmospheric deposition from rainfall (7.7%). The self-organizing map (SOM) further revealed three nitrate regimes, including natural and agricultural, mixed, and sewage dominated conditions, indicating a clear downstream gradient of increasing anthropogenic influence. The results suggest that efficient nitrogen management in megacity rivers requires improving biological nutrient removal in wastewater treatment, regulating fertilizer application in upstream areas, and maintaining ecological base flow for natural denitrification. This integrated framework provides a quantitative basis for nitrate control and supports sustainable water governance in highly urbanized watersheds. Full article

The nitrogen and phosphorus pollution in water is highly related to the land use pattern in the watershed. The impacts of the land use patterns on total nitrogen (TN) and total phosphorus (TP) exports in an agricultural watershed of the Lijiang River Basin were studied using the Soil and Water Assessment Tool (SWAT). The SWAT model performed well in simulating runoff, TN, and TP exports, and the R² values were all above 0.67. The model simulation results showed that the total nitrogen (TN) and total phosphorus (TP) outputs in the wet season were 13.97 tons and 1.37 tons, respectively, approximately three times those in the dry season, highlighting that outputs of TN and TP predominantly occurred in the wet season in the basin. The correlation analysis showed that the forest land and water in the sub-basin had negative impacts on TN and TP exports, while the orchard, cultivated land, and building land had a positive correlation with TN and TP exports. Then, scenario simulations were conducted using the calibrated and validated SWAT model. A total of 55 scenarios were set up, involving five land use types with five conversion ratios (10%, 20%, 30%, 40%, and 50%), to analyze the impacts of dynamic land use changes on TN and TP exports. The results showed that the TN and TP exports significantly increased under the conversion of the other land use types into building land, cultivated land, and orchards, and the increasing rate decreased in order, while the TN and TP exports declined with the rising forest and water body area. Generally, the changing rates of TN exports under land use conversion were higher than those of TP exports, except for the orchard conversion. This study revealed that the reasonable planning of land use could alleviate nitrogen and phosphorus pollution, which was helpful for aquatic ecosystem restoration. It provided scientific references for land use planning, aquatic ecosystem restoration, and the achievement of sustainable development goals related to water environment protection in similar karst basins. Full article

Microplastics, as a pervasive emerging pollutant, pose a critical threat to freshwater ecosystems and have emerged as a pressing global environmental concern. This study employed methods such as microscopic observation and Raman spectroscopy analysis to characterize the abundance, morphology, and polymer composition of microplastics in surface water and sediments from the lower Minjiang River (Fujian Province, China) in July and November 2024. By integrating socioeconomic indicators with water quality parameters, we dissected the pollution sources, and employed the Pollution Load Index (PLI), Polymer Chemical Toxicity Hazard Index (PHI), and Potential Ecological Risk Index (PERI) to quantify ecological risks in the study area. Findings reveal that the lower Minjiang River exhibits moderate microplastic contamination compared to domestic and international river systems, with mean abundances of 19.90 ± 1.56 n/L (flood-season surface water), 22.87 ± 1.32 n/L (dry-season surface water), and 728.17 ± 20.51 n/kg (dry-season sediments). Spatiotemporal dynamics demonstrate significantly higher microplastic loads in dry-season surface water versus flood-season counterparts, and markedly elevated concentrations in sediments relative to water column, underscoring medium-specific contamination gradients. Microplastic particles predominantly comprised transparent fibrous/fragmentary entities <500 μm, with polymeric constituents dominated by PP and PE. Urbanization-driven wastewater discharge emerged as the primary contamination vector. Notably, PLI assessment confirmed moderate pollution, whereas PHI and PERI analyses indicated elevated risks, with highly toxic polymers, such as PVC and PAN, contributing disproportionately to risk indices. Full article

