Sensitivity analysis of factors influencing pollutant removal from shallow groundwater by the PRB method based on numerical simulation

Permeable reactive barrier (PRB) is one of the most promising in situ treatment methods for shallow groundwater pollution. However, optimal design of PRB is very difficult due to a lack of comprehensive understanding of various complex influencing factors of PRB remediation. In this study, eight of the main factors of PRB, including hydraulic gradient I, permeability coefficient K-PRB of PRB material, PRB length L, PRB width W, PRB distance from pollution source Dist., the ratio of the maximum adsorption capacity to Langmuir constant of PRB material Q(max)/K-L, the discharge rate of pollution source DR, and recharge concentration RC were investigated, to carry out the sensitivity analysis of PRB removal efficiency. The simulation experiments for Morris analysis were designed, and pollutant removal efficiency was numerically simulated by coupling MODFLOW and MT3DMS under two scenarios of high and low permeability and dispersivity. For a typical low permeability with low dispersity medium, the sensitivity ranking of factors from high to low is DR, RC, I, W, L, Dist., Q(max)/K-L, and K-PRB, and for a typical high permeability with a high dispersity medium, the sensitivity ranking of factors from high to low is I, W, DR, Q(max)/K-L, L, RC, Dist., and K-PRB. When considering multiple factors in PRB design, the greater the K-PRB, L, W, Q(max)/K-L is, the higher the removal efficiency is; the greater the RC, I is, the lower the removal efficiency is. The rest factors remain ambiguous enhancement to removal efficiency.

Automated summary

In this study, the sensitivity analysis of the factors affecting the removal efficiency of permeable reactive barrier (PRB) was conducted. Through simulation experiments and numerical simulations, the influence of different factors on PRB removal efficiency was analyzed. The results showed that the sensitivity ranking of factors varied in different permeability and dispersity conditions, and the study also identified some key factors that affect the removal efficiency.