Experimental Investigation of Rheological Behavior and Wax Deposition of Waxy Oil–Disulfide Oil Systems

Natural Resources Research - In this work, we studied the feasibility of using disulfide oil (DSO) as a solvent for wax prevention in pipelines. Several tests were carried out to determine the...     

Column Bioleaching of Arsenic and Heavy Metals from Gold Mine Tailings by Aspergillus fumigatus

A column bioleaching experiment was carried out to compare the effectiveness of the fungus Aspergillus fumigatus to bioleach arsenic (As) and heavy metals from the tailings using two different methods. In the first method, which is named as distribution method (DM), the fungus was distributed in the column by means of vertical and horizontal layers of coarse sand. In the other method, named as surface applied method (SAM), the fungus was cultivated on the surface of the tailings, which was covered with a few centimeters of coarse sand. Results showed that in the DM, oxalic acid production was stimulated and maximum removal of As, Fe, Mn, and Zn was 53, 51, 81, and 62%, respectively. However, Pb removal was low (8%), which might be due to the precipitation of Pb as its oxalates. On the other hand, the maximum removal of As, Fe, Mn, Pb, and Zn were 22, 28, 37, 64, and 34%, respectively, for the SAM. Results of the sequential extraction study showed that the DM was effective in removing the water soluble, exchangeable, carbonate, and Fe/Mn oxide fractions of As, Fe, Mn, and Zn. Our study suggested that A. fumigatus has a potential to be used in remediation of heavy metal contaminated sites. Distributing the fungus throughout the entire tailings columns improved the bioleaching of heavy metals by the fungus.     

Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent

Snow and glacial melt processes are an important part of the Himalayan water balance. Correct quantification of melt runoff processes is necessary to understand the region's vulnerability to climate change. This paper describes in detail an application of conceptual GR4J hydrological model in the Tamor catchment in Eastern Nepal using typical elevation band and degree‐day factor approaches to model Himalayan snow and glacial melt processes. The model aims to provide a simple model that meets most water planning applications. The paper contributes a model conceptualization (GR4JSG) that enables coarse evaluation of modelled snow extents against remotely sensed Moderate Resolution Imaging Spectroradiometer snow extent. Novel aspects include the glacial store in GR4JSG and examination of how the parameters controlling snow and glacial stores correlate with existing parameters of GR4J. The model is calibrated using a Bayesian Monte Carlo Markov Chain method against observed streamflow for one glaciated catchment with reliable data. Evaluation of the modelled streamflow with observed streamflow gave Nash Sutcliffe Efficiency of 0.88 and Percent Bias of <4%. Comparison of the modelled snow extents with Moderate Resolution Imaging Spectroradiometer gave R² of 0.46, with calibration against streamflow only. The contribution of melt runoff to total discharge from the catchment is 14–16% across different experiments. The model is highly sensitive to rainfall and temperature data, which suffer from known problems and biases, for example because of stations being located predominantly in valleys and at lower elevations. Testing of the model in other Himalayan catchments may reveal additional limitations. © 2016 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.     

Assessment of heavy metal contamination in the surficial sediments from the lower Meghna River estuary,Noakhali coast,Bangladesh

Sediment samples were collected from ten selected sites of the lower Meghna River estuary,and six heavy metals were analyzed with Atomic Absorption Spectrophotometry(AAS)to assess the contamination level and the metals’association with sediment grain size.The current results revealed that the mean concentrations of the studied metals were ranked in descending order of iron(Fe)(1.29×103 mg/kg)>zinc(Zn)(42.41 mg/kg)>lead(Pb)(12.48 mg/kg)>chromium(Cr)(10.59 mg/kg)>copper(Cu)(6.22 mg/kg)>cadmium(Cd)(0.28 mg/kg).The geo-accumulation,contamination,and pollution load indexes suggested that the lower Meghna river estuary was not contaminated by Fe,Zn,Pb,Cr,and Cu.The mean size of the sediment ranged from 28.92 to 126.2 mm,and the Pearson correlation coefficient showed a significant association between Fe and Pb(coefficient of determination,r2=0.836;p<0.05),and no significant correlation was found between individual metals and grain size,indicating no or low influence on the metals distribution.     

