Search Results (198)

As one of the nine largest plateau lakes in Yunnan Province, China, Qilu Lake is considered significantly affected by extensive anthropogenic pollution. However, the pollution status and integrated risks posed by organochlorine pesticides and heavy metals in the lake’s sediments remain poorly understood. This study analyzed the concentrations of organochlorine pesticides and heavy metals in 22 surface sediment samples from the Qilu Lake, and assessed their combined ecological and health risks. Results showed that the mean concentrations of five target organochlorine pesticides (α-hexachlorocyclohexane, β-hexachlorocyclohexane, γ-hexachlorocyclohexane, p,p′-dichlorodiphenyltrichloroethane, and o,p′-dichlorodiphenyltrichloroethane) were consistently low, whereas most heavy metals, except for arsenic, significantly exceeded Yunnan Province background values, with mercury and cadmium exhibiting the most pronounced enrichment. Source analysis indicated that the heavy metals mainly derived from a mixed agricultural-industrial-traffic source, a natural geogenic source, and industrial-traffic emissions, while the organochlorine pesticides originated from both historical residues and ongoing agricultural applications. A linear model was identified as optimal function for characterizing the adsorption-accumulation relationship between organochlorine pesticides and heavy metals. Ecological risks were dominated by heavy metals, especially cadmium, and the evaluated results showed that the health risks were higher for children than adults. Although non-carcinogenic risks were negligible, carcinogenic risks, particularly from chromium, warrant special attention, especially for children. This study enhances the understanding of combined pollution in rural plateau lakes and provides a scientific basis for achieving sustainable water environment management by (1) establishing an integrated risk assessment framework for pollutants, (2) identifying a priority control pollutant list, and (3) laying a theoretical foundation for targeted ecological restoration strategies, directly supporting the implementation of Sustainable Development Goal (SDG) 6 (clean water and sanitation). Full article

Unsupervised learning methods significantly benefit various practical applications by effectively identifying intrinsic patterns within unlabelled data. However, inherent data noise and uncertainties often compromise model reliability, result interpretability, and the overall effectiveness of unsupervised learning strategies, particularly in complex fields such as biomedical, engineering, and physics research. To address these critical challenges, this study proposes SUN (Stochastic UNsupervised learning), a novel approach that integrates probabilistic unsupervised techniques—specifically Gaussian Mixture Models—into the RUN-ICON unsupervised learning algorithm to achieve optimal clustering, systematically reduce data noise, and quantify inherent uncertainties. The SUN methodology strategically leverages probabilistic modelling for robust classification and detection tasks, explicitly targeting particle dispersion scenarios related to environmental pollution and airborne viral transmission, with implications for minimising public health risks. By combining advanced uncertainty quantification methods and innovative unsupervised denoising techniques, the proposed study aims to provide more reliable and interpretable insights than conventional methods while alleviating issues such as computational complexity and reproducibility that limit traditional mathematical modelling. This research contributes to enhanced trustworthiness and interpretability of unsupervised learning systems, offering a robust methodological framework for handling significant uncertainty in complex real-world data environments. Full article

Exposure to environmental pollutants, including toxic metals, volatile organic compounds (VOCs), and per- and polyfluoroalkyl substances (PFAS), has been increasingly linked to impaired kidney function. However, the combined effects of these exposures, along with essential elements, on kidney health remain poorly understood. This study aimed to evaluate the independent and cumulative or mixture effects of toxic metals (cadmium, lead, and mercury), essential elements (iron, manganese, and selenium), PFAS (PFOA and PFOS), and VOCs (m-/p-xylene and o-xylene) on kidney function as measured by estimated glomerular filtration rate (eGFR). Using data from the National Health and Nutrition Examination Survey (NHANES), we applied multiple imputation to address missing data and implemented statistical techniques, including Bayesian Kernel Machine Regression (BKMR), quantile g-computation, and Weighted Quantile Sum Regression (WQSR) to assess complex exposure–response relationships, including non-linear, potential synergistic, and antagonistic effects. The results indicated that several exposures were correlated, particularly o-xylene with m-/p-xylene (r = 0.77), Cd with Pb (r = 0.46), and PFOS with PFOA (r = 0.61). eGFR was negatively associated with Pb, PFOS, PFOA, and Hg. In the BKMR analysis, overall posterior inclusion probabilities (PIPs) highlighted PFOS, Cd, Se, Mn, and Fe as the most influential exposures. Quantile g-computation highlighted Cd and Mn as major contributors, while WQSR modeling confirmed Mn as a key contributor. The findings underscore the importance of considering complex interactions in environmental exposure assessments. While essential elements may offer protective effects, toxic metals, PFAS, and VOCs remain critical contributors to kidney dysfunction. These insights highlight the need for integrative risk assessment approaches and public health strategies aimed at mitigating harmful exposures while promoting optimal nutrient balance. Full article

