Search Results (1,461)

Eutrophication is a form of pollution caused by elevated nutrient concentrations in water bodies, leading to excessive algal growth and subsequent oxygen depletion. This process poses significant risks to aquatic ecosystems and overall water quality. This study investigates the spatial distribution of eutrophication in the Almyros Stream, aiming to develop a rapid and high-resolution approach for identifying eutrophication patterns and selecting representative sampling sites. Almyros is an urban stream in the western Heraklion Basin (Crete, Greece) that is subjected to considerable pressures from agricultural, industrial, urban, and tourism-related activities. Data for this study were collected using a drone equipped with a multispectral sensor. The multispectral bands, together with remote sensing indices associated with chlorophyll presence, served as input data. Chlorophyll presence is a key indicator of phytoplankton biomass and is widely used as a proxy for nutrient enrichment and eutrophication intensity in aquatic ecosystems. The k-means clustering algorithm was then applied to classify the data and reveal the eutrophication spatial patterns of the study area. The results show that the methodology successfully identified spatial variations in eutrophication-related conditions and generated robust eutrophication pattern maps. These findings underscore the potential of integrating remote sensing and machine learning techniques for efficient monitoring and management of water bodies. Full article

Italy is estimated to host thousands of abandoned mines, many of which contain large volumes of mine residues that negatively affect land and aquatic ecosystems, also posing a risk to human health. This study evaluates the effectiveness of spaceborne imaging spectroscopy combined with laboratory spectroscopy for characterizing the mineralogy and geochemistry of residues from the abandoned Montevecchio sulfide mine in southwestern Sardinia, a site recognized as a significant source of environmental pollution. Mine tailings and their downstream dispersion along the Rio Irvi River were systematically studied and sampled in the field. Collected samples were analyzed in the lab using an Analytical Spectral Device (ASD) spectroradiometer, complemented by powder X-ray Diffraction (XRD) for mineralogical characterization. Affected zones were subsequently mapped using the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite data at a 30 m spatial resolution, by applying a polynomial fitting technique to the image spectra. The results reveal the presence of Fe- and Zn-bearing sulfates and oxy/hydroxides, indicative of acidic-to-circum-neutral drainage conditions in the mine tailings and along affected streams. Specifically, EnMAP was able to detect jarosite and subtle chemical and physical variations in Fe-hydroxides. This integrated approach enabled the delineation of environmental conditions and zones with varying acidity based on the spectral characteristics of secondary minerals. Overall, the study demonstrates the potential of EnMAP data for mapping acid mine drainage and assessing environmental impacts in legacy mining areas. Full article

Black carbon (BC) is a short-lived climate pollutant with substantial warming and health impacts, yet its contribution from maritime activities in data-limited regions remains poorly constrained. This study conducts a methodological and uncertainty-focused evaluation of tier-based emission inventory approaches from the European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA) Guidebook, examining fuel-based (Tier I) and activity-based (Tier III) methodologies using national fuel statistics, port call activity, vessel registry data, and an operational Philippine Coast Guard dataset. Monte Carlo uncertainty analysis, spatial mapping, and hotspot intensity analysis are applied to evaluate how each tier responds to data limitations and parameter uncertainty rather than to reconcile absolute emission magnitudes. Results indicate that Tier I provides scalability for national reporting but exhibits substantial uncertainty for gasoline-dominated segments due to reliance on particulate matter-based proxies, underscoring the role of Tier II as a targeted refinement option. Tier III applies an activity-based formulation using fuel consumption resolved by operational phase and phase-specific emission factors, consistent with EMEP/EEA Tier III guidance. These findings are integrated into a decision-oriented synthesis to support informed selection and combination of tiered emission approaches under data-limited maritime conditions aligned with national and international climate commitments. Full article

