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How fast, how far: Diversification and adoption of novel methods in aquatic microplastic monitoring
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Despite calls for standardized methods, the rate of new microplastic sampling, extraction, and quantification methods has accelerated since 2011, but most researchers continue using well-established techniques, and inconsistencies in reporting units stem more from researcher choices than from method selection.
Since 2004, there has been a marked diversification in the methods used to determine aquatic microplastic (MP) concentrations. Despite calls for a unified approach to MP sampling, the proliferation of new methods has accelerated in recent years. Both minor method adaptations and entirely novel approaches have been introduced to overcome barriers to reliable MP sampling, extraction and quantification, resulting in a variety of complimentary but also competing approaches. However, there is little clarity regarding the extent to which new methods are acknowledged and adopted, or of the apparent drivers of, as well as barriers to, said adoption. To explore these issues, the rate of method diversification was examined in a systematic review. The rate and degree of diversification were determined by scoring each method by its "degree of novelty": highly novel methods, secondary adaptations of existing methods and smaller, tertiary adaptations of existing methods. This analysis revealed that the rate of method diversification has been greatest since 2011. Our results indicate limited use of these novel methods and adaptations in the subsequent literature, with many researchers falling back on methods that are well established in the existing literature. Importantly, there is little consistency in the units used when reporting MP concentrations. However, these differences are seldom driven by method selection and are rather the result of discrepancies between researchers. Thus, in understanding the requirements of comparability and consistent reporting for monitoring purposes, we can apply a diverse approach to sampling whilst maintaining the applicability and usefulness of the resulting data.
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