The importance of ensuring representative sample volumes in microplastic monitoring - A predictive methodology DOI Creative Commons
Richard K. Cross, S. Craig Roberts, Monika D. Jürgens

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Abstract A large body of literature is available quantifying microplastic contamination in freshwater and marine systems across the globe. “Microplastics” do not represent a single analyte. Rather, they are usually operationally defined based on their size, polymer shape, dependent sample collection method analytical range measurement technique. In absence standardised methods, significant variability uncertainty remains as to how compare data from different sources so consider exposure correