Environment
Zaenal Zaenal; Noor Fauzi Isniarno; Delina Mutiara; Sofie Nur’aini; Hasyim Fadhilah; Elfida Moralista; Andrieanto Nurrochman
Abstract
Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards ...
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Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations.
Environment
Debasmita Basu; Smriti Mishra
Abstract
This study presents a comprehensive analysis of community perceptions regarding the impacts of reclamation strategies for abandoned coal mines in India, with a specific focus on the Manikpur Coal Mine. Through a structured survey administered to residents in the vicinity of the mine, the research investigates ...
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This study presents a comprehensive analysis of community perceptions regarding the impacts of reclamation strategies for abandoned coal mines in India, with a specific focus on the Manikpur Coal Mine. Through a structured survey administered to residents in the vicinity of the mine, the research investigates the economic, socio-cultural, and environmental impacts of reclamation efforts. Utilizing Structural Equation Modeling (SEM), the study identifies key factors influencing community perceptions, including the perceived benefits of reclamation, levels of community involvement, and overall satisfaction with mining operations. The findings reveal significant relationships among these factors, such as the positive influence of reclamation availability/requirement (path coefficient = 0.633) on satisfaction and the negative impact of involvement on satisfaction (-0.805). Indirect effects highlight the interplay between constructs, with experience positively influencing involvement (0.673) and satisfaction (0.162) while negatively affecting reclamation availability/requirement (-0.194). Variations in latent variable scores for satisfaction (-1.63 to 3.031) and reclamation availability/requirement (-1.42 to 1.903) underscore the diverse respondent experiences. These insights emphasize the importance of effective community engagement and tailored reclamation strategies. Policy recommendations are provided to enhance the sustainability and effectiveness of reclamation efforts, emphasizing the need for holistic approaches that integrate economic viability, socio-cultural acceptance, and environmental sustainability. The study contributes to the field of mine reclamation by offering valuable insights into resident perceptions and practical guidelines for improving reclamation practices in mining-affected areas.
Debasmita Basu; Smriti Mishra
Abstract
Destination image positioning plays a pivotal protagonist in the accomplishment of mining tourism. By strategically shaping the perception of a mining destination, marketers can entice visitors who are interested in the exceptional experiences and cultural heritage associated with mining. The lack of ...
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Destination image positioning plays a pivotal protagonist in the accomplishment of mining tourism. By strategically shaping the perception of a mining destination, marketers can entice visitors who are interested in the exceptional experiences and cultural heritage associated with mining. The lack of destination image positioning and mining tourism research can hinder the growth and advancement of mining tourism destinations. Without a clear understanding of the unique attributes and market positioning of a mining destination, it becomes challenging to effectively target and attract the right audience. Insufficient research on mining tourism also limits the ability to identify and capitalize on the destination's potential, such as its cultural heritage, environmental sustainability, or adventure offerings. Without a well-defined destination image and research-backed strategies, marketing efforts may fall short of conveying the value and appeal of mining tourism experiences. Therefore, stakeholders and researchers must invest in studying and understanding the market dynamics, visitor preferences, and the prospective welfares that mining tourism can fetch to local economies and communities. This research can inform effective destination image positioning strategies and help unlock the full potential of mining tourism destinations. Therefore, current environmental, social, and economic viewpoints on the sustainability of this type of tourism growth are outlined in a review of the literature in this area for the Indian scenario.
