Determining the Gravitational Gradient Tensor Using Satellite-Altimetry Observations over the Persian Gulf

With the advent of satellite altimetry in 1973, new scientific applications became available in oceanography, climatology, and marine geosciences. Moreover, satellite altimetry provides a significant source of information facilitated in the geoid determination with a high accuracy and spatial resolution. The information from this approach is a sufficient alternate for marine gravity data in the high-frequency modeling of the marine gravity field quantities. The gravity gradient tensor, consisting of the second-order partial derivatives of the gravity potential, provides more localized information than gravity measurements. Marine gravity observations always carry a high noise level due to environmental effects. Moreover, it is not possible to model the high frequencies of the Earth's gravity field in a global scale using these observations. In this article, we introduce a novel approach for a determination of the gravity gradient tensor at sea level using satellite altimetry. Two numerical techniques are applied and compared for this purpose. In particular, we facilitate the radial basis functions (RBFs) and the harmonic splines. As a case study, the gravitational gradient tensor is determined and results presented in the Persian Gulf. Validation of results reveals that the solution of the harmonic spline approach has a better agreement with a theoretical zero-value of the trace of the Marussi gravitational gradient tensor. However, the data-adaptive technique in the RBF approach allows more efficient selection of the parameters and 3-D configuration of RBFs compared to a fixed parameterization by the harmonic splines.     

Heavy Metal Concentrations in the Intertidal Gastropod Nerita lineata and Their Relationships to Those in Its Habitats： A Case Study in Dumai Coastal Waters

Concentrations of heavy metals in the intertidal gastropod Nerita lineata collected from 4 sampling stations in the intertidal zone of Dumai coastal waters have been analyzed and correlated with the respective metal concentrations in their habitat. The objective of this study was to evaluate the possible relationships between heavy metal concentrations in the N.lineata with metal concentrations in their habitat, which include seawater, algal mat, suspended particulate matter, and surface sediment. The results showed that heavy metal concentrations in the sediment, N.lineata, suspended particulate matter, algal mat and surface seawater were found to be higher in stations closed to Dumai city center which has more industrial and anthropogenic activities. The mean concentrations of Cd, Cu, Pb, Zn, Ni and Fe in the N.lineata collected from Dumai intertidal area were 4.14 μg/g, 5.90 μg/g, 44.43 μg/g, 3.74 μg/g, 20.73 μg/g, 24.91 μg/g in shell; 4.16 μg/g, 7.31 μg/g, 51.78 μg/g, 17.63 μg/g, 23.52 μg/g, 30.60 μg/g in operculum and 0.71 μg/g, 15.09 μg/g, 9.41 μg/g, 94.42 μg/g, 5.10 μg/g, 398.24 μg/g dry weight in the total soft tissue, respectively. Cd, Pb and Ni concentrations of parts of the N.lineata were in the order: that of operculum>shell>soft tissue whilst Cu, Zn and Fe concentrations of parts of the N.lineata in the order: that of soft tissue>operculum>shell. This suggested that N.lineata could be used as biomonitoring agent for Cu, Zn and Fe (soft tissues) and for Cd, Pb and Ni (operculum). However, the shell had significant correlations (P<0.05) with metal concentrations in the sediment, algal mat, suspended particulate matter and seawater. Bioconcentration factor (BCF) values of the shell also revealed greater capacity for non-essential metals bioaccumulation from all environmental phases. This might suggest that the shell of N.lineata could be better used as biomonitoring agent for heavy metal pollution.     

Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques

Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R² = 0.97, ANFIS R² = 0.92 and Marciano R² = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data.     

Quantification of seismic potential failure modes in concrete dams

A comprehensive framework for potential failure modes (PFM) identification and quantification of concrete dams subjected to seismic excitation is presented. A quantifiable indicator of PFM is presented in the context of both linear and nonlinear analyses. As an illustrative example, a thin arch dam subjected to a set of ground motions at different seismic intensity levels is investigated and corresponding PFM quantified. An outcome of this analysis is the probabilistic‐based correlation between linear and nonlinear analyses and identification of the optimal intensity measure parameter. This study, is an adaptation and extension of well‐accepted procedures defined by the performance‐based earthquake engineering paradigm in buildings. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing flow paths in a karst aquifer based on multiple dye tracing tests using stochastic simulation and the MODFLOW-CFP code

