Abstract
The food industry is considered to be one of the greatest sources of environmental contamination produced by dyes. Moreover, a large number of commercial food dyes and their by-products have been shown to be toxic, having chronic effects on human health. The search for efficient processes with which to treat these compounds is, therefore, necessary. In this work, the photo-peroxidation and photo-Fenton processes using UV-C and sunlight radiations were evaluated in order to degrade two synthetic dyes commonly found in food industry wastewater, sunset yellow and tartrazine, in an aqueous mixture. The preliminary results showed that the photo-Fenton/UV-C system was the most efficient. The ANOVA analysis results indicated a good fit of the model. The higher degradations were obtained using 50 mg L−1 of [H2O2], 1 mg L−1 of [Fe], a pH of 3.5, and a lower surface area/volume ratio (0.02 cm2 mL−1). In the kinetic study, a good fit was found for the kinetic model proposed by Chan and Chu. Degradations higher than 99% and 78% were obtained for the chromophore and aromatic groups, respectively, in 180 min. Toxicity tests showed that post-treatment samples did not interfere in the development of Lactuca sativa seeds and Escherichia coli and Salmonella enteritidis bacteria strains. The photo-Fenton/UV-C system can, therefore, be considered an efficient treatment for the degradation of the mixture of sunset yellow and tartrazine dyes under the conditions evaluated.
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The authors are very grateful to the following Brazilian fostering agencies and institutions: Fundação de Amparo a Ciência e Tecnologia de Pernambuco (FACEPE), Fundação de Apoio ao Desenvolvimento da Universidade Federal de Pernambuco (FADE/UFPE), Núcleo de Química Analítica Avançada do Estado de Pernambuco (NUQAAPE/FACEPE), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and the Laboratory of Protein Biochemistry of UFPE.
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do Nascimento, G.E., Cavalcanti, V.O.M., Santana, R.M.R. et al. Degradation of a Sunset Yellow and Tartrazine Dye Mixture: Optimization Using Statistical Design and Empirical Mathematical Modeling. Water Air Soil Pollut 231, 254 (2020). https://doi.org/10.1007/s11270-020-04547-5
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DOI: https://doi.org/10.1007/s11270-020-04547-5