Search Results (263)

Agricultural non-point source pollution (AGNPSP) is one of the core challenges facing global water environment management. Existing research mainly focuses on post-event estimation of pollution loads and source analysis, while studies on proactive risk warning for watershed non-point source pollution are relatively limited, especially those that integrate with agricultural production practices. Therefore, this study takes the River Tongyang Watershed as the research object and establishes a fertilization warning and regulation model based on short-term meteorological data. First, it simulates the migration and transformation processes of pollutants within the watershed under different meteorological conditions and analyzes their spatiotemporal evolution characteristics. Then, combined with real-time water quality monitoring data at the lake inlet, it calculates the residual environmental capacity for pollutants in the river water. Finally, based on this environmental capacity and the farmland area, it back-calculates the maximum safe fertilization amount for each plot under different meteorological scenarios to achieve precise fertilization management. When the planned fertilization amount does not exceed this maximum safe value, environmental risks are within a controllable range; if exceeded, fertilization should be proportionally reduced to prevent non-point source pollution. The results indicate that this model can accurately predict the concentration trends of non-point source pollutants and can develop differentiated fertilization strategies based on rainfall scenarios. The “fertilization determined by water” decision-making framework established in this study provides a technically significant pathway for shifting watershed agricultural non-point source pollution management from passive treatment to active prevention. Full article

Marine sediments are a key component of aquatic ecosystems, linking diverse water uses, functions, and services. Chemical contamination of sediments is a global concern, with many jurisdictions striving to prevent future pollution and manage existing contamination. This study evaluates the contamination status of Rovinj’s coastal waters using an integrated approach that combines sediment and biota chemical analyses. Sediments were analyzed to assess long-term contaminant accumulation (D8.C1), while the Mediterranean mussel (Mytilus galloprovincialis) served as a bioindicator of bioavailable contaminants and their cumulative effects on marine habitats (D8.C2). Sediment samples were collected from five sites (S1–S5), and mussels were caged using Mussel Watch installations for approximately 120 days at a control site (Lim Bay) and within Rovinj harbor. Both matrices were analyzed for heavy metals (As, Cd, Cu, Cr, Hg, Ni, Pb, and Zn), polycyclic aromatic hydrocarbons (16 PAHs), and polychlorinated biphenyls (PCBs), following the EU Water Framework Directive. All sampled locations showed a reduction in sediment contamination relative to 2011 data, with most concentrations below ecotoxicological thresholds. Exceptions included elevated ΣPAH and PCB concentrations in the harbor (S1 = 3.18 mg/kg DW; 0.33 mg/kg DW) and marina (S2 = 3.64 mg/kg DW; 0.89 mg/kg DW), as well as Ni levels (S3 = 30 mg/kg DW; S4 = 34 mg/kg DW). Despite higher contaminant loads at some locations, mussel contaminant bioaccumulation remained limited, and their vitality and survival were only moderately affected in the harbor. Although localized increases in some contaminants were detected, all calculated Q_PECm values remained below 1.0, indicating no significant ecological risk. However, a moderate-to-high probability of toxic effects (P) may occur with long-term exposure for biota inhabiting harbor and marina areas. The results of this study demonstrate continued improvement in the environmental quality of Rovinj’s coastal waters compared to the previous decade. Full article

This study investigates the residue characteristics and potential ecological risks of glyphosate (GLY) and its primary metabolite, aminomethylphosphonic acid (AMPA), in the karst region, specifically focusing on the Yangmei River sub-basin. Water samples from the river were collected in April, June, August, and October of 2023, alongside 20 soil samples taken based on agricultural tillage practices. The residue characteristics of GLY and AMPA were analyzed, and their potential ecological risks were assessed using the Risk Quotient (RQ) method. The results indicated that the residues of GLY and AMPA in the soil of the Yangmei River basin exhibited spatial heterogeneity. The GLY content in the soil ranged from non-detectable (nd) to 888.85 μg/kg, with an average concentration of 262.53 μg/kg. The AMPA content varied from 47.90 to 2102.10 μg/kg, with an average of 465.52 μg/kg. Glyphosate pollution in the soil of the Yangmei River basin was determined to pose a moderate ecological risk. In the water of the Yangmei River basin, GLY concentrations ranged from 0 to 204.0 μg/L, with an average of 50.91 μg/L, while AMPA concentrations varied from 0 to 127.26 μg/L, averaging 26.51 μg/L. The highest GLY concentration was recorded in June, with detection rates for GLY being higher in April and June. The spatial distribution of GLY and AMPA was uneven. Glyphosate pollution in the water environment of the Yangmei River basin also presents a moderate ecological risk. Moreover, glyphosate has negatively impacted the aquatic environment, and its effects on water eutrophication should not be overlooked in efforts to prevent and control this phenomenon. Full article

