Removal of cadmium and humic acid from aqueous solutions using surface modified nanozeolite A

The sorption of cadmium and humic acids from aqueous solutions using surface-modified nanozeolite A has been investigated under various examination conditions. The morphology of untreated and treated nanozeolite was studied under scanning electron microscope and transmission electron microscope. Isotherms of cadmium adsorption onto surface-modified nanozeolite A were studied at different pH, solid to liquid ratio, adsorbate concentration and interaction time. Kinetic and equilibrium studies were conducted and the equilibrium data have been analyzed using Langmuir and Freundlich isotherm models. The study revealed that experimental results were in agreement with the Freundlich model. The Langmuir monolayer adsorption capacity was found to be 1666.67 g cadmium and 6.75 g humic acid per gram of modified nanozeolite A, which is higher than that of reported value for other zeolites. The sorption ability was enhanced by surface modification and reduction in size and enabled the zeolite to adsorb cadmium. The adsorption of cadmium and humic acid on nanozeolite was found to be the highest at pH 6 and 3, respectively. Results showed that solid to liquid ratio and pH are the most important factors for cadmium and humic acid removal, respectively. Effect of competitive ions was studied and results showed that there is no competition between cadmium and humic acid sorption and presence of these ions.     

Time dependent chemical study of contracting interstellar clouds. I. Abundances of nitrogen- and carbon-bearing molecules

We have constructed a reaction system containing the chemical families of H, C, O, N, S, Si, Cl, metals (Me) and grains. A total of 104 species have been included and a network of 557 reactions has been studied. The chemical kinetic equations were integrated as a function of time by using gear program. The chemical reaction system was followed at low, intermediate and high cloud densities i.e. from 10–10⁷ particles cm^-3. The calculated fractional abundances of N₂, CN, HCN, and CH which are in good agreement with the results of observations and with those of previous theoretical studies.     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Mass Estimation in the Globular Cluster M4

Using the kinematical data we try to solve the Jeans equation to provide mass estimation of the globular cluster M4. The proper motion data provides two independent velocity dispersion profiles which we need in this estimation. Moreover, we have calculated the density distribution function and checked the anisotropy of the velocity dispersion. The comparison of the dynamical mass with the estimation stellar mass for M4 gives the best estimation of the mass inside a spherical shell centered on the cluster, with a radius corresponding to 840 arcsec on the sky, as 4-5× 10⁴ M . This revised version was published online in July 2006 with corrections to the Cover Date.     

Probabilistic Convective Initiation Nowcasting with Reduced Satellite - NWP Predictors over Iran

Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used.     

Physicochemical behavior of tropical laterite soil stabilized with non-traditional additive

Non-traditional soil stabilizers are widely used for treating weak materials. These additives are cost- and time-effective alternatives to more traditional materials such as lime and cement. It has been well established that the treatment of natural soil with chemical additives will gradually affect the size, shape, and arrangement of soil particles. Furthermore, the degree of improvement is dependent on the quantity and the pattern of new products formed on and around the soil particles. In this paper, unconfined compressive strength (UCS) test was performed as an index of soil improvement on mix designs treated with calcium-based powder stabilizer (SH-85). The time-dependent changes in shear strength parameter and compressibility behavior of treated soil were also studied using standard direct shear and one-dimensional consolidation tests. In order to better understand the shape and surface area of treated particles, FESEM, N₂-BET, and particle size distribution analysis were performed on soil-stabilizer matrix. From engineering standpoint, the UCS results showed that the degree of improvement for SH-85-stabilized laterite soil was roughly five times stronger than the untreated soil at the early stages of curing (7-day period). Also, a significant increase in the compressibility resistance of treated samples with curing time was observed. Based on the results, less porous and denser soil fabric was seen on the surface of clay particles. FESEM images of the treated mix designs showed the formation of white lumps in the soil fabric with the cementitious gel filling the pores in the soil structure.     

A comparative study of kriging and simulation-based methods in classifying ore and waste blocks

Ore grade is the most important source of uncertainty in a mining operation which plays an important role to classify run-of-mine (ROM) material into ore and waste parcels. As a widely used method, kriging estimator is used to estimate the grade of ore blocks. In conventional mining practices, if the estimated grade of a parcel is above the cut-off grade, this parcel is classified as ore, otherwise, is labelled as a waste parcel. An alternative approach is to simultaneously consider the grade of parcels and the economic consequences of sending parcels to destinations by applying simulation-based methods. In this study, kriging and simulation-based methods including loss and profit functions are applied on a real-world case study to classify ore/waste material based on the initial exploration data. Then, the actual known data, collected from blast holes samples, are compared with the estimated results in order to validate the performance of the presented methods. Outcomes show that simulation-based methods can perform better and show more adjustability with real data.     

Application of the WEPP model to determine sources of run‐off and sediment in a forested watershed

This study investigates critical run‐off and sediment production sources in a forested Kasilian watershed located in northern Iran. The Water Erosion Prediction Project (WEPP) watershed model was set up to simulate the run‐off and sediment yields. WEPP was calibrated and validated against measured rainfall–run‐off–sediment data. Results showed that simulated run‐off and sediment yields of the watershed were in agreement with the measured data for the calibration and validation periods. While low and medium values of run‐off and sediment yields were adequately simulated by the WEPP model, high run‐off and sediment yield values were underestimated. Performance of the model was evaluated as very good and satisfactory during the calibration and validation stages, respectively. Total soil erosion and sediment load of the study watershed during the study period were determined to be 10 108 t yr^?1 and 8735 t yr^?1, respectively. The northern areas of the watershed with dry farming were identified as the critical erosion prone zones. To prioritize the subwatersheds based on their contribution to the run‐off and sediment production at the watershed's main outlet, unit response approach (URA) was applied. In this regard, subwatersheds close to the main outlet were found to have the highest contribution to sediment yield of the whole watershed. Results indicated that depending on the objective of land and water conservation practices, particularly, for controlling sediment yield at the main outlet, critical areas for implementing the best management practices may be identified through conjunctive application of WEPP and URA. Copyright © 2014 John Wiley & Sons, Ltd.