Environmental Science and Pollution Research最新文献

This study presents a comprehensive performance evaluation of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis total aerosol optical depth (AOD) over India. We first use AOD observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003 to 2020 to evaluate the spatial and temporal patterns of AOD simulated by CAMS. Owing to the lack of aerosol speciation in MODIS, we complement it with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), which provides individual aerosol species such as dust, black carbon, organic carbon, sulfate, and sea salt. The results demonstrate that CAMS exhibit high AOD, similar to MODIS, particularly over the Indo-Gangetic Plain, despite some underestimations in total AOD. Temporal trend analysis indicates that CAMS exhibited rising AOD trends similar to MODIS across Indian regions, though discrepancies arise in western India during pre-monsoon and monsoon seasons. Spatial differences in different aerosol species between CAMS and MERRA-2 suggest potential differences in model parameterizations. Principal component analysis (PCA) further reveals that the first mode of AOD (PCA-1) in both CAMS and MERRA-2 shows a strong correlation (> 0.7) with MODIS during all seasons except pre-monsoon. This enhances our understanding of aerosol distribution and its implications for regional climate and air quality. Consequently, this study provides valuable insights into the performance of CAMS, supporting advancements in climate modeling, air quality management, and environmental policy-making in densely populated and rapidly developing regions.

5-Fluorouracil (5-FU) is among the chemotherapeutic drugs considered emerging contaminants, highly water-soluble, persistent, and mobile organic compounds (PMOCs) detected in water bodies, which have harmful and cytotoxic effect on aquatic biota. Its high hydrophilicity causes conventional water treatment processes, including adsorption, to exhibit low efficiencies of its removal. The novelty of this work lies in the study of the electroadsorption behavior of 5-FU on commercial granular activated carbon used as a packed working electrode in a three-electrode asymmetrical electrochemical cell, as a viable option for its removal from water. The polarization of electrodes was carried out at a controlled potential in a range from 0.3 to 0.9 V vs. Ag/AgCl, which favored an increase in adsorption capacity with the highest removal of 42% vs. 13% with no polarization, and an increase of the initial adsorption rate from 0.0018 at OCP to 0.0035 mg 5-FU min^-1 L^-1 at 0.9 V. This occurred even if 5-FU is a nonionic molecule (at neutral pH). Its electroactive nature makes it electrochemically oxidized on the adsorbent electrode at 1 V vs. Ag/AgCl, after the electroassisted adsorption process and its degradation mechanism were studied by liquid chromatography-ion trap mass spectrometer. The electrochemical oxidation mechanism was mediated by OH anions, causing the rupture of pyrimidine rings and loss of fluoride. This oxidation allowed the electrode reactivation with a maximum of 70 ± 1.3% of removal.

This study evaluates the effectiveness of metal-organic acid mordanting systems as sustainable alternatives to high-concentration metallic mordants in natural dyeing of wool yarns using Rubia tinctorum (madder) and Reseda luteola (weld). A total of 46 treatments involving single metals, organic acids, and their binary combinations were applied, and the dyed yarns were assessed for colorimetric parameters (K/S), ultraviolet protection factor (UPF), and antioxidant activity. The results demonstrated that binary mordant systems, especially those combining copper and iron salts with citric or tartaric acid, significantly enhanced color strength (K/S up to 12.05), UV protection (UPF > 90), and radical scavenging activity (up to 96.10%), often surpassing the performance of conventional 5% metal mordants. Non-parametric Kruskal-Wallis tests showed statistically significant improvements (p < 0.05) for weld-dyed samples in all functional categories, while correlation matrices revealed strong associations between K/S, UPF, and antioxidant values in weld, but weaker links in madder. These findings confirm that binary metal-acid mordanting systems can effectively reduce metallic load without compromising or even enhancing dye performance, photoprotection, and bioactivity contributing to both textile sustainability and functional innovation.

Mountain catchments play a critical role in water supply for lowland regions, contributing significantly to national water resources despite their limited geographic coverage. This study investigates the impact of wastewater treatment plant (WWTP) construction on the hydrochemistry of the Stara River in the Carpathian Foothills (southern Poland). Long-term hydrochemical data (2001-2024) and spatial surveys were analyzed to assess temporal trends and spatial variability before and after the WWTP installation. Results indicate that the WWTP significantly altered river water chemistry, increasing concentrations of conservative ions (Na, Cl) and nutrients (NO₃, PO₄), particularly during the vegetative season. Principal Component Analysis (PCA) revealed a shift from diffuse pollution linked to high flow rates prior to 2011, to a dilution-dominated regime after infrastructure development. Seasonal analyses highlighted the winter accumulation of nitrites, likely due to inhibited nitrification under low temperatures. Spatial profiles showed elevated ion concentrations immediately downstream of the WWTP, with partial attenuation further downstream. These findings demonstrate that the implementation of wastewater treatment infrastructure, while improving certain aspects of sanitary safety, may also introduce persistent shifts in river hydrochemistry, particularly under conditions typical for mountain catchments (low buffering capacity, high hydrological variability). To mitigate these effects, we recommend the following: (i) enhanced monitoring of nutrient and ion loads downstream of WWTP outlets, (ii) optimization of treatment processes during low-temperature seasons, and (iii) integration of hydrological modeling into local wastewater management planning. Such measures are essential to maintain ecological resilience and long-term water quality in mountain-fed river systems.