Water removal is crucial in natural gas processing to minimize water content, ensure safe transmission, and prevent operational issues like equipment corrosion and hydrate formation. Glycol absorption could be considered as one of the most effective methods used for natural gas dehydration and dew point control. However, during solvent regeneration, some pollutants, like benzene, toluene, ethylbenzene, and xylene (BTEX), are released to the atmosphere, resulting in catastrophic physical and mental health problems. Minimizing such pollutants that have negative impacts is highly needed to avoid the related negative environmental consequences. The objective of the current work is to investigate alternative strategies targeted to minimize BTEX emissions and guarantee efficient control of the dew point. Two strategies are introduced and investigated in this work; the first strategy is based on revamping an existing unit by adding a new cooler upstream glycol inlet separator, while the second strategy is based on using alternative glycols. The proposed strategies are applied to an Egyptian natural gas dehydration unit to select the optimum scenario that achieves the minimum BTEX emissions with efficient dew point control. It is found that natural gas dehydration using monoethylene glycol (MEG) is the best scenario in reducing BTEX emissions with efficient dew point control. The impact of operating conditions on BTEX emissions, along with natural gas water content, is also investigated. Lingo optimization software, v. 18, as well as HYSYS, v. 14, are used to find the optimum operating conditions for efficient dew point control with minimum BTEX emissions. It is demonstrated that stripping gas, MEG circulation rate, and inlet feed gas temperature have remarkable effects on BTEX emissions. Two quadratic correlations are also introduced in this study to efficiently relate BTEX emissions and water dew point to the influencing operating conditions. Full article

Antibiotic resistance genes (ARGs) are regarded as a major threat to public health and ecological security globally. The Dongliao River Basin is a typical farming–pastoral ecotone in the northeast of China. It is of great practical significance to explore the pollution characteristics and sources of ARGs in the Dongliao River. In this study, the Dongliao River Basin was taken as the research object, and water samples were collected at five points in the wet season, the normal season and the dry season, and the qPCR technology was used to detect the ARGs, revealing its spatial and temporal distribution characteristics. The results show that the temporal difference in ARGs was mainly in the wet season, and the contribution rates of sulfonamides (SAs) and aminoglycosides (AMs)ARGs were the largest, with relative abundance reaching 13–27% and 7–37%. In the normal season, the contribution rate of AMs ARGs further increased to 26–37%, while the contribution rate of SAs and tetracyclines (TCs) ARGs also showed a high level, accounting for 12–20% and 11–16%. In dry season, the ARGs of AMs and TCs reached 29–43% and 16–22%. As far as the spatial distribution characteristics were concerned, the absolute abundance of ARGs reached the maximum value of 3.79 × 10⁶ copies/mL in the sampling section of Chengzishang during the wet season. In the normal season, the absolute abundance of ARGs was the largest at the sampling section of Heqing River, which was 2.62 × 10⁶ copies/mL; While in the dry season, the absolute abundance of ARGs reached the maximum at the sampling section of Sishuang Bridge, which was 5.30 × 10⁶ copies/mL. Furthermore, using principal component analysis–multiple linear regression (PCA–MLR) model, sul1, sul2, aadA2–03, aadA–01 genes with high absolute abundance was selected for source analysis, so as to reveal the source of ARGs pollution in Dongliao River. The results indicated that sulfonamide resistance genes (sul1, sul2) were primarily driven by nutrient salt contamination; aminoglycoside resistance genes (aadA2–03, aadA–01) exhibit sensitivity to temperature gradients, with significant proliferation during high–temperature seasons. This study provided a scientific basis for the prevention and control strategy of ARGs pollution in the Dongliao River Basin. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

