Response Surface Methodology in the Photo-Fenton Process for COD Reduction in an Atrazine/Methomyl Mixture
Source abstract
Abstract
This study optimized a homogeneous photo-Fenton process for the simultaneous degradation of the emerging pesticides atrazine and methomyl in water using Response Surface Methodology (RSM). A synthetic agricultural effluent containing 2.0 mg L−1 of each pesticide (COD = 103.2 mg O2 L−1; TOC = 26.1 mg C L−1; BOD5 = 45.8 mg O2 L−1) was treated in a recirculating UV–H2O2/Fe2+ reactor. A 23 factorial design with replication and five central points identified the H2O2/Fe2+ ratio and irradiation time as the main factors controlling mineralization, achieving up to 88.9% COD removal in the best screening run. Steepest-ascent experiments were then performed to approach the region of maximum response, followed by a rotatable Central Composite Design (20 runs). The resulting quadratic model explained 98.14% of the COD variance (R2 = 0.9814; adjusted R2 = 0.9646; predicted R2 = 0.8591; CV = 0.2736%) and predicted a maximum COD removal of 94.5% at a volumetric flow rate of 0.466 L min−1, a Fenton ratio of 12.713 mg mg−1, and a treatment time of 71.0 min. Experimental validation under these optimized conditions yielded highly reproducible removals of 94.2 ± 0.04% COD and 81% TOC, confirming the predictive capability of the RSM model and demonstrating a high degree of organic mineralization. The response surfaces revealed that increasing the Fenton ratio enhances oxidation up to an optimum, beyond which hydroxyl-radical self-scavenging slightly decreases efficiency. Overall, the integration of multivariable experimental design and RSM provided a robust framework to maximize photo-Fenton performance with moderate reagent consumption and operating time, consolidating this process as a viable alternative for the mitigation of pesticide-laden agricultural wastewaters.
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Research context
OpenAlex topics: Advanced oxidation water treatment · Pesticide and Herbicide Environmental Studies · Adsorption and biosorption for pollutant removal
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Chemical and Textile Engineering, Universidad Nacional de Ingeniería, Lima 15333, Peru
Faculty of Environmental Engineering and Natural Resources, Universidad Nacional del Callao, Callao 07011, Peru
Escuela de Posgrado, Universidad Nacional Agraria La Molina, Lima 15024, Peru
Facultad de Ciencias Agrarias, Universidad Nacional Santiago Antúnez de Mayolo, Huaraz 02002, Peru
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