Sorption of uranyl and arsenate on SiO2, Al2O3, TiO2 and FeOOH

Migration of uranium and arsenic in aquatic environments is often controlled by sorption on minerals present along the water flow path. To investigate the sorption behaviour, batch experiments were conducted for uranium and arsenic as single components and also solutions containing both uranium and arsenic in the presence of SiO₂, Al₂O₃, TiO₂ and FeOOH at a pH ranging from 3 to 9. In solutions containing only U(VI) or As(V) with the minerals, the sorption of U(VI) was low at acidic pH range and increases with increasing pH, whereas As(V) showed opposite sorption behaviour to Al₂O₃, TiO₂ and FeOOH from acidic pH range to alkaline condition. For the As(V)–SiO₂ system, the sorption was low for almost all pH. Sorption of U(VI) and As(V) on SiO₂ and FeOOH is almost similar in solutions containing either U(VI) or As(V) separately, or both together. In the U(VI)–As(V)–Al₂O₃ system, a significant retardation in uranyl sorption and an enhancement in arsenate sorption on Al₂O₃ were observed for a wide range of pH. The sorption behaviour of U(VI) and As(V) was changed when Al₂O₃ was replaced by TiO₂, where an increase in sorption was observed for both elements. The sorption behaviour of uranyl and arsenate in the U(VI)–As(V)–TiO₂ system gives evidence for the formation of uranyl–arsenate complexes. The change in sorption retardation/enhancement of U(VI) and As(V) could be explained by the formation of uranyl–arsenate complexes or due to the competitive sorption between uranyl and arsenate species.     

Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.     

An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm

The challenge of obtaining training data for supervised classifications of satellite images has led researchers to unsupervised algorithms, i.e. cluster analysis. Numerous researches have been conducted to improve quality and decrease uncertainty of results of this analysis. This study proposes a hybrid cost function as well as a hybrid clustering algorithm-Artificial Bee Colony optimization approach for the clustering of high-resolution satellite images. In order to evaluate viability of the proposed methodology, it is compared to some other classic clustering algorithms such as modified K-Means, K-Medoids, Fuzzy C-Means, and Kernel-based Fuzzy C-Means methods over three different study areas selected from a WorldView-2 satellite image. The Shannon entropy technique, Kappa coefficient, compactness, and separation criteria are used as quality and uncertainty indicators for the evaluation. The results of the study show that, compared to other methods, the hybrid algorithm obtained from the proposed cost function, Kernel-based Fuzzy C-Means method, and ABC algorithm provide clustering capabilities of higher quality and lower uncertainty levels.     

Impact of local site conditions on portfolio earthquake loss estimation for different building types

This article presents a sensitivity analysis investigating the impact of using high-resolution site conditions databases in portfolio earthquake loss estimation. This article also estimates the effects of variability in the site condition databases on probabilistic earthquake loss ratios and their geographical pattern with respect to structural characteristics of different building types. To perform the earthquake loss estimation here, the OpenQuake software developed by Global Earthquake Model is implemented in Clemson University’s supercomputer. The probabilistic event-based risk analysis is employed considering several notional portfolios of different building types in the San Francisco area as the inventory exposure. This analysis produces the stochastic event sets worth for 10,000 years including almost 8000 synthetically simulated earthquakes. Then, the ground motion prediction equations are used to calculate the ground motion per event and incorporate the effect of five site conditions, on amplifying or de-amplifying the ground motions on notional building exposure locations. Notional buildings are used to account for various building characteristics in conformance with the building taxonomy represented in HAZUS software. The HAZUS damage functions are applied to model the vulnerability of various structural types of buildings. Finally, the 50-year average mean loss and probabilistic loss for multiple values for probability of exceedance (2, 10, 20, and 40%) in 50 years are calculated, and the impact of different site condition databases on portfolio loss ratios is investigated for different structural types and heights of buildings. The results show the aggregated and geographical variation of loss and loss ratio throughout the region for various site conditions. Comparing the aggregated loss and loss ratio, while considering different databases, represents normalized differences that are limited to 6% for all building taxonomy with various heights and for all PoEs. However, site-specific loss ratio errors are significantly greater and in some cases are more than 20%.     

Quantitative analysis of earthquake fatalities: case of Iran

Earthquakes in Iran are major and chronic disasters, but there is a seemingly downward trend in the number of lives lost from earthquake disasters in the past five decades. This paper particularly examines whether factors such as urbanization, literacy rate, wealth, and retrofitting measures have contributed to the declining trend of fatalities over past decades. Data records of 1960–2010 have been used to demonstrate the fatalities trend and a shorter series of 1990–2010 has been used to carry out a statistical analysis due to limited availability of information on retrofitting practices in the country. Regression models run in two stages. The first stage consists of normalized fatalities of Iran’s earthquakes, regressed on urbanization, wealth, and retrofitting. The second stage involves the measure of retrofit as the dependent variable regressed over urbanization and/or wealth as explanatory variables. The resulted regression models clearly explain the importance of retrofitting measure in saving lives in earthquakes disasters, as well as providing economic advantages and robust environment to the population. The study provides significant guidance for public policy. Undoubtedly, retrofitting, started in the country from 1990, has decreased the number of deaths; nonetheless, the measure is found to be mostly enforced in the cities and not in rural areas. Decision makers can use the results of this study to prioritize retrofit in rural areas, more than ever, and as much as possible.     

Garnetization as a ground preparation process for copper mineralization: evidence from the Mazraeh skarn deposit, Iran

The Mazraeh Cu–Fe skarn deposit, NW Iran is the result of the intrusion of an Oligocene–Miocene granitic pluton into Cretaceous calcareous rocks. The pluton ranges in composition from monzonite to quartz monzonite, monzogranite, tonalite and granodiorite with I-type, calc-alkaline, and weakly peraluminous characteristics. The Mazraeh pluton was emplaced in a volcanic arc setting in an active continental margin at a depth of ~8 km. Pyroxene skarn, garnet skarn, and epidote skarn zones were formed during the intrusive phase. The garnet skarn developed as exoskarn and endoskarn from the calcareous wall rocks and the pluton, respectively, prior to mineralization. Garnet skarn from the exoskarn zone is identified by relict layering inherited from the precursor calcareous lithologies. Mass balance calculation of garnet skarn in the endoskarn zone indicates that hydrothermal fluids originating from the cooling magma introduced Si, Fe, Mn, Ca, Mg, P, Ag, Cu, Zn, La, Pb, Cd, Mo, and Y. The main mass loss in the garnet skarn was due to destruction of feldspars in the Mazraeh plutonic rocks and leaching of K₂O and Na₂O. Released Ca has been fixed in the andraditic garnet. Garnetization of the Mazraeh pluton was accompanied by mass and volume increase. The magnitude of these changes depends mainly on the degree of alteration and composition of the precursor. The brittle behavior of the endoskarn zone was increased due to formation of massive garnet which subsequently fractured. These fractures not only facilitated movement of hydrothermal fluids but also provided new locations for Cu mineralization. Therefore locating strongly garnetized zones may be a vector to ore in skarn deposits.