Search Results (146)

Illegal mining poses significant environmental and economic challenges, and effective monitoring is essential for regulatory enforcement. This study evaluates the potential of the U-Net deep learning model for detecting mining activities using Sentinel-2 satellite imagery over the Strzegom region in Poland. We prepared annotated datasets representing various land cover classes, including active and inactive mineral extraction sites, agricultural areas, and urban zones. U-Net was trained and tested on these data, and its classification accuracy was assessed against common Google Earth Engine (GEE) classifiers such as Random Forest, CART, and SVM. Accuracy metrics, including Overall Accuracy, Producer’s Accuracy, and F1-score, were computed. Additional analyses compared model performance for detecting licensed versus potentially illegal mining areas, supported by integration with publicly available geospatial datasets (MOEK, MIDAS, CORINE). The results show that U-Net achieved higher detection accuracy for mineral extraction sites than the GEE classifiers, particularly for small and spatially heterogeneous areas. This approach demonstrates the feasibility of combining deep learning with open geospatial data for supporting mining activity monitoring and identifying potential cases of unlicensed extraction. Full article

Solid waste for incineration and wastewater from the country’s largest city, Mexico City (CDMX), is transported to the southern region of Valle del Mezquital (MV). This area also hosts an oil refinery, a thermoelectric plant (PEMEX-CFE), cement factories, industrial corridors, and mining operations, all of which harm environmental and public health. From a Political Ecology (PE) perspective, we examine the mechanisms of accumulation, emphasizing the allocation of property titles and the extraction of rent as an environmental reservoir. We also explore the power of socio-environmental movements to provide a comprehensive understanding of environmental conflict. Based on economic power structures, we identify a geopolitical configuration that deepens the spatial divisions between labor in the MV and consumption in CDMX, exacerbating health disparities. We conclude that an unequal geography has been built that has produced capitalist and rentier landowners who are exempt from the externalities that have produced a sacrifice zone. The Mexican State is a key stakeholder, collaborating with the industrial elite in both legal and illegal spheres. Within this sacrifice zone, the inhabitants of the MV have resisted pollution and industrial accidents for over 50 years. Despite publicizing their struggle internationally and collaborating with academics, members of the movement have been assassinated. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage. Full article

Amazonian parrots (Psittacidae) are essential to ecosystem balance. Already vulnerable to habitat fragmentation and weak environmental regulations, they are now increasingly threatened by heavy metal contamination. This review synthesizes evidence on the sources, transgenerational bioaccumulation, and physiological impacts of metals such as mercury (Hg), lead (Pb), cadmium (Cd), zinc (Zn), and arsenic (As) in these birds. Anthropogenic activities, including illegal gold mining, agricultural intensification, and urban expansion, release metals that biomagnify along food webs. Parrots, as long-lived, high-trophic consumers, accumulate metals in vital tissues, leading to severe neurotoxic effects, immunosuppression, reproductive failure, and reduced survival. Furthermore, maternal transfer of contaminants to eggs exacerbates genetic erosion and threatens population viability. While biomonitoring tools and habitat restoration have been proposed, current strategies are insufficient against the synergistic pressures of pollution and climate change. Addressing heavy metal exposure is critical to conserving Amazonian biodiversity and safe-guarding ecosystem services. Future efforts should prioritize multidisciplinary predictive models, bioremediation actions, and the strengthening of international environmental governance to ensure the survival of these sentinel species. Full article

The genus Coturnix, comprising migratory Old World quails, includes Common Quail (Coturnix coturnix) and Japanese Quail (Coturnix japonica), which are nearly indistinguishable morphologically. This similarity poses challenges in species identification, leading to conservation issues such as the illegal trade of wild Common Quail in the name of farmed Japanese Quail. To address this issue, we employed two approaches: (1) mining species-specific short sequence repeats (SSRs) and (2) designing a PCR-restriction fragment length polymorphism (PCR-RFLP) assay targeting the COX1 gene to distinguish these species. While SSR markers proved unreliable, the PCR-RFLP assay successfully distinguished between Common Quail and Japanese Quail, leveraging the unique BsaBI restriction site in the Common Quail COX1 gene. This method demonstrated high specificity and reproducibility, offering a robust tool for forensic and conservation applications. Our findings provide a reliable, efficient, and accessible technique for wildlife managers and researchers to regulate the illegal trade of Coturnix quails and support conservation efforts. Full article

