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Leila Gholami Abdulvahed Khaledi Darvishan Ataollah Kavian 《Arabian Journal of Geosciences》2016,9(19):729
The soil conditioner in processes of soil conservation is important especially in heavily eroded areas. Because in this study done in Educational and Research Forest Watershed of Tarbiat Modares University, north of Iran, the experiments created four treatments of control and different wood chips with rates of 0.5, 1, and 1.5 kg m?2, by rainfall simulation in rainfall intensity of 60 mm h?1, and plot scale of 1 m2 on changing ponding time, runoff coefficient, sediment concentration, and soil loss. The results showed that the average change ponding time in control treatment and wood chip treatments with rates of 0.5, 1, and 1.5 kg m?2 were 4.25, 7.48, 11.63, and 12.45 min. Also, the average change runoff coefficient in control treatment and wood chip treatments with rates of 0.5, 1, and 1.5 kg m?2 were 50.03, 26.27, 15.28, and 13.17 %. The results also indicated that the wood chips could decrease average soil loss with the rates of ?52.15, ?82.18, and ?89.35 % compared with control treatment for 0.5, 1, and 1.5 kg m?2 of wood chips, respectively. The one-way ANOVA results showed that the runoff coefficient, sediment concentration, and soil loss decreased with increasing wood chip amount, and the effect of conservation treatment was significant on study variables (R 2 = 0.99). But, the ponding time increased with increasing wood chip amount, and this effect was significant on study variables (R 2 = 0.99). 相似文献
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Use of magnetic measures to assess soil redistribution following deforestation in hilly region 总被引：1，自引：0，他引：1
Parisa Mokhtari Karchegani Shamsollah Ayoubi Sheng Gao Lu Naser Honarju 《Journal of Applied Geophysics》2011,75(2):227-236
Limited information is available about the use of magnetic susceptibility property to determine soil redistribution in hilly areas of the semi-arid regions. This study was conducted to evaluate the use of magnetic properties to determine soil redistribution along a hill slope following deforestation. The study area is located in the Quaternary hilly region of Lordegan district in west Iran. Ten transects were established in two land uses that included natural Querqus forested and cultivated lands. Soil samples were collected at different positions along the slope and magnetic properties (χlf, χhf, SIRM, ARM, and χfd) and selected physico-chemical properties were determined. The results (based on the χfd, SIRM/ARM) showed that the magnetic susceptibly in the calcareous materials is pre-dominantly derived during the pedogenic processes and the superparamagnetic particles which were transported to lower positions of hill slope following deforestation. The results confirmed that this methodology could be applied for monitoring soil redistribution along the slope in calcareous hilly areas in the semi-arid regions. 相似文献
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Specification and prediction of nickel mobilization using artificial intelligence methods 总被引：1，自引：0，他引：1
Raoof Gholami Mansour Ziaii Faramarz Doulati Ardejani Shahoo Maleki 《Central European Journal of Geosciences》2011,3(4):375-384
Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment. 相似文献
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Ian Hers John T. Wilson Ravi V. Kolhatkar Matthew A. Lahvis Emma Luo Parisa Jourabchi 《Ground Water Monitoring & Remediation》2022,42(1):65-80
Several regulatory agencies recommend screening petroleum vapor intrusion (PVI) sites based on vertical screening distance between a petroleum hydrocarbon source in soil or groundwater and a building foundation. U.S. Environmental Protection Agency (U.S. EPA) indicate the risk of PVI is minimal at buildings that are separated by more than 6 feet (1.8 m) from a dissolved-phase source and 15 feet (4.6 m) from a light nonaqueous phase liquid (LNAPL) source. This vertical screening distance method is not, however, recommended at sites with leaded gasoline sources containing ethylene dibromide (EDB) because of a lack of field data to document EDB attenuation in the vadose zone. To help address this gap, depth-discrete soil-gas samples were collected at a leaded gasoline release site in Sobieski, Minnesota (USA). The maximum concentration of EDB in groundwater (175 μg/L) at the site was high relative to those observed at other leaded gasoline release sites. Soil gas was analyzed for EDB using a modification of U.S. EPA Method TO-14A that achieved analytical detection limits below the U.S. EPA Vapor Intrusion Screening Level (VISL) for EDB based on a 10−6 cancer risk (<0.16 μg/m3). Concentrations of EDB in soil gas above LNAPL reached as high as 960 μg/m3 and decreased below the VISL within a source-separation distance of 7 feet. This result coupled with BioVapor model predictions of EDB concentrations indicate that vertical screening distances recommended by regulatory agencies at PVI sites are generally applicable for EDB over the range of anticipated source concentrations and soil types at most sites. 相似文献
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Hossein Tabari Parisa Hosseinzadehtalaei Patrick Willems Christopher Martinez 《水文科学杂志》2013,58(3):610-619
ABSTRACTIn this work, the applicability of 12 solar radiation (RS) estimation models and their impacts on daily reference evapotranspiration (ETo) estimates using the Penman‐Monteith FAO-56 (PMF-56) method were tested under cool arid and semi-arid conditions in Iran. The results indicated that the average increase in accuracy of the ETo estimates by the calibrated RS models, quantified by the decrease in RMSE, was 2.8% and 6.4% for semi-arid and arid climates, respectively. Mean daily deviations in the estimated ETo by the calibrated RS equations in semi-arid climates varied from ?0.283?mm/d-1 for the Glover‐McCulloch model to 0.080?mm/d for the El-Sebaii model, with an average of ?0.109?mm/d-1, and in arid climates, they ranged from ?0.522?mm/d-1 for the Samani model to 0.668?mm/d for the El-Sebaii model, with an average of 0.125?mm/d-1.
Editor D. Koutsyiannis; Associate editor Not assigned 相似文献
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Raoof Gholami Vamegh Rasouli Andisheh Alimoradi 《Rock Mechanics and Rock Engineering》2013,46(5):1199-1209
Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures. 相似文献