Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method

Groundwater is an important source of freshwater for domestic, agricultural and industrial uses in Iran. Groundwater quality assessment and environmental evaluation are considered as critical issues in recent years. Intensive human activities have resulted in significant changes in environment leading to serious groundwater contamination. This research proposes a two-part systematic approach to tackle heavy metals contamination problem in Rayen Basin (southeast Iran). The first part consists of determining geochemical characteristics and evaluating groundwater quality through application of water quality index and heavy metal pollution indices (i.e. HPI and MI). The second part includes ranking sampling stations based on heavy metals concentration in groundwater using linear assignment method. Six types of water could be identified according to the dominant cations and anions in samples: Ca–HCO₃, Ca–SO₄, Na–Cl, Na–HCO₃, Na–SO₄ and mixed water type. Calculation of indices revealed that natural and anthropogenic activities are playing a vital role in degrading groundwater quality in the study area. The proposed methodology can help in groundwater resource management and preventative activities by identifying risk factors and recognizing their pollution level. The results of this research provide useful and effective information for water pollution control and management and can be used in environmental studies in order to protect groundwater resources in the future.     

Evaluation of local site effect from microtremor measurements in Babol City,Iran

Every year, numerous casualties and a large deal of financial losses are incurred due to earthquake events. The losses incurred by an earthquake vary depending on local site effect. Therefore, in order to conquer drastic effects of an earthquake, one should evaluate urban districts in terms of the local site effect. One of the methods for evaluating the local site effect is microtremor measurement and analysis. Aiming at evaluation of local site effect across the city of Babol, the study area was gridded and microtremor measurements were performed with an appropriate distribution. The acquired data was analyzed through the horizontal-to-vertical noise ratio (HVNR) method, and fundamental frequency and associated amplitude of the H/V peak were obtained. The results indicate that fundamental frequency of the study area is generally lower than 1.25 Hz, which is acceptably in agreement with the findings of previous studies. Also, in order to constrain and validate the seismostratigraphic model obtained with this method, the results were compared with geotechnical, geological, and seismic data. Comparing the results of different methods, it was observed that the presented geophysical method can successfully determine the values of fundamental frequency across the study area as well as local site effect. Using the data obtained from the analysis of microtremor, a microzonation map of fundamental frequency across the city of Babol was prepared. This map has numerous applications in designing high-rise building and urban development plans.     

Titanium‐in‐biotite thermometry in porphyry copper systems: Challenges to application of the thermometer

Empirical geothermometer dealing with Ti solubility in the Fe‐Mg biotites was originally proposed for biotites in graphitic, peraluminous metapelites containing ilmenite or rutile that equilibrated roughly at 4–6 kbar. Given that biotites are abundant in the porphyry copper systems, this geothermometer has frequently been used for the determination of magmatic–hydrothermal temperatures in the porphyry copper systems. Common associations of porphyry copper deposits (PCDs), that is, low Al content of biotite, biotite chloritization (causes the biotite to become more magnesian and to lose Ti), and biotite formation by amphibole replacement, as well as disequilibrium, local equilibrium, or re‐equilibration of biotites, especially through potassic alteration, may provide significant uncertainty in the temperatures estimated a by Ti‐in‐biotite geothermometer. In addition, besides the calibration range of thermometer for pressure (400–600 MPa), the temperatures of major sulfide precipitation in PCDs (>~400°C) does not fit with the temperature range of thermometer calibration (480–800°C). Worth noting, as confirmed by fluid inclusion data in the Sarkuh PCD, regardless of presence of mineralogical requirements, obtained temperatures of sulfide mineralization using Ti in biotite thermometer could be overestimated. This may be due to the difference between general conditions of sulfide mineralization and calibration range of Ti in the biotite thermometer for pressure and temperature, as well as the metaluminous nature of biotites in PCDs.     

Teleparallel gravity with scalar and vector fields

We consider a cosmological model in which a scalar field is non-minimally coupled to scalar torsion and a vector field through two coupling functions in the framework of teleparallel gravity. The explicit forms of the coupling functions and the scalar field potential are explored, under the assumption that the Lagrangian admits the Noether symmetry in the Friedmann–Lemaître–Robertson–Walker (FLRW) space–time. The existence of such symmetry allows to solve the equations of motion and achieve exact solutions of the scale factor, scalar and vector fields. It is found that the vector field contributes significantly in the accelerating expansion of the universe in the early times, while the scalar field plays an essential role in the late times.     