The Amazonian region comprises a set of ecosystems that play an essential role in stabilizing global climate and regulating carbon and water cycles. However, several environmental issues of anthropogenic origin threaten climate stability in this region: agribusiness, illegal mining, illegal timber exports, pesticide use, and biopiracy, among others. These actions lead to deforestation, soil erosion, fauna biodiversity loss, water resource contamination, land conflicts, violence against indigenous peoples, and epidemics. The present study aims to feature the current degradation process faced by the Amazonian biome and identify strategic alternatives based on science to inhibit and minimize the degradation of its biodiversity and water resources. This applied research, based on a systematic review, highlighted the complexity, fragility, and importance of the functioning of the Amazonian ecosystem. Although activities such as mining and agriculture notoriously cause soil degradation, this research focused on the scenarios of biodiversity and water resource degradation. The dynamics of the current Amazon degradation process associated with human activity and climate change advancement were also described. Ultimately, the study emphasizes that, given the invaluable importance of the Amazon’s biodiversity and natural resources for global climate balance and food and water security, anthropogenic threats endanger its sustainability. Beyond the well-known human-induced impacts on the forest and life, the findings highlight the need for strategies that integrate forest conservation, sustainable land management, and public policies focused on the region’s sustainable development. These strategies, supported by partnerships, include reducing deforestation and burning, promoting environmental education, engaging local communities, enforcing public policies, and conducting continuous monitoring using satellite remote sensing technology. Full article

The Yanomami Indigenous Land (YIL) is heavily impacted by illegal gold mining, leading to significant contamination by methylmercury, a neurotoxin that poses severe risks to human health. The fetal brain is particularly susceptible to the neurotoxic effects of methylmercury, which can result in mild mental retardation (MMR). The goal of this study was to estimate the burden of disease (BoD) associated with methylmercury exposure in the YIL and its economic implications. The BoD calculations followed World Health Organization (WHO) methodologies. To estimate the local BoD, hair samples were collected from women of childbearing age in the Waikás, Mucajaí, Paapiu, and Maturacá regions. For broader estimates, data from the scientific literature were used. The average hair methylmercury concentrations in these investigated regions were 6.21 µg/g, 3.86 µg/g, 3.53 µg/g, and 2.96 µg/g, respectively. The MMR incidence rate (IR) in children ranged from 2.08 to 4.47 per 1000 in these regions. The Disability-Adjusted Life Years (DALYs) per 1000 births varied from 24.8 to 53.4. In the Worst-Case Scenario, MMR-IR reached 9 per 1000, with DALYs per 1000 births rising to 109.6. The estimated economic impact of methylmercury exposure ranged from USD 716,750 to USD 3,153,700. This study is the first to quantify the MMR incidence due to mercury in the YIL, highlighting the severe threat posed by gold mining to the health and survival of the Yanomami people. Full article

In Africa, the legacy of mining has not only contributed to economic growth, employment, and prosperity but also brought risks to pollution exposure associated with detrimental health effects, ecological degradation, and social upheaval, such as the rapid Zama-Zamas. Due to this, illegal mining on the African continent has numerous environmental implications. While illegal mining operations have an adverse impact on the environment, government and academic research into the Zama-Zamas in South Africa has focused mainly on the socio-economic aspects of the illegal mining aspect, so environmental factors have been overlooked. Most government reports and academic literature on illegal mining activities in the country typically emphasize the socio-economic impact with little or no environmental consideration. Zama-Zamas have a major socioeconomic impact; however, their adverse impact on the environment cannot be ignored as well. This is especially true with Zama-Zamas’ illegal activities, which result in environmental pollution that may affect the entire ecosystem as a result. Based on this background, the purpose of this paper is to explore the significant environmental implications of Zama-Zama’s illegal mining in the South African context. The current study has discovered that there is little documentation regarding the environmental implications of illegal mining within communities where it occurs within South Africa’s mining sector, despite its infiltration by Zama-Zamas illegal mining activities. This is a cause for concern, especially within countries like South Africa where illegal mining has become a national crisis, yet the environmental impacts of illegal mining from the literature point of view are not well documented. The limited literature on this issue highlights the need for urgent attention to the environmental damage caused by illegal mining. Thus, this appraisal advocates for the inclusion of environmental impact studies alongside the socio-economic impacts widely reported on illegal mining. For a country striving to achieve sustainable development, understanding the holistic potential risks of illegal mining activities by Zama-Zama is essential. Full article