Urban streams often suffer from poor water quality, in part due to nutrient pollution, especially in highly developed areas. Poor water quality, driven by high concentrations of nitrate and phosphate entering waterways from runoff, wastewater, and stormwater systems, contributes to urban stream syndrome. This study evaluates the long-term performance of a floating wetland (FW) system installed in a canal of the North Branch of the Chicago River near Goose Island, an area heavily impacted by urban runoff. From 2018 to 2023, surface and subsurface water samples were collected upstream and downstream of a 90 m² FW system and analyzed for nitrate as nitrogen (NO₃-N) and phosphate (PO₄³⁻) using ion chromatography. A paired t-test and two-way ANOVA revealed statistically significant reductions (p < 0.001) in NO₃-N (mean: 1.31 mg/L surface, 1.02 mg/L at 0.3 m) and PO₄³⁻ (mean: 0.64 mg/L surface, 0.57 mg/L at 0.3 m) between waters entering and exiting the FW, with no significant seasonal differences in removal efficiency. These results highlight the FW’s consistent, year-round nutrient mitigation performance driven by plant uptake and microbial processes. Over the five-year period of the study, the FW served as a means of improving the water quality, delivering a sustainable, low-maintenance solution for urban stream management with broader implications for ecological resilience and water quality enhancement. Full article

The detrimental effects of anthropogenic pollution are often magnified across ecosystems due to the interconnected nature of land, rivers, and oceans. Phytoremediation is an accessible technique that leverages the ability of plants to absorb and sequester pollutants and can potentially mitigate contaminants entering the ocean. It is a cost-effective and minimally invasive alternative to traditional water treatment methods. This study investigates the potential of the grapevine species Cissus verticillata (L.), a native plant from Puerto Rico, to be used in the phytoremediation of a creek in a highly urbanized site impacted by contaminated runoff due to heavy rainfall and sanitary waters. A mesocosm experiment was conducted using distilled water mixed with nutrients and known concentrations of cadmium (Cd) and lead (Pb) salts to assess whether C. verticillata could accumulate heavy metals in its tissues. Results showed that C. verticillata successfully absorbed heavy metals, with removal efficiencies of 80.13% (±0.16 SE) for Pb and 44% (±1 SE) for Cd. Results indicated a translocation factor <1 for both cadmium and lead, meaning C. verticillata is not a hyperaccumulator, but a metal stabilizer, as evident by the below detection limit (BDL) of the metals in Juan Mendez Creek. Despite evidence of new vegetative growth among individuals, no significant changes in total biomass or chlorophyll concentration were detected, indicating that C. verticillata maintained physiological stability under heavy metal exposure. Therefore, C. verticillata’s wide availability, adaptability to various environments, and climbing nature—which makes it less vulnerable to runoff and strong currents during rainy seasons—position it as a promising candidate for conservation initiatives and pollution management strategies. Full article

Escherichia coli is an important indicator microorganism of fecal contamination in water. However, routine government monitoring often fails to capture the actual state of pollution, because E. coli concentrations in urban rivers are highly variable. This study presents a case study on factors influencing E. coli concentrations in an urban river draining a fully sewered area. An approximately 70-fold higher concentration compared with the average dry-weather concentration (1.9 CFU/mL) was observed under wet-weather conditions, probably due to the effects of combined sewer overflows. A very short survival of E. coli (less than one day) was expected in the unfiltered overlying water, due to the contributions of bacteriophages, protozoan predation, and bacterial competition, whereas a longer survival was expected in the sediment. Such a short survival may be a characteristic of the target watershed, where treated wastewater accounted for approximately 75% of the total flow. The highly variable antimicrobial resistance among E. coli populations under dry-weather conditions was possibly caused by the regrowth of a limited number of E. coli individuals in the sediment. Rising temperatures due to global warming are expected to decrease the concentration of E. coli in the target watershed, where E. coli populations are strongly suppressed by predation and competition. Full article