Environmental pollution is becoming increasingly unpredictable over time due to its complexity, given the number of new chemicals produced annually and the constantly changing environmental conditions. Regulation has yet to keep pace with the rapid changes posed by chemical mixtures, especially in the Global South. Understanding the potential outcomes of co-exposure to multiple compounds can be challenging, even for professionals with a background in sustainability and mixture toxicity, due to the complexity of the issue. Some tools have been developed to tackle this uncertainty like the Species Sensitivity Distribution curve (SSD), the Adverse Outcome Pathways (AOP), and the Mixture Assessment Factor (MAF). This study aims to bridge the gap between knowledge generated in the field of mixture toxicity and regulatory practices by proposing sustainable management practices at the local scale, particularly for countries in the Global South. The proposed framework is called GlORIES and comprises the following measures. The first proposed step is to describe the chemicals used in industries or identified in existing environmental studies and/or monitoring campaigns on a watershed basis. Having a watchlist of compounds and organisms present in the region, and by generating a regionalized SSD, it is possible to use models such as AOPs to try to predict which compounds could potentially interact and thus generate a correcting factor, such as a MAF. A MAF could then be incorporated into regulations to further protect the environment by reducing the concentration of the compound in the mixture. Including local communities in reporting human and environmental health alterations could be a key to identifying the possible harmful emissions. It is proposed that watershed management committees be established to integrate all stakeholders and promote workshops organized by academia, industry, regulatory agencies, and civil society, leveraging existing structures to conserve energy in the process. The proposed framework can improve the sustainability of the process and the knowledge flow from academia to regulatory bodies, increasing the efficacy of the chosen water quality thresholds by adapting to real-life scenarios. Full article

Microwave heating is a method with a uniform heating effect and environmental friendliness in in-place hot recycling, but the microwave absorption capacity of traditional asphalt mixtures is still insufficient. As an excellent microwave-absorbing material, magnetite powder has the characteristics of high temperature resistance, corrosion resistance, and good thermodynamic stability. This study selects it as the microwave-absorbing material, prepares AC (Asphalt Concrete) type and SMA (Stone Mastic Asphalt) type microwave asphalt mixtures by adjusting its content, and investigates its influence on the microwave-heating characteristics and pavement performance of the mixtures. Simulations of the microwave-heating process of AC-type mixtures using COMSOL software (COMSOL Multiphysics 6.2) show that magnetite powder achieves optimal performance in terms of heating effect and economic efficiency when its content is 0.5%. Subsequently, laboratory tests are conducted to study the wave absorption and temperature rise performance of AC and SMA microwave asphalt mixtures; combined with economic factors, the optimal contents of magnetite powder for the two types of mixtures are determined to be 0.5% and 1%, respectively, and at the same time, these results are explained based on multiple physical theories. Furthermore, pavement performance is investigated through laboratory tests, including high-temperature rutting tests, low-temperature bending tests, immersed Marshall tests, and freeze–thaw cycle durability tests, and the results indicate that the high-temperature performance, low-temperature performance, and water stability of the microwave asphalt mixtures all meet the specification requirements for pavement performance. Subsequently, after 15 freeze–thaw cycles, the splitting tensile strength retention rate and stiffness modulus of the two types of mixtures show minimal differences from those of ordinary mixtures, and there is no durability degradation caused by the incorporation of magnetite powder. Finally, outdoor environment verification is carried out, and the results show that under complex conditions such as environmental factors, the wave absorption and temperature rise rates of AC and SMA mixtures at optimal contents are 52.2% and 14.6% higher than those of ordinary AC and SMA asphalt mixtures, respectively. In addition, these microwave asphalt mixtures have the advantages of both sustainability and reduced carbon emissions. By combining simulation methods and experimental verification, this study finally prepared two types of microwave asphalt mixtures with excellent performance, not only improving the microwave absorption and heating performance of asphalt mixtures, but also reducing environmental pollution and energy consumption, which conforms to the development of green transportation. Full article

