The variability of ruthenium in chromite from chassignite and olivine‐phyric shergottite meteorites: New insights into the behavior of PGE and sulfur in Martian magmatic systems

The Martian meteorites comprise mantle‐derived mafic to ultramafic rocks that formed in shallow intrusions and/or lava flows. This study reports the first in situ platinum‐group element data on chromite and ulvöspinel from a series of dunitic chassignites and olivine‐phyric shergottites, determined using laser‐ablation ICP‐MS. As recent studies have shown that Ru has strongly contrasting affinities for coexisting sulfide and spinel phases, the precise in situ analysis of this element in spinel can provide important insights into the sulfide saturation history of Martian mantle‐derived melts. The new data reveal distinctive differences between the two meteorite groups. Chromite from the chassignites Northwest Africa 2737 (NWA 2737) and Chassigny contained detectable concentrations of Ru (up to ~160 ppb Ru) in solid solution, whereas chromite and ulvöspinel from the olivine‐phyric shergottites Yamato‐980459 (Y‐980459), Tissint, and Dhofar 019 displayed Ru concentrations consistently below detection limit (<42 ppb). The relatively elevated Ru signatures of chromite from the chassignites suggest a Ru‐rich (~1–4 ppb) parental melt for this meteorite group, which presumably did not experience segregation of immiscible sulfide liquids over the interval of mantle melting, melt ascent, and chromite crystallization. The relatively Ru‐depleted signature of chromite and ulvöspinel from the olivine‐phyric shergottites may be the consequence of relatively lower Ru contents (<1 ppb) in the parental melts, and/or the presence of sulfides during the crystallization of the spinel phases. The results of this study illustrate the significance of platinum‐group element in situ analysis on spinel phases to decipher the sulfide saturation history of magmatic systems.     

Temporal variations of fluoride concentration in Isparta public water system and health impact assessment (SW-Turkey)

Water–rock interaction is one of the prime factors affecting the fluoride contents of surface and groundwater. If fluoride concentration of drinking water has been neglected, excess fluoride can cause serious dental and medical problems on human health, which is well known at Golcuk-Isparta region. In the research area, Egirdir lake, Golcuk lake and surrounding springs have been utilized as drinking water sources. Golcuk lake water and surrounding groundwaters have high fluoride content (1.4–4.6 mg/l), which is above the WHO standards. Fluoride is predominantly supplied by dissolution of fluoride within the fluormicas of volcanics during the circulation of water. Fluoride concentrations of waters have shown variations for dry and rainy seasons depending on the degree of interaction between groundwater and volcanic rocks. It tends to decrease in rainy seasons and increase in dry seasons for all years. In this study, temporal variations and spatial distribution of fluoride concentration in public water system of Isparta were investigated to get benefit using GIS techniques from1990 to 2003 years. Extremely fluoride concentrations were measured in the public water system in 1990 at almost every district of the city. In 2003, fluoride content of the public water system decreased in some district of city due to drinking water has started obtaining from Egirdir lake in 1995. The fluoride contents of Isparta drinking water ought to be modified with suitable mixture of lake waters and groundwater point of view to health impact.     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units

This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closed-form analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers’ physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.

A Fuzzy Rule Based Remedial Priority Ranking System for Contaminated Sites

Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases.     

Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the Senirkent-Uluborlu Basin (Isparta, Turkey)

Aquifer vulnerability has been assessed in the Senirkent-Uluborlu Basin within the Egirdir Lake catchment (Turkey) using the DRASTIC method, based on a geographic information system (GIS). There is widespread agriculture in the basin, and fertilizer (nitrate) and pesticide applications have caused groundwater contamination as a result of leaching. According to hydrogeological data from the study area, surface water and groundwater flow are towards Egirdir Lake. Hence, aquifer vulnerability in the basin should be determined by water quality in Egirdir Lake. DRASTIC layers were prepared using data such as rainfall, groundwater level, aquifer type, and hydraulic conductivity. These data were obtained from hydrogeological investigations and literature. A regional-scale aquifer-vulnerability map of the basin was prepared using overlay analysis with the aid of GIS. A DRASTIC vulnerability map, verified by nitrate in groundwater data, shows that the defined areas are compatible with land-use data. It is concluded that 20.8% of the basin area is highly vulnerable and urgent pollution-preventions measures should be taken for every kind of relevant activity within the whole basin.     

Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks

Measuring unconfined compressive strength (UCS) using standard laboratory tests is a difficult, expensive, and time-consuming task, especially with highly fractured, highly porous, weak rock. This study aims to establish predictive models for the UCS of carbonate rocks formed in various facies and exposed in Tasonu Quarry, northeast Turkey. The objective is to effectively select the explanatory variables from among a subset of the dataset containing total porosity, effective porosity, slake durability index, and P-wave velocity in dry samples and in the solid part of samples. This was based on the adjusted determination coefficient and root-mean-square error values of different linear regression analysis combinations using all possible regression methods. A prediction model for UCS was prepared using generalized regression neural networks (GRNNs). GRNNs were preferred over feed-forward back-propagation algorithm-based neural networks because there is no problem of local minimums in GRNNs. In this study, as a result of all possible regression analyses, alternative combinations involving one, two, and three inputs were used. Through comparison of GRNN performance with that of feed-forward back-propagation algorithm-based neural networks, it is demonstrated that GRNN is a good potential candidate for prediction of the unconfined compressive strength of carbonate rocks. From an examination of other applications of UCS prediction models, it is apparent that the GRNN technique has not been used thus far in this field. This study provides a clear and practical summary of the possible impact of alternative neural network types in UCS prediction.     

An integration of GIS and remote sensing in groundwater investigations: A case study in Burdur, Turkey

The importance of groundwater is growing based on an increasing need and decreasing spring discharges in the Burdur area. Remote Sensing and the Geographic Information System (GIS) have been used for investigation of springs, which are an important groundwater source. The chemical composition of groundwater is not of drinking water quality in Burdur city and water in the Burdur residential area is being obtained from the Cine plain.The purpose of this study was to investigate new water sources by using remote sensing and GIS methods. Geology, lineament and land use maps of the research area were prepared using the Landsat TM satellite image composed of different analyses on the TM 7–4-1 band. In addition, contours, creeks, roads and springs were digitized using a topographic map of 1/100,000 scale to produce a drainage density map. A groundwater potential map was produced which integrated thematic maps, such as annual rainfall, geology, lineament density, land use, topography, slope and drainage density. According to this investigation, the surrounding villages of Askeriye, Bugduz, Gelincik, Taskap and Kayaalt were determined to be important from the point of view of groundwater potential in the research area.