List of Articles ظرفیت تبادل کاتیونی

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  1 - Effects of different levels of biochar on soil physical properties with different textures
  Fatemeh Razzaghi ناهید رضایی
  Biochar is a charcoal made from biomass and animal manure, which is produced by thermal decompositionunder a limited or zero supply of oxygen. Therefore, the current research was conducted to evaluate the effect ofdifferent biochar levels on some soil physical and chemi More

  Biochar is a charcoal made from biomass and animal manure, which is produced by thermal decompositionunder a limited or zero supply of oxygen. Therefore, the current research was conducted to evaluate the effect ofdifferent biochar levels on some soil physical and chemical properties in four soil types under greenhouseconditions. The experiment was performed in completely randomized design with four levels of biocharproduced from wheat straw in 500oC (0 (as control), 25, 50 and 75 ton ha-1) and four soils having varioustextures (sandy loam, loam, clay loam and clay) in three replications. Forty days after mixture of soil and biocharand determination of soil moisture content at field capacity, other physical and chemical parameters (soilmoisture at permanent wilting point, bulk and particle density, saturated hydraulic conductivity and cationexchange capacity) were measured by taking soil samples from the pots. The results showed that application ofbiochar enhanced soil physical properties. Increasing biochar levels from 0 to 75 ton ha-1 increased soil availablewater content, porosity, hydraulic conductivity and cation exchange capacity by 45, 13, 95 and 52 %,respectively. It is concluded that biochar can be used as a soil amendment in coarse textured soil to increasewater holding capacity and in fine textured soil to improve the drainage and infiltration. Manuscript profile
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  2 - Three years effect of iron and magnesium nano-particles on the stability of aggregates and some soil chemical properties
  Elahe Daraei Hossein Bayat Pouya Zamani
  Little is known about the long term effects of nanoparticles on soil properties. Therefore, the objective of this study was to investigate the three years effects of nanoparticles on aggregate stability and some of the soil chemical properties. Different amounts (1, 3 a More

  Little is known about the long term effects of nanoparticles on soil properties. Therefore, the objective of this study was to investigate the three years effects of nanoparticles on aggregate stability and some of the soil chemical properties. Different amounts (1, 3 and 5 percentage by weight) of two types of nanoparticle of metal oxides, MgO and Fe3O4 were mixed with a loamy soil in three replications and their possible effects on different properties of the soil after three years were investigated. The results showed that application of nanoparticles, increased the pH of the soil from 7.7 in the control to 8.1- 9.3 and the electrical conductivity from 0.31 in the control to 0.34 -0.56 dSm-1, due to the increase in the alkali cations. The percentage of calcium carbonate increased from 19.75% in the control to 20.5-22.7% due to the accumulation of nanoparticles in the soil, with the highest increase in three variables with 5% magnesium nano oxide. 3% nano iron oxide significantly increased the cation exchange capacity from 23.50 in the control to 24.28 cmolc/kgsoil. Also the nanoparticles increased the mean weight diameter, due to their high specific surface area, with the greater effect of magnesium nano oxide (increased from 33 to 1242 percentage compared to the control) than iron nano oxide (increased from 97 to 173 percentage compared to the control). In general, the results of this study showed that, nanoparticles with specific physico-chemical properties can affect some properties of soil. Manuscript profile
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  3 - Comparison of Impact of Carbonate Content, Cation Exchange Capacity and Specific Surface Area in the Retention of Heavy Metal Contaminant by Bentonite, Kaolinite, and Nano-Clay
  Mohammad Amiri Vahid Reza Ouhadi
  10.22034/jest.2018.15062.2357
  Background and Objective: Carbonate, Cation exchange capacity and Specific surface area are the three factors which  play a significant role in the retention of heavy metal contaminants by the soil. However, the amount and role of each of these three factors in hea More

