Managing saline water discharges from mining operations is a global environmental challenge. Measuring the location and extent of surface efflorescence can indicate solute movement before changes in electrical conductivity (EC) are detected in waterways. We hypothesised through the use of a case study that ground-based reflectance spectrometry and airborne hyperspectral (450-2500 nm) analysis of surface efflorescence could be a rapid method for monitoring large regions of the surrounding environment, including downstream of remote mines. X-ray diffraction and X-ray fluorescence were used to determine mineralogy and elemental composition of surface salts around a uranium mine. Salt samples were found to be mixtures of magnesium sulfate. The reflectance of field spectra varied depending on the hydration of the mineral, mainly hexahydrite and starkeyite. A constrained energy minimisation technique was used to match the field reflectance spectra to the airborne data. Airborne matches were confirmed at the field sampling sites and surrounds. Salts were also detected at lower matches at mine water irrigation areas where excess mine water had previously been applied. Hence, hyperspectral remote sensing is a potentially rapid and sensitive method for mapping magnesium sulfates over large areas in operating and rehabilitated mines. It was successfully demonstrated as a tool for monitoring and assessment of efflorescence as a result of saline processes.
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http://dx.doi.org/10.1016/j.scitotenv.2018.05.396 | DOI Listing |
Plant Biotechnol J
December 2024
School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
Tomato fruit ripening is a complex developmental process that is important for fruit quality and shelf life. Many factors, including ethylene and several key transcription factors, have been shown to play important roles in the regulation of tomato fruit ripening. However, our understanding of the regulation of tomato fruit ripening is still limited.
View Article and Find Full Text PDFPhys Chem Chem Phys
November 2024
Centre of Excellence for Energy and Environmental Studies, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat 131039, Haryana, India.
This study introduces a novel approach to synthesizing magnesium oxide (MgO) nanoparticles through the use of (tulsi seed) extract combined with the thermal polymerization of MgO-doped graphitic carbon nitride (MgCN) nanocomposites. The nanocomposites were prepared at varying MgO concentrations (0.5 mM, 1.
View Article and Find Full Text PDFSci Rep
October 2024
Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous, Syria.
This study addresses the critical need for effective groundwater (GW) management in Muzaffarabad, Pakistan, amidst challenges posed by rapid urbanization and population growth. By integrating Support Vector Machine (SVM) and Weight of Evidence (WOE) techniques, this study aimed to delineate GW potential zones and assess water quality. This study fills the gap in applying advanced machine learning and geostatistical methods for accurate GW potential mapping.
View Article and Find Full Text PDFJ Environ Manage
November 2024
Geosciences, Environment and Geomatics Laboratory (GEG), Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir, Morocco; MARE-Marine and Environmental Sciences Centre - Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal; Laboratory for Sustainable Innovation and Applied Research, Universiapolis-International University of Agadir, Agadir, Morocco. Electronic address:
In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. Additionally, we employed the ReliefF and Shapley Additive exPlanations (SHAP) methods to elucidate the underlying factors driving water quality. We first estimated the water quality index (WQI) to model seven water quality parameters: total hardness (TH), pH, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), calcium (Ca), magnesium (Mg).
View Article and Find Full Text PDFHeliyon
September 2024
Department of Agriculture, Food & Science, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada.
Spatial variability in soil pH is a major contributor to within-field variations in soil fertility and crop productivity. An improved understanding of the spatial variability of soil pH within agricultural fields is required to determine liming requirements for precision farming. This study with the use of proximal sensors, firstly assessed the spatial pattern of soil pH and how it can be used to determine site-specific, spatially variable lime requirements.
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