Articles
Geopersia (22287817)15(1)pp. 119-136
The study of karst water potential is one of the most important and attractive studies of this type of distinctive landform. This study has been carried out with the aim of identification of areas of extensive karstification and consequently groundwater resource potential carbonate rocks in Iran considering the role of the chief factors affecting karst water potential. Accordingly, 25 different data layers where interrogated in a GIS platform. Subjective karst map was developed on the basis of what is considered to be a proper combination of these factors. The most important parameters are categorized to three driving factors include chemical, physical, and hydrogeological factors. Thematic map of each parameter was prepared using geographic information system (GIS). Measuring the rate and weight of the maps was performed using analytical hierarchical process (AHP), respectively. The final output map showed different zones of groundwater prospective potential, which was divided into five grades. According to the results, out of the total area of 174,000 km2 of carbonate outcrops in Iran, the highest grade of karst water potential was found in the Kopet-Dagh zone in the northeast. So, the significance of karst formations in the Kepet-Dagh region is at least as great as that of the Zagros and Alborz ranges, if not greater. Validation of karstification potential map was done with the existence and location of springs and karst aquifers in the Kopet-Dagh area. © (2025), (University of Tehran). All rights reserved.
Iranian Journal Of Earth Sciences (2228785X)17(1)
Groundwater inflow into rock tunnels depends on the geologic and hydrological conditions. There is no complete standard method for estimating the exact volumes and locations of groundwater inflow that may be encountered in the rock tunnels. The discharge rate of the water inflow into the tunnel depends on several factors like permeability and groundwater head. Site Groundwater Rating (SGR) system based on initial site investigations to classify tunnel length qualitatively and quantitatively from the point of groundwater seepage hazard view. In this rating system, we score parameters like frequency and aperture of joints, schistosity, crushed zones, karstification, soil permeability, water head above the tunnel, and annual rainfall. According to the SGR method, the conditions of the tunnel with respect to the main parameters of the risk of leakage of underground water and the way it is done are evaluated. But this method needs to be completed. Some parameters must be corrected and even deleted, and some new parameters must be proposed. In this paper, the SGR method has been optimized with respect to new parameters. According to the experiences of the Kerman and Karaj tunnel excavations, the authenticity of the SGR method validations was studied. As a result, the optimized SGR method (SGRm) is introduced. The results of SGRm compared to the results of SGR are closer to the actual results. This method is a new method to estimate the water entrance to the tunnel. © 2025 The Author(s).
Saberinasr, A.,
Kalantari, N.,
Ghelichpour, H.,
Morsali, M. Mine Water and the Environment (16161068)44(1)pp. 30-54
We used hydrogeochemical and isotopic analyses to determine the source of groundwater in the Gohar-Zamin iron mine in south-central Iran. Through three phases of groundwater sample collection from seeps and boreholes, a total of 75 samples were gathered for analysis, including 12 samples containing metals and semi-metals and stable isotopic data (D and 18O), five samples containing 14C and 13C, and another five samples containing 3H data. Statistical analysis, as well as a time series of groundwater levels recorded in boreholes and multi-level vibrating wire piezometers, confirmed the multi-layered nature of the aquifer system, which includes at least three to four distinct aquifers. All the samples were saline and brackish water (EC > 4 mS cm−1), with a predominant sequence of Cl−–SO42−–HCO3−–NO3−, and Na+–Ca2+–Mg2+–K+ for anions and cations, respectively. Conservative tracers (Cl, Br, and B) and stable isotopes demonstrated that the Kheirabad Salt Lake (≈ 13 km north of the mine) is not likely the groundwater source. Radiocarbon and tritium age dating suggest that most of the groundwater in the mining area infiltrated during the Holocene and late Pleistocene epochs (paleowater) rather than being replenished by recent rainfall. However, hydrochemical variations observed in samples collected during the wet season are generally attributed to the mixing of groundwater with surface water infiltrating through fractures around the mine pit. © The Author(s) under exclusive licence to International Mine Water Association 2025.
International Journal of Environmental Science and Technology (17351472)21(2)pp. 1619-1636
The waste collection problem is one of the critical problems in today’s world, and ignoring this issue or the existence of a fault in this system can cause huge costs and damages. The advanced countries in the world are trying to improve the efficiency of their waste collection system with modern methods to solve the challenges of this system. The application of Internet of Things (IoT) and RFID tags is an interesting field of research in urban waste management systems. This study develops three models for urban waste collecting. The ST model is a traditional and static method currently used in many cities. The DSA is a semi-modern model based on greedy algorithms in which RFID tags are installed on garbage bins. The DAIoT model is a modern system working with IoT equipment installed on trucks and waste bins. This model uses a combination of greedy algorithm and harmony search metaheuristics. The main purpose of this study is to schedule the waste collection system and vehicle routing to reduce trucks' gas emissions and empty garbage bins on time. The results on Isfahan city show that compared to the traditional ST model, the DSA model causes a 2% reduction in gas emissions and a 6.7% reduction in the number of required trucks and improves system performance in critical situations. The DAIoT model, as the best model, causes a 33.9% reduction in greenhouse gas emissions and a 60% reduction in the number of trucks compared to the traditional ST model. © 2023, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.
Bulletin of Engineering Geology and the Environment (14359537)80(7)pp. 5725-5742
Water inflow caused by tunneling can have severe impacts on the springs’ discharge rate. If these impacts have not been predicted beforehand, technical, economic, and environmental challenges could occur. While there are a few methods for evaluating the risk of water drawdown, their shortcomings create the need to develop a new one. First, in this research, five main tunneling projects in Iran were studied for evaluating the influence of tunneling on spring’s discharge, and a comprehensive database that contains information on 111 springs located in the vicinity of these tunneling projects was developed. Then, by learning from previously developed methods’ shortcomings and using an appropriate decision analysis method (Analytic Hierarchy Process or AHP), a new model was proposed for evaluating the risk of discharge reduction in springs located in the vicinity of tunneling projects. This new model, named TIS (Tunneling Impacts on Springs), was developed based on four important parameters of a) volume of water inflow toward the tunnel, b) distance between spring and tunnel, c) hydraulic connectivity, and d) aquifer recharge potential. In the next step, using data recorded in the database, TIS values were calculated for each spring, and using suitable statistical methods, the obtained TIS values were classified based on the actual behavior of springs. For using this model in practice, all springs must be checked using a screening process. In this process, according to some limitation criteria (including distance from the tunnel, groundwater condition in tunnel, spring origin), unimportant springs are excluded from the list and only springs with possible influence from tunneling are considered for further assessments. This helps to investigate the in-risk springs more effectively. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
