Experimental study of the effect of material and arrangement of electrodes and voltage on the electro-remediation of saturated clays containing chloride and sulfate ions

Electro-remediation process (ERP) is used for dewatering and decontaminating of the soil and improving its mechanical properties. In this study, the effect of voltage, electrode materials, and their arrangement on ERP were studied. The saturated clayey soil was remolded in four boxes at a dry density of 16.2 kN/m³. The process was performed in three stages. The chemical tests at the end of ERP indicated that the absorption of chloride ions in the anode may differ from 89 to 642% due to electro-migration phenomenon. The pH of the soil decreases in anode zone and increases towards the cathode zone. Up to 92% of the sulfate ions were transported and accumulated in the anode. However, in highly contaminated soil, the absorption ratio is 5% in the cathode due to the domination of electro-osmosis process. It was indicated that the electro-osmosis permeability coefficient (K_e) depends on the applied voltage, the soil type, and temperature and its reduction versus time is inversely related to applied voltage. Also, sulfate ions reduced by 74% in the anode due to a change in absorption direction. The results showed that the optimum configuration is linear along with a voltage of 18 V.     

4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station,case study: northwest of Iran

Iran enjoys a variety of climatological conditions. Moreover, numerical weather prediction (NWP) models are not assimilated with the meteorological data in Iran, the country suffering from poor spatial and temporal resolution of radiosonde measurements. These facts make modeling of troposphere impossible using the measurements and NWP. On the other hand, the global positioning system (GPS) has been emerged as a valuable tool for modeling and remote sensing of Earth’s atmosphere. This research is the first attempt to address the tropospheric wet refractivity modeling by GPS measurements in Iran. Changes of topography in the study area are taken into account. As a leading work, virtual reference stations (VRS) are used to fix the rank deficiency of the problem. The model space resolution matrix is used to achieve the optimum spatial resolution of the tomographic model and the optimum number of VRS stations. The accuracy of the developed model (KNTU1) is investigated by deploying radiosonde measurements.     

Integration between morphometric parameters,hydrologic model,and geo-informatics techniques for estimating WADI runoff (case study WADI HALYAH—Saudi Arabia)

In arid and semiarid areas, the only surface and groundwater recharge source is the runoff generated through flash floods. Lack of hydrological data in such areas makes runoff estimation extremely complicated. Flash floods are considered catastrophic phenomena posing a major hazardous threat to cities, villages, and their infrastructures. The objective of this study is to assess the flash flood hazard and runoff in Wadi Halyah and its sub-basins. Integration of morphometric parameters, geo-informatics, and hydrological models has been done to overcome the challenge of scarcity of data.Advanced Spaceborne Thermal Emission and Reflection (ASTER) data was used to prepare a digital elevation model (DEM) with 30-m resolution, and geographical information system (GIS) was used in the evaluation of network, geometry, texture, and relief features of the morphometric parameters. Thirty-eight morphometric parameters were estimated and have been linked together for producing nine effective parameters for evaluation of the flash flood hazard in the study basin.Flash flood hazard in Wadi Halyah and its sub-basins was identified and grouped into three classes depending on nine effective parameters directly influencing the flood prone areas. Calculated runoff volume of Wadi Halyah ranges from 26.7 × 10⁶ to 111.4 × 10⁶ m³ with an inundation area of 15 and 27 km² at return periods of 5 and 100 years, respectively. Mathematical relationships among rainfall depth, runoff volume, infiltration losses, and rainfall excess demonstrate a strong directly proportional relationships with correlation coefficient of about 0.99.     

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects

In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.     

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.     

Thresholds of land cover to control runoff and soil loss

This research focused on the determination of land cover thresholds that have a significant impact on runoff generation and soil loss at the pedon scale. For this purpose, six erosion micro-plots were set up on grassland and shrubland types of rangeland in the northeast of Iran, and the amounts of vegetation cover, litter, runoff and soil loss on them were measured. A factorial statistical analysis was carried out on the completely randomized design using land cover and rainfall factors. The results show that the effect of rainfall on soil loss and runoff was greater than that of land cover. Also, the effect of land cover on soil loss was greater than that on runoff generation. Furthermore, two specific thresholds were identified: the first was from 10 to 30% of landcover and the second from 50 to 70%.     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...     

Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production

Natural Resources Research - Offshore oil and gas reservoirs comprise a significant portion of the world’s reserve base, and their development is expected to help close a potential gap in the...     

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.     

Discriminating Weathering Degree by Integrating Optical Sensor and SAR Satellite Images for Potential Mapping of Groundwater Resources in Basement Aquifers of Semiarid Regions

Natural Resources Research - Unlike in coastal and sedimentary basins, regional-scale exploration of groundwater resources using only geophysical methods is costlier in consolidated rocks such as...