From mechanisms to machine learning: an AI-enabled framework for biochar-based remediation of polyaromatic hydrocarbons (PAHs).

Biochar is a carbon-rich byproduct of biomass pyrolysis. It has emerged as a promising tool in environmental biomanufacturing specifically for the remediation of polycyclic aromatic hydrocarbons (PAHs). PAHs are persistent organic pollutants with significant ecotoxicological and human health risks. This review provides a comprehensive analysis of PAH formation during biochar production, advanced analytical techniques for PAH quantification, and the role of biochar in mitigating PAH contamination in soil and water systems. The effectiveness of biochar in PAH remediation is governed by several mechanisms including π-π interactions, pore-filling, electrostatic forces, and hydrogen bonding. These processes influence sorption efficiency and bioavailability. Moreover, biochar-based bioremediation strategies, including microbial-assisted degradation and engineered biochar composites, offer innovative solutions for contaminated environments. Yet there are several challenges that persists such as PAH desorption, biochar stability, and large-scale deployment persist. To bridge lab performance and field reliability, this study outlines an AI-enabled pipeline for data standards, predictive modelling, and inverse design of biochar tailored to site chemistry. Followed by embedding sustainability through cradle-to-grave life-cycle assessment and decision frameworks that weigh trade-offs among sorption efficacy, by-product risks, and deployment logistics. Future research should prioritize optimizing biochar functionalization, enhancing microbial synergistic interactions, and integrating life cycle assessments to advance scalable and sustainable biomanufacturing approaches for restoring polluted environment.