Comparison of fast elimination of Cr(VI) by alumina nanofiber and alumina nanoporous

The goal of this study is examination of the mixture between adsorption and permeation process for removing chromium (VI) from the water. Two types of supported membranes are developed: The first one which was made by sol–gel method is called nanoporous and the second one which was made by electrospinning is called nanofiber. The sorption capacity of nanoporous and nanofiber is examined in single batch experiments at various pH values, and it is found that maximum chromium removal is observed for both nanoporous and nanofiber at pH 3.5. Adsorption studies illustrated that the Cr(VI) adsorption onto alumina nanoporous and nanofiber is affected by changes in pH, contact time, dosage of adsorbent, concentration of chromium and solution volume. Langmuir and Freundlich isotherms can be used to explain the adsorption equilibria of Cr(VI) onto alumina nanoporous and nanofiber. It was found that balance adsorption data adequate Langmuir isotherm more than Freundlich model. Adsorption kinetics was found to be fitted to pseudo-second order and Weber and Morris model. The output of multiple linear regressions was run for the second-order response surface model implied that the linear agents of pH, sorbent dosage and Cr(VI) concentration are more significant factors. Manufacturing electrospun alumina nanofiber and sol–gel nanoporous with these cheap materials, renewable and fast methods are so important although the removal percentage is significant.     

Intensity measures for the seismic response evaluation of buried steel pipelines under near-field pulse-like ground motions

Ground-motion Intensity Measures (IMs) are used to quantify the strength of ground motions and evaluate the response of structures. IMs act as a link between seismic demand and seismic hazard analysis and therefore, have a key role in performance-based earthquake engineering. Many studies have been carried out on the determination of suitable IMs in terms of efficiency, sufficiency and scaling robustness. The majority of these investigations focused on ordinary structures such as buildings and bridges, and only a few were about buried pipelines. In the current study, the optimal IMs for predicting the seismic demand of continuous buried steel pipelines under near-field pulse-like ground motion records is investigated. Incremental dynamic analysis is performed using twenty ground motion records. Using the results of the regression analysis, the optimality of 23 potential IMs are studied. It is concluded that specific energy density (SED) followed by \(\sqrt {VSI[{\omega _1}(PGD + RM{S_d})]} \) are the optimal IMs based on efficiency, sufficiency and scaling robustness for seismic response evaluation of buried pipelines under near-field ground motions.

     

Assessment of agricultural groundwater users in Iran: a cultural environmental bias

Many environmental problems are rooted in human behavior. This study aimed to explore the causal effect of cultural environmental bias on ‘sustainable behavior’ among agricultural groundwater users in Fars province, Iran, according to Klockner’s comprehensive model. A survey-based research project was conducted to gathering data on the paradigm of environmental psychology. The sample included agricultural groundwater users (n = 296) who were selected at random within a structured sampling regime involving study areas that represent three (higher, medium and lower) bounds of the agricultural-groundwater-vulnerability spectrum. Results showed that the “environment as ductile (EnAD)” variable was a strong determinant of sustainable behavior as it related to groundwater use, and that EnAE had the highest causal effect on the behavior of agricultural groundwater users. The adjusted model explained 41% variance of “groundwater sustainable behavior”. Based on the results, the groundwater sustainable behaviors of agricultural groundwater users were found to be affected by personal and subjective norm variables and that they are influenced by casual effects of the “environment as ductile (EnAD)” variable. The conclusions reflect the Fars agricultural groundwater users’ attitude or worldview on groundwater as an unrecoverable resource; thus, it is necessary that scientific disciplines like hydrogeology and psycho-sociology be considered together in a comprehensive approach for every groundwater study.     

Basin progress: active deformation analysis by tectonostratigraphic elements and geophysical methods on North Anatolian Fault System (Eastern Marmara Region,Turkey)

Natural Hazards - The Northern Branch of the North Anatolian Fault System controls and deforms the Izmit Basin and the Sapanca Lake Basin in the study area. Unlike the Sapanca Lake Basin, the...     

Improving soil moisture accounting and streamflow prediction in SWAT by incorporating a modified time‐dependent Curve Number method

The objective of this study is to incorporate a time‐dependent Soil Moisture Accounting (SMA) based Curve Number method (SMA_CN) in Soil and Water Assessment Tool (SWAT) and compare its performance with the existing CN method in SWAT by simulating the hydrology of two agricultural watersheds in Indiana, USA. Results show that fusion of the SMA_CN method causes decrease in runoff volume and increase in profile soil moisture content, associated with larger groundwater contribution to the streamflow. In addition, the higher amount of moisture in the soil profile slightly elevates the actual evapotranspiration. The SMA‐based SWAT configuration consistently produces improved goodness‐of‐fit scores and less uncertain outputs with respect to streamflow during both calibration and validation. The SMA_CN method exhibits a better match with the observed data for all flow regimes, thereby addressing issues related to peak and low flow predictions by SWAT in many past studies. Comparison of the calibrated model outputs with field‐scale soil moisture observations reveals that the SMA overhauling enables SWAT to represent soil moisture condition more accurately, with better response to the incident rainfall dynamics. While the results from the modification of the CN method in SWAT are promising, more studies including watersheds with various physical and climatic settings are needed to validate the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models

Natural Hazards - On March 25, 2019, widespread flood events occurred across Iran’s provinces and set a new record for socioeconomic losses and casualties. In hindsight, it opened an...     

FEA of Urban Rock Tunnels Under Impact Loading at Targeted Velocity

Geotechnical and Geological Engineering - This paper presents the effect of impact load and weathering of surrounding rockmass on the deformation behavior of urban underground structures. The FEM...     

WASP-121b Secondary Eclipses Revisited Using TESS Observation

Astronomy Reports - We present the detection and characterization of the ultrahot Jupiter WASP-121b ( $${{R}_{p}} \simeq 1.865{\kern 1pt} {{R}_{J}}$$ , $${{M}_{p}} \simeq 1.184{\kern 1pt}...     

Gas-oil gravity drainage mechanism in fractured oil reservoirs: surrogate model development and sensitivity analysis

Computational Geosciences - In this study, a surrogate model is developed based on single porosity modelling approach to predict the gas-oil gravity drainage recovery curves. The single porosity...