Search Results (41)

The escalating environmental crisis and the threat posed by environmental crime demand more effective prevention strategies. The predictive mapping of environmental crimes can address this challenge by improving monitoring and response. This study proposes an analysis and modelling of the occurrence of environmental crimes in the Canary Islands, a territory of exceptional ecological value and strong tourism and urban sprawl pressures. Four types of illegal activity were examined: buildings and constructions, mining and tilling, solid waste dumping, and liquid waste discharging. A predictive modelling framework based on Random Forest (RF) machine learning algorithms was applied to identify spatial patterns and environmental crime potential. A colour-based environmental crime potential map was generated for each island, showing the likelihood of 0, 1, 2, 3, or all 4 types of environmental crime. Findings reveal that 43.2% of the surface area of the islands could potentially be affected by at least one crime type. Potential occurrences are lower in protected natural areas, in islands with lower population densities and in inland areas compared to coastal regions. The methodology provides a foundation for future research which could assist policymakers and environmental protectors in combating and preventing environmental crimes more effectively and contribute to the preservation of their ecosystems. Full article

Chimpanzees (Pan troglodytes) rescued from the illegal wildlife trade often suffer from chronic, traumatic injuries that require specialized and prolonged medical treatment in wildlife rehabilitation centers. We present the case report of a two-year-old male chimpanzee admitted at the Tchimpounga Chimpanzee Rehabilitation Center in the Republic of Congo with a chronic periorbital abscess, likely caused by a machete wound sustained during the poaching of his mother. Despite receiving extended antimicrobial therapy, his condition was never fully controlled and progressed to a chronic orbital infection, causing him discomfort and producing chronic purulent discharge. Enucleation was performed under general anesthesia using ketamine and medetomidine, with surgical approach adapted to the distinctive orbital anatomy of chimpanzees. During the procedure, ligation of the optic nerve and ophthalmic vessels was required due to the confined orbital apex and extensive vascularization, ensuring adequate haemostasias and procedural safety. The chimpanzee made an uneventful postoperative recovery, resuming normal feeding and social behavior within 48 h, with complete wound healing occurring within two weeks. This case report highlights the importance of prompt surgical intervention when conservative medical management fails to resolve refractory ocular infections in chimpanzees. It also emphasizes the importance of specific anesthetic protocols, refined surgical techniques and tailored postoperative care in wildlife rehabilitation centers. Documenting and sharing detailed case reports such as this contributes to the limited veterinary literature on great ape surgery and supports evidence-based clinical decision-making to improve the welfare and treatment outcomes of rescued chimpanzees. Full article

Antimicrobial resistance has been stated to be a global health problem. In Chile, the use of antibiotics should be declared by medical prescription, but it is unknown what happens to the drugs once the treatment ends. Among the possibilities for their disposal are the trash or the drain; regardless of which scenario arises, antibiotics could accumulate in the environment, stimulating the emergence of antimicrobial resistance mechanisms and their transfer between microorganisms. Unfortunately, sometimes wastewater ends up in bodies of water, due to the dragging of elements by rain, or by the presence of illegal water discharges. In this work, shotgun metagenomics was used to elucidate the functional and microbial composition of biohazard elements in the bay of Puerto Varas City, Chile. As expected, a high diversity of microorganisms was found, including bacterial elements described as human or animal pathogens. Also, a diverse repertory of antimicrobial resistant genes (ARGs) was detected, which confers mainly resistance to macrolides, beta-lactams, and tetracyclines, consistent with the families of antibiotics most used in Chile. Similar ARGs were identified in DNA mobile elements. In addition, we tested the antimicrobial susceptibility in 14 bacterial strains isolated from Llanquihue Lake. This is the first report of the presence of genomic elements that could constitute a health problem, considering the importance of the interconnection between environmental, animal, and human health, a concept known as One Health. Full article