In plain river networks, a sluggish flow due to the flat terrain and hydraulic structures significantly reduces water’s capacity for self-purification, leading to persistent water pollution that threatens aquatic ecosystems and human health. Despite being critical, effective water quality prediction proves challenging in such regions, with current models lacking either physical interpretability or temporal accuracy. To address this gap, both a process-based model (MIKE 21) and a deep learning model (CNN-LSTM-Attention) were developed in this study to predict key water quality indicators—dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP)—in a typical river network area in Jiaxing, China. This site was selected for its representative complexity and acute pollution challenges. The MIKE 21 model demonstrated strong performance, with R² values above 0.88 for all indicators, offering high spatial resolution and mechanistic insight. The CNN-LSTM-Attention model excelled in capturing temporal dynamics, achieving an R² of 0.9934 for DO. The results indicate the complementary nature of these two approaches: while MIKE 21 supports scenario-based planning, the deep learning model enables highly accurate real-time forecasting. The findings are transferable to similar river network systems, providing a robust reference for selecting modeling frameworks in the design of water pollution control strategies. Full article

This study presents the level of polychlorinated biphenyl (PCB) pollution in the transboundary Ile River in 2015, 2018, 2019, 2023, and 2024. PCB contamination of water, as well as the presence of a large number of individual congeners, including strictly controlled ‘marker’ and dioxin-like congeners, were detected along the entire length of the river within Kazakhstan. Water samples were analyzed using a Chromos GH-1000 gas chromatograph. Significant interannual variability of river water contamination and a noticeable decrease in 2023 and 2024 compared to the previous periods have been identified. The study examined the PCB concentration transformation in the Ile River, from the transboundary section to the river’s confluence with Lake Balkhash, assessing not only fluctuations in total PCB concentration, but also their congener composition. The main natural and anthropogenic PCB sources and factors causing the transformation of the toxicant along the river course were identified. The total amount of transboundary PCB discharge both into Kazakhstan and into Lake Balkhash was calculated. The results can be used by state and local environmental protection agencies for the development of measures to protect rivers from pollution by these highly toxic pollutants, which is in line with the requirements of the Stockholm Convention on POPs. Full article

Based on monitoring data from 2019 to 2024 at 270 typical surface drinking water sources (SDWS) in the Pearl River Basin (PRB), the occurrence and health risks of metal and metalloid pollutants (MMPs) were analyzed from a large watershed scale and long-term evolution. The results indicated that the overall pollution status of 8 MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) were at a low level and the concentrations of Cd, Pb, Ni, Ba, and V exhibited downward trends from 2019 to 2024. The distribution of MMPs exhibited significant regional differences with the main influencing factors including geological conditions, industrial activities, and urban development. River-type drinking water sources might be more affected by pollution from human activities such as industrial wastewater discharge, and the concentration levels of MMPs were generally higher than those in lake-type drinking water sources. Monte Carlo simulation revealed that 33.08% and 12.90% of total carcinogenic risks (TCR) exceeded the threshold of 10⁻⁶ for adults and children, respectively. Ba and Ni were the main contributors to the TCR, while As posed a certain non-carcinogenic risk to children. Sensitivity analysis indicated that concentrations of As and Ba were the main factors contributing to health risks. Although highly stringent water pollution control and a water resource protection policy have been implemented, it is still suggested to strengthen the control of As, Ba, and Ni in industrial-intensive areas and river-type water sources in the PRB. Full article

Traditional river quality models struggle to accurately predict river water quality in watersheds dominated by non-point source pollution due to computational complexity and uncertain inputs. This study addresses this by developing a novel coupling model integrating a gradient boosting algorithm (Light GBM) and a long short-term memory network (LSTM). The method leverages Light GBM for spatial data characteristics and LSTM for temporal sequence dependencies. Model outputs are reciprocally recalculated as inputs and coupled via linear regression, specifically tackling the lag effects of rainfall runoff and upstream pollutant transport. Applied to predict the concentrations of chemical oxygen demand digested by potassium permanganate index (COD) in South China’s Jiuzhoujiang River basin (characterized by rainfall-driven non-point pollution from agriculture/livestock), the coupled model outperformed individual models, increasing prediction accuracy by 8–12% and stability by 15–40% than conventional models, which means it is a more accurate and broadly applicable method for water quality prediction. Analysis confirmed basin rainfall and upstream water quality as the primary drivers of 5-day water quality variation at the SHJ station, influenced by antecedent conditions within 10–15 days. This highly accurate and stable stack coupling method provides valuable scientific support for regional water management. Full article