Emerging pollutants such as pharmaceuticals, microplastics, and industrial chemicals are increasingly detected in water systems worldwide, raising concerns about long-term risks to ecosystems and human health. A new study highlights how artificial intelligence can transform biochar into a powerful, cost-effective solution for removing these persistent contaminants from water.
In a comprehensive review, researchers present a new framework that combines biochar engineering with artificial intelligence to design next-generation materials tailored for specific pollutants. The work outlines how advanced data-driven approaches can accelerate the development of sustainable water treatment technologies.
“Emerging pollutants are difficult to remove using conventional treatment methods, and their diversity makes the challenge even more complex,” said the corresponding author. “Our study shows that by combining biochar with artificial intelligence, we can design smarter materials that are both effective and scalable.”
Biochar, a carbon-rich material produced from biomass such as agricultural waste, has gained attention for its environmental benefits and low production cost. Compared to advanced nanomaterials, which can cost thousands to millions of dollars per ton, biochar can be produced for around 144 dollars per ton, making it an attractive option for large-scale water treatment.
However, pristine biochar has limitations. Its pollutant removal performance is often moderate and largely relies on physical adsorption processes. To address this, the researchers propose a tiered strategy that classifies biochar systems into three levels: pristine biochar, modified biochar, and advanced biochar composites.
At the basic level, pristine biochar removes contaminants through mechanisms such as pore filling, hydrophobic interactions, and electrostatic attraction. Modified biochar enhances these capabilities by introducing functional groups or increasing surface area through activation and doping techniques. At the highest level, biochar composites integrate materials such as nanoparticles or graphene, enabling additional mechanisms including catalytic degradation and photocatalysis.
The study highlights that while advanced composites can achieve superior performance, they often face challenges related to cost, scalability, and environmental safety. As a result, the researchers advocate for a balanced approach that prioritizes simpler biochar systems where effective and reserves complex materials for more challenging pollutants.
A key innovation in the study is the integration of artificial intelligence to guide material design. Machine learning models can analyze large datasets to predict how factors such as feedstock type, pyrolysis conditions, and surface chemistry influence pollutant removal. These models can identify optimal combinations of parameters, significantly reducing the need for time-consuming trial-and-error experiments.
For example, AI models can predict how changes in biochar structure affect its ability to remove specific contaminants such as PFAS, pharmaceuticals, or microplastics. By linking material properties with pollutant characteristics, researchers can design targeted solutions that maximize efficiency under real-world conditions.
The study also emphasizes the importance of scalability and environmental impact. While laboratory studies demonstrate high performance, real-world applications must consider factors such as production cost, energy use, and potential ecotoxicity. The authors call for standardized datasets, green synthesis methods, and more pilot-scale studies to bridge the gap between research and implementation.
“Our goal is not just to develop high-performance materials, but to ensure they can be safely and economically deployed at scale,” the author added.
By combining sustainable materials with advanced computational tools, the research provides a roadmap for tackling one of the most pressing challenges in water treatment. The findings suggest that AI-guided biochar engineering could play a critical role in safeguarding global water resources in the years ahead.
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Journal Reference: Wada, O.Z., McKay, G., Al-Ansari, T. et al. AI-driven biochar engineering for emerging pollutants removal from water: performance, mechanisms, and environmental perspectives. Biochar 8 , 61 (2026).
https://doi.org/10.1007/s42773-025-00565-w
About Biochar
Biochar (e-ISSN: 2524-7867) is the first journal dedicated exclusively to biochar research, spanning agronomy, environmental science, and materials science. It publishes original studies on biochar production, processing, and applications—such as bioenergy, environmental remediation, soil enhancement, climate mitigation, water treatment, and sustainability analysis. The journal serves as an innovative and professional platform for global researchers to share advances in this rapidly expanding field.
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Biochar
Literature review
AI-driven biochar engineering for emerging pollutants removal from water: performance, mechanisms, and environmental perspectives
25-Feb-2026