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. Full article

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions. Full article

In the Pra Basin of Ghana, groundwater is increasingly becoming the alternative water supply due to the continual pollution of surface water resources through illegal mining and indiscriminate waste discharges into rivers. However, our understanding of hydrogeology and the dynamics of groundwater quality remains inadequate, posing challenges for sustainable water resource management. This study aims to characterize groundwater recharge by determining its origin and mechanism of recharge prior to entering the saturated zone and to provide spatial estimates of groundwater recharge using stable isotopes and water level measurements relevant to groundwater management in the basin. Ninety (90) water samples (surface water and groundwater) were collected to determine stable isotope ratios of oxygen (${\delta }^{18}\mathrm{O}$) and hydrogen (${\delta }^2\mathrm{H}$) and chloride concentration. In addition, ten boreholes were installed with automatic divers to collect time series data on groundwater levels for the 2022 water year. The Chloride Mass Balance (CMB) and the Water Table Fluctuation (WTF) methods were employed to estimate the total amount and spatial distribution of groundwater recharge for the basin. Analysis of the stable isotope data shows that the surface water samples in the Pra Basin have oxygen (${\delta }^{18}\mathrm{O}$) and hydrogen (${\delta }^2\mathrm{H}$) isotope ratios ranging from −2.8 to 2.2‰ vrs V-SMOW for ${\delta }^{18}\mathrm{O}$ and from −9.4 to 12.8‰ vrs V-SMOW for ${\delta }^2\mathrm{H}$, with a mean of −0.9‰ vrs V-SMOW and 0.5‰ vrs V-SMOW, respectively. Measures in groundwater ranges from −3.0 to −1.5‰ vrs V-SMOW for ${\delta }^{18}\mathrm{O}$ and from −10.4 to −2.4‰ vrs V-SMOW for ${\delta }^2\mathrm{H}$, with a mean of −2.3 and −7.0‰ vrs V-SMOW, respectively. The water in the Pra Basin originates from meteoric source. Groundwater has a relatively depleted isotopic signature compared to surface water due to the short residence time of infiltration within the extinction depth of evaporation in the vadose zone. Estimated evaporative losses in the catchment range from 51 to 77%, with a mean of 62% for surface water and from 55 to 61% with a mean of 57% for groundwater, respectively. Analysis of the stable isotope data and water level measurements suggests a potential hydraulic connection between surface water and groundwater. This hypothesis is supported by the fact that the isotopes of groundwater have comparatively lower values than surface water. Furthermore, the observation that the groundwater level remains constant in months with lower rainfall further supports this conclusion. The estimated annual groundwater recharge in the catchment ranges from 9 to 667 mm (average 165 mm) and accounts for 0.6% to 33.5% (average 10.7%) of mean annual precipitation. The total estimated mean recharge for the study catchment is 228 M ${\mathrm{m}}^3$, higher than the estimated total surface water use for the entire Pra Basin of 144 M ${\mathrm{m}}^3$ for 2010, indicating vast groundwater potential. Overall, our study provides a novel insight into the recharge mechanism and spatial quantification of groundwater recharge, which can be used to constrain groundwater flow and hydrogeochemical evolution models, which are crucial for effective groundwater management within the framework of the Pra Basin’s Integrated Water Resources Management Plan. Full article

Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night. Full article

The crystalline basement aquifer of the Pra Basin in Ghana is essential to the water supply systems of the region. This region is experiencing the ongoing pollution of major river networks from illegal mining activities. Water management is difficult due to the limited knowledge of hydrochemical controls on the groundwater. This study investigates its evolution based on analyses from a previous groundwater sampling campaign and mineralogical investigation of outcrops. The dominant reactions driving the average groundwater composition were identified by means of a combinatorial inverse modelling approach under the hypothesis of local thermodynamical equilibrium. The weathering of silicate minerals, including albite, anorthite, plagioclase, K-feldspar, and chalcedony, explains the observed median groundwater composition in the transition and discharge zones. Additional site-specific hypotheses were needed to match the observed composition of the main recharge area, including equilibration with carbon dioxide, kaolinite, and hematite in the soil and unsaturated zones, respectively, and the degradation of organic matter controlling the sulfate/sulfide content, thus pointing towards kinetic effects during water–rock interactions in this zone. Even though an averaged water composition was used, the inverse models can “bridge” the knowledge gap on the large basin scale to come up with quite distinct “best” mineral assemblages that explain observed field conditions. This study provides a conceptual framework of the hydrogeochemical evolution for managing groundwater resources in the Pra Basin and presents modelling techniques that can be applied to similar regions with comparable levels of heterogeneity in water chemistry and limited knowledge of aquifer mineralogy. The combinatorial inverse model approach offers enhanced flexibility by systematically generating all plausible combinations of mineral assemblages from a given pool of mineral phases, thereby allowing for a comprehensive exploration of the reactions driving the chemical evolution of the groundwater. Full article

