Numerical modeling and performance evaluation of passive convergence-permeable reactive barrier (PC-PRB)

Highlights

● A 2D finite-element solute transport model, PRB-Trans, is developed.

● PC-PRB can significantly improve the remediation efficiency of PRB.

● PC-PRB can considerably reduce the required PRB dimensions and materials costs.

● The required PRB length decreases with the increase of pipe length, L _p.

Abstract

The passive convergence-permeable reactive barrier (PC-PRB) was proposed to address the limitations of traditional PRB configurations. To evaluate the hydraulic and pollutant removal performance of the PC-PRB system, we developed a simulation code named PRB-Trans. This code uses the two-dimensional (2D) finite element method to simulate groundwater flow and solute transport. Case studies demonstrate that PC-PRB technology is more efficient and cost-effective than continuous permeable reactive barrier (C-PRB) in treating the same contaminated plume. Implementation of PC-PRB technology results in a 33.3% and 72.7% reduction in PRB length (L_PRB) and height (H_PRB), respectively, while increasing 2D horizontal and 2D vertical pollutant treatment efficiencies of PRB by 87.8% and 266.8%, respectively. In addition, the PC-PRB technology has the ability to homogenize the pollutant concentration and pollutant flux through the PRB system, which can mitigate the problems arising from uneven distribution of pollutants in the C-PRB to some extent. The L_PRB required for PC-PRB decreases as the water pipe length (L_p) increases, while the H_PRB required initially decreases and then increases with increasing L_p. The effect of passive well height (H_w) on H_PRB is not as significant as that of L_p on H_PRB. Overall, PC-PRB presents a promising and advantageous PRB configuration in the effective treatment of various types of contaminated plumes.

Graphical abstract

Keywords

Passive convergence-permeable reactive barrier / Numerical modeling / Hydraulic behavior assessment / Pollutant treatment performance evaluation / Influential factors analysis

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Acknowledgements

This study was funded by the National Key R&D Program of China (2018YFC1802306) and the National Natural Science Foundation of China (No. 42177177).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1731-z and is accessible for authorized users.

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