Accurate air quality forecasting is vital for public health, urban planning, and environmental policy. Traditional models often fail to capture the complex temporal, spatial, and topological dependencies inherent in air quality data. This study presents a hybrid deep learning model that integrates long short-term memory (LSTM) networks for sequential prediction, convolutional neural networks (CNNs) for spatial feature extraction, and graph neural networks (GNNs) for modeling spatial correlations between monitoring stations. To further enhance prediction robustness, ensemble learning techniques-stacking and boosting-are employed to fuse the outputs of individual models. The model is evaluated using real-world datasets from air quality monitoring agencies, covering multiple pollutants and meteorological parameters. Experimental results show significant improvements in performance metrics, achieving R² scores exceeding 0.92 and reduced prediction errors compared to baseline models. Visualization of predicted vs. actual AQI values and residuals confirms the model's accuracy and consistency. Specifically, the hybrid model achieved an MAE of 11.30, RMSE of 15.60, and R² of 0.89 on the Beijing dataset, outperforming standalone LSTM (MAE 14.55, R² 0.80), CNN (MAE 15.12, R² 0.78), and GNN (MAE 13.67, R² 0.83). In cross-dataset evaluation, the model attained an MAE of 12.62 and R² of 0.85 when trained on Beijing and tested on Los Angeles, confirming its robustness and transferability. The proposed framework demonstrates strong generalizability and interpretability, making it a promising tool for environmental monitoring and decision-making in pollution-sensitive regions.

The urban heat island (UHI) effect driven by urbanization induced land use and land cover (LULC) change has emerged as one of the serious environmental issues. Urbanization is one of the prime drivers of the UHI effect in cities. Therefore, it is essential to understand the impact of the urbanization on UHI. This study aims to understand the relationship between urbanization and surface urban heat island (SUHI) using machine learning approach in Kolkata Metropolitan Area (KMA), India. A spatial urbanization index (SUI) has been developed to assess the spatial urbanization and its relationship with UHI using spatial landscapes metrics. A spatial gradient approach was also applied to understand the changes of land surface temperature (LST) along urban-rural gradient (URG). A correlation and regression analysis were employed to understand the relationship between urbanization and SUHI. The findings showed that the impervious surface (IS) substantially increased from 2000 to 2020 with a net increase of 128%. The area under high and very high SUHI intensity increased by 287%, and 207% The IS and LST from the core decreased with increasing distance along URG. The RF model showed that the area mean (AREA_AM) (built-up) had the greatest influence on spatial urbanization, followed by class area (CA), Cohesion (COHE), and percentage of landscape (PLAND). Among the spatial landscape metrics, largest patch index (LPI) had the greatest influence on SUHI followed by patch density (PD), division (DIV), number of patches (NP), and Interspersion and Juxtaposition index (IJI). Lastly, the spatial urbanization had a positive correlation with SUHI intensity. The findings emphasize the pressing necessity of sustainable urban planning strategies to alleviate the detrimental consequences of urban expansion on thermal environments. The integration of green and blue infrastructure into urban planning policies is essential for the effective combat of UHI.

As illicit drug trafficking becomes more widespread, it is increasingly important to assess the extent of drug abuse for health and safety reasons. However, on the one hand, administering questionnaires to consumers is time-consuming and inconclusive, and on the other hand, seizures and clinical data evaluate service performance in response to the flow of illicit drugs rather than the flow itself. Wastewater-based epidemiology has ushered in a new era, but it tends to provide snapshots and encourages the bias of assuming that recalculated consumption is stable between samples. In Mayotte, synthetic cannabinoid receptor agonists (SCRAs), the main constituents of "chimique," have achieved an unusual status as an iconic hard drug in a disorganized political context. Assuming a "worst-case scenario," i.e., insularity and the specificity of SCRAs drug abuse in Mayotte, this approach aimed to explore the evolution of the nature and concentration of SCRAs, as well as their location. Over the course of a year, eight "chimique" sampling campaigns were conducted in Mayotte, collecting samples from street users and in places of open consumption for 1 week each. Consumers were offered (i) an interview (standard of living, sources of supply, consumption, co-addictions, side effects on health, and social life) and (ii) the opportunity to provide a sample of their "chimique" dose for analysis and to be informed of its composition. The collected "chimique" samples were analyzed using a liquid chromatography method with high-resolution mass spectrometry detection method and (i) "in-house" and (ii) online (HighResNPS) spectral libraries. Illegal drugs were identified during each campaign, for a total of 195 samples (187 cigarettes and 8 powders). Thirteen different SCRAs were observed in association with a plant matrix (tobacco was predominant, mixed or unmixed with cannabis). Co-formulants included cannabinoids, pharmaceuticals/precursors, and/or other new psychoactive substances. The results showed that month after month, the market became more homogeneous due to the products offered by SCRA pharmaceutical manufacturers and the growing hegemony of a gang of traffickers. However, the profile of the samples collected changed rapidly, sometimes becoming unrecognizable from one campaign to the next.

