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.     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

Mineral chemistry of a Cenozoic igneous complex,the Urumieh–Dokhtar magmatic belt,Iran: Petrological implications for the plutonic rocks

The Niyasar plutonic complex, one of the Cenozoic magmatic assemblages in the Urumieh‐Dokhtar magmatic belt, was the subject of detailed petrographic and mineralogical investigations. The Niyasar magmatic complex is composed of Eocene to Oligocene mafic rocks and Miocene granitoids. Eleven samples, representing the major rock units in the Niyasar magmatic complex and contact aureole were chosen for mineral chemical studies and for estimation of the pressure, temperature, and oxygen fugacity conditions of mineral crystallization during emplacement of various magmatic bodies. The analyzed samples are composed of varying proportions of quartz, plagioclase, K‐feldspar, hornblende, biotite, titanite, magnetite, apatite, zircon, garnet, and clinopyroxene. Application of the Al‐in‐hornblende barometer indicates pressures of around 0.2 to 0.4 kbar for the Eocene–Oligocene mafic bodies and around 0.5 to 1.7 kbar for the Miocene granitoids. Hornblende‐plagioclase thermometry yields relatively low temperatures (661–780 °C), which probably reflect late stage re‐equilibration of these minerals. The assemblage titanite–magnetite–quartz as well as hornblende composition were used to constrain the oxygen fugacity and H₂O content during the crystallization of the parent magmas in the Miocene plutons. The results show that the Miocene granitoids crystallized from magmas with relatively high oxygen fugacity and high H₂O content (~5 wt% H₂O). The Miocene granitoids show similar range of oxygen fugacity, H₂O contents and mineral chemical compositions, which indicate a common source for their magmas. Although the crystallization pressures of the Miocene plutons discriminate various categories of plutonic bodies emplaced at depths of about 5.7–6.5 km (Marfioun pluton), about 4.2 km (Ghalhar pluton) and 1.9–2.3 km (Poudalg pluton), they were later uplifted to the same level by vertical displacement of faults. The emplacement depths of the Niyasar plutons suggest that the central part of the Urumieh‐Dokhtar magmatic belt has experienced an uplift rate of ca. 0.25–0.4 mm/yr from the Miocene onwards.     

Development of a mathematical expression for the variation of amorphization phenomenon during intensive milling of minerals

Attempts have been made to develop a mathematical expression on the basis of dislocation theory to describe the effect of intensive milling time on the changes of dislocation density as well as amorphization degree of a mineral substance during intensive milling process. Validity of the proposed expression was verified by the results of experiments performed on a natural chalcopyrite mineral as well as those reported in the literature. It was concluded that the expression satisfied the experimental results with a good accuracy.     

Les Volcans Dôméens du Néogène de la Région de Qom (Iran Central). Essai de Classification de l’Activité Volcanique Dôméenne

Some domean volcanoes built by acidic to intermediate lavas are described. They were intruded, probably in Pliocene, into the neogenic volcano-sedimentary formations of the volcanic Tabriz — Bazman Zone (Central Iran). Some of them are intrusive domes which certainly did not reach the existing topographical surface when emplaced; others are extrusive domes which exhibit some interesting featuresviz.: hypogenous emplacement of the Suleghan dome, typical ring shape of the Dastjerd dome, etc. A provisional classification of domean volcanism in proposed in conclusion.     

Lithostratigraphie et histoire paléozoïque à paléocène des complexes métamorphiques de la région de Muteh, zone de Sanandaj–Sirjan (Iran méridional)Lithostratigraphy and Palaeozoic to Palaeocene history of some metamorphic complexes from Muteh area, Sanandaj–Sirjan zone (southern Iran)

A detailed analysis of metamorphic complexes outcropping in the Muteh area in central Iran leads to establish the regional stratigraphical column, and to propose a Palaeozoic age for the metamorphic protolith that mainly consists of volcano-sedimentary units. ⁴⁰K---⁴⁰Ar ages for minerals suggest the Mesozoic age of the metamorphic amphiboles and the Palaeocene ones for a late or even post metamorphic bimodal magmatism. To cite this article: N. Rachidnejad-Omran et al., C. R. Geoscience 334 (2002) 1185–1191.     

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Development and Characterization of Lignin‐Based Hydrogel for Use in Agricultural Soils: Preliminary Evidence

In arid and semi‐arid regions of the world, agricultural production is greatly limited by water scarcity and inefficient water use. Water‐absorbent hydrogels are a technological solution that can retain soil water for plants. A lignin‐based hydrogel as a natural plant‐based water absorbent is prepared from lignin alkali polymers and poly(ethylene glycol) diglycidyl ether (PEGDGE) in adjusted alkali (NaOH) solution. The maximum swelling capacity of the hydrogel is achieved in 1.5 m NaOH with 0.5 mmol PEGDGE g ${}_{\mathrm{lignin}}^{?1}$. Water swelling capacity is 34 g g ${}_{\mathrm{Hydrogel}}^{?1}$ dry weight of hydrogel in distilled water, which is reduced to 53% and 64% in 0.1 m NaCl and 0.1 m CaCl₂ solution, respectively. Biodegradability and phytotoxicity tests show that 6.5% of the sample mass decomposed after 40 days of incubation in soil solution media and the hydrogel is not phytotoxic to wheat seeds. These findings support the use of the lignin‐based hydrogel as an environmentally friendly additive to promote water retention in dry, saline soils. Due to the limitations of this study, further assessments are needed in order to understand the efficiency of lignin‐based hydrogel application in different soils with different biota.     

Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks

In this paper, three types of artificial neural network (ANN) are employed to prediction and interpretation of pressuremeter test results. First, multi layer perceptron neural network is used. Then, neuro-fuzzy network is employed and finally radial basis function is applied. All applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance in two stages is determined. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models.