The effects of human activities and different land-use on trace element pollution in urban topsoil of Isfahan (Iran)

The excessive input of trace elements into urban soil has become one of the most important concerns in industrial and crowded cities all over the world. The contamination of urban soils can affect the health of people living in urban areas, and the surrounding ecosystems. Current study was conducted to assess the effects of human activities as well as different land-use on accumulation of trace elements in urban topsoil and also identify the potential risks to human health in Isfahan (Iran). A total of 95 topsoil samples were taken from different localities of Isfahan City and analyzed for Zn, Pb, Cu and Cr using the atomic absorption spectrophotometric method. Pollution index (PI) was calculated for each trace element to identify the rate of trace element accumulation with respect to the background values. Land-use map and geochemical maps were also created for evaluating of spatial distribution of pollution index and trace elements’ concentration in the studied area. Overlapping the concentrations’ map and land-use map revealed that the highest values of pollution index and trace elements’ concentration were located in central part of the city and highways with a great vehicle traffic load and also in the vicinity of industrial factories that increased potential health hazards to the local community. On the other hand, lowest values of trace elements were located in green-lands with strict vehicle traffic laws. These results indicated that different land-use and human activities have affected quality of urban topsoil of Isfahan resulting in great apprehensions regarding public health in crowded parts of the city.     

A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean

Abstract

The Pettitt test is a non-parametric test that has been used in a number of hydroclimatological studies to detect abrupt changes in the mean of the distribution of the variable of interest. This test is based on the Mann-Whitney two-sample test (rank-based test), and allows the detection of a single shift at an unknown point in time. This test is often used to detect shifts in extremes because of the lack of distributional assumptions. However, the downside of not specifying a distribution is that the Pettitt test may be inefficient in detecting breaks when dealing with extremes. Here we adopt a Monte Carlo approach to examine the sensitivity of the Pettitt test in detecting shifts in the mean under different conditions (location of the break within the series, magnitude of the shift, record length, level of variability in the data, extreme vs non-extreme records, and pre-assigned significance level). These simulation results show that the sensitivity of this test in detecting abrupt changes increases with the increase in the magnitude of the shift and record length. The number of detections is higher when the time series represents the central part of the distribution (e.g. changes in the time series of medians), while the skill decreases as we move toward either low or high extremes (e.g. changes in the time series of maxima). Furthermore, the number of detections decreases as the variability in the data increases. Finally, abrupt changes are more easily detected when they occur toward the center of the time series.

Editor D. Koutsoyiannis Associate editor K. Hamed     

Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study

This study compares formal Bayesian inference to the informal generalized likelihood uncertainty estimation (GLUE) approach for uncertainty-based calibration of rainfall-runoff models in a multi-criteria context. Bayesian inference is accomplished through Markov Chain Monte Carlo (MCMC) sampling based on an auto-regressive multi-criteria likelihood formulation. Non-converged MCMC sampling is also considered as an alternative method. These methods are compared along multiple comparative measures calculated over the calibration and validation periods of two case studies. Results demonstrate that there can be considerable differences in hydrograph prediction intervals generated by formal and informal strategies for uncertainty-based multi-criteria calibration. Also, the formal approach generates definitely preferable validation period results compared to GLUE (i.e., tighter prediction intervals that show higher reliability) considering identical computational budgets. Moreover, non-converged MCMC (based on the standard Gelman–Rubin metric) performance is reasonably consistent with those given by a formal and fully-converged Bayesian approach even though fully-converged results requires significantly larger number of samples (model evaluations) for the two case studies. Therefore, research to define alternative and more practical convergence criteria for MCMC applications to computationally intensive hydrologic models may be warranted.     

Investigating the capability of deformation rate analysis method in stress estimation: a case study of water conveyance tunnel of Gotvand Dam

Knowledge of the magnitude and orientation of the initial in situ stress of rock mass in underground spaces in mining, construction, and oil projects are so vital; hence, putting it aside could not only cost a lot rather incur some irrecoverable damage. Various methods are available to estimate in situ stress in rock mass. However, the most commonly used one, i.e., hydraulic fracturing (HF) method is considered expensive and time consuming. As a matter of fact, laboratory methods based on drilled “core” have become prevalent these days considering them simple, cheap, and quick. Taking into account one such procedure, i.e., deformation rate analysis (DRA), the current research tries to review the DRA capability in determining the magnitude of initial in situ stress is in different parts of stress–strain curve. Further, an investigation was made about the usage of DRA method for both brittle and ductile rocks. To compare the DRA and hydraulic fracturing methods in in situ stress measurement, the water conveyance tunnel of Gotvand Dam was selected as a case study. The DRA tests were conducted on core samples prepared from blocks of tuff (as brittle) and soft sandstones (as ductile) from shallow quarry. The results show that the DRA method is suitable for all types of intact rock and that this could easily estimate in situ stress values. A comparison between in situ stress values obtained by DRA and those of HF method show the feasibility of geotechnical project, simplicity, speed, and low cost.     

