AI-driven biochar engineering for emerging pollutants removal from water: performance, mechanisms, and environmental perspectives

Abstract

The global prevalence of emerging pollutants (EPs) in aqueous systems presents a significant environmental threat that conventional treatments cannot adequately address. This review provides a comprehensive analysis of biochar-based systems as a sustainable solution, charting a path from foundational material science to advanced, data-driven engineering. We critically evaluate these solutions through a tiered framework: starting with Tier 1 (Pristine Biochar), which is highly reliant on physisorption mechanisms; moving to Tier 2 (Modified Biochar) with enhanced surface properties through activation and/or heteroatom doping; and culminating in Tier 3 (Advanced Composites) incorporating materials like nanoparticles and graphene, which offer superior removal mechanisms, including chemisorption and photocatalysis. A central focus is placed on the transformative role of Artificial Intelligence (AI), which enables predictive modelling and optimization to accelerate the design of tailored, high-performance adsorbents. Beyond performance, this review delves into the critical aspects of scalability, presenting a detailed analysis of the economic trade-offs and environmental/ecotoxicity considerations that govern real-world deployment. We demonstrate how this tiered approach leads to targeted solutions for challenging EPs, such as cationic composites for per- and polyfluoroalkyl substances and engineered surface porosity for the physical entrapment of micro- and nanoplastics. Ultimately, we advocate for an AI-guided strategy, prioritizing sustainable pristine biochar where effective and strategically deploying advanced composites as a last resort. This work concludes by outlining a roadmap for future research, emphasizing the need for standardized and robust datasets, green synthesis protocols, and rigorous safety assessments to ensure the responsible development of these next-generation water treatment technologies.

Graphical Abstract

Keywords

Micropollutants / Resource recovery / AI-Engineering / Nanomaterials / Wastewater treatment / Biomass valorization

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