It is generally found that enhancement in catalytic activity comes at the expense of selectivity or stability. In this study, an SO_x²⁻-modified Mn₂O₃ (SO-Mn₂O₃) solid catalyst was prepared using a simple oxalate precipitation method. This catalyst exhibited not only high catalytic activity but also high selectivity and good cycling stability. The degradation ratio of bisphenol A (BPA) under SO-Mn₂O₃ activated potassium peroxymonosulfate (PMS) achieved over 99% within 10 min, and the mineralization ratio increased to 83.2%. Particularly, the degradation rate for BPA under the SO-Mn₂O₃/PMS system was 15 times that of Mn₂O₃. Furthermore, the degradation ratio remained at 93.3% after five consecutive cycles. Multiple experimental characterizations confirmed that the introduction of SO_x²⁻ into Mn₂O₃ shifted the oxidative degradation pathway from a mixture of radical and non-radical routes to a predominantly non-radical pathway. This suppressed radical generation promoted the selective formation of high-valence metallic-oxo (Mn(V)=O) species and singlet oxygen (¹O₂), thereby significantly enhancing the catalytic activity. In addition, the SO-Mn₂O₃/PMS system exhibited broad applicability towards the degradation of other phenolic pollutants, strong anti-interference capability against complex water matrices, and suitability for efficient removal of organic contaminants in such environments. This research offers new perspectives for the design of selective catalysts for PMS activation. Full article

The discharge of dye-laden effluents remains an environmental challenge since conventional treatments remove color but not the organic load. This study systematically compared anodic oxidation (AO), electro-Fenton (EF), and photoelectro-Fenton (PEF) processes for three representative industrial dyes, such as Coriasol Red CB, Brown RBH, and Blue VT, and their ternary mixture, using boron-doped diamond (BDD) and Ti/IrO₂–SnO₂–Sb₂O₅ (MMO) anodes. Experiments were conducted in a batch reactor with 50 mM Na₂SO₄ at pH = 3.0 and current densities of 20–60 mA cm⁻². Kinetic analysis showed that AO-BDD was most effective at low pollutant loads, EF-BDD became superior at medium loads due to efficient H₂O₂ electrogeneration, and PEF-MMO dominated at higher loads by fast UVA photolysis of surface Fe(OH)²⁺ complexes. In a ternary mixture of 120 mg L⁻¹ of dyes, EF-BDD and PEF-MMO achieved >98% decolorization in 22–23 min with pseudo-first-order rate constants of 0.111–0.136 min⁻¹, whereas AO processes remained slower. COD assays revealed partial mineralization of 60–80%, with EF-BDD providing the most consistent reduction and PEF-MMO minimizing treatment time. These findings confirm that decolorization overestimates efficiency, and electrode selection must be tailored to dye structure and effluent composition. Process selection rules allow us to conclude that EF-BDD is the best robust dark option, and PEF-MMO, when UVA is available, offers practical guidelines for cost-effective electrochemical treatment of textile wastewater. Full article