The middle Eocene high-K magmatism in Eastern Iran Magmatic Belt: constraints from U-Pb zircon geochronology and Sr-Nd isotopic ratios

ABSTRACT

Intrusive rocks are well-exposed in the south Birjand around the Koudakan is herein compared to previously studied outcrops along the middle Eocene to late Oligocene Eastern Iran Magmatic Belt. This pluton is composed mainly of monzonite, quartz-monzonite, and granite with high-K calc-alkaline to shoshonitic affinities. The U-Pb zircon geochronology from monzonite and quartz-monzonite reveals the crystallization ages of 40.96 ± 0.48 to 38.78 ± 0.78 Ma (Bartonian). The monzonite, quartz-monzonite, and granite rocks show similar REEs and trace element patterns, as well as limited variations in εNd_(i) and ⁸⁷Sr/⁸⁶Sr_(i) ratio, suggesting that they are a comagmatic intrusive suite. The chondrite and primitive mantle normalized rare earth and trace element patterns show enrichment in the light rare earth elements, K, Rb, Cs, Pb, Th, and U and depletion in heavy rare earth elements, Nb, Zr, and Ti. The εNd_(i) and ⁸⁷Sr/⁸⁶Sr_(i) values range from +1.32 to +1.68 and 0.7044 to 0.7047, respectively, identical to island-arc basalt composition. The whole-rock Nd model age (T_DM) for the intrusive rocks range between 0.69 and 0.73 Ga. These geochemical and isotopic signatures indicate a subduction-related sub-continental lithospheric mantle source for these rocks. Our new geochemical, isotopic, and geochronological studies integrated with previously published data indicate that the middle Eocene to late Oligocene magmatism in eastern Iran was formed in a post-collisional tectonic environment. We suggest the northeastward subduction of the Neo-Tethys ocean beneath the Lut block and the eastward subduction of the Sistan ocean beneath the Afghan block caused mantle wedge to be metasomatized by slab components. At a later stage, a collision between the Lut and Afghan blocks was accompanied by the lithospheric delamination, and the subsequent asthenospheric upwelling led to the melting of the metasomatized sub-continental lithospheric mantle and the generation of middle Eocene to late Oligocene magmatism in the Eastern Iran Magmatic Belt.     

System identification and modeling of a dynamically tested and gradually damaged 10‐story reinforced concrete building

This paper discusses the dynamic tests, system identification, and modeling of a 10‐story reinforced concrete building. Six infill walls were demolished in 3 stages during the tests to introduce damage. In each damage stage, dynamic tests were conducted by using an eccentric‐mass shaker. Accelerometers were installed to record the torsional and translational responses of the building to the induced excitation, as well as its ambient vibration. The modal properties in all damage states are identified using 2 operational modal analysis methods that can capture the effect of the wall demolition. The modal identification is facilitated by a finite element model of the building. In turn, the model is validated through the comparison of the numerically and experimentally obtained modal parameters. The validated model is used in a parametric study to estimate the influence of structural and nonstructural elements on the dynamic properties of the building and to assess the validity of commonly used empirical formulas found in building codes. Issues related to the applicability and feasibility of system identification on complex structures, as well as considerations for the development of accurate, yet efficient, finite element models are also discussed.     

A step beyond susceptibility: an adaptation of risk framework for monetary risk estimation of gully erosion

Natural Hazards - In an effort to improve the previous gully susceptibility assessments in Iran, we attempted to conglomerate the notions of susceptibility, vulnerability, and exposure associated...     

Multiple Classifier Systems for Hyperspectral Remote Sensing Data Classification

One of the most widely used outputs of remote sensing technology is Hyperspectral image. This large amount of information can increase classification accuracy. But at the same time, conventional classification techniques are facing the problem of statistical estimation in high-dimensional space. Recently in remote sensing, support vector machines (SVMs) have shown very suitable performance in classifying high dimensionality problem. Another strategy that has recently been used in remote sensing is multiple classifier system (MCS). It can also improve classification accuracy by combining different classifier methods or by a diversity of the same classifier. This paper aims to classify a Hyperspectral data using the most common methods of multiple classifier systems i.e. adaboost and bagging and a MCS based on SVM. The data used in the paper is an AVIRIS data with 224 spectral bands. The final results show the high capability of SVMs and MCSs in classifying high dimensionality data.     

Evaluating the efficacy of SVMs,BNs, ANNs and ANFIS in wave height prediction

Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.     

Strength and Compressibility Characteristics of a Soft Clay Subjected to Ground Treatment

The compressibility properties, undrained shear strength, and stress history are essential for reliable calculations of settlement and bearing capacity of soft soils. However, it is sometimes a challenge to determine representative parameters for very soft and high plasticity clays, which are often found in Brazilian coastal areas. In this study an extensive site investigation was planned aiming to interpret the behaviour of a trial embankment on stabilized soft ground. The site investigation was carried out in a test area located in the west of city of Rio de Janeiro, consisted of three clusters in which standard penetration tests, vane shear tests, and piezocone tests (CPTu) were performed. A number of correlations was developed and compared with empirical equations in order to verify their reliability. The results of these tests made it possible to define geotechnical parameters of the soft clay to use in the numerical and analytical computations of the embankment on reinforced ground.