Predicting Lead Concentration of Soil using Readily Available Properties Based on Artificial Neural Network Model

Increased generation of pollutants such as heavy metals is one of the serious and developing environmental issues threatening human society. Heavy metal pollution not only affects the physical and chemical properties of the soil but also it is dangerous to human health through entering into the food chain and finding its way into the groundwater. The present study was conducted to predict soil lead concentration, as one of the most important heavy metals, using readily available soil properties based on artificial neural network model. For this purpose, 63 soil samples were collected from 60-cm depth of the land surrounding Kashafrud River located in Mashhad City. Measured parameters included pH, electrical conductivity, particle size distribution, organic carbon, and Pb content in soil. The multilayer perceptron (MLP) as an artificial neural network model was used to predict the Pb concentration in soil. The performance of this model was assessed by the coefficient of determination (R2), mean absolute error (MAE), and also root mean square error (RMSE). The results showed that artificial neural network model is a suitable method to determine Pb concentration in soil rather than the direct laboratory measurement, which is an expensive and time-consuming method

Reducing the 2, 4 D+MCPA Antagonism from Hard Spray Waters by Ammonium Sulfate

Introduction: Water is the main carrier of herbicides (HC) that its quality plays an important role in herbicide performance hard water has a high concentration of Ca++ and Mg++ and reviews have shown that calcium, manganese and zinc are the main factors reducing the effectiveness of weak acid herbicides. Weak acid herbicides such as glyphosate, paraquat, clethodim and 2, 4 D are compounds that release the H+ ions once dissolved in water, but just slightly. Therefore, herbicides that are weak acids partially dissociate. Herbicides not dissociated (the compound remains whole) are more readily absorbed by plant foliage than those that dissociate. Dissociated herbicide molecules have a negative charge. After being dissociated, herbicides might remain as negatively charged molecules, or they might bind with other positively charged cations. Binding to some cations improves herbicide uptake and absorption, binding to others such as Ca++ and Mg++ antagonizes herbicide activity by decreasing absorption or activity in the cell. To correct such carriers, the use of adjuvants, such as ammonium sulphate (AMS), is recommended, which can reduce the use of herbicides and cause economic savings. The aim of this study was to investigate the simple effects and interactions between different amounts of AMS and carrier hardness (CH) levels on 2, 4 D + MCPA herbicide efficacy in controlling white clover (Trifolium repens L.) in turf grass. Materials and Methods: The experiment was laid out in a RCBD with three replications for each treatment during spring-summer 2013 in 10 years old mixed cold season turf grass (Festuca rubra + Poa pratensis + Poa pratensis) dominated by white clover in Mashhad (Iran). The treatments were the factorial combination of four carrier hardness (CH) rates (Deionized, 45, 90 and 180 ppm of Ca++ +Mg++) and three Ammonium Sulfate (AMS) rates (0, 2, 3 and 4 Kg per100 L of carrier water) were studied. The turf was sprayed with 2, 4 D + MCPA (67.5% SL) at 1.5 L-ha applied once on July. The density and dry matter of clover and turf were recorded. Results and Discussion: Full performance of 2, 4 D + MCPA herbicide to control clover, regardless of the amount of ammonium sulfate used, was obtained in soft water. Adding just 4%, AMS to Carrier water with a hardness of 45 ppm could recover effectiveness of herbicide up to DI water, whereas in 90 ppm of hardness adding only 2 percent ammonium sulfate was enough to increase herbicide efficacy to twice as no ammonium sulfate treatment. The most significant antagonism effect was obtained in 180 ppm hardness level without AMS reducing 84% of 2, 4 D + MCPA performance compared to soft water. The highest antagonism effect of the herbicide carrier went to 180 ppm, 90 ppm and 45 ppm of hardness respectively. Overall, the study revealed that only in 45 ppm of CH the addition of 4% of AMS will help to restore the toxicity of 2, 4 D + MCPA while in 90 ppm and 180 ppm of CH add more than 2% of AMS to 2, 4 D + MCPA carrier water will not benefit the herbicide toxicity. Most reports have considered sufficient two percent of AMS to neutralize the inhibitory effect of CH on the weak acid herbicides. Three weeks after spraying, no phytotoxicity was found in the grass. At the same time interaction between CH and AMS on the lawn dry weight was significant (

Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea

Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model