Per- and polyfluoroalkyl substances (PFASs) have attracted global attention due to their persistence and biological toxicity, becoming critical emerging contaminants in river and lake environments worldwide. Building upon existing studies, this work aims to comprehensively understand the pollution patterns, environmental behaviors, and potential risks of PFASs in freshwater systems, thereby providing scientific evidence and technical support for precise pollution control, risk prevention, and the protection of aquatic ecosystems and human health. Based on publications from 2002 to 2025 indexed in the Web of Science (WoS), bibliometric analysis was used to explore the temporal evolution and research hotspots of PFASs, and to systematically review their input pathways, pollution characteristics, environmental behaviors, influencing factors, and ecological and health risks in river and lake environments. Results show that PFAS inputs originate from both direct and indirect pathways. Direct emissions mainly stem from industrial production, consumer product use, and waste disposal, while indirect emissions arise from precursor transformation, secondary releases from wastewater treatment plants (WWTPs), and long-range atmospheric transport (LRAT). Affected by source distribution, physicochemical properties, and environmental conditions, PFASs display pronounced spatial variability among environmental media. Their partitioning, degradation, and migration are jointly controlled by molecular properties, aquatic physicochemical conditions, and interactions with dissolved organic matter (DOM). Current risk assessments indicate that PFASs generally pose low risks in non-industrial areas, yet elevated ecological and health risks persist in industrial clusters and regions with intensive aqueous film-forming foam (AFFF) use. Quantitative evaluation of mixture toxicity and chronic low-dose exposure risks remains insufficient and warrants further investigation. This study reveals the complex, dynamic environmental behaviors of PFASs in river and lake systems. Considering the interactions between PFASs and coexisting components, future research should emphasize mechanisms, key influencing factors, and synergistic control strategies under multi-media co-pollution. Developing quantitative risk assessment frameworks capable of characterizing integrated mixture toxicity will provide a scientific basis for the precise identification and effective management of PFAS pollution in aquatic environments. Full article

As one of the world’s most widely used packaging materials, paper obtains its properties from its major component: wood. Variations in the species of wood result in variations in the paper’s mechanical properties. The pulp and paper production industry is known to be a polluting industry and a consumer of a large amount of energy but remains an essential heavy industry globally. Paper production, based largely on the kraft process, is mainly intended for the food packaging sector and, thus, is associated with contamination risks. The lack of standardized regulations and the different analytical techniques used make information on the subject complex, particularly for inorganic elements where little information is available in the literature. Most research in this field is based on sample preparation using mineralization via acid digestion to obtain a liquid and homogeneous matrix, mainly with a HNO₃/H₂O₂ mixture. The most commonly used techniques are Atomic Absorption Spectrometry (AAS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), each with its advantages and disadvantages, which complicates the use of these tech-niques for routine analyses on an industrial site. In the same field of inorganic compound analysis, Microwave Plasma Atomic Emission Spectrometry (MP-AES) has become a real alternative to techniques such as AAS or ICP-AES. This technique has been used in several studies in the food and environmental fields. This publication aims to examine, for the first time, the state of the art regarding the analysis of inorganic elements in food packaging and different matrices using MP-AES. The entire manufacturing process is studied to identify possible sources of inorganic contaminants. Various analytical techniques used in the field are also presented, as well as research conducted with MP-AES to highlight the potential benefits of this technique in the field. Full article

