Recycling heavy metal-contaminated soil with biochar, mineral powder and cement into sustainable subgrade material
Source abstract
Abstract
Heavy metal contamination in soils, exacerbated by anthropogenic activities such as mining and smelting, has become increasingly severe, necessitating efficient remediation technologies to safeguard environmental and human health. This study developed a ternary binder (BMPC) comprising biochar (BC), mineral powder (MP), and ordinary Portland cement (OPC) to solidify contaminated soil for reuse as subgrade materials. Solidification efficacy and mechanisms were investigated through unconfined compressive strength (UCS), California Bearing Ratio (CBR), dry-wet cycling tests, complemented by microstructure and chemical composition analysis. An improved comprehensive pollution assessment model was established to evaluate the pollution risk of the solidified soils based on batch leaching tests. Results indicated that the optimal BC: MP: OPC mass ratio was 3:17:80. With 10% BMPC treatment, the 28-day UCS and the 7-day CBR of the contaminated soil reached 904.79 kPa and 36.23%, both satisfying the Chinese highway subgrade requirements (UCS ≥ 0.8 MPa and CBR ≥ 8% as per JTG/T 3610-2019). Its dry-wet cycle durability improved. The comprehensive heavy metal pollution risk of the contaminated soil was mitigated by 77.44%, downgrading the risk level from severe to low. Compared to the conventional OPC treatment, BMPC application reduced costs by 22.74% and CO 2 emissions by 37.69%. Microstructural analysis revealed that BMPC synergistic effects promoted the formation of calcium silicate hydrate (C-S-H) and ettringite (AFt), creating a dense hydration product network that effectively encapsulated and immobilized heavy metals.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Landfill Environmental Impact Studies · Microbial Applications in Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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