The quality of groundwater resources in the Pra Basin (Ghana) is threatened by ongoing river pollution from illegal mining. To date, there are very limited data and literature on the hydrochemical characteristics of the basin. For the first time, we provide regional hydrochemical data on surface water and groundwater to gain insight into the geochemical processes and quality for drinking and irrigation purposes. We collected 90 samples from surface water (rivers) and groundwater (boreholes) and analysed them for their chemical parameters. We performed a water quality assessment using conventional water quality rating indices for drinking water and irrigation. Cluster and factor analysis were performed on the hydrochemical data to learn the chemical variations in the hydrochemical data. Bivariate ion plots were used to interpret the plausible geochemical processes controlling the composition of dissolved ions in surface water and groundwater. The water quality assessment using Water Quality Index (WQI) revealed that 74% of surface water and 20% of groundwater samples are of poor drinking quality and, therefore, cannot be used for drinking purposes. For irrigation, surface water and groundwater are of good quality based on Sodium Adsorption Ratio (SAR), Wilcox diagram and United States Salinity (USSL) indices. However, Mn and Fe (total) concentrations observed in most surface water samples are above the acceptable limit for irrigation and therefore require treatment to avoid soil acidification and loss of availability of vital soil nutrients. Manganese and iron (total) are identified as the main contaminants affecting the basin’s water quality. The hierarchical cluster analysis highlights the heterogeneity in the regional hydrochemical data, which showed three distinct spatial associations based on elevation differences. Groundwater composition chemically evolves from a Ca–${\mathrm{HCO}}_3$ to a Na–${\mathrm{HCO}}_3$ and finally to a Na–Cl water type along the flow regime from the recharge to the discharge zone. The bivariate ion plot and the factor analysis underscore silicate weathering, carbonate dissolution and ion exchange as the most likely geochemical processes driving the hydrochemical evolution of the Pra Basin groundwater. Going forward, geochemical models should be implemented to elucidate the dominant reaction pathways driving the evolution of groundwater chemistry in the Pra Basin. Full article

Vinyl, such as those in the form of mulching and vinyl houses, is used to improve agricultural productivity. It is generated as an agriculture waste vinyl (AWV) after use. The collected AWV is transported to a recycling facility and shredded, washed, and compressed to be recycled. Recycled materials can contribute to the circular economy of agriculture as they are used again as an agricultural plastic product. However, in Korea, there are concerns about the illegal disposal (landfill, incineration) of AWV. So, a new management model is needed in which stakeholders voluntarily establish an AWV management system. In this study, a sustainable management strategy was proposed. This strategy is reinforcing the responsibility of the producers of AWV and forms a value chain in the proper discharge after consumption by applying the deposit system proposed to recover AWV. Local governments and the National Agricultural Cooperative Federation (NH) proposed education to curb the illegal disposal of AWV, and for managing areas where a collection system has not yet been established, biodegradable mulching vinyl (BMV) was proposed to minimize the environmental pollution caused by AWV. It was calculated that the EPR contribution was 0.16 USD/kg, and the introduction of BMV was 0.42 USD/kg in Korea. This study will provide a new alternative in countries struggling with AWV management. Full article

Anthropogenic activities along coastal areas have contributed to the unwarranted discharge of toxic metals into mangrove swamps, posing risks to marine deposits and ecological environments. In this research, we studied the Isaka–Bundu tidal swamp area in the Niger Delta, which is an impacted mangrove creek located along the Bonny river, exposed to pollution pressures. The ecological risks (Er) of toxic metals in the sediments and water of the Isaka–Bundu tidal mangrove swamp followed a decreasing order (Cu > Zn > Cd > Cu > Pb > As), according to our results, while the potential ecological risk index (PERI) of the toxic metals in the sediments and water of the Isaka–Bundu tidal mangrove swamp can be said to have a very high ecological risk (PERI ≥ 600). The sediment pollution load index (PLI) was higher than 1 in all three analyzed stations, suggesting extremely toxic pollution. The enrichment evaluation shows that the studied stations have a moderate potential ecological risk of Cd, with the enrichment value for Pb showing low potential ecological risk. Our study shows that the Isaka–Bundu tidal mangrove swamp has a significant level of toxic metal pollution, which is evidence of the illegal activities performed in the Niger Delta. Full article

As an informal environmental regulation, public participation plays a vital role in the multi-governance environmental system. Based on the evolutionary game theory, this paper constructs the game models of government enterprise, public enterprise and government public enterprise, and analyzes the impact of different intensity of government behavior and public participation on enterprise behavior strategies. The results show that: (1) In the two-party evolutionary game, the behavior of each stakeholder is related to its costs and benefits. Still, effective public participation allows the enterprise to choose legal discharge, even if the benefits of legal discharge are smaller than illegal discharge. (2) In the three-party evolutionary game, the steady-state conditions of government and the public are the same as those in two-party evolutionary game models. However, the decision-making behavior of enterprises also needed to consider the impact of public whistle-blowing on their reputation and image. (3) With the increase of the government’s ecological protection publicity, subsidies, fines, public concern, and whistle-blowing, the evolution speed of the enterprise towards legal discharge is faster. Full article

