Pristine and engineered biochar for the removal of contaminants co-existing in several types of industrial wastewaters: A critical review

Biochar has been widely studied as an adsorbent for the removal of contaminants from wastewater due to its unique characteristics, such as having a large surface area, well-distributed pores and high abundance of surface functional groups. Critical review of the literature was performed to understand the state of research in utilizing biochars for industrial wastewater remediation with emphasis on pollutants that co-exist in wastewater from several industrial activities, such as textile, pharmaceutical and mining industries. Such pollutants include organic (such as synthetic dyes, phenolic compounds) and inorganic contaminants (such as cadmium, lead). Multiple correspondence analyses suggest that through batch equilibrium, columns or constructed wetlands, researchers have used mechanistic modelling of isotherms, kinetics, and thermodynamics to evaluate contaminant removal in either synthetic or real industrial wastewaters. The removal of organic and inorganic contaminants in wastewater by biochar follows several mechanisms: precipitation, surface complexation, ion exchange, cation-π interaction, and electrostatic attraction. Biochar production and modifications promote good adsorption capacity for those pollutants because biochar properties stemming from production were linked to specific adsorption mechanisms, such as hydrophobic and electrostatic interactions. For instance, adsorption capacity of malachite green ranged from 30.2 to 4066.9 mg g⁻¹ depending on feedstock type, pyrolysis temperature, and chemical modifications. Pyrolyzing biomass at above 500 °C might improve biochar quality to target co-existing pollutants. Treating biochars with acids can also improve pollutant removal, except that the contribution of precipitation is reduced for potentially toxic elements. Studies on artificial intelligence and machine learning are still in their infancy in wastewater remediation with biochars. Meanwhile, a framework for integrating artificial intelligence and machine learning into biochar wastewater remediation systems is proposed. The reutilization and disposal of spent biochar and the contaminant release from spent biochar are important areas that need to be further studied.