Development and evaluation of operational parameter driven mechanistic and empirical kinetic models for photocatalytic degradation of emerging contaminants

Abstract

Emerging contaminants (ECs) are ubiquitous in various aquatic systems due to their unique physicochemical properties and low environmental concentrations. Over the years, photocatalysis has shown significant promise in irradicating ECs; however, it is characterized by high operational costs, and its efficacy reduces when applied to real-time samples. This is typically attributed to inconsistent contaminant concentrations, overuse or underuse of photocatalyst dose, irradiation time, and light intensity. Hence, optimization of the photocatalytic systems is the need of the hour. In this context, 6 different mechanistic and empirical models have been used to model 7 datasets in this study. Intrinsic kinetic models demonstrated significantly better performance across all datasets compared to Langmuir–Hinshelwood and pseudo-first-order kinetic models, with R² values ranging from 0.87 to 0.98. Model 1, which considered the roles of both hydroxyl and superoxide radicals in photocatalytic degradation, outperformed the other models for most datasets (average R² = 0.937, RMSE = 8.057). Models 2, 3, and 5, which were developed based on system behavior, driving radicals being hydroxyl radicals, and the power law, respectively, also exhibited good fitting (average R² = 0.918, RMSE = 8.976). The results suggested that these models can be effectively used for system optimization, minimizing resource inputs, and maximizing contaminant removal, thereby bridging the gaps between laboratory and pilot-scale studies.