This study assesses groundwater quality and nitrate-related health risks in the Skhirat coastal aquifer (Morocco) using a multidisciplinary approach. A total of thirty groundwater wells were sampled and analyzed for physico-chemical properties, including major ions and nutrients. Multivariate statistical analyses were employed to explore contamination sources. Pollution indices such as the Groundwater Pollution Index (GPI) and Nitrate Pollution Index (NPI) were computed, and Monte Carlo simulations (MCSs) were conducted to assess nitrate-related health risks through ingestion and dermal exposure. Furthermore, Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR) with radial basis function kernel, and Artificial Neural Networks (ANN) models were tested for predicting groundwater pollution indices. Results of hydrochemical facies revealed Na⁺-Cl⁻ dominance in 47% of the samples, suggesting strong marine influence, while nitrate concentrations reached up to 89.3 mg/L, exceeding World Health Organization (WHO) limits in 26.7% of the sites. Pollution indices indicated that 33.3% of samples exhibited moderate to high GPI values, with 36.7% of the samples exceeding the threshold for NPI. The MCS for nitrate health risk revealed that 43% of the samples posed non-carcinogenic health risks to children (Hazard Index (HI) > 1). RF outperformed other models in predicting GPI (R² = 0.76) and NPI (R² = 0.95). Spatial prediction maps visualized contamination hotspots aligned with intensive horticultural activity. This integrated methodology offers a robust framework to diagnose groundwater pollution sources and predict future risks. Full article

Topography plays a crucial role in shaping local urban microclimates and can drive the formation of cold-air pools in valley bottoms. This study examines the Eiras Valley (Coimbra, Portugal), a rapidly growing peri-urban area, to identify the conditions under which cold-air pools form and to characterize their spatial and vertical dynamics. Field measurements were carried out using Tinytag Plus 2 data loggers at the surface (≈1.5 m above ground) and mounted on an unmanned aerial vehicle (UAV) for vertical profiles, complemented by high-resolution thermal mapping through Empirical Bayesian Kriging. The results show that a nocturnal cold-air pool develops within the valley under clear, anticyclonic winter conditions, persisting into the early morning hours and dissipating after sunrise due to solar heating. In contrast, under overcast or summer conditions, no cold-air pooling was observed. The temperature inversion capping the cold-air pool was found at approximately 275 m altitude, inhibiting vertical mixing and trapping pollutants near the ground. These findings underscore the importance of topoclimatology in urban and regional planning, with implications for thermal comfort, air quality, and public health. The study contributes to urban climate research by highlighting how local topography and seasonal atmospheric stability govern cold-air pool formation in valley environments, supporting the development of mitigation strategies aligned with urban sustainability goals. Full article

Coal gangue sorting is crucial for improving coal quality and reducing environmental pollution; however, traditional methods suffer from resource wastage, high cost, and intensive labor demands. To address these challenges, this paper investigates an image recognition-based coal gangue sorting technique and proposes an improved YOLOv7-tiny detection model tailored for edge GPU devices with limited computational power and memory. YOLOv7-tiny is selected as the baseline due to its balanced performance in detection accuracy, architectural maturity, and deployment stability on edge GPUs. Compared to newer lightweight detectors such as YOLOv8-N and YOLOv6-N, YOLOv7-tiny adopts an ELAN-based modular design, which facilitates structural optimization without relying on anchor-free reconstruction or complex post-training strategies, making it particularly suitable for engineering enhancements in real-time industrial sorting under resource constraints. To tackle the limitations in computing and storage, we first introduce an ELAN-PC feature extraction module based on partial convolution and ELAN. Secondly, a GhostCSP module is proposed by integrating cross-stage aggregation and Ghost bottleneck concepts. These modules replace the original ELAN structures in the backbone and neck networks, significantly reducing floating-point operations (FLOPs) and the number of parameters. Furthermore, the SIoU loss function is adopted to replace the original bounding box loss, enhancing detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv7-tiny, the improved model increases mAP0.5 from 86.9% to 88.7% (a gain of 1.8%), reduces FLOPs from 13.2 G to 9.2 G (a decrease of 30%), and cuts parameters from 6.0 M to 4.3 M (a reduction of 28%). In dynamic sorting tests, the model achieves a coal gangue sorting rate of 82.2% with a misclassification rate of 8.1%, indicating promising practical applicability. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

