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The increasing presence of emerging pollutants (EPs) in water poses significant environmental and health risks, necessitating effective treatment solutions. Originating from industrial, agricultural, and domestic sources, these contaminants threaten ecological and public health, underscoring the urgent need for innovative and efficient treatment methods. TiO 2 -based semiconductor photocatalysts have emerged as a promising approach for the degradation of EPs, leveraging their unique band structures and heterojunction schemes. However, few studies have examined the synergistic effects of operating conditions on these contaminants, representing a key knowledge gap in the field. This review addresses this gap by exploring recent trends in TiO 2 -driven heterogeneous photocatalysis for water and wastewater treatment, with an emphasis on photoreactor setups and configurations. Challenges in scaling up these photoreactors are also discussed. Furthermore, Machine Learning (ML) models play a crucial role in developing predictive frameworks for complex processes, highlighting intricate temporal dynamics essential for understanding EPs behavior. This capability integrates seamlessly with Computational Fluid Dynamics (CFD) modeling, which is also addressed in this review. Together, these approaches illustrate how CFD can simulate the degradation of EPs by effectively coupling chemical kinetics, radiative transfer, and hydrodynamics in both suspended and immobilized photocatalysts. By elucidating the synergy between ML and CFD models, this study offers new insights into overcoming traditional limitations in photocatalytic process design and optimizing operating conditions. Finally, this review presents recommendations for future directions and insights on optimizing and modeling photocatalytic processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Porous-activated carbons (ACs) derived from Moroccan pine cones (PC) were synthesised by a two step-chemical activation/carbonisation method using phosphoric acid (PC-H) and zinc chloride (PC-Z) as activating agents and used for the adsorption of bisphenol A (BPA) from water. Several techniques (TGA/DTA, FT-IR, XRD, SEM and BET) were used to determine the surface area and pore characterisation and variations during the preparation of the adsorbents. The modification significantly increased the surface area of both ACs, resulting in values of 1369.03 m 2  g -1 and 1018.86 m 2  g -1 for PC-H and PC-Z, respectively. Subsequent adsorption tests were carried out, varying parameters including adsorbent dosage, pH, initial BPA concentration, and contact time. Therefore, the highest adsorption capacity was observed when the BPA molecules were in their neutral form. High pH values were found to be unfavourable for the removal of bisphenol A from water. The results showed that BPA adsorption kinetics and isotherms followed pseudo-second-order and Langmuir models. Thermodynamic studies indicated that the adsorption was spontaneous and endothermic. Besides, the regeneration of spent adsorbents demonstrated their reusability. The adsorption mechanisms can be attributed to physical adsorption, hydrogen bonds, electrostatic forces, hydrophobic interactions, and π-π intermolecular forces., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)