排序方式： 共有35条查询结果，搜索用时 31 毫秒
21.
N. Mansouriieh M. R. Sohrabi M. Khosravi 《International Journal of Environmental Science and Technology》2016,13(5):1393-1404
Bimetallic Fe/Ni nanoparticles were synthesized and used for the removal of profenofos organophosphorus pesticide from aqueous solution. These novel bimetallic nanoparticles (Fe/Ni) were characterized by scanning electron microscopy, energy-dispersive X-ray analysis spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. The effect of the parameters of initial pesticide concentration, pH of the solution, adsorbent dosage, temperature, and contact time on adsorption was investigated. The adsorbent exhibited high efficiency for profenofos adsorption, and equilibrium was achieved in 8 min. The Langmuir, Freundlich, and Temkin isotherm models were used to determine equilibrium. The Langmuir model showed the best fit with the experimental data (R 2 = 0.9988). Pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were tested to determine absorption kinetics. The pseudo-second-order model provided the best correlation with the results (R 2 = 0.99936). The changes in the thermodynamic parameters of Gibb’s free energy, enthalpy, and entropy of the adsorption process were also evaluated. Thermodynamic parameters indicate that profenofos adsorption using Fe/Ni nanoparticles is a spontaneous and endothermic process. The value of the activation energy (E a = 109.57 kJ/mol) confirms the nature of the chemisorption of profenofos onto Fe/Ni adsorbent. 相似文献
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Khabat Khosravi Rahim Barzegar Shaghayegh Miraki Jan Adamowski Prasad Daggupati Mohammad Reza Alizadeh Binh Thai Pham Mohammad Taghi Alami 《Ground water》2020,58(5):723-734
While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources. 相似文献
23.
Hassan Khosravi Ali Azareh Hadi Eskandari Dameneh Elham Rafiei Sardoii Hamed Eskandari Dameneh 《Arabian Journal of Geosciences》2017,10(4):93
The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively. 相似文献
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25.
Assessment of drought hazard impact on wheat cultivation using standardized precipitation index in Iran 总被引：1，自引：0，他引：1
Mohammad Reza Mansouri Daneshvar Ali Bagherzadeh Mahmood Khosravi 《Arabian Journal of Geosciences》2013,6(11):4463-4473
The assessment of drought hazard impacts on wheat cultivation as a strategic crop in Iran is essential for making mitigation plans to reduce the impact of drought. Standardized precipitation index has gained importance in recent years as a potential drought indicator and is being used more frequently for assessment of drought hazard in many countries. In the present study, the calculated standardized precipitation index for 48 stations dataset in the 30-year time scale fulfilled 30 statistical matrices. The drought hazard index map was produced by sum overlaying the spatial representations of 30 statistical matrices and categorized into four levels of low, moderate, high, and very high, which demonstrated probability of drought occurrences of 10–20 %, 20–30 %, 30–40 %, and 40–50 %, respectively. Finally, after the general division of zonal statistics in drought hazard index map of Iran, major drought hazard zones were geographically classified into five zones. The statistical analysis showed a significant correlation (R 2?=?0.701 to 0.648) between drought occurrences and wheat cultivation including surface area and total production for these drought hazard zones. 相似文献
26.
Binh T. Pham Indra Prakash Khabat Khosravi Kamran Chapi Phan T. Trinh Trinh Q. Ngo 《国际地球制图》2013,28(13):1385-1407
AbstractIn this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides. 相似文献
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Sinan Q. Salih Ahmad Sharafati Khabat Khosravi Hossam Faris Ozgur Kisi Hai Tao 《水文科学杂志》2020,65(4):624-637
ABSTRACTSuspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process. 相似文献
29.
Vahid Khosravi Faramarz Doulati Ardejani Saeed Yousefi 《Environmental Earth Sciences》2017,76(13):459
Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R 2 values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method. 相似文献
30.
M. Taghi ganji M. Khosravi R. Rakhshaee 《International Journal of Environmental Science and Technology》2005,1(4):265-271
The adsorption of heavy metals onto treated Azolla filiculoides by H2O2/MgCl2, as a cosmopolitan free-floating waterfern, was investigated from aqueous solutions in the batch biosorption experiments. The maximum uptake capacities of the collected Azolla from rice field at the optimal conditions for Pb, Cd, Cu and Zn ions were approximately 228, 86, 62 and 48 mg/g (dry Azolla), respectively. On the other hand, the maximum uptake capacities of the collected Azolla from the Anzali International Wetland in the north part of Iran at the same conditions for these heavy metals were about 124, 58, 33 and 34 mg/g (dry Azolla), respectively. Such decrease of uptakes is due to the pollution of Anzali International Wetland, which reduces the capacity uptake of metals. The recovery of biosorbed heavy metals from the rice field Azolla was carried out by HCl and NaCl desorbents that the recovery of 64–86% and 51–72% was occurred, respectively. 相似文献