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Enhancing wastewater pretreatment for microplastic detection: optimization of Fenton’s reagent for organic matter removal

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Researchers optimized Fenton's reagent—a hydrogen peroxide and iron ion mixture—for removing organic matter from wastewater samples prior to microplastic detection. They found that higher H2O2 dosage and longer reaction times (up to 48 hours) drove the greatest organic matter removal (up to 70%), while iron dosage had less influence, and developed a predictive model for estimating removal efficiency.

Study Type Environmental

Microplastics, pervasive environmental pollutants, poses significant risks to ecosystems and human health. However, their effective detection and analysis in environmental samples are often hindered by high organic matter content. Therefore, organic digestion is often taken as a pre-treatment step before microplastic analysis. Fenton’s reagent, a combination hydrogen peroxide (H2O2) and iron ions, is widely used for organic digestion due to its efficiency. However, its application has largely relied on empirical practices, leading to waste of reagents, release of extra contaminants into the environment, and inconsistent and noncomparable results across studies. This study evaluated and optimized the use of Fenton’s reagent for organic degradation to improve microplastic detectability. By varying the digestion time, and the dosages of H2O2 and Fe(II), the study identified optimal conditions for organic matter removal. Results showed that higher H2O2 dosage and extended reaction time significantly improved degradation, while Fe(II) dosage was less influential. Organic matter removal increased rapidly during the first 6 hours of reaction, then gradually slowed. The highest removal achieved was 70% under conditions of 150 mL H2O2 (30%), 5 mL Fe(II) (80 mM), and 48 hours, per gram of organic matter. Statistical analysis and modeling with ANOVA and Response Surface Methodology (RSM) evaluated the significance of factors. A predictive equation for the estimation of removal rate under various conditions was developed based on a regression model. Visual analysis confirmed effective organic matter removal with minimal impact on microplastic morphology, although minor fragmentation was observed in weathered particles. The study offers an optimized, reproducible pre-treatment protocol that improves microplastic analysis while minimizing reagent use and environmental impact.

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