Uncertainty and Reliability Analysis Applied to Slope Stability: A Case Study From Sungun Copper Mine

Nowadays, there are many new methods for slope stability analysis; including probabilistic methods assessing geotechnical uncertainties to develop safety factors. In this paper, a reliability index analysis for the Sungun copper mine slope stability is evaluated based on three methods of uncertainties consisting Taylor series method, Rosenblueth point estimate method and Monte-Carlo simulation method. Sungun copper mine will be one of the Iran’s biggest mines with final pit’s height of 700 meters. For this study two of its main slopes were assessed, one dipping to the NE (030) and the other to the SE (140). Probability density function of cohesion and angle of friction for the slopes were developed using limit equilibrium methods. These shear strengths were then used to determine the probability density function of safety factor and reliability index using the probabilistic methods. Results of the probabilistic analysis indicate that with ascending values of the uncertainties the reliability index decreases. Furthermore, it was determined that with the Monte Carlo simulation the seed number used has little effect on the reliability index of the safety factor especially with seed numbers in excess of 1200. Variations in the overall reliability index of safety factor were observed between the two slopes and this difference is explained by the differences in complexities of the geology within the cross-section.     

Application of the strain energy to estimate the rock load in squeezing ground condition of Eamzade Hashem tunnel in Iran

Estimation of rock load is a very important issue because the selection of a support system is highly related to this parameter. Several methods are used to estimate this parameter such as experimental, empirical, and numerical methods. This study propose a new empirical method to estimate the rock load in squeezing ground condition using actual collapses data of Emamzade Hashem tunnel of Iran based on the ration of the post-failure residual strain energy to the pre-failure stored strain energy. Prediction of squeezing ground condition in this study is performed based on Jethwa, Singh, and Hoek criterions. Results show that some sections in shale and sandstone of the Shemshak formation are prone to squeezing. Finally, the relation between the rock load and the ratio of the post-failure residual strain energy to the pre-failure stored strain energy, Ψ, in squeezing ground condition is estimated. Based on the statistical analysis, the maximum correlation between both parameters is achieved when Alejano’s equations are used to estimate the drop modulus. As the rock mass behavior changes from elastic–plastic to elastic–brittle, the drop modulus changes from 0 to infinite. The reason is that by increasing the quality of rock mass and reducing the minimum principal stresses, the ratio of post-failure residual strain energy to pre-failure stored strain energy and rock load height (H _p) reduce. So, regression analysis is used to investigate the relation between the rock load height and the ratio of post-failure residual strain energy to pre-failure stored strain energy, and finally, a formulation is presented to determine rock load height based on power function.     

Life cycle greenhouse gas footprint of shale gas: a probabilistic approach

With the increase in natural gas (NG) production in recent years, primarily from shale gas, some sources, including the US Environmental Protection Agency (EPA), have suggested that upstream methane emissions are increasing. Much of the recent controversy has centered on emissions during well drilling, testing, and completion even though emissions downstream of the wellhead are also of concern. The study critically assessed the current state of knowledge about the life cycle GHG footprint of NG, analyzed the assumptions, data and analysis methodologies used in the existing literature. This study comprehensively analyzed the emission of methane from different stage of the life of well for conventional and unconventional NG using the EPA’s revised 2011 estimates as well as other existing literature and publicly available government data. The study proposed a probabilistic model to estimate the range of total GHG footprint of NG with varying probabilities. Through the bottom up approach starting from the well construction to the delivery of NG to the small user and using Monte Carlo simulation, the study identified the critical sources of fugitive emissions from the NG. As expected, emissions from well completion and periodic emissions (e.g. liquid unloading in the case of onshore conventional wells and workovers in the case of unconventional wells) are significant contributors to the overall GHG footprint of NG, and possess large opportunity for reduction. Finally the application of probabilistic model is demonstrated through a case study using the data from the Montney and Horn River shale gas basins in the Northern British Columbia to estimate the range of total GHG footprint of shale gas with varying probabilities. The study found that the GHG footprint of Montney and Horn River wells are much smaller than that of Barnett shale (which is representative of US shale gas) due to strict flaring regulations followed in BC. The study also undercuts the outcome of Howarth et al. (Clim Chang Lett 106:679–690, 2011), which states that the GHG footprint of shale gas is at least 20 % greater than coal.     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...