Karst systems show high spatial variability of hydraulic parameters over small distances and this makes their modeling a difficult task with several uncertainties. Interconnections of fractures have a major role on the transport of groundwater, but many of the stochastic methods in use do not have the capability to reproduce these complex structures. A methodology is presented for the quantification of tortuosity using the single normal equation simulation (SNESIM) algorithm and a groundwater flow model. A training image was produced based on the statistical parameters of fractures and then used in the simulation process. The SNESIM algorithm was used to generate 75 realizations of the four classes of fractures in a karst aquifer in Iran. The results from six dye tracing tests were used to assign hydraulic conductivity values to each class of fractures. In the next step, the MODFLOW-CFP and MODPATH codes were consecutively implemented to compute the groundwater flow paths. The 9,000 flow paths obtained from the MODPATH code were further analyzed to calculate the tortuosity factor. Finally, the hydraulic conductivity values calculated from the dye tracing experiments were refined using the actual flow paths of groundwater. The key outcomes of this research are: (1) a methodology for the quantification of tortuosity; (2) hydraulic conductivities, that are incorrectly estimated (biased low) with empirical equations that assume Darcian (laminar) flow with parallel rather than tortuous streamlines; and (3) an understanding of the scale-dependence and non-normal distributions of tortuosity.     

Biosorption of 4-chlorophenol by dried anaerobic digested sludge: artificial neural network modeling,equilibrium isotherm,and kinetic study

In this study, dried anaerobic digested sludge (DADS) was utilized to remove 4-chlorophenol (4-CP) from aqueous solutions. Batch biosorption experiments were carried out to investigate the effects of physicochemical parameters such as pH, contact time, biosorbent dosage, and initial concentration. Artificial neural network (ANN) was then used to predict the removal efficiency of the process. The comparison between predicted and experimental results provided a high degree of determination coefficient (R² = 0.98), indicating that the model could predict the biosorption efficiency with reasonable accuracy. Biosorption data were successfully described by the Freundlich isotherm and pseudofirst-order model. The Weber–Morris kinetic model indicated that intraparticle diffusion was not the only rate-controlling step, and other mechanisms may be involved in the biosorption process. The optimum pH was detected to be 3 for DADS. By increasing contact time and biosorbent dosage, the removal efficiency of 4-CP increased. Also, a decreasing trend was observed when initial concentrations were increased. The findings suggested that the results predicted by ANN are very close to the experimental values, and DADS as an available adsorbent can efficiently remove 4-CP from aqueous solutions.     

An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios

Remotely sensed image analysis using spectral-spatial information plays a key role in modern remote sensing applications. This article presents a new semi-automatic framework for spectral-spatial classification of hyperspectral images. The proposed framework benefits from a combination of pixel-based and object-based classification scenarios in which the main parameters are adaptively tuned. In order to reduce the complexity of the method, an unsupervised band selection technique is used as well. Meanwhile, the wavelet thresholding is applied in order to smooth the selected bands. The classification results after applying the proposed method to well-known standard hyperspectral datasets are better than those of the most of the other state-of-the-art approaches. As an example, the overall classification accuracy achieved by applying the proposed semi-automatic spectral-spatial classification framework to the Salinas dataset is more than 99% for 10% training samples per class. Moreover, the vital parameters are adaptively set in our approach.     

Removal of cadmium and humic acid from aqueous solutions using surface modified nanozeolite A

The sorption of cadmium and humic acids from aqueous solutions using surface-modified nanozeolite A has been investigated under various examination conditions. The morphology of untreated and treated nanozeolite was studied under scanning electron microscope and transmission electron microscope. Isotherms of cadmium adsorption onto surface-modified nanozeolite A were studied at different pH, solid to liquid ratio, adsorbate concentration and interaction time. Kinetic and equilibrium studies were conducted and the equilibrium data have been analyzed using Langmuir and Freundlich isotherm models. The study revealed that experimental results were in agreement with the Freundlich model. The Langmuir monolayer adsorption capacity was found to be 1666.67 g cadmium and 6.75 g humic acid per gram of modified nanozeolite A, which is higher than that of reported value for other zeolites. The sorption ability was enhanced by surface modification and reduction in size and enabled the zeolite to adsorb cadmium. The adsorption of cadmium and humic acid on nanozeolite was found to be the highest at pH 6 and 3, respectively. Results showed that solid to liquid ratio and pH are the most important factors for cadmium and humic acid removal, respectively. Effect of competitive ions was studied and results showed that there is no competition between cadmium and humic acid sorption and presence of these ions.