Data-driven scale-up design of H₂O₂-assisted PRBs for COD and NH₄⁺ removal in landfill-contaminated groundwater: Linking removal, retardation, and hydraulic conductivity.

Authors

Yuqing You, Li Zhao, Liangtong Zhan

Published in

Water research. Volume 309. Issue Pt A. Pages 127110. Oct 07, 2026. Epub Oct 07, 2026.

Abstract

H₂O₂-assisted permeable reactive barriers (PRBs), containing sequential carbon and zeolite layers, show promise for remediating landfill-contaminated groundwater with high COD and NH₄⁺ concentrations. However, site constraints and experimental costs make multiscale studies at the same site difficult, while interactions among influential variables complicate quantitative identification of scale-up relationships. To support PRB design, this study integrated batch, column, and pilot-scale data and applied Pearson correlation analysis and single-factor fitting to identify key variables controlling reactive and hydraulic performance, leading to four main findings. First, H₂O₂ pre-oxidation achieved 40∼60% COD removal when source COD exceeded 75 mg/L but showed negligible removal below 50 mg/L, with no obvious scale effect under sufficient oxidant contact. Second, removal capacity generally decreased with increasing seepage length and local velocity, but NH₄⁺ removal in the carbon layer showed the opposite trend, increasing from 0.597∼0.782 mg/g in columns to 1.435∼4.995 mg/g in pilots and approaching COD values (2.489∼2.959 mg/g), suggesting that both contaminants should be considered in carbon-layer design. Third, retardation factor was strongly associated with removal capacity and shared several sensitive factors. Adjusting the carbon/sand ratio according to the influent COD/NH₄⁺ ratio may balance contaminant removal and material cost. In zeolite layers, retardation increased with pore volume, with a potential turning point beyond 200 PV that may indicate the service-life limit. Fourth, hydraulic conductivity showed a lagged response to geochemical changes. Therefore, decreasing pH and EC did not necessarily indicate reduced clogging risk and could instead imply that hydraulic deterioration had already occurred. Based on these findings, a scale-up design framework was developed, including thickness design using effective removal capacity and retardation factor and geochemistry-based hydraulic performance assessment. The framework may support intelligent management of contaminated sites by reducing reliance on computationally intensive numerical simulations while providing physically meaningful constraints and multiscale datasets for machine-learning-based PRB design.

PMID:
42860087
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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