To assess the uncertainty of contaminant transport MODELs in column EXPERIMENTs it is essential to select reliable MODELs for simulating column EXPERIMENTs and estimation of reliable parameters in order to identify the processes governing contaminant transport. So the overall goal of this study is to specify the uncertainty associated with the MODEL development by defining several conceptual MODELs and selecting the most probable MODEL and the best estimate of contaminant transport MODEL parameters and MODEL prediction. For exploring uncertainty in column EXPERIMENT we considered four MODELs from the literature including the equilibrium and non-equilibrium convection dispersion MODELs i.e. CDE1 and CDE2 convection-dispersion MODELs and MIM1 and MIM2 mobile-immobile MODELs. The MODEL selection criteria were used to evaluate the probabilities of the four MODELs. Using the full covariance matrix that considersresidual correlation has caused definitive recommendations on the basis of AIC, AICc, BIC, and KIC statistics and the MODEL probability. The result showed that for the slow flow case due to the satisfaction of the equilibrium assumption the CDE2 and for the high flow case MIM1 were receiving the highest MODEL probability and the least uncertainty. Finally it is concludes that the reliability of the MODEL’s data interpretation can be improved by considering alternative transport processes and quantifying uncertainty in the EXPERIMENT.

To assess the uncertainty of contaminant transport MODELs in column EXPERIMENTs it is essential to select reliable MODELs for simulating column EXPERIMENTs and estimation of reliable parameters in order to identify the processes governing contaminant transport. So the overall goal of this study is to specify the uncertainty associated with the MODEL development by defining several conceptual MODELs and selecting the most probable MODEL and the best estimate of contaminant transport MODEL parameters and MODEL prediction. For exploring uncertainty in column EXPERIMENT we considered four MODELs from the literature including the equilibrium and non-equilibrium convection dispersion MODELs i.e. CDE1 and CDE2 convection-dispersion MODELs and MIM1 and MIM2 mobile-immobile MODELs. The MODEL selection criteria were used to evaluate the probabilities of the four MODELs. Using the full covariance matrix that considers residual correlation has caused definitive recommendations on the basis of AIC, AICc, BIC, and KIC statistics and the MODEL probability. The result showed that for the slow flow case due to the satisfaction of the equilibrium assumption the CDE2 and for the high flow case MIM1 were receiving the highest MODEL probability and the least uncertainty. Finally it is concludes that the reliability of the MODEL’s data interpretation can be improved by considering alternative transport processes and quantifying uncertainty in the EXPERIMENT.

Introduction: Bioremediation and biodegradation are considered as environmental friendly techniques for contaminants’ removal in polluted environment. In this study the removal and kinetics of Tetrachloroethene (PCE) and Trichloroethene (TCE) microbial degradation, their inhibitory effects and the rate of dehalogenation capacity at high concentration of PCE were investigated. Materials and Methods: Dechlorinating culture was provided by Bioclear B. V. from a PCE-contaminated site (Evenblij in Hoogeveen-The Netherlands). The batch apparatuses were placed in an orbital shaker at 150 rpm at room temperature. In all the 18 batches, 6 different concentrations of PCE were measured from 0. 1 mM to 0. 6 mM. The degradation rate of PCE, Trichloroethene (TCE), and cis-1, 2-dichloroethene (cDCE) were determined by the PHREEQC MODEL. Results: The results revealed that the final product was ethene and the rate of dechlorinating of PCE increased gradually. The degradation process started after 3 days in batch modes (0. 1 mM). After 10 days, the dechlorination of PCE to TCE was obtained in a low concentration of PCE (0. 1 mM). Also, the TCE concentration became close to zero after 10 days. However, the start point was longer than PCE and the rate of biodegradation of TCE was faster than PCE. PCE did not show any progress in the dechlorinating procedure at 13th and 14th batch series and none of the daughter products were observed. Conclusions: It should be concluded that there was no single organism that could dechlorinate PCE to ethene, directly. Therefore, the best consortium of microorganisms to dechlorinate PCE to ethene faster, with less production of VC as the most hazardous compound, should be studied.

EXPERIMENTation is one of the ways to learn the basics and general results of science that compel students to think and ultimately learn optimally. EXPERIMENT-based education is a robust method for maintaining the visual process of teaching and principles for furthering educational objectives in chemistry. The laboratory is the complementary sector and the focus of empirical science education, which provides students with participation in science-related activities and learning the scientific method. Since most of the findings of empirical sciences are obtained through observation and implementation of EXPERIMENTs, therefore, the best way to effectively learn empirical sciences is to conduct EXPERIMENTs and live observation of scientific evidences. In the present research, at first, the role of the laboratory in the teaching of EXPERIMENTal sciences, including chemistry, will be discussed, the goals and approach to implementing this method will be addressed, and the views of several experts regarding the importance of laboratory teaching, the stages of the laboratory teaching method, and also separately, the ways of presenting laboratory activities, merits and limitations of this method are examined, suggestions for using this method as much as possible in schools in order to improve chemistry education are presented. In the end, the process of making colored crystals by first-secondary students is described.

The empirical Bayes estimators of treatment effects in a factorial EXPERIMENT were derived and their asymptotic properties were explored. It was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. A Bootstrap analysis was performed to illustrate the theoretical results empirically.