The presence of persistent emerging pollutants in aquatic ecosystems presents an environmental challenge that standard municipal water treatment facilities cannot fully mitigate. Compounds such as pharmaceuticals, per- and polyfluoroalkyl substances, microplastics, endocrine disruptors, and synthetic pesticides frequently bypass traditional filtration systems, accumulating at trace concentrations and threatening marine ecosystems and drinking water safety. While adsorption technologies provide an accessible remediation strategy, choosing the optimal adsorbent material involves balancing financial constraints, removal efficiency, and ecological sustainability. Research published in the journal Biochar by authors Ojima Z. Wada, Gordon McKay, Tareq Al-Ansari, and Khaled A. Mahmoud evaluates a tiered deployment framework—ranging from pristine biochar (Tier 1) to modified biochar (Tier 2) and advanced composites (Tier 3)—while detailing how artificial intelligence (AI) and life-cycle economics can guide responsible deployment.

Pristine biochar produced from thermochemical conversion of agricultural residues, wood waste, or biosolids represents a highly sustainable, low-cost baseline material, with production costs estimated at approximately 144 USD per ton. However, raw biochar relies predominantly on non-selective physisorption mechanisms, such as pore-filling and hydrophobic interactions, which often struggle to capture persistent or polar contaminants. On the opposite end of the performance spectrum lie advanced nanomaterials, including two-dimensional MXenes, graphene oxide, and metal-organic frameworks. While these advanced materials display exceptional adsorption capacities and enable specialized degradation pathways like photocatalysis, Fenton reactions, and molecular sieving, their commercial deployment is severely constrained by extreme production costs—exceeding 20.33 million USD per ton for pure MXenes—and potential ecotoxicity risks from nanoparticle washout into aquatic environments.

To reconcile this performance-cost divide, the review highlights biochar-based composites, which integrate small fractions of advanced materials into a sustainable carbon platform. Incorporating just ten percent MXenes into a biochar matrix creates a hybrid material costing approximately 2.03 million USD per ton, representing an eighteen-fold cost reduction compared to pure MXene while retaining superior removal mechanisms. Similarly, functionalizing biochar with cationic polymers like polyethyleneimine enables strong electrostatic interactions with negatively charged sulfonate and carboxylate head groups of perfluoroalkyl substances, achieving ultra-high sorption capacities. For microplastics and nanoplastics, high-temperature biochars and magnetic iron-oxide composites immobilize plastic particles through physical entrapment, surface complexation, and electrostatic attraction within porous honeycomb structures.

Accelerating the design of these tailored adsorbents relies increasingly on machine learning algorithms that eliminate traditional trial-and-error laboratory methods. Tree-based ensemble models—such as Random Forest, Gradient Boosting Regression, and XGBoost—outperform deep learning architectures when applied to biochar datasets, achieving high predictive accuracy (R2 values up to 0.98) for surface area, pore volume, and removal efficiency. By evaluating multi-dimensional inputs across feedstockFeedstock refers to the raw organic material used to produce biochar. This can include a wide range of materials, such as wood chips, agricultural residues, and animal manure. More chemistry, pyrolysis conditionsThe conditions under which pyrolysis takes place, such as temperature, heating rate, and residence time, can significantly affect the properties of the biochar produced.   More, solution pHpH is a measure of how acidic or alkaline a substance is. A pH of 7 is neutral, while lower pH values indicate acidity and higher values indicate alkalinity. Biochars are normally alkaline and can influence soil pH, often increasing it, which can be beneficial More, and pollutant properties, AI models can forecast optimal material configurations and continuous-flow fixed-bed column behaviors.

From an economic and environmental perspective, techno-economic and life-cycle assessments confirm that biochar feasibility depends on feedstock selection, transportation distances, and energy co-recovery. Lignocellulosic feedstocks like wood residues achieve net-negative global warming potential (up to -420 kilograms of carbon dioxide equivalent per ton) due to carbon stabilization and bio-oil or syngasSyngas, or synthesis gas, is a fuel gas mixture consisting primarily of hydrogen and carbon monoxide. It is produced during gasification and can be used as a fuel source or as a feedstock for producing other chemicals and fuels.   More energy capture. In contrast, wet feedstocks like sewage sludge incur higher drying costs and global warming potential, though lower feedstock acquisition costs maintain their economic viability. Furthermore, incorporating electro-Fenton or heat-activated persulfate regeneration restores active binding sites over multiple operational cycles, drastically reducing lifetime treatment costs while preventing secondary waste.

Source: Wada, O. Z., McKay, G., Al-Ansari, T., & Mahmoud, K. A. (2026). AI-driven biochar engineering for emerging pollutants removal from water: performance, mechanisms, and environmental perspectives. Biochar, 8, Article 61.