This study uses biochar as a catalyst to evaluate the effectiveness of heterogeneous advanced oxidation processes (HAOPs) for removing three widely detected CECs: triclosan, caffeine, and parabens. Two types of biochar, derived from coffee grounds and sewage sludge, were selected for their ability to drive oxidative degradation of these pollutants. Biochar, functionalized to enhance its catalytic properties, offers a sustainable and low-cost approach to wastewater treatment by utilizing waste-derived materials (Liu et al., 2019; Keerthanan et al. 2020). Batch experiments were conducted to evaluate the degradation of triclosan, caffeine, and parabens from greywater, employing response surface methodology (RSM) and principal component analysis (PCA) to optimize critical operational parameters. Parameters such as pH, biochar dosage, hydrogen peroxide concentration, and reaction time were systematically varied to determine the optimal conditions for maximum pollutant removal. PCA was utilized to identify the key factors influencing pollutant degradation, providing a clearer understanding of the relationships between variables and their effects on the efficiency of the process

Removal of contaminants of emerging concern from greywater using heterogeneous advanced oxidation processes

Antonio Faggiano
;
Maria Ricciardi;Oriana Motta;Antonio Proto
2025

Abstract

This study uses biochar as a catalyst to evaluate the effectiveness of heterogeneous advanced oxidation processes (HAOPs) for removing three widely detected CECs: triclosan, caffeine, and parabens. Two types of biochar, derived from coffee grounds and sewage sludge, were selected for their ability to drive oxidative degradation of these pollutants. Biochar, functionalized to enhance its catalytic properties, offers a sustainable and low-cost approach to wastewater treatment by utilizing waste-derived materials (Liu et al., 2019; Keerthanan et al. 2020). Batch experiments were conducted to evaluate the degradation of triclosan, caffeine, and parabens from greywater, employing response surface methodology (RSM) and principal component analysis (PCA) to optimize critical operational parameters. Parameters such as pH, biochar dosage, hydrogen peroxide concentration, and reaction time were systematically varied to determine the optimal conditions for maximum pollutant removal. PCA was utilized to identify the key factors influencing pollutant degradation, providing a clearer understanding of the relationships between variables and their effects on the efficiency of the process
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4966335
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