Optimization of photochemical decomposition acetamiprid pesticide from aqueous solutions and effluent toxicity assessment by Pseudomonas aeruginosa BCRC using response surface methodology

• Main Authors: Ali Toolabi, Mohammad Malakootian, Mohammad Taghi Ghaneian, Ali Esrafili, Mohammad Hassan Ehrampoush, Maesome Tabatabaei, Mohsen AskarShahi
• Format: Article
• Language: English
• Published: SpringerOpen 2017-08-01
• Series: AMB Express
• Subjects: Photocatalytic decomposition; Acetamiprid; Toxicity assessment; Pseudomonas aeruginosa BCRC
• Online Access: http://link.springer.com/article/10.1186/s13568-017-0455-5

Abstract Contamination of water resources by acetamiprid pesticide is considered one of the main environmental problems. The aim of this study was the optimization of acetamiprid removal from aqueous solutions by TiO2/Fe3O4/SiO2 nanocomposite using the response surface methodology (RSM) with toxicity assessment by Pseudomonas aeruginosa BCRC. To obtain the optimum condition for acetamiprid degradation using RSM and central composite design (CCD). The magnetic TiO2/Fe3O4/SiO2 nanocomposite was synthesized using co-precipitation and sol–gel methods. The surface morphology of the nanocomposite and magnetic properties of the as-synthesized Fe3O4 nanoparticles were characterised by scanning electron microscope and vibrating sample magnetometer, respectively. In this study, toxicity assessment tests have been carried out by determining the activity of dehydrogenase enzyme reducing Resazurin (RR) and colony forming unit (CFU) methods. According to CCD, quadratic optimal model with R2 = 0.99 was used. By analysis of variance, the most effective values of each factor were determined in each experiment. According to the results, the most optimal conditions for removal efficiency of acetamiprid (pH = 7.5, contact time = 65 min, and dose of nanoparticle 550 mg/L) was obtained at 76.55%. Effect concentration (EC50) for RR and CFU test were 1.950 and 2.050 mg/L, respectively. Based on the results obtained from the model, predicted response values showed high congruence with actual response values. And, the model was suitable for the experiment’s design conditions.