High-resolution Observations of H$\alpha$ Spectra with a Subtractive Double Pass

High-resolution imaging spectroscopy in solar physics has relied on Fabry–Pérot interferometers (FPIs) in recent years. FPI systems, however, become technically challenging and expensive for telescopes larger than the 1 m class. A conventional slit spectrograph with a diffraction-limited performance over a large field of view (FOV) can be built at much lower cost and effort. It can be converted into an imaging spectro(polari)meter using the concept of a subtractive double pass (SDP). We demonstrate that an SDP system can reach a similar performance as FPI-based systems with a high spatial and moderate spectral resolution across a FOV of \(100^{\prime\prime} \times100^{\prime \prime}\) with a spectral coverage of 1 nm. We use H\(\alpha\) spectra taken with an SDP system at the Dunn Solar Telescope and complementary full-disc data to infer the properties of small-scale superpenumbral filaments. We find that the majority of all filaments end in patches of opposite-polarity fields. The internal fine-structure in the line-core intensity of H\(\alpha\) at spatial scales of about 0\(.\!\!^{\prime \prime }\)5 exceeds that in other parameters such as the line width, indicating small-scale opacity effects in a larger-scale structure with common properties. We conclude that SDP systems in combination with (multi-conjugate) adaptive optics are a valid alternative to FPI systems when high spatial resolution and a large FOV are required. They can also reach a cadence that is comparable to that of FPI systems, while providing a much larger spectral range and a simultaneous multi-line capability.     

Modeling Fate and Transport of Volatile Organic Compounds (VOCs) Inside Sewer Systems

Hazardous waste site investigations have shown that volatile organic compounds (VOCs) can be transported via sewer pipes and migrate into indoor spaces. Despite field data confirming the presence of this exposure pathway, there is lack of context-based numerical models that provide guidance to characterize and predict VOCs concentration in sewer gas at vapor intrusion sites. Particularly, this poses a challenge when assessing and mitigating risks associated with these exposure pathways. Therefore, a numerical model has been developed to simulate the concentration of VOCs in sewer gas in different stages throughout the sewer lines. The developed model considers various input parameters, including temperature, sewer liquid depth, groundwater depth, and sewer construction characteristics to incorporate local and operational conditions. The model's output is verified using field data from a sewer system constructed near a Superfund site. Moreover, a sensitivity analysis was conducted to evaluate the model's response to variation of the external input parameters. To the best of our knowledge, this study is the first attempt to model VOCs concentration in sewer gas, particularly to address vapor intrusion. The developed model can be used as a numerical tool to support the development of sewer assessment guidelines, risk assessment studies, and mitigation strategies.     

Application of neural networks to predict net present value in mining projects

Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R ²?=?0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV.     

Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam,Iran

An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data from Bam city is presented. A feed forward back propagation network was developed employing 45 sets of input data. The input variables in the ANN model are the electrical resistivity, water table as a Boolean value and depth; the output is the mean grain size. To demonstrate the authenticity of this approach, the network predictions are compared with those from interpolation methods and the same data. This comparison shows that the ANN approach performs better results. The predicted and observed mean grain size values were compared and show high correlation coefficients. The ANN approach maps show a high degree of correlation with well data based grain size maps and can therefore be used conservatively to better understand the influence of input parameters on sedimentological predictions.     

Chemistry of the Karst Sarabkalan Spring,Iran, and Controls of PDO and ENSO Climate Indices on It

The Sarabkalan Spring serves as a primary water supply to irrigation and domestic use in the Sirvan Region, Iran. As it has a highly variable discharge, understanding its teleconnections with large-scale climate variability is crucial. In this study, we first characterize the springshed and its corresponding karst aquifer system using genetic algorithm analysis on the spring discharge, water balance calculations, temporal variations of physicochemical parameters, and stable isotopes along with considering its geological settings. Then, the large-scale climate indices teleconnections with precipitation and spring discharge are studied using wavelet analysis. Results reveal that the springshed contains two karst subaquifers resulting from geological and morphological settings. Unlike most developed karst systems in Zagros, which show one peak, the spring has two principal flow peaks over most hydrological years where the second peak occurs over the dry season. It takes ∼99 d (from lag correlation over 2008-2019) and ∼145 d (from δ¹⁸O measurements over 2018-2019) for rain water to reach the Sarabkalan Spring. Moreover, intense precipitations would result in an increase in discharge and a decrease in electrical conductivity, Ca + Mg, HCO₃, SO₄, Cl, ionic strength, and δ¹⁸O of the spring because of the developed karst conduit(s). It is further found that a positive Pacific Decadal Oscillation phase coupled with El Niño causes an increase in both the precipitation and spring discharge, signifying the influence of the atmospheric circulations of the Pacific Ocean on the spring behavior.     

Input Selection Based on Information Theory for Constructing Predictor Models of Solar and Geomagnetic Activity Indices

Various phenomena with solar origin and their mutual dependence must be studied in order to predict behaviors in solar – terrestrial system. Linear statistical methods prevalent in analyzing natural systems may not be able to detect nonlinear dependencies among solar and geomagnetic processes. When relations, whether linear or nonlinear, between indices and their changes over time are revealed, better predictions can be made through appropriate modeling techniques. Selection of nonredundant input variables to build suitable models for prediction of solar and geomagnetic activity is of utmost importance. Mutual information is a tool that is capable of capturing all dependencies for detecting nonlinear relations and selecting the best subset of input variables by means of an applicable algorithm that maximizes information about the output and minimizes the shared information between inputs. High generalization power and improved interpretability of the selected inputs are the consequences of this analysis.