Managed aquifer recharge (MAR) can address challenges pertaining to water quality and security, land subsidence, and aquifer degradation. This study has been conducted in the irrigated plains of Indus River Basin (IRB) of Pakistan, where groundwater is being used for drinking, agriculture, industries, and other commercial purposes and where the Punjab Government is implementing the MAR project. The study aims to assess the existing level of heavy metals and trace elements contamination in the groundwater and to set baseline data for the suitability of the site for the MAR project. Groundwater samples from 20 tubewells were collected from an area of 1522 km² to investigate the level of heavy metals concentration in groundwater and to assess its suitability for irrigation and drinking. Samples were analyzed for Aluminum (Al), Arsenic (As), Barium (Ba), Cadmium (Cd), Cobalt (Co), Copper (Cu), Chromium (Cr), Lead (Pb), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), Selenium (Se), Strontium (Sr), and Zinc (Zn). To elucidate the contamination trend of these metals, the Heavy Metal Pollution Index (HPI), Heavy Metal Index (HI), geostatistical description, Pearson correlation analysis, and geospatial mapping were employed. Results showed that groundwater in the study area is not suitable for drinking and may pose serious health risks. It should be, however, generally suitable for irrigation. This concludes that the site is suitable for the implementation of a MAR project where the intended use of groundwater is for irrigation. It has been recommended that the groundwater may not be used for direct human consumption in the study area. It has been recommended, too, that targeted monitoring of identified hotspots and assessment of soil and crop uptake are conducted so that industrial or wastewater discharge into irrigation supplies may be prevented and controlled. For policy decisions, distinguishing irrigation suitability from potable-water safety is essential. Full article

Water pollution has caused serious consequences for human health and aquatic systems. Therefore, analyzing and predicting water quality is of great significance for the early prevention and control of water pollution. Aiming at the shortcomings of the Gated Recurrent Unit (GRU) water quality prediction model, such as the low utilization rate of early information and poor deep feature extraction ability of the hidden state mechanism, this study combines the temporal attention (TA) mechanism with the bidirectional superimposed neural network. A time-focused bidirectional gated recurrent unit (TA-Bi-GRU) model is proposed. Taking the actual water quality data of the water source reservoir in Xiduan Village as the research object, this model was used to predict four core water quality indicators, namely pH, ammonia nitrogen (NH₃N), total nitrogen (TN), and dissolved oxygen (DOX). Predictions are made within multiple time ranges, with prediction periods of 7 days, 10 days, 15 days, and 30 days. In the long-term prediction of the TA-Bi-GRU model, its average R² was 0.858 (7 days), 0.772 (10 days), 0.684 (15 days), and 0.553 (30 days), and the corresponding average MAE and MSE were both lower than those of the comparison models. The experimental results show that the TA-Bi-GRU model has higher prediction accuracy and stronger generalization ability compared with the existing GRU, bidirectional GRU (Bi-GRU), Time-focused Gated Recurrent Unit (TA-GRU), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Deep Temporal Convolutional Networks-Long Short-Term Memory (DeepTCN-LSTM) models. Full article

During rapid social and economic growth, large amounts of organic matter, nitrogen, and phosphorus are released into the environment with wastewater, and constructed wetlands (CWs) play a key role in water pollution prevention and control. This study employed six test columns to evaluate the pollutant removal performance of various filter media combinations in CWs when treating synthetic sewage under different pollution and hydraulic loads. The results showed that all columns containing bio-ceramsite exhibited superior pollutant removal performance, especially for organics and phosphorus. A synergistic effect was observed between bio-ceramsite and volcanic rock in enhancing pollutant removal, with average removal rates of 88.02%, 69.69%, 62.96%, and 88.22% for COD, NH₄⁺, TN, and TP, respectively, under the nine experimental conditions. Scanning Electron Microscopy (SEM), BET surface area testing, and microbial community structure analysis were conducted to investigate the reasons for the differences in pollutant removal efficiency among the columns. The results showed that bio-ceramsite exhibits a highly microporous structure and a large surface area of 1.3816 m²/g, which provides abundant adsorption sites for microorganisms and pollutant molecules. The microbial community structure on bio-ceramsite remained highly consistent across all column tests, with dominant microbial species playing a key role in enhancing pollutant removal efficiency. The conclusions of this study indicate the potential application of some filter media combinations in CW design for environmental conservation. Full article

