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Application of electrostatic separation and differential scanning calorimetry for microplastic analysis in river sediments
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Researchers developed a combined method integrating electrostatic separation, density separation, and differential scanning calorimetry for microplastic analysis in river sediments, demonstrating that the approach can process 100-1000 g sample masses and achieved 98% mass reduction in sand matrices and 70-78% in sediments before DSC polymer identification.
A method with the potential for comprehensive microplastic monitoring in river sediments is presented in this study. We introduce a novel combination of electrostatic separation, density separation, and differential scanning calorimetry (DSC). Currently, microplastic analysis in sediments is limited in terms of sample masses, processing time, and analytical robustness. This work evaluated a method to process large sample masses efficiently and still obtain robust results. Four particulate matrices, including commercial sands and river sediments, were spiked with PCL, LD-PE, and PET microplastic particles (63–200 µm). Samples with a mass of 100 g and 1,000 g (sand only) contained 75 mg of each microplastic. After electrostatic separation, the mass of sand samples was reduced by 98%. Sediment samples showed a mass reduction of 70–78%. After density separation, the total mass reduction of sediment samples was above 99%. The increased concentration of total organic carbon seems to have the highest impact on mass reduction by electrostatic separation. Nevertheless, the recovery of microplastic was independent of the particulate matrix and was polymer-specific. In 100 g samples, the average recovery rates for PCL, LD-PE, and PET were 74 ± 9%, 93 ± 9%, and 120 ± 18%, respectively. The recoveries of microplastic from 1,000 g samples were 50 ± 8%, 114 ± 9%, and 82 ± 11%, respectively. In scale up experiments, high recoveries of all microplastics were observed with a decrease in standard deviation. Moreover, the biodegradable polymer PCL could be used as an internal standard to provide quality assurance of the process. This method can overcome the current limitations of routine microplastic analysis in particulate matrices. We conclude that this method can be applied for comprehensive microplastic monitoring in highly polluted sediments. More studies on electrostatic separation and polymer-specific recovery rates in complex matrices are proposed.
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