Response Surface Methodology for the Optimization of Zn-Contaminated Soil Remediation by Soil Washing with Water-Soluble Chitosan

Abstract

Soil washing is an important method for the remediation of contaminated soil. This research presents the optimization of soil washing conditions in the remediation of Zn-contaminated soils with water-soluble chitosan (WSCS). Response surface methodology (RSM) was used to optimized the washing conditions after single factor experiments. The central composite design (CCD) with three factors and five levels was applied to the optimization of the removal efficiency of Zn from soils, and WSCS concentration, pH value, and washing time were evaluated variables in the washing process. Results indicated that the pH value (p < 0.0001) was the most significant factor which mainly affected the distribution and content of metal species in aqueous solution, ion exchange and adsorption/desorption behavior of metals, solubility of chelating agent, as well as readsorption of metal complexes. The optimal conditions for the Zn removal from soils were WSCS concentration of 1.5%, pH of 3.3, and washing time of 72 min. The removal efficiency could reach 65.4% under the optimized conditions, which was close to the predicted value of 68.3% by the response surface method. Therefore, it could be found that the response surface methodology was an effective method to determine the optimal conditions for the removal of metals from contaminated soils by soil washing.

Synthesis and Impurity Profiling of a Vital Intermediate in Gabapentin and Bispidine Derivative Pathways by Response Surface Methodology

“…In addition, Table shows that the difference between Pred- R 2 (0.9572) and Adj- R 2 (0.9820) was within a certain range (<0.2), which indicates that the model is appropriate . A higher level of precision was also indicated by the relatively low coefficient of variation (0.7116), which also validated the reliability of the experiments . Adequate precision, which denotes the signal-to-noise ratio (a measure of the design space in predicted response relative to its error), must be greater than 4.…”

“…Significant effects can be seen when the F-value is high, and the p -value is low (prob ≫ F). A probability value of p < 0.05 means that the interactions between the variables are significant and ensure the accuracy of the model. , Table presents the results of the ANOVA analysis. From Table , it can be concluded that the quadratic equation is satisfactory for predicting the relationship between the actual response and the independent variables that influence the reaction yield.…”

“…37 A higher level of precision was also indicated by the relatively low coefficient of variation (0.7116), which also validated the reliability of the experiments. 33 Adequate precision, which denotes the signal-to-noise ratio (a measure of the design space in predicted response relative to its error), must be greater than 4. A good signal is indicated by the model's "adequate precision" value of 30.9373.…”

Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology

“…However, it is essential to note that adding additional variables to the model will always increase the R 2 value, regardless of whether these variables are statistically significant or not. Therefore, it was said that a high R 2 value does not necessarily imply the adequacy of the model [ 27 , 28 ].…”