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Description
The occurrence of micro-and nanoplastic (MNP) debris in the environment is a research area of considerable public health concern. Various combinations of methods for extraction, isolation, and quantification of MNP have been applied but literature studies evaluating the appropriateness and efficacy of these protocols are lacking. A meta-analysis of the

The occurrence of micro-and nanoplastic (MNP) debris in the environment is a research area of considerable public health concern. Various combinations of methods for extraction, isolation, and quantification of MNP have been applied but literature studies evaluating the appropriateness and efficacy of these protocols are lacking. A meta-analysis of the literature (n=134; years 2010-2017) was conducted to inventory and assess the appropriateness of methodologies employed. Some 30.6% of studies employed visual identification only, which carried a calculated misidentification error of 25.8-74.2%. An additional 6.7% of studies reported counts for particles smaller than the cutoff value of the selected collection pore size, and 9.7% of studies utilized extraction solution densities which exclude some of the polymers commonly occurring in the environments investigated. A composite value of data vulnerability of 43.3% was determined for the sample, indicating considerable weaknesses in the robustness of information available on MNP occurrence and type. Additionally, the oxidizing solutions documented in the literature frequently were deemed unsuccessful in removing interfering organic matter. Whereas nanoplastics measuring <1 µm in diameter are likely principal drivers of health risk, polymer fragments reported on in the literature are much larger, measuring 10+ µm in diameter due to lack of standardized methods. Thus, current inventories of MNP in the environmental MNP feature data quality concerns that should be addressed moving forward by using more robust and standardized techniques for sampling, processing and polymer identification to improve data quality and avoid the risk of misclassification.
ContributorsCook, Cayla R (Author) / Halden, Rolf U. (Thesis advisor) / Hamilton, Kerry (Committee member) / Mascaro, Giuseppe (Committee member) / Arizona State University (Publisher)
Created2018