Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

Abstract

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two-layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions.Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC C ] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them.A classical variance-based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two-layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.

Sensitivity Analysis for Modeling of Cr(VI) Transfer From Soil to Surface Runoff

“…The γ is vital to the simulation result due to the high σ value resulting from the interaction of the strong parameters. Despite the parameter p, the GSA results are consistent with the previous study with the variance-based GSA (Xia et al, 2018). Therefore, it shows that the low computational effort of the Morris trajectory method is able to give reasonable results for the model in this study.…”

“…Laboratory test data was used to examine the modeling works with linear isotherm adsorption equation (Henry equation) by Wang (2012) and nonlinear isotherm adsorption equation (Langmuir and Temkin equation) by Wu et al (2014Wu et al ( , 2015. In addition, the selection impact of different adsorption models on the Cr(VI) loss simulation has been evaluated, and it suggested the Langmuir model is preferable (Xia et al, 2018). The model under each adsorption assumption is accompanied by a set of uncertain/ unknown parameters.…”

“…At this point, the global sensitivity analysis (GSA) is vital for investigating the effects of parameters systematically and the uncertainty of simulation results, thereby enhancing the interpretation of the system (Saltelli and Annoni 2010). A variety of GSA techniques have been applied to hydrogeological models, such as the screening method (Saltelli et al, 2008), regression analysis (Tiscareno-Lopez et al, 1993), meta-modeling method (Petropoulos et al, 2009), variance-based method (Xia et al, 2018), etc. Based on sensitivity analysis of fitting parameters in the two-layer incomplete mixing model (Tong et al, 2010) with different adsorption equations (such as α, γ, h mix , k d , K, S max , and N).…”

“…Based on sensitivity analysis of fitting parameters in the two-layer incomplete mixing model (Tong et al, 2010) with different adsorption equations (such as α, γ, h mix , k d , K, S max , and N). It suggested that the most important influencing factor is h mix , and the sensitivity of adsorption parameters in the model gradually increases with time (Wu et al, 2015;Xia et al, 2018).…”

Integration of moment equations in a reduced-order modeling strategy for Monte Carlo simulations of groundwater flow

“…A stark example is given by groundwater related scenarios, where soil parameters, such as porosity and hydraulic conductivity, are frequently conceptualized as spatial random fields. In this context, MC methods play a significant role in dealing with uncertainty quantification [4][5][6], uncertainty reduction (e.g., in the framework of data assimilation techniques, such as the ensemble Kalman filter and/or smoother [7][8][9][10]), and variance-or momentbased global sensitivity analyses [11][12][13][14][15]. Implementation of MC relies on performing multiple forward simulations of the selected forward FSM upon using a collection of independent realizations of the uncertain model parameters.…”