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Taking control of microplastics data: A comparison of control and blank data correction methods
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Researchers compared six commonly used methods for correcting microplastic data using procedural controls and blanks, finding significant variability in results depending on the correction method chosen and highlighting the urgent need for standardized data quality protocols.
Although significant headway has been achieved regarding method harmonisation for the analysis of microplastics, analysis and interpretation of control data has largely been overlooked. There is currently no consensus on the best method to utilise data generated from controls, and consequently many methods are arbitrarily employed. This study identified 6 commonly implemented strategies: a) No correction; b) Subtraction; c) Mean Subtraction; d) Spectral Similarity; e) Limits of detection/ limits of quantification (LOD/LOQ) or f) Statistical analysis, of which many variations are possible. Here, the 6 core methods and 45 variant methods (n = 51) thereof were used to correct a dummy dataset using control data. Most of the methods tested were too inflexible to account for the inherent variation present in microplastic data. Only 7 of the 51 methods tested (six LOD/LOQ methods and one statistical method) showed promise, removing between 96.3 % and 100 % of the contamination data from the dummy set. The remaining 44 methods resulted in deficient corrections for background contamination due to the heterogeneity of microplastics. These methods should be avoided in the future to avoid skewed results, especially in low abundance samples. Overall, LOD/LOQ methods or statistical analysis comparing means are recommended for future use in microplastic studies.
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This paper reviewed how blank and control sample data are currently used in microplastic research and found a significant lack of consensus on best practices, potentially undermining the reliability of published contamination measurements. Researchers proposed standardized approaches to collecting, analyzing, and reporting control data to better account for contamination introduced during sample processing. Improving control practices is essential for ensuring that measured microplastic concentrations reflect environmental reality rather than laboratory artifacts.
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This study highlights the critical importance of blank controls in microplastic research, demonstrating that laboratory and environmental contamination during sample processing can significantly compromise data reliability if not properly accounted for.
Microplastics are overestimated due to poor quality control of reagents
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This study demonstrated that inadequate quality control of chemical reagents used in microplastic analysis can introduce contamination that leads to systematic overestimation of microplastic concentrations in environmental samples, highlighting a critical methodological flaw in the field.
Solving the Nonalignment of Methods and Approaches Used in Microplastic Research to Consistently Characterize Risk
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Researchers proposed and tested methods to rescale microplastic data collected using different sampling and analysis approaches, addressing the lack of standardization that hampers comparisons across studies. They developed correction factors to account for differences in particle size ranges, sampling volumes, and analytical techniques. The study provides practical tools for harmonizing microplastic research data, which could significantly improve global pollution assessments.
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