Related Experiment Video
Updated: May 7, 2026

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites
Published on: November 28, 2014
Mechanistic and Data-Guided Design of Biochar Adsorbents for Gas Phase Pollution Control: A State-of-the-Art Review
Hammad Khan1, Muhammad Arshad2, Ahsan Jalal3
1Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan. Hammad@giki.edu.pk.
None:
Air pollution remains critical global challenge, with sulfur oxides (SOx), nitrogen oxides (NOx), and volatile organic compounds (VOCs) contributing to environmental degradation and adverse health outcomes. Among mitigation technologies, biochar (BC) has gained attention as sustainable adsorbent for gas-phase pollutant control due to its hierarchical porosity, tunable surface chemistry, and production from renewable biomass. This review examines mechanistic foundations and design strategies of engineered biochar for removal of SOx, NOx, and VOCs, while comparing its performance with conventional technologies that are pollutant-specific, energy-intensive, or limited under industrial conditions. Key synthesis routes including pyrolysis, hydrothermal carbonization, and co-pyrolysis are discussed alongside modification strategies such as activation, heteroatom doping, and metal functionalization, which enhance pore structure, surface reactivity, and pollutant selectivity. Reported studies indicate that engineered biochars achieve adsorption capacities up to ~ 200 mg g-1 for SO₂ and ~ 245 mg g-1 for aromatic VOCs such as toluene, while demonstrating effective NOx removal under flue-gas conditions. These performances are governed by hierarchical porosity, defect-rich carbon structures, and oxygen-containing functional groups that promote acid-base interactions, π-π stacking, and redox-mediated adsorption pathways. Computational tools increasingly support adsorbent design: Density Functional Theory provides atomistic insight, while Machine Learning enables rapid prediction across datasets. Despite progress, challenges remain, including regeneration energy demand, reduced selectivity under humid conditions, and limited industrial scalability. By integrating experimental insights with computational approaches, this review outlines a predictive framework for developing efficient and durable advanced biochar adsorbents for next-generation air pollution control.