  Background and Objective: Carbonate, Cation exchange capacity and Specific surface area are the three factors which  play a significant role in the retention of heavy metal contaminants by the soil. However, the amount and role of each of these three factors in heavy metal retention process is not clearly known. Accordingly, this experimental study attempts to examine the role of each of these factors on the heavy metal retention process. This study has been performed by the use of bentonite clay sample (which has 8% natural carbonate, significantly large specific surface area  and cation exchange capacity), kaolinite (which has 4% natural carbonate, small specific surface area and cation exchange capacity), industrial nano-clay called Cloisite®Na+ (free of carbonate, large specific surface area and considerable cation exchange capacity), industrial nano-clay called Cloisite®30B (free of carbonate, large specific surface area  and small cation exchange capacity), and laboratory sample of nano-clay called SLB (Surface Layer Bentonite) (free of carbonate, large specific surface area  and considerable cation exchange capacity). Materials and methods: In this regard, by conducting a series of geotechnical and geo-environmental experiments, the interaction process of kaolinite clay samples, bentonite, industrial Cloisite®Na+, industrial Cloisite®30B, and laboratory nano-clay SLB with heavy metal contaminants of lead and copper were experimentally explored and studied. Results and discussions: The analysis of experimental studies including soil buffering capacity, X-ray diffraction test and the measurement of heavy metal retention by soil samples indicate that in comparing of carbonate content, cation exchange capacity, and specific surface area of soil samples the significant role of each parameter in heavy metal retention is as follows, respectively:    Carbonate > Cation exchange capacity (CEC) > Specific surface area (SSA).   Manuscript profile
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  4 - بررسی توابع انتقالی رگرسیون چند متغیره، شبکه پرسپرترون چند لایه و توابع پایه شعاعی جهت برآورد ظرفیت تبادل کاتیونی خاک‌های شمال اهواز
  علی صالحی کامران محسنی فر علی غلامی
  برای تخمین ظرفیت تبادل کاتیونی خاک (CEC) به روش غیر مستقیم از توابع انتقالی استفاده می‌شود. چون (CEC) یکی از شاخص‌های مهم حاصلخیزی خاک است که به دلیل هزینه‌بر و وقت‌گیر بودن کمتر به صورت مستقیم اندازه‌گیری می‌شود. هدف از این تحقیق برآورد (CEC) خاک با استفاده از رگرسیون More

  برای تخمین ظرفیت تبادل کاتیونی خاک (CEC) به روش غیر مستقیم از توابع انتقالی استفاده می‌شود. چون (CEC) یکی از شاخص‌های مهم حاصلخیزی خاک است که به دلیل هزینه‌بر و وقت‌گیر بودن کمتر به صورت مستقیم اندازه‌گیری می‌شود. هدف از این تحقیق برآورد (CEC) خاک با استفاده از رگرسیون چند متغیره و شبکه‌های عصبی مصنوعی از روی خصوصیات زودیافت خاک می‌باشد. به این منظور اندازه‌گیری‌ها برای 100 نمونه خاک شامل 1000 اندازه‌گیری شامل اندازه توزیع ذرات خاک، جرم مخصوص ظاهری، مواد آلی، آهک، تخلخل، میانگین هندسی قطر و انحراف معیار هندسی، انجام شد. پس از شناسایی داده‌های پرت و حذف آنها آزمون نرمال بودن داده‌ها صورت گرفت. با استفاده از نرم‌افزار SPSS رگرسیون چند متغیره بین (CEC) و ویژگی‌های زودیافت خاک برقرار شد. سپس بسط توابع انتقالی برای ظرفیت تبادل کاتیونی خاک با استفاده از پارامترهای موجود با شبکه عصبی پرسپترون چند لایه (MLP) و شبکه عصبی تابع پایه شعاعی (RBF) انجام شد. نتایج نشان داد مواد آلی و رس خاک که منابع اصلی بار منفی خاک مـیباشـند با بالاترین ضریب تبیین 0.97 در برآورد CEC نقش دارند و مدل رگرسیون چند متغیره به طور کلی با ضریب تبیین 0.87 روش نسبتا مناسبی جهت برآورد CEC می‌باشد و شبکه MLP،  با تابع انتقال تانژانت سیگموئید در لایه میانی و تابع انتقال خطی در لایه خروجی و الگوریتم آموزشی بیزین با ضریب تبیین 97/0 و میانگین مربعات خطای013/0 قادر است CEC را با خطای کمتری برآورد کند. برای شبکه RBF ضریب تبیین برابر 55/0 و خطای 017/0 در مرحله تست شبکه بدست آمد. درمجموع با توجه به نتایج حاصل مشخص شد که MLP به دلیل اینکه برای دادههایی که به صورت خطی قابل تفکیک نیستند را میتواند بهتر متمایز کند، دارای خطای کمتر و بعد از آن رگرسیون چند متغیره بهترین مدل‌ها در مدل‌سازی و تخمینCEC می باشد درصورتی که شبکه های RBF  به دلیل حساس بودن به ورودیها از دقت کمی در منطقه مورد مطالعه برخوردار می‌باشند. Manuscript profile