Dicamba Degradation Using a Low-Cost Chlorine/Ferrous-Based AOP: ANN-PSO Model Development, Intermediate Identification, and Toxicity Assessment Using Microalgae

In this study, the degradation of Dicamba methyl ester (DME) was investigated using a low-cost chlorine/ferrous process. The degradation yield was determined by examining the influence of several factors, such as NaClO concentration, dicamba concentration, FeSO4 catalyst mass, and initial solution pH, over a 15 min period. To determine the optimal conditions for DME degradation, an artificial neural network (ANN) model with 4-5-1 architecture was developed. The particle swarm optimization (PSO) algorithm was then utilized in conjunction with the ANN model to identify the optimal factor levels predicted yield of 88%. The following optimal conditions were identified: [NaClO] = 422.3 μM, [Dicamba] = 4.4 mg/L, [FeSO4] = 9.5 mg/L, and a pH of 2.56. GC/MS analysis was conducted to identify the byproducts that were generated during DME degradation. Benzene, 1,2,4-trichloro-3-methoxy DME-BP (m/z 210) was the only identified byproduct that contained chlorine in its structure. A proposed reaction pathway for the DME degradation was suggested based on the obtained mass spectra. In the final stage of the study, total organic carbon (TOC) removal was analyzed using a Fenton-like process under optimized conditions for a duration of 195 min. To confirm the effectiveness of DME and its byproduct degradation, toxicity assessments were performed using the Chlorella vulgaris microalgae as a model organism. The results indicated a low toxicity of 20% when the DME mineralization reached 62.52%. These findings provide strong evidence that support the effectiveness of the proposed low-cost system for DME removal.This research was financially supported by the Directorate-General for Scientific Research and Technological Development (DGRSDT) Algeria. This research was funded by the Korea Environment Industry and Technology Institute (KEITI) through National Research Foundation of Korea (NRF) (no. 2022R1A2C2011252) and supported by the Industrial Strategic Technology Development Program (1415186317, 20010276) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).Peer reviewe