Sequential extraction and leaching characteristics of heavy metals in abandoned tungsten mine tailings sediments

The chemical speciation of potentially toxic elements (As, Cd, Cu, Pb, and Zn) in the contaminated soils and sulfides-rich tailings sediments of an abandoned tungsten mine in Korea was evaluated by conducting modified BCR sequential extraction tests. Kinetic and static batch leaching tests were also conducted to evaluate the potential release of As and other heavy metals by acidic rain water and the leaching behaviors of these heavy metals. The major sources of the elements were As-, Zn- and Pb-bearing sulfides, Pb carbonates (i.e., cerussite), and Pb sulfates (i.e., anglesite). The biggest pollutant fraction in these soil and tailing samples consists of metals bound to the oxidizable host phase, which can be released into the environment if conditions become oxidative, and/or to residual fractions. No significant difference in total element concentrations was observed between the tailings sediments and contaminated soils. For both sample types, almost no changes occurred in the mobility of As and the other heavy metals at 7 days, but the mobility increased afterwards until the end of the tests at 30 days, regardless of the initial pH. However, the mobility was approximately 5–10 times higher at initial pH 1.0 than at initial pHs of 3.0 and 5.0. The leached amounts of all the heavy metal contents were higher from tailings sediments than from contaminated soils at pH > 3.0, but were lower at pH < 3.0 except for As. Results of this study suggest that further dissolution of heavy metals from soil and tailing samples may occur during extended rainfall, resulting in a serious threat to surface and groundwater in the mine area.     

Quantifying fracture geometry with X-ray tomography: Technique of Iterative Local Thresholding (TILT) for 3D image segmentation

This paper presents a new method—the Technique of Iterative Local Thresholding (TILT)—for processing 3D X-ray computed tomography (xCT) images for visualization and quantification of rock fractures. The TILT method includes the following advancements. First, custom masks are generated by a fracture-dilation procedure, which significantly amplifies the fracture signal on the intensity histogram used for local thresholding. Second, TILT is particularly well suited for fracture characterization in granular rocks because the multi-scale Hessian fracture (MHF) filter has been incorporated to distinguish fractures from pores in the rock matrix. Third, TILT wraps the thresholding and fracture isolation steps in an optimized iterative routine for binary segmentation, minimizing human intervention and enabling automated processing of large 3D datasets. As an illustrative example, we applied TILT to 3D xCT images of reacted and unreacted fractured limestone cores. Other segmentation methods were also applied to provide insights regarding variability in image processing. The results show that TILT significantly enhanced separability of grayscale intensities, outperformed the other methods in automation, and was successful in isolating fractures from the porous rock matrix. Because the other methods are more likely to misclassify fracture edges as void and/or have limited capacity in distinguishing fractures from pores, those methods estimated larger fracture volumes (up to 80 %), surface areas (up to 60 %), and roughness (up to a factor of 2). These differences in fracture geometry would lead to significant disparities in hydraulic permeability predictions, as determined by 2D flow simulations.     

An orthotropic interface damage model for simulating drying processes in soils

The study of drying process in soils has received an increased attention in the last few years. This is very complex phenomenon that generally leads to the formation and propagation of desiccation cracks in the soil mass. In recent engineering applications, high aspect ratio elements have proved to be well suited to tackle this type of problem using finite elements. However, the modeling of interfaces between materials with orthotropic properties that generally exist in this type of problem using standard (isotropic) constitutive model is very complex and challenging in terms of the mesh generation, leading to very fine meshes that are intensive CPU demanding. A novel orthotropic interface mechanical model based on damage mechanics and capable of dealing with interfaces between materials in which the strength depends on the direction of analysis is proposed in this paper. The complete mathematical formulation is presented together with the algorithm suggested for its numerical implementation. Some simple yet challenging synthetic benchmarks are analyzed to explore the model capabilities. Laboratory tests using different textures at the contact surface between materials were conducted to evaluate the strengths of the interface in different directions. These experiments were then used to validate the proposed model. Finally, the approach is applied to simulate an actual desiccation test involving an orthotropic contact surface. In all the application cases the performance of the model was very satisfactory.     

Potential for localized groundwater contamination in a porous pavement parking lot setting in Rhode Island

The control of polluted surface runoff and the assessment of possible impacts on groundwater is a concern at the local and regional scale. On this background, a study investigates possible impacts of organic and inorganic pollutants (including bacteria) originating from a permeable asphalt parking lot on the water quality immediately beneath it. The functioning of the permeable pavement, including clogging and restricted vertical percolation, was also evaluated. Four nested sample ports (shallow and deep) were installed below low- and high-traffic areas, including one port outside the parking lot. At least initially there was a good hydraulic connection between the parking surface and the shallow sample ports. The presence of a geotextile layer at the base of the parking lot structure, however, was identified in lab tests as one factor restricting vertical percolation to the deeper ports. Clogging of the permeable surface was most pronounced in heavy traffic areas and below snow pile storage areas. Corroborated by high electric conductivity and chloride measurements, sand brought in by cars during winter was the principal cause for clogging. No bacteria or BOD were found in percolating water. Polycyclic aromatic hydrocarbons (PAH) were present at concentrations near minimum detection limit. Nutrients (nitrate and phosphate) were being leached into the ground via the permeable parking lot surface at annual flux rates of 0.45–0.84 g/m²/year. A multi-species tracer test demonstrated a retention capacity of the permeable parking lot structure of >90% for metals and 27% for nutrients, respectively.     

