Search Results (10)

Identifying land use mix (LUM) in urban areas is challenging, often requiring extensive human intervention and fieldwork. Accurate classification of LUM is crucial for various disciplines, including urban planning, urban economics, and public health. This study addresses this need by employing Voronoi triangulation and an entropy-based LUM formula using point-based geospatial data collected from publicly available sources. The methodology was tested in two distinct urban settings: Ankara and Kadıköy. Ankara, the capital city, provides a large and diverse urban environment, while Kadıköy, a district in Istanbul known for its dynamic urban life, offers a contrasting scenario. Results were analyzed concerning local spatial autocorrelation and point of interest (POI) intensity. The comparative analysis demonstrated that the approach performs well across different urban contexts, with improved results observed in Kadıköy due to its higher density of mixed-use development. Specifically, we managed to identify mixed land use areas with an accuracy of up to 78% and an F1-score of 83% in urban regions. These findings highlight the robustness and applicability of our approach in diverse urban environments, providing valuable insights for city planners and policymakers in optimizing the allocation of urban resources and enhancing land use efficiency. Full article

Due to climate change risks, the public, regulators, and investors require solid actions to minimize the greenhouse gas (GHG) emissions of mineral extraction and metals production. The mining sector considers alternatives to reduce its carbon footprint by transforming the business and adopting new technologies into operations. Given the capital intensity, technical characteristics, and business structure involved, a shift in the mining industry necessitates impartial insights into the trade-offs and risks. Considering the low-carbon transition trade-offs and risks in mining, this study presents the application of system dynamics modeling (SDM) in mining projects to analyze the impact of decarbonization alternatives with respect to carbon footprint and costs. A system dynamics model of an open-pit copper mine is developed to quantify greenhouse gas (GHG) emissions, as well as capital and operational costs, during the project life cycle. The change in GHG emissions in the business-as-usual scenario with diesel equipment haulage versus the alternative scenario with electric overland conveyor haulage is compared concerning GHG emissions and associated costs. The results unequivocally demonstrated that electrifying material mobility offers significant decarbonization in open-pit mining if the on-site electricity has a low emission factor. The findings also indicate that the substantial cost difference between electrification and diesel alternatives is another major obstacle to implementing electrification in an open-pit copper mine. This research proves that implementing SDM in the mining industry can offer impartial insights into decision-making and enable a thorough evaluation of options using quantitative criteria. It effectively assesses and communicates the trade-offs and risks of transitioning to low-carbon alternatives because it analyzes project variables quantitatively and holistically and is easy to run. Full article

Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites—Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals. Full article

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration. Full article

Detecting the formation of explosive methane–air mixtures in a longwall face is still a challenging task. Even though atmospheric monitoring systems and computational fluid dynamics modeling are utilized to inspect methane concentrations, they are not sufficient as a warning system in critical regions, such as near cutting drums, in real-time. The long short-term memory algorithm has been established to predict and manage explosive gas zones in longwall mining operations before explosions happen. This paper introduces a novel methodology with an artificial intelligence algorithm, namely, modified long short-term memory, to detect the formation of explosive methane–air mixtures in the longwall face and identify possible explosive gas accumulations prior to them becoming hazards. The algorithm was trained and tested based on CFD model outputs for six locations of the shearer for similar locations and operational conditions of the cutting machine. Results show that the algorithm can predict explosive gas zones in 3D with overall accuracies ranging from 87.9% to 92.4% for different settings; output predictions took two minutes after measurement data were fed into the algorithm. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions are possible using the proposed algorithm compared to computational fluid dynamics and atmospheric monitoring systems. Full article

Responsible mine closure and repurposing are key to contributing to sustainable development by ensuring successful environmental rehabilitation and reducing socioeconomic risks. However, mine closure has primarily focused on remediation and rehabilitation of mined lands with limited consideration of stakeholder perspectives and the broader social, economic, and cultural impacts of closure. In this paper, we use stakeholder input to evaluate and compare three different repurposing alternatives for the tailings dam area of a mine in the state of Colorado, USA, which is expected to close in the next twenty years. By using multi-attribute utility theory (MAUT), we determine which alternative better reflects stakeholder preferences and results in the most economically, environmentally, and socially sustainable outcome. Our results show that although stakeholder groups have different ideas about what constitutes sustainable development in the context of mine closure and repurposing, it is possible to identify to what extent different scenarios can address these perspectives. We argue that integrating stakeholder views into mine closure design and repurposing can lead to more responsible and sustainable mine closure that is unique to a particular setting and stakeholder needs, and we provide a methodology that mining companies may use to understand stakeholder priorities and preferences. Full article

In this paper, we propose a system dynamics (SD) model to examine the dynamics of an informal artisanal and small-scale gold mining (ASGM) supply chain that has interactions with the illegal gold supply chain in the Amazon rainforest region, Madre de Dios (MdD), Peru. In order to examine the system under climate impacts and validate the model, we run it under a flood scenario, which is one of the main climate impacts that causes disruption in mining activities. Our findings suggest that the dynamics of informal mines are highly affected by the illegal mercury supply, fuel supply, and availability of workers. In addition, the model under the flood scenario suggests that any external variable that could directly affect fuel and mercury supply would result in a disruption of informal and illegal gold production. Full article

Sustainability reporting is one of the tools that contribute to incorporating sustainable development in the design of extractive operations (i.e., “Design for Sustainability”), and the demand for sustainability reports is increasing due to the increased focus on sustainable development and sustainable financing efforts. The extractive industries are believed to have unique strengths to contribute to achieving the Sustainable Development Goals. Nonetheless, companies are expected to be transparent and accountable not only to investors but to all stakeholders, including communities, suppliers, clients, employees, and governments. Therefore, extractive industries require effective sustainability accounting and reporting to transition and contribute to sustainable development. Through a data-driven approach, this paper examines the scope and consistency of sustainability indicators used in the sustainability reports of eight oil and gas and eight mining companies from 2012 to 2018. Through content analysis and relevant statistical methods, we analyze the ways in which companies reported on their contributions to sustainable development, with a focus on indicators used and trends over time both within each industry and between industries. We demonstrate that extractive industries’ sustainability reporting practices are not consistent over time and that internal issues are better represented than external issues, in particular transportation and supply chain issues. Furthermore, while there are similar trends across the industries in terms of social and environmental indicator reporting, there are significant differences in economic reporting. We conclude that although both industries have established sustainability reporting practices, there are trends that demonstrate what companies are focusing on more, as well as areas for improvement. We see this as an initial step for conceptualizing how these industries can more objectively, consistently, and effectively assess and contribute to sustainable development. Full article

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field. Full article

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management. Full article