Atmospheric deposition through rainfall plays a significant role in transporting various anthropogenic contaminants to terrestrial and aquatic ecosystems. However, rainwater’s integrated ionic and molecular composition remains underexplored in semiurban environments. This study provides a comprehensive chemical characterization of rainwater collected during seven precipitation events from February to April 2025 in Athens, Georgia, USA. This semiurban area is characterized by substantial vehicular traffic, seasonal agricultural activities, and ongoing construction, while lacking significant industrial emissions. Targeted spectrophotometric analyses revealed heightened concentrations of nitrate (ranging from 2.0 to 4.3 mg/L), sulfate (17 to 26 mg/L), and phosphate (2.4 to 3.1 mg/L), with peak concentrations observed during high-intensity rainfall events. These findings are consistent with enhanced wet scavenging of atmospheric emissions. Concurrently, both targeted and non-targeted gas chromatography-mass spectrometry (GC-MS) analyses identified a diverse array of organic pollutants in the rainwater, including organophosphate, organochlorine, and triazine pesticides; polycyclic aromatic hydrocarbons (PAHs); plasticizers; flame retardants; surfactant degradation products; and industrial additives such as bisphenol A, triclosan, and nicotine. Furthermore, several legacy contaminants, such as organochlorines, were detected alongside currently utilized compounds, including glyphosate and its metabolite aminomethylphosphonic acid (AMPA). The concurrent presence of elevated anion and organic pollutant levels during significant storm events suggests that atmospheric washout can be the primary deposition mechanism. These findings underscore the capability of semiurban atmospheres to accumulate and redistribute complex mixtures of pollutants through rainfall, even in the absence of large-scale industrial activity. The study emphasizes the importance of integrated ionic and molecular analyses for uncovering concealed pollution sources. It highlights the potential of rainwater chemistry as a diagnostic tool for monitoring atmospheric contamination in urbanizing environments. Full article

Microcystin–LR (MC-LR) and phenanthrene (Phen), which commonly co-occur in eutrophic waters, have been extensively studied as individual contaminants, but their combined ecotoxicological effects on submerged macrophytes remain unclear. In this study, we examined the individual and combined toxicity of MC-LR (2, 10, 50, 250, and 1000 μg/L) and Phen (0.2, 1, 5, 25, and 100 μg/L) on the submerged macrophyte Vallisneria natans over a 7-day exposure. Key toxicity biomarkers, including growth, photosynthetic efficiency, and antioxidant responses (catalase, superoxide dismutase, glutathione S-transferase, and malondialdehyde), were evaluated. The results showed that high concentrations of each contaminant alone (MC-LR ≥ 1000 μg/L; Phen ≥ 100 μg/L) significantly inhibited growth and reduced photosynthetic efficiency. In contrast, synergistic toxicity was observed at much lower combined concentrations (≥50 + 5 μg/L), with effects substantially exceeding those of individual exposures. Co-exposure intensified antioxidant activity, but it was insufficient to mitigate oxidative damage. Notably, Phen at concentrations above 25 μg/L significantly enhanced the bioaccumulation of MC-LR in V. natans. These findings demonstrate that environmentally relevant mixtures of MC-LR and Phen induce remarkable toxicity even at concentrations where individual compounds show negligible effects. The results highlight that co-existing cyanotoxins and polycyclic aromatic hydrocarbons may present greater ecological risks than predicted from single-contaminant assessments, underscoring the need to update current ecological risk frameworks for the accurate evaluation of complex pollution scenarios in freshwater systems. Full article

Plastic pollution from polyethylene-based materials is a critical environmental concern due to their high persistence. Here, we report the first proof-of-concept application of a multitechnique analytical framework for quantifying linear low-density polyethylene (LLDPE) in Tenebrio molitor frass. Artificially enriched frass–LLDPE mixtures were analyzed using thermogravimetric analysis (TGA), TGA coupled with Fourier-Transform Infrared Spectroscopy (FTIR) and Mass Spectrometry (MS), TGA under inert atmosphere, and solid-state ¹³C nuclear magnetic resonance spectroscopy with Cross-Polarization and Magic Angle Spinning (CP-MAS NMR) ¹³C CP-MAS NMR combined with interval Partial Least Squares (iPLS) modeling. Thermal methods provided insight into decomposition pathways but showed reduced specificity at <1% w/w due to matrix interference. CP-MAS NMR offered matrix-independent quantification, with characteristic signals in the 10–45 ppm region and a calculated LOD and LOQ of 0.173% and 0.525% w/w, respectively. The LOQ lies within the reported ingestion range for T. molitor (0.8–3.2% w/w in frass), confirming biological relevance. This validated workflow establishes CP-MAS NMR as the most robust tool for quantifying polyethylene residues in complex matrices and provides a foundation for in vivo biodegradation studies and environmental monitoring. Full article

