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Recommended best practices for collecting, analyzing, and reporting microplastics in environmental media: Lessons learned from comprehensive monitoring of San Francisco Bay
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Researchers developed and tested standardized methods for collecting, analyzing, and reporting microplastics during a comprehensive monitoring study of San Francisco Bay. They identified best practices for sampling across water, sediment, and fish tissue, and highlighted how methodological differences can lead to incomparable results between studies. The study provides a practical framework that other monitoring programs can adopt to improve the consistency and reliability of microplastic data.
Microplastics are ubiquitous and persistent contaminants in the ocean and a pervasive and preventable threat to the health of marine ecosystems. Microplastics come in a wide variety of shapes, sizes, and plastic types, each with unique physical and chemical properties and toxicological impacts. Understanding the magnitude of the microplastic problem and determining the highest priorities for mitigation require accurate measures of microplastic occurrence in the environment and identification of likely sources. The field of microplastic pollution is in its infancy, and there are not yet widely accepted standards for sample collection, laboratory analyses, quality assurance/quality control (QA/QC), or reporting of microplastics in environmental samples. Based on a comprehensive assessment of microplastics in San Francisco Bay water, sediment, fish, bivalves, stormwater, and wastewater effluent, we developed recommended best practices for collecting, analyzing, and reporting microplastics in environmental media. We recommend factors to consider in microplastic study design, particularly in regard to site selection and sampling methods. We also highlight the need for standard QA/QC practices such as collection of field and laboratory blanks, use of methods beyond microscopy to identify particle composition, and standardized reporting practices, including suggested vocabulary for particle classification.
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