The reclamation of illegal landfills poses a significant threat to the environment. An example of such a case is Łomianki near Warsaw, where an illegal landfill contained alarming levels of arsenic and chromium, posing a potential risk to the health of local residents due to the possibility of these metals contaminating a nearby drinking water source. Initial geochemical tests revealed high concentrations of these metals, with chromium reaching up to 24,660 mg/kg and arsenic up to 10,350 mg/kg, well above international environmental standards. This study presents effective reclamation strategies that can be used in similar situations worldwide. The reclamation allowed this land to be used for the construction of the M1 shopping center while minimizing environmental hazards. The study is based on a case study of the reclamation of this illegal landfill. The methods used in this project included the relocation of approximately 130,000 m³ of hazardous waste to a nearby site previously used for sand mining. Bentonite mats and geotextiles were used to prevent the migration of contaminants into the groundwater. The waste was layered with sand to assist in the structural stabilization of the site. In addition, proper waste segregation and drainage systems were implemented to manage water and prevent contamination. Eight years after the reclamation, post-remediation soil surveys showed significant improvements in soil quality and structural stability. Specifically, the Proctor Compaction Index (I_S) increased from an estimated 0.5–0.7 (for uncontrolled slope) to 0.98, indicating a high degree of compaction and soil stability, while arsenic and chromium levels were reduced by 98.4% and 98.1%, respectively. Reclamation also significantly reduced permeability and settlement rates, further improving the site’s suitability for construction. The cost-benefit analysis showed a cost saving of 37.7% through local waste relocation compared to off-site disposal, highlighting the economic efficiency and environmental benefits. The main conclusions of this study are that land reclamation effectively reduced environmental hazards; innovative solutions, such as bentonite mats, advanced waste sorting, geotextiles, and drainage systems, improved environmental quality; and the Łomianki case serves as a model for sustainable waste management practices. Full article

Deforestation in the municipality of Puerto Guzmán, located in the Colombian Amazon, has increased significantly in recent years with negative consequences for the region’s ecosystems. This paper article aims to explore local perceptions of the causes and actors of deforestation in Puerto Guzmán through a qualitative approach. Semi-structured interviews and documentary review were used as data collection techniques. A total of 25 interviews were conducted with different stakeholders between June and October 2022. ATLAS.ti 9 software was used for data processing. The study found that the main perceived causes of deforestation in Puerto Guzmán are extensive cattle ranchers, illegal mining and illicit crops. The main actors of deforestation include businesspeople and raising cattle in Caquetá, as well as local armed groups involved in illicit crop cultivation and illegal mining, which generate violence and intimidation in the community. Almost all of the actors belong to the local community, with the exception of some members of the armed groups who come from other regions. The various actors interviewed identified education as possible alternative solution and suggested improvements to the illicit crops substitution programmes. It was concluded that there is a loss of trust among the actors living in Puerto Guzmán, particularly in relation to the management of international cooperation funds intended to support efforts to reduce deforestation. The communities are aware of these resources and claim that they belong to them and therefore expect them to be given directly to them. Full article

Gas explosions are the most serious type of accident in coal mines in China. This study analyzed 125 gas explosion accidents that occurred between 2010 and 2020. The results showed that the number of gas explosion accidents and deaths in 2010–2020 was stable and decreasing. The number of larger gas explosion accidents in 2010–2020 is the largest, but the death toll from major accidents was much greater. Coal faces, headings, and roadways are the main locations where gas explosions are initiated. The coal mines in which gas explosions occur in coal faces and headings are mainly “township” enterprises and private mines, all of which engage in illegal operations. The main cause of gas accumulations in roadways is ventilation system failure; these failures can be reduced with improved ventilations system management. The number of gas explosion accidents and related deaths in the Sichuan, Guizhou, and Heilongjiang provinces are very high. The annual change in the frequency of gas explosion accidents, the quarterly distribution of gas explosion accidents, and time during a mining shift when gas explosion accidents occur are closely related to national policies and regulations, company annual production goals, and the mental status of miners, respectively. Full article

The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region. Full article

The Yangtze River Economic Belt in China, a major economic and ecological region, faces critical challenges in the sustainable management of dredged sand, exacerbated by illegal sand mining practices. This study advances the understanding of integrated management models for dredged sand utilization by systematically analyzing six pilot projects through field investigations and theoretical methods. It identifies three novel management models: the traditional government-led model, the integrated “operation + concession” model, and the separated “operation + concession” model. These models provide structured approaches to enhance stakeholder collaboration, streamline resource distribution, and standardize regulatory frameworks. Furthermore, this study underscores the necessity of tailored strategies to align with local conditions, enabling sustainable and resource-efficient practices. By addressing critical gaps in prior research and proposing an actionable framework, this research offers valuable insights for global efforts to mitigate the sand scarcity crisis through innovative sand management. Full article

Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining. Full article