With the development of mobile crowdsensing systems (MCSs), wireless network transmission efficiency has attracted widespread attention. Network coding can be used in wireless communication to improve network throughput and robustness, which allows intermediate nodes to perform arbitrary coding operations on data packets. However, the data packet in network coding systems is vulnerable to pollution attacks. The special operation of intermediate nodes makes some security protocols in traditional store-and-forward networks unavailable in network coding systems. To address this problem, an efficient certificate-based linearly homomorphic signature scheme against pollution attacks in network coding systems is presented. A novel homomorphic contraction mapping technique is introduced to reduce the computational cost of signature generation. In the proposed scheme, the computational cost of both signature generation and verification is independent of the data packet size. Furthermore, a construction is provided to simultaneously defend against both eavesdropping attacks and pollution attacks in unicast networks. The security of the certificate-based linearly homomorphic signature scheme is formally proved in the random oracle model (ROM), and the scheme is implemented using the Java Pairing-Based Cryptography (JPBC) library. Simulation results demonstrate that the scheme is efficient and practical for real-world deployments in public environments without requiring secure channels. Full article

Mediterranean peri-urban forests play a crucial role in urban sustainability, yet their ecosystem services remain underexplored. This study quantifies and maps six regulating ecosystem services—carbon sequestration, air pollutant removal, surface runoff retention, precipitation interception, soil water regulation, and wildlife refuge—in a representative Mediterranean peri-urban forest, Monte de El Pardo (Spain). The analysis integrates cartographic and environmental data, biophysical modelling (i-Tree), and field surveys to provide a spatially explicit assessment. The results reveal that riparian formations and mixed stone pine–broadleaved woodlands provide the highest values across most services, while holm oak forests and dehesas contribute substantially due to their extensive coverage. Total annual carbon sequestration was estimated at 27,917,803 kg C yr⁻¹, equivalent to 102,329,511 kg CO₂e yr⁻¹. Hydrological regulation was also significant, with 94.5% of the area showing medium soil permeability and over half the territory presenting complex, multi-layered vegetation structure. Overall, Mediterranean peri-urban forests function as major carbon sinks, hydrological regulators, and biodiversity cores, reinforcing their importance as ecological and climatic stabilisers in metropolitan regions. Full article

Population growth and agricultural expansion are threatening groundwater resources in the Maze Zenti catchment, Southern Ethiopia. This study evaluated groundwater suitability for drinking and irrigation by analyzing 30 samples using an integrated approach. This approach included GIS-based IDW interpolation, hydrochemical characterization, drinking water quality index, entropy weight, pollution index of groundwater, multivariate statistics, Piper, Gibbs, and Wilcox diagrams, ANOVA, and irrigation indices based on WHO standards. The correlation matrix revealed strong associations between Na⁺-TDS (r = 0.77) and Na⁺-Ca²⁺ (r = 0.68), indicating mineral dissolution, ion exchange, and agricultural inputs as key factors. Weak correlations were found for NO₃⁻ and F⁻, reflecting localized anthropogenic and geogenic influences. Component analysis identified four components explaining 78.2% (wet season) and 81.2% (dry season) of the variance, highlighting mineralization and anthropogenic inputs. Hydrochemical facies were mainly Ca-Mg-HCO₃ with some localized Na-HCO₃, suggesting that rock–water interactions are the primary source of geochemical control. Drinking water quality assessment showed that, during the wet season, 52.8% of the catchment had excellent water quality, 45.8% was good, and 1.4% was poor–very poor. In the dry season, 51.6% was excellent, 47.4% was good, 0.8% was poor, and 0.2% was very poor. The results of the entropy-weighted analysis indicated seasonal improvement, with excellent areas increasing from 13.1% to 31.4% and poor zones decreasing from 7.5% to 3.4%. Irrigation indices (Na%, PI, MAR, SAR) and Wilcox analysis (86.4% C2S1) suggested low sodicity and salinity hazards. This study provides the first integrated seasonal mapping of drinking and irrigation water quality, entropy-weighted water quality, and pollution index for the Maze Zenti catchment, establishing a hydrogeochemical baseline. Overall, groundwater in the area is generally suitable for drinking and irrigation. However, localized monitoring and sustainable land-use practices are recommended to mitigate contamination risks. Full article

