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Isolation and Extraction of Microplastics from Environmental Samples: An Evaluation of Practical Approaches and Recommendations for Further Harmonization

Applied Spectroscopy 2020 200 citations

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Isolation and extraction methods for microplastics from environmental samples were evaluated and compared, tracing the evolution of methodologies from early studies in the 1970s to current approaches. The review identified persistent inconsistencies in extraction efficiency across methods as a major obstacle to comparing microplastic contamination data across studies and locations.

Researchers have been identifying microplastics in environmental samples dating back to the 1970s. Today, microplastics are a recognized environmental pollutant attracting a large amount of public and government attention, and in the last few years the number of scientific publications has grown exponentially. An underlying theme within this research field is to achieve a consensus for adopting a set of appropriate procedures to accurately identify and quantify microplastics within diverse matrices. These methods should then be harmonized to produce quantifiable data that is reproducible and comparable around the world. In addition, clear and concise guidelines for standard analytical protocols should be made available to researchers. In keeping with the theme of this special issue, the goals of this focal point review are to provide researchers with an overview of approaches to isolate and extract microplastics from different matrices, highlight associated methodological constraints and the necessary steps for conducting procedural controls and quality assurance. Simple samples, including water and sediments with low organic content, can be filtered and sieved. Stepwise procedures require density separation or digestion before filtration. Finally, complex matrices require more extensive steps with both digestion and density adjustments to assist plastic isolation. Implementing appropriate methods with a harmonized approach from sample collection to data analysis will allow comparisons across the research community.

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