Car wash wastewaters (CWW) bring growing environmental challenges due to the increasing number of vehicles worldwide and they require novel, optimized and sustainable treatment methods. They are highly heterogenous, typically containing complex mixtures of detergents, waxes, oils, petroleum derivatives, corrosion inhibitors and salts, with the composition depending on installation age, geographic location, season, and weather. This study aimed to select bacteria resistant to variable and potentially toxic CWW, capable of biodegrading organic pollutants. A total of 81 strains isolated from various environmental sites were screened for tolerance to CWW environments by performing growth inhibition tests in 20 real wastewater samples with chemical oxygen demand (COD) ranging from 122 to 2267 mg O₂/dm³. Seventeen strain candidates were chosen, identified with molecular proteomics, and further evaluated for biodegradation potential. Based on the most robust isolates, six microbial consortia were developed and examined. Biodegradation experiments were conducted at ambient temperature without active pH control and nutrient supplementation to reflect real conditions occurring in wastewater treatment practice. The best-performing consortium reduced COD by 86% within 7 days. These findings should help improve the treatment of complex CWW by highlighting the potential of thoroughly selected bacteria as effective tools for bioremediation of extremely harsh environments. Full article

Urban rivers, as crucial components of ecosystems, serve multiple functions, including flood control, drainage, and landscape services. However, with the acceleration of urbanization, factors such as industrial wastewater discharge, domestic sewage leakage, and surface runoff pollution have led to increasingly severe degradation of water quality in urban rivers. Unmanned aerial vehicle (UAV) remote sensing technology, with its sub-meter spatial resolution and operational flexibility, demonstrates significant advantages in the detailed monitoring of complex urban water systems. This study proposes an Ensemble Self-Attention Enhanced Mixture Density Network (ESA-MDN), which integrate an ensemble learning framework with a mixture density network and incorporates a self-attention mechanism for feature enhancement. This approach better captures the nonlinear relationships between water quality parameters and remote sensing features, achieving high-precision modeling of water quality parameter distributions. The resulting spatiotemporal distribution maps provide valuable support for pollution source identification and management decision making. The model successfully retrieved five water quality parameters, Chl-a, TSS, COD, TP, and DO, and validation metrics such as R², RMSE, MAE, MSE, MAPE, bias, and slope were utilized. Key metrics for the ESA-MDN test set were as follows: Chl-a (R² = 0.98, RMSE = 0.31), TSS (R² = 0.93, RMSE = 0.27), COD (R² = 0.93, RMSE = 0.39), TP (R² = 0.99, RMSE = 0.02), and DO (R² = 0.88, RMSE = 0.1). The results indicated that ESA-MDN can effectively extract water quality parameters from multispectral remote sensing data, with the generated spatiotemporal water quality distribution maps providing crucial support for pollution source identification and emergency response decision making. Full article

Environmental perception is crucial for achieving autonomous driving of auxiliary haulage vehicles in underground coal mines. The complex underground environment and working conditions, such as dust pollution, uneven lighting, and sensor data abnormalities, pose challenges to multimodal fusion perception. These challenges include: (1) the lack of a reasonable and effective method for evaluating the reliability of different modality data; (2) the absence of in-depth fusion methods for different modality data that can handle sensor failures; and (3) the lack of a multimodal dataset for underground coal mines to support model training. To address these issues, this paper proposes a coal mine underground BEV multiscale-enhanced fusion perception model based on dynamic weight adjustment. First, camera and LiDAR modality data are uniformly mapped into BEV space to achieve multimodal feature alignment. Then, a Mixture of Experts-Fuzzy Logic Inference Module (MoE-FLIM) is designed to infer weights for different modality data based on BEV feature dimensions. Next, a Pyramid Multiscale Feature Enhancement and Fusion Module (PMS-FFEM) is introduced to ensure the model’s perception performance in the event of sensor data abnormalities. Lastly, a multimodal dataset for underground coal mines is constructed to provide support for model training and testing in real-world scenarios. Experimental results show that the proposed method demonstrates good accuracy and stability in object-detection tasks in coal mine underground environments, maintaining high detection performance, especially in typical complex scenes such as low light and dust fog. Full article