Ingestion of <Emphasis Type="Italic">Brachionus plicatilis</Emphasis> under different microalgae conditions

The effects of four microalgae,Chlorella vulgaris,Platymonas helgolandicavar,Isochrysis galbana,and Nitzschia closterium on the grazing and filtering rates of the marine rotifer,Brachionus plicatilis,were evaluated under laboratory conditions.The grazing rates in separate cultures of the four microalga were as follows:C.vulgaris > P.helgolandicavar > I.galbana > N.closterium.However,the filtering rates occurred in the following order:P.helgolandicavar > N.closterium > C.vulgaris > I.galbana.A mixed diets ex...     

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Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

Geochemical processes in a Mediterranean Lake: a high-resolution study of the last 4,000 years in Zoñar Lake,southern Spain

High-resolution geochemical analysis of a 6-m-long sediment core from Zoñar Lake, southern Spain, provides a detailed characterization of major changes in lake and watershed processes during the last 4,000 years. Geochemical variables were used as paleolimnological indicators and complement Zoñar Lakes’s paleoenvironmental reconstruction based on sedimentological and biological proxies, which define periods of increasing allochthonous input to the lake and periods of dominant autochthonous sedimentation. Chemical ratios identify periods of endogenic carbonate formation (higher Ca/Al, Sr/Al and Ba/Al ratios), evaporite precipitation (higher S/Al, Sr/Al ratios), and anoxic conditions (higher Mo/Al, U/Th ratios and Eu anomaly). Higher productivity is marked by elevated organic carbon content and carbonate precipitation (Mg/Ca). Hydrological reconstruction for Zoñar Lake was based on sedimentological, mineralogical and biological proxies, and shows that lower lake levels are characterized by Sr-rich sediments (a brackish lake with aragonite) and S-rich sediments (a saline lake with gypsum), while higher lake levels are characterized by sediments enriched in elements associated with alumino-silicates (Al, K, Ti, Fe, trace and rare earth elements), reflecting fresher conditions. Geochemical indicators also mark periods of higher detrital input to the lake related to human activity in the watershed: (1) during the Iberian Roman Humid Period (650 BC–AD 300), around the onset of the Little Ice Age (AD 1400), during the relatively drier Post-Roman and Middle Ages (AD 800–1400), and over the last 50 years, due to mechanized farming practices. Heavy metal enrichment in the sediments (Cu and Ni) suggests intensification of human activities during the Iberian Roman Period, and the use of fertilizers during the last 50 years.     

Impacts of climate change and human activities on the aboveground production in alpine grasslands: a case study of the source region of the Yellow River,China

Changes in aboveground net primary productivity (ANPP) in alpine grasslands are the consequence of climate change and human activities, but it is difficult to disentangle their relative contributions. Based on monthly remote-sensed vegetation index and meteorological data during the period 1982–2010, we analysed the long-term variation of annual ANPP in the source region of the Yellow River and quantified the effects of climate and human activities including grazing on ANPP variability, using the Carnegie–Ames–Stanford Approach (CASA) model and the ANPP-based residual trend analysis method. Our results suggested that ANPP increased in 80% of alpine grasslands. Areas with negative changes in ANPP were found mainly in the eastern portion of the region, accounting for 0.3% of alpine grasslands. ANPP was positively correlated with the monthly mean temperature from June to September and the sunshine duration in September at a regional scale. Moreover, ANPP was negatively correlated with the total livestock numbers. Using the residual trend analysis method, we demonstrated that climate and human activities accounted for 76.6 and 23.4%, respectively, of the variability in ANPP for the entire study region in 1982–2010. We concluded that climate change alleviated climatic constraints, in particular temperature limitations and sunshine duration, resulting in a significant increase in ANPP. Overgrazing was supposed to be the primary driver for grassland degradation in the eastern region. Our study has implications for grassland management and its sustainability to minimize the risk of grassland degradation and desertification processes in geo-ecologically and socially important regions such as the study region in China.     

油气资源评价的多参数约束改进油气田(藏)规模序列法及其应用

油气田(藏)规模序列法是油气资源评价常用的方法之一,其优点是不但可以给出总的资源量,而且还可以给出各油气田(藏)的规模和序列,不足是其预测结果带有多解性。在多年的资评工作中,形成了一套求取规模序列中关键参数的科学方法:依据其他方法(成因法等)求取的资源量来约束规模序列法求取的地质资源量;依据高勘探区地质资源量与最大单一油气田(藏)规模统计关系来约束评价区最大单一油气田(藏)规模;根据预测单元可能存在的圈闭数(包含隐蔽圈闭)来限制预测的油气田(藏)个数。通过采用总量约束、最大单一规模约束和油气田(藏)个数约束,获得的结果克服了原方法存在的一些问题,使评价结果更科学,更符合地质实际。以该方法在黄河口凹陷等地区的应用实例证明了评价结果与勘探认识吻合较好。