The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed. Full article

Soil contamination by potentially toxic elements (PTEs) is a growing environmental concern, particularly in agricultural regions where soil quality directly affects crop safety and human health. This study evaluates PTE concentrations and ecological risks in agricultural soils of Hautat Sudair, central Saudi Arabia, using contamination indices, multivariate statistics, and GIS-based spatial modeling supported by RS-derived land use/land cover (LULC) mapping. The results show that the mean concentrations of Ni (35.97 mg/kg) and Mn (1230 mg/kg) exceed international thresholds in several locations, while Pb (8.34 mg/kg), Cr (33.00 mg/kg), Zn (60.09 mg/kg), and As (4.25 mg/kg) remain within permissible limits in most samples. Contamination indices, including the Enrichment Factor (EF), Contamination Factor (CF), and Geo-Accumulation Index (Igeo), highlight hotspot behavior, with isolated sites showing elevated concentrations approaching screening levels (e.g., Pb up to 32.0 mg/kg and Cr up to 52.0 mg/kg), whereas Ni and Mn exhibit the most pronounced local enrichment. The Pollution Load Index (PLI) varies from 0.24 to 0.80, indicating low to moderate contamination levels, while the Risk Index (RI) ranges from 10.43 to 41.38, signifying low ecological risk. Multivariate statistical analyses, including correlation matrices and principal component analysis (PCA), reveal that Ni, Cr, and Mn share a common source, possibly linked to anthropogenic inputs and natural geological background. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirm the adequacy of the dataset for PCA (KMO = 0.797; χ² = 563.845, p < 0.001). Spatial distribution maps generated using GIS and RS highlight contamination hotspots, reinforcing the necessity for periodic monitoring. By integrating indices, multivariate patterns, and spatial context, this study provides a replicable, research-driven framework for interpreting PTE controls in arid agricultural soils. Full article

Industrial heritage sites hold significant historical and architectural value and their attractive urban locations make them frequent targets for adaptive reuse. Yet decades of industrial activity have left hazardous residues embedded in building fabric, posing risks to public health. Current conservation practice rarely incorporates systematic identification and mapping of such contamination, creating a critical gap that can undermine both safety and the authenticity and integrity of historical material layers. This article proposes an interdisciplinary methodological framework for identifying, analysing, and managing contamination in post-industrial heritage. The model extends the Conservation Management Plan (CMP) by integrating chemical and toxicological analyses, GIS-based diagnostics, and ontological data modelling (CIDOC CRM). It supports value-based decision-making by enabling the safe recognition and preservation of historical layers that may contain toxic residues. The framework is being tested at the former Gdańsk Shipyard through integrated historical research, conservation surveys, and laboratory analyses to assess its applicability and scalability. The proposed approach is intended as a transferable tool for managing polluted heritage environments, aligned with SDGs 11 and 12. Full article

The accumulation of heavy metals in fish tissues is widely recognized as an indicator of aquatic environmental pollution, and the analysis of their content provides a basis for assessing ecological risk and the safety of aquatic food. The European eel (Anguilla anguilla) is a species frequently used as a bioindicator in environmental studies due to its wide geographic distribution, long life cycle, and high capacity for bioaccumulation of heavy metals in various tissues. The aim of this study was to assess the variation in the accumulation of heavy metals, i.e., mercury (Hg), lead (Pb), arsenic (As), and cadmium (Cd), in the tissues (muscle, liver, gonads, and gills) of European eels caught in two locations in Polish inland waters. The obtained results showed significant differences both in the concentration levels of individual elements and in their co-occurrence in the examined tissues. The statistical methods used, including correlation analysis, heat maps, and principal component analysis (PCA), allowed for a comprehensive assessment of the relationships between metals and the identification of factors differentiating the studied populations. The obtained results clearly indicate that fish residing in similar environments for long periods exhibit significant differences in heavy metal content in various fish tissues. Fish obtained from environments with potentially higher levels of heavy metal inputs, such as the Oder River EMU compared with the Vistula River EMU, showed higher levels of heavy metal accumulation in tissues. This study also found that the concentration of heavy metals tested did not exceed the safe standards for human fish consumption. Full article

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping. Full article