AI-guided investigation of biochar’s efficacy in Pb immobilization for remediation of Pb contaminated agricultural land

Abstract

Abstract This study evaluated the lead (Pb) immobilization efficiency of biochar in contaminated agricultural soil. The biochar was produced from a range of major biomass residues and pyrolyzed under well-controlled conditions. Ten different types of standard biochar samples were derived from five different feedstocks (i.e., softwood, miscanthus straw, rice husk, oilseed rape straw, wheat straw) and pyrolyzed at 550 ℃ and 700 ℃. Pb-contaminated soil near an abandoned mine was incubated with 2.5% (w w− 1) of biochar. Incubation was conducted for various durations at room temperature under both short-term (21 days) and long-term (214 days) conditions. This variation explicitly accounted for the simulated microplastic contamination during the long-term incubation period. A novel framework has been developed to predict the long-term immobilization effect of various biochar types using a machine-learning approach, following the successful identification of optimal biochar implementations. This prediction method utilizes a small on-field dataset by employing a data augmentation approach, showcasing an innovative approach to forecasting the effects of different biochar types over time. After the incubation period, soil samples were analyzed for their chemical properties. As a result, oil seed rape biochar was the highest in pH, EC, exchangeable Ca2+, Mg2+, and K+, total nitrogen content, soil organic matter content, and available phosphate. In return, OSR 700 treated soils showed the highest content of exchangeable cations and the lowest content of available Pb after the incubation period. The most efficient biochar for immobilizing lead (Pb) in soil appears to be OSR 700, based on the available evidence.

Keywords

• Heavy metal immobilization
• Land use
• UN SDGs
• Life on land
• Machine learning
• Soil remediation