Under the increasing pressures of land-based pollution and intensive coastal development, marine ecosystems are facing unprecedented challenges, highlighting the urgent need for enhanced protection and management of marine environmental quality. This study examines the spatiotemporal distribution and pollution risks of seven potentially toxic elements (Hg, Cd, Pb, Cr, As, Zn, and Cu) in the coastal waters of Bohai Bay, China, based on monitoring data collected from 2020 to 2023. Results show a significant decline in annual average concentrations of Pb (from 3.23 ± 1.11 μg/L to 0.10 ± 0.06 μg/L) and Hg (from 0.05 ± 0.02 μg/L to 0.01 ± 0.00 μg/L), reflecting effective pollution control measures. In contrast, Cu concentrations nearly doubled, rising from 0.90 ± 0.50 μg/L in 2020 to 1.98 ± 0.42 μg/L in 2023, while Zn exhibited a “V”-shaped fluctuation over the study period. Spatially, Zn, Pb, and Hg displayed pronounced clustering patterns, with coefficients of variation (CV) of 1.04, 1.49, and 1.17, respectively. The Pollution Load Index (PLI) decreased from 1.82 in 2020 to 0.94 in 2023, indicating an overall improvement in ecological quality. However, the Risk Index (RI) reached a maximum of 672.5 at Site 11 in 2020, with Hg and Cd contributing 49.6% and 22.7% to the total risk, respectively. Health risk assessment revealed non-carcinogenic risks (Hi) below the safety threshold (Hi < 1) across all sites. In contrast, carcinogenic risks (CR) ranged from 5.7 × 10⁻⁴ to 9.1 × 10⁻⁴, approaching the acceptable upper limit of 10⁻³, primarily due to dermal exposure to Hg and the high toxicity of Cd. Principal Component Analysis (PCA) suggested familiar sources for Hg, Pb, and Zn, whereas As appeared to originate from distinct pathways. Overall, this study establishes an integrated “pollution–ecological–health” assessment framework, offering scientific support for targeted pollution prevention and zonal management strategies in coastal environments. Full article

Accurate identification of groundwater pollution sources is crucial for the socio-economic development of a region. In highly urbanized areas, where human activities have a pronounced impact on groundwater, however, the hydrochemical evolution patterns and sources of pollutants remain unclear. Taking Shenzhen, a highly urbanized city in China, as a case study, this research employed a combination of multivariate statistical techniques and the Positive Matrix Factorization (PMF) model to elucidate the hydrochemical evolution and quantitatively parse the pollution sources of groundwater in such regions. The results revealed that the pH of groundwater in the study area ranged from 4.24 to 7.31, indicating weak acidity to neutrality. The exceedance rates for pH, NH₄⁺, COD, Mn, and Fe were as high as 67.1%, 44.3%, 44.3%, 34.3%, and 31.4%, respectively. The Water Quality Index assessment revealed that 32.9% of the groundwater samples were classified as poor, highlighting the significant impact of human activities. Land-use types significantly affected groundwater quality, with urban areas exhibiting higher concentrations of the COD, NO₃⁻, Mn, and Fe compared to agricultural and forested areas. The predominant hydrochemical type of groundwater in the study area was HCO₃·Cl—Ca·Na, with rock weathering (primarily silicate weathering) being the dominant process controlling groundwater chemistry. The PMF model identified three major pollution sources in the highly urbanized region: domestic and industrial wastewater, enhanced water-rock interactions leading to the release of hydrochemical components, and agricultural fertilizers, contributing 43.9%, 37.0%, and 19.1% to groundwater pollution, respectively. Geostatistical spatial interpolation techniques demonstrated that in urban areas, groundwater quality was primarily controlled by the discharge of domestic and industrial wastewater, while in agricultural areas, excessive fertilizer application was the main driver of groundwater degradation. These findings provide a scientific basis for groundwater pollution prevention and sustainable utilization in highly urbanized regions. Full article

Although adding iron salts can improve phosphorus removal in membrane bioreactor (MBR) processes, overdosing iron salts may result in excessive iron concentrations in the effluent and pose risks of surface water contamination. In this study, an optimized iron salt dosing method was proposed to comprehensively investigate its effects on the performance of MBRs and the control of iron leakage. The results showed that batch dosing of solid iron salts (Fe₂(SO₄)₃) into the influent or activated sludge maintained an effluent Fe³⁺ concentration below 1.0 mg/L and a total phosphorus (TP) concentration below 0.30 mg/L. Long-term operation of the MBR (under conditions of HRT = 4.3 h, SRT = 20 d, and MLSS = 12 g/L) showed that batch dosing of solid iron salts led to an increase in the effluent ammonia–nitrogen (NH₃-N) concentration, and the nitrification effect was restored after supplementing the alkalinity. Iron salts increased the TP removal rate by approximately 40% while inhibiting the biological phosphorus removal capacity. The average Fe³⁺ concentration in the membrane effluent (0.23 ± 0.11 mg/L) met China’s Environmental Quality Standard for Surface Water (GB3838-2002). This study demonstrates that batch dosing of solid iron salts effectively controls iron concentration in the MBR effluent while preventing secondary pollution. The mechanisms of the impact of iron salts on MBR performance provide crucial theoretical and technical support for MBR process optimization. Full article