Ports are crucial coastal areas, constantly exposed to contaminants like polycyclic aromatic hydrocarbons (PAHs). Maritime activities in the Red Sea, mainly through the Suez Canal, are vital for global trade, with its ports being among the busiest worldwide. This study assessed PAHs in the surficial sediments of six northern Red Sea ports, focusing on sediment quality, toxicity, sources, and factors affecting PAH accumulation. The average concentrations of ∑16PAHs showed the following order: Al-Zaitiya port (558 ng/g dw) > Port Tawfiq (535 ng/g dw) > Safaga port (205 ng/g dw) > Nuweiba port (178 ng/g dw) > Hurghada port (156 ng/g dw) > Sharm El Sheikh port (153 ng/g dw). Port activity and structure were key factors influencing PAH levels. Diagnostic ratios indicated that most PAHs originated from pyrogenic sources, with some stations showing petrogenic origins. The principal component analysis supported these findings. Sediment quality guidelines (SQG) suggested occasional adverse effects. Port Tawfiq had the highest toxic equivalent (TEQ) and mutagenic equivalent (MEQ) quotients, with 72.15 ng TEQ/g dw and 69.53 ng MEQ/g dw, respectively. In contrast, Sharm El Sheikh port showed the lowest values (27.27 ng TEQ/g dw and 25.22 ng MEQ/g dw). Under these criteria, sediments from all six investigated ports are classified within the safe range, reflecting low potential for toxic and mutagenic effects.

Perfluorinated alkyl substances (PFAS) are used in the manufacture of various items, such as food packaging, non-stick utensils and waterproof textiles, and solvents. The two main substances of this class, which are the most studied and reported, are PFOA-perfluorooctanoic acid and PFOS-perfluorooctanesulfonic acid. The most used method to extract these substances from aqueous matrices employs solid phase extraction (SPE), and in this work we tested the use of low temperature partition extraction (LTPE)-a simpler and cheaper method-to extract PFOA and PFOS from sewage and sludge matrices. Validation of the analytical method, which employed acetonitrile as solvent and LC-MS/MS equipment with Phenyl-Hexyl column, was satisfactory for the analysis of these perfluorinated compounds in both matrices, with the lowest correlation coefficient (r²) equal to 0.9884 for PFOS in sewage samples. In such matrix, the detection and quantification limits of the method were 1.50 µg/L and 4.54 µg/L for PFOA, and 2.11 µg/L and 6.40 µg/L for PFOS, respectively. As for the sludge matrix, the limits of detection and quantification for PFOA were 1.34 µg/kg dw (dry weight) and 4.05 µg/kg dw, whereas for PFOS, they were 0.32 µg/kg dw and 0.96 µg/kg dw. Samples collected at two Brazilian sewage treatment plants (STP) which employ different treatment technologies were analyzed, and PFOA was detected in four sludge samples in the range of 9.09-10.26 µg/kg. On the other hand, PFOS was quantified in all sewage samples (with concentrations ranging from 17.05 to 53.58 µg/L) and all sludge sample in concentrations that varied from 8.14 to 12.50 µg/kg dw. The obtained LQs confirm the applicability of the method for µg-level screening and monitoring of sewage treatment plants, especially in the context of limited analytical resources. Importantly, this represents the first reported application of low-temperature partition extraction (LTPE) for PFAS analysis in sewage and sludge, demonstrating the feasibility and potential of this simplified approach for future environmental investigations.

Groundwater is a vital source of fresh water for human consumption, cooking, and irrigation. In populations with endemic goiter, high nitrate concentrations have been reported in groundwater, raising concerns about its impact on thyroid function. This study evaluated the effects of chronic exposure to groundwater on Xenopus laevis larvae throughout the entire metamorphic process, focusing on thyroid function. Larvae were divided into three groups: control (C, drinking water), groundwater-exposed (G), and positive control (PC, drinking water plus potassium perchlorate). Metamorphic progression, thyroid histology, and sodium-iodide symporter (NIS) protein expression were assessed. Metamorphosis was completed in 100% of C, 37.7% of G, and 0% of PC larvae. At NF stage 60, G showed reduced wet weight and snout-to-tail length compared to C and PC, whereas hind limb length did not differ significantly. At NF stage 62, G had lower wet weight than C, and PC exceeded C; hind limb length was greater in G and PC than in C. Thyroid histology revealed hyperplasia, epithelial hypertrophy, and colloid depletion in G and PC at both stages. NIS protein expression increased at NF stage 60 in G and PC relative to C but was suppressed at NF stage 62. Groundwater contained nitrates (24-83 mg/L). These results indicate that nitrate-contaminated groundwater disrupts thyroid function in Xenopus laevis, highlighting potential environmental and regulatory concerns.