With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetric Synthetic Aperture Radar (Pol-SAR) system has been widely used in marine oil spill detection. However, in the studies of the oil spill extraction in SAR images, there are some problems that limit large-scale oil spill detection work. As a transition from single-polarized to full-polarized, the dual-polarized system carries some polarization information and can be obtained in large quantities for free, which has become a major breakthrough in solving the problem of large-scale oil spill detection. In order to optimize the multisource features that can be extracted from dual-polarized SAR images, greatly improve the utilization rate of dual-polarized SAR oil spill images under the premise of reducing workload, and ensure the accuracy of marine oil spill extraction, this paper adopts the metric of inter-class separability, the Jeffries–Matusita distance, which improves on the traditional K-means algorithm by focusing on the noise sensitivity defect of the K-means algorithm; the artificial influence of J–M distance in measuring the separability between classes improves the algorithm in three aspects: sample selection, distance calculation, and data evaluation. Finally, using the inter-sample J–M distance of multisource features, the overall accuracy of image segmentation, the F1-score, and the results of correlation analysis between features, three advantageous features and three subdominant features are selected that can be used for marine oil spill detection. Full article

Pawara area is a mining district in the eastern region of Cameroon. Mining in the area is generally artisanal and semi-mechanized, practiced by the local miners and immigrants from neighboring African countries and China. The lack of strict regulations and control of mining activities permits the miners to use illegal substances, especially Hg in gold separation. These expose the area to toxic and heavy metals pollution. This study highlights the source of heavy metals concentration in the Pawara soils and the potential adverse effects of Hg on gold separation to the environment and health. Three mining sites and one control site were investigated, namely Site A, Site B and Site C. The control Site 0 (background) is an area where no mining and agricultural activities have taken place. Soil samples were collected at depth of 20 cm, with six from each site (24 samples). Samples were analyzed for Al, Cd, Cr, Cu, Fe, Hg, Pb, Cd and Zn content using atomic absorption spectrophotometry in a graphite furnace. The metals, except for Fe, show high values for all three sites exceeding the background levels in the soils. Hg shows the highest concentration on Site A with a value of 1590 mg kg⁻¹. Pb is highest on Site B with a concentration of 12,274 mg kg⁻¹. The contamination degree was assessed with the help of contamination indices (Igeo—index of geo-accumulation; PLI—pollution load index; RI—potential ecological risk; Eri—ecological risk; Pi—single pollution index; CF—contamination factor) and all parameters show a high degree of contamination on all three sites compared to the control site. Hg, Pb, Cd, Cr and Cu as single pollutants show the highest ecological risk on Site A and Site B where intense mining is taking place. The absence of industrial and large-scale agricultural activities in the Pawara area, the nonexistence of contaminants on the control site and the presence of contaminants on Site C where farming is high and mining is low jointly show that the discharge of mine wastes onto the soils and stream channels are the main source of contaminants and potential pollutants of the Pawara ecological environment. Full article

The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field of machine learning (ML) have become a niche of interest for the community of remote sensing. Specifically, a major challenge is the automatic classification of ships from satellite imagery, which is needed for traffic surveillance systems, the protection of illegal fisheries, control systems of oil discharge, and the monitoring of sea pollution. Deep learning (DL) is a branch of ML that has emerged in the last few years as a result of advancements in digital technology and data availability. DL has shown capacity and efficacy in tackling difficult learning tasks that were previously intractable. Specifically, DL methods, such as convolutional neural networks (CNNs), have been reported to be efficient in image detection and recognition applications. In this paper, we focused on the development of an automatic ship detection (ASD) approach by using DL methods for assessing the Airbus ship dataset (composed of about 40 K satellite images). The paper explores and analyzes the distinct variations of the YOLO algorithm for the detection of ships from satellite images. A comparison of different versions of YOLO algorithms for ship detection, such as YOLOv3, YOLOv4, and YOLOv5, is presented, after training them on a personal computer with a large dataset of satellite images of the Airbus Ship Challenge and Shipsnet. The differences between the algorithms could be observed on the personal computer. We have confirmed that these algorithms can be used for effective ship detection from satellite images. The conclusion drawn from the conducted research is that the YOLOv5 object detection algorithm outperforms the other versions of the YOLO algorithm, i.e., YOLOv4 and YOLOv3 in terms accuracy of 99% for YOLOv5 compared to 98% and 97% respectively for YOLOv4 and YOLOv3. Full article