Colorectal cancer (CRC) has a complex etiology involving genetic, lifestyle, and environmental factors. This study investigates the association between exposure to water contaminants and the risk of developing CRC in Lebanon. Population Attributable Fraction (PAF) was calculated using exposure prevalence from environmental reports and water quality assessments. Relative risks (RR) were obtained from multiple meta-analyses and epidemiological studies to quantify the contribution of five water contaminants to CRC incidence: nitrates, mercury, arsenic, trihalomethanes (THMs), and microbial pollutants (E. coli). A synthetic cohort was simulated using national exposure data and probabilistic techniques, employing multivariate logistic regression models to ensure precise CRC risk and controlling for lifestyle covariates. Adjusted PAF values were calculated using regression data to determine the proportion of CRC cases attributable to each water contaminant. THMs and E. coli showed the strongest association with CRC, with adjusted PAF values of 25.76% and 23.65%, respectively. Exposure to nitrates, arsenic, and mercury contributed less to CRC risk (1.02%, 0.52% and 2.20%, respectively). Multivariate regression confirmed that THMs and E. coli remained the strongest independent predictors of CRC risk (OR = 1.37, p = 0.001) and (OR = 1.79, p < 0.0001) among water contaminants, respectively. Our model-based approach carries limitations due to reliance on indirect exposure and risk estimates, which may introduce uncertainty stemming from data gaps and underlying assumptions. This study highlights the importance of water quality management in CRC prevention as exposure to drinking water contaminants contributes meaningfully to disease burden. Full article

Microplastics have become a global environmental issue, and this challenge has also emerged in river environments. In this study, the Three Gorges Reservoir (TGR) was selected as the research area, and microplastics in water and sediments were chosen as the objects. The occurrence characteristics of microplastics were presented in rivers of the TGR through the methods of field sample collection and laboratory testing, and the risks of microplastics were evaluated using the pollution risk index (PRI) and potential ecological risk index. The results showed that the average abundances of microplastics in water and sediments of the rivers from the TGR were 15,464 particles/m³ and 1838 particles/kg, respectively. Furthermore, the main colors of microplastics in water and sediments of the TGR were black and blue, and the particle sizes were concentrated in the range of 200 to 500 μm. Polypropylene and polyethylene were the major polymers of microplastics in the water of the TGR, while the polypropylene–polyethylene copolymer was dominant in sediments. The PRIs in the water of the Yangtze River and its tributary from the TGR area were 2.45 and 1.72, respectively, and their PRIs were 2.43 and 2.01 in sediments, respectively, showing a low risk of pollution (level I). The average comprehensive potential ecological risk indices (RIs) for the Yangtze River and its tributaries were 27.28 and 18.82 in the water of the TGR, respectively, indicating low and moderate risk grades. However, there was a significantly high risk in sediment, and the average RI was 130.57 for the Yangtze River and 70.05 for the tributaries. Importantly, the neglected risks of microplastics in the river environment of the TGR area have been revealed, which provides a critical basis for the prevention and control of microplastic pollution here. Full article

Potentially toxic element pollution caused by coal mining activities, especially the accumulation of cadmium, has become a major threat to the global environment and health. Long-term mining activities in China, a major coal consumer, caused a large accumulation of coal gangue. Gangue weathering and leaching release Cd, which threatens the ecological safety of the surrounding soil and water bodies. Although the government has implemented ecological restoration projects in the mining areas, there is still a lack of systematic evaluation of pollution control of downstream farmlands. For this study, remote sensing analyses of fractional vegetation coverage (FVC), geo-accumulation index (I_geo), and potential ecological risk index (EI) data, as well as the pollution characteristics and ecological risks of Cd, were evaluated for a coal mining area in Jiangxi Province. Coal gangue, restoration cover soil, downstream farmland soil, irrigation water, and sediment samples were used in the analyses. After restoration, the Cd concentration in the mining cover soil (0.23 mg/kg) was significantly lower than that of the coal gangue (1.18 mg/kg), while the Cd concentration in the downstream farmland soil (0.44 mg/kg) was roughly an average of the two. The geo-accumulation index indicates that the farmland soil is mainly unpolluted (with an average I_geo of −0.25). However, some points have reached the level of no pollution to moderate pollution. Coal gangue poses a relatively high ecological risk (with an average EI of 118), while cover soil and farmland soil pose low risks (with an average EI of 22.5 and 39.86, respectively). The restoration project significantly reduced the Cd input in the downstream farmlands. The study revealed the effective blocking of external soil cover on Cd migration, providing a key scientific basis for the optimization of ecological restoration strategy and risk prevention and control in similar mining areas worldwide. Full article