Journal of Degraded and Mining Lands Management 2024-07-01T05:21:58+00:00 Editorial Team Open Journal Systems <p><strong>p-ISSN : <a title="ISSN Printed Version" href="" target="_blank" rel="noopener">2339-076X</a> | e-ISSN : <a title="ISSN Electronic Version" href="" target="_blank" rel="noopener">2502-2458</a></strong></p> <p><strong>Journal of Degraded and Mining Lands Management</strong> is managed by the Soil Department, Faculty of Agriculture, Brawijaya University, and International Research Centre for the Management of Degraded and Mining Lands (<a title="Profile IRC-MEDMIND" href="" target="_blank" rel="noopener"><strong>IRC-MEDMIND</strong></a>), a research collaboration between Brawijaya University, Mataram University, Massey University, and Institute of Geochemistry-Chinese Academy of Sciences.</p> <p>Papers dealing with results of original research and critical reviews on aspects directed to the management of degraded and mining lands covering landscape topography, soil and water quality, biogeochemistry, ecosystem structure and function, and environmental, economic, social, and health impacts are welcome. Journal of Degraded and Mining Lands Management is published in four issues every year, generally published in <strong>January</strong>,<strong> April</strong>, <strong>July,</strong> and <strong>October</strong>.</p> <p>Welcome to, the online submission and editorial system of the Journal of Degraded and Mining Lands Management. To submit an article, go to <a href="">Online Submissions</a>. New authors (for the first time in this journal) intending to submit articles for publication may contact the editor for free registration. If authors have any difficulty using the online submission system, please kindly contact the editor via this email: <a href=""></a>.</p> Harnessing hyperaccumulator (Brassica oleracea var. alboglabra) extract for green synthesis of nickel oxide nanoparticles: A prospective route for post-phytoremediation 2024-07-01T05:21:43+00:00 Abd Mujahid Hamdan Zahratul Maulida Syafrina Sari Lubis Arif Sardi Rhyan Prayuddy Reksamunandar Khairun Nisah Jamaludin Malik <p>Even though phytoremediation is considered a green technology for remediating heavy metals, there are some problems with the application of this technology, particularly when it comes to managing the biomass that is used. So, processing biomass needs to be given a lot of attention. This study outlined the utilization of extracts obtained from the hyperaccumulator plant <em>Brassica oleracea</em> var. <em>alboglabra</em> to synthesize nickel oxide nanoparticles. Subsequently, the nanoparticle underwent testing to determine its suitability as an absorbent for heavy metals, specifically lead, as well as its efficacy as an antifungal agent against <em>Fusarium</em> sp. strain. The characterization of nickel oxide nanoparticles involved several measurements, such as scanning electron microscopy analysis, high- and low-resolution transmission electron microscopy, energy dispersive spectroscopy, X-ray diffraction, and hysteresis curve acquisition. The research findings indicate that the extract from hyperaccumulators can be utilized for the synthesis of NiO, which exhibits an absorption capacity exceeding 98% and serves as an efficient antifungal agent against <em>Fusarium</em> sp. pathogens. The approach utilized in this study not only prioritizes "green" and sustainability factors but also takes into account the economic aspects associated with the items being manufactured. The research has important implications in two areas. Firstly, it demonstrates the utilization of natural resources (<em>B. oleracea var. alboglabra</em>) in the production of nickel oxide, which serves as a safer and more eco-friendly substitute for dangerous chemicals. Furthermore, it aids in the advancement of novel techniques for effectively managing biomass hyperaccumulators.</p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 Journal of Degraded and Mining Lands Management Content of heavy metals in soils of Bidoup Nui Ba National Park (Southern Vietnam) 2024-07-01T05:21:46+00:00 Cam Nhung Pham Yaroslav Lebedev Anna Drygval Roman Gorbunov Tatiana Gorbunova Andrey Kuznetsov Svetlana Kuznetsova Dang Hoi Nguyen Vladimir Tabunshchik <p>The study of technogenic pollution of soils with heavy metals (HM) is an essential task for ecology. The analysis of the content of HMs in the park's soils shows the degree of pollution and the sources of its occurrence. The study of the elemental composition of soils is an objective method for assessing the state of the ecosystem. To determine the current state of heavy metal contamination in forest soils, the concentrations of their total forms were analyzed. Heavy metals, including Zn, Pb, Cr, Cu, Hg, Cd, and As, were found in the study area. In addition, the threat of contamination with Cd and As has been identified. The calculation of the total pollution coefficient allows us to assess the level of pollution for the dry season (Zc = 18.45-28.24, average 22.45) as average (moderately hazardous) and for the wet season (Zc = 0.01-5.11, average 1.96) as permissible. This indicates an unfavorable environmental situation. The content of heavy metals in soils depends on the season. Observations show that at the end of the wet season, the concentration of heavy metals decreases, while it increases in the period after the dry season. </p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 Journal of Degraded and Mining Lands Management Understanding the knowledge of Mogpog residents about heavy metal pollution due to mining and its associated health risk 2024-07-01T05:21:51+00:00 Ronnel C. Nolos Janice B. Sevilla-Nastor Jessica D. Villanueva-Peyraube Marisa J. Sobremisana <p>The municipality of Mogpog in the Philippines was one of the severely hit areas during the 1993 mining disaster in the province of Marinduque. After three (3) decades, the aftermath of the disaster still lingers in the municipality and even in the whole province. This study was conducted to assess the relationship between the social demographics of the residents of Mogpog and their knowledge about heavy metal (HM) pollution and its associated health risks. A cross-sectional survey was conducted among the 314 residents of Mogpog. Six (6) social demographics were considered, such as age, sex, marital status, highest education attainment, monthly household income, and whether the respondents were government employees or not. Results of the binomial logistic regression analysis showed that the social demographics affecting the respondents’ knowledge about HM pollution were marital status and monthly household income (significant at the 0.05 level). Married individuals may have larger and more diverse social networks, which could expose them to a wider range of information, including environmental issues. On the other hand, those with lower incomes may have limited access to formal education or information resources, which could result in lower environmental awareness. The results underscore the need for specific interventions and educational initiatives to enhance the understanding of the adverse health impacts associated with HM pollution among residents in Mogpog</p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 Journal of Degraded and Mining Lands Management Biodegradation of sodium lauryl ether sulfate (SLES) contamination by Pseudomonas aeruginosa isolates 2024-07-01T05:21:55+00:00 Hussein Ali Awadh AL-Zamili Ithar Kamil Al-Mayaly <p>Sodium lauryl ether sulfate (SLES) is a surfactant commonly used in the formulation of detergents, which is typically disposed of in wastewater treatment plants. The current study describes the effectiveness of bacteria isolated from Iraqi wastewater to remove SLES. 16S rRNA genetic analysis revealed that this strain is <em>Pseudomonas aeruginosa</em>. Three temperatures (30, 35, and 40<sup>o</sup>C) and pH values (5,7, and 9) were chosen for this study, and three concentrations of SLES (25, 50, and 100 mg/L) were used. The SLES anionic surfactant showed that the best biodegradation by <em>Pseudomonas aeruginosa</em> was at a temperature of 30<sup>o</sup>C and both pH 7 and 9, while the removal percentages for them were 98.44% and 96.36%, respectively, at 25 mg/L of SLES. The outcomes of this study revealed the potential and significance of SLES removal in actual effluents by aerobic biodegradation. The ability of this bacterium to degrade SLES makes the bacterium an important tool for bioremediation.</p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 Journal of Degraded and Mining Lands Management A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation 2024-07-01T05:21:58+00:00 Gangamma Hediyalad K Ashoka Govardhan Hegade Pratibha Ganapati Gaonkar Azizkhan F Pathan Pratibhaa R Malagatti <p>Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning algorithms. Initially, the current status of land degradation in India is presented, along with a brief discussion on the overview of widely used factors, evaluation parameters, and algorithms used. Consequently, merits and demerits related to machine learning-based land degradation identification are presented. Additionally, solutions are prescribed in order to reduce existing problems in the detection of land degradation. Since one of the major objectives is to explore the future perspectives of machine learning-based land degradation detection, areas including the application of remote sensing, mapping, optimum features, and algorithms have been broadly discussed. Finally, based on a critical evaluation of existing related studies, the architecture of the machine learning-based desertification process has been proposed. This technology can fulfill the research challenges in the detection of land degradation and computation difficulties in the development of models for the detection of land degradation.</p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 Journal of Degraded and Mining Lands Management