The genesis of Mo-Cu deposits and mafic igneous rocks in the Senj area,Alborz magmatic belt,Iran

Mineralogy and Petrology - The geochemical and isotopic investigations were provided on the Upper Eocene Senj mafic intrusion and Mo-Cu mineralization to better understand the tectono-magmatic...     

Development of a new wavelet-based hybrid model to forecast multi-scalar SPEI drought index (case study: Zanjan city,Iran)

Theoretical and Applied Climatology - Drought forecasting plays a vital role in managing drought and reducing its effects on agricultural systems and water resources. In the present study, three...     

Nematode community structure in relation to metals in the southern of Caspian Sea

Spatial distribution and structure of nematode assemblages in coastal sediments of the southern part of the Caspian Sea were studied in relation to environmental factors. By considering metals, organic matter, Shannon diversity index(H), maturity index(MI) and trophic diversity(ITD), ecological quality status of sediment was also determined. Fifteen nematode species belonging to eleven genera were identified at the sampling sites. Average density of nematode inhabiting in sediment of the studied area was 139.78±98.91(ind. per 15.20 cm~2). According to redundancy analysis(RDA), there was high correlation between metals and some species. Based on biological indicators, the studied area had different environmental quality. Generally, chemical and biological indices showed different results while biological indices displayed similar results in more sites.     

Arsenic in the Muteh gold mining district, Isfahan, Iran

Following the appearance of symptoms of arsenic toxicity in the inhabitants of villages in the Muteh gold mining region, central Iran, the concentration of this element in various parts of biogeochemical cycle is investigated. For this purpose, rock, groundwater, soil, plant, livestock hair and wool, and human hair samples are collected and analysed. Total arsenic content ranges from 23 to 2,500?mg/kg in rock samples, 7?C1,061???g/l in water, 12?C232?mg/kg in soil, 0.5?C16?mg/kg in plant samples, 4.10?C5.69?mg/kg in livestock hair and wool, and 0.64?C5.82?mg/kg in human hair. Arsenic concentration in various parts of biogeochemical cycle near the gold deposit in a metamorphic complex, and also close to the gold-processing plant, is very high and decreases exponentially with increasing distance from them. Arsenic concentration in water from a well close to the Muteh gold mine is above 1?mg/L. Arsenic in hair samples taken from local inhabitants is above the recommended levels, and the control samples in Shahre-Kord city. Arsenic concentration is higher in male population and correlates positively with age. It is suggested that arsenic resulting from the decomposition of ore mineral such as orpiment (As₂S₃), realgar (As₂S₂) and arsenopyrite (FeAsS) is responsible for polluting natural resources and the human intake via drinking water and the food chain. Gold mining and processing has undoubtedly enhanced the release of arsenic and intensified the observed adverse effects in Muteh area.     

Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.     

Probabilistic Convective Initiation Nowcasting with Reduced Satellite - NWP Predictors over Iran

Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used.     

Gorgan Bay:a microcosm for study on macrobenthos species-environment relationships in the southeastern Caspian Sea

The relationship between spatial patterns of macrobenthos community characteristics and environmental conditions(salinity, temperature, dissolved oxygen, organic matter content, sand, silt and clay) was investigated throughout the Gorgan Bay in June 2010. Principal components analysis(PCA) based on environmental data separated eastern and western stations. The maximum(4500 ind./m²) and minimum(411 ind./m²) densities were observed at Stas 1 and 6, respectively. Polychaeta was the major group and Streblospio gynobranchiata was dominant species in the bay. According to Distance Based Linear Models results, macrofaunal total density was correlated with silt percentage and salinity and these two factors explaining 64% of the variability while macrofaunal community structure just correlated with salinity(22% total variation). In general, western part of the bay showed the highest number of species and biodiversity while, the highest density was found at Sta. 1 and in the middle part of the bay. Furthermore, relationship between diversity indices and macrobenthic species with measured factors is also discussed. Our results confirm the effect of salinity as an important factor on distribution of macrobenthic fauna in south Caspian brackish waters.     

Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km², respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km², respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km². Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha^?1 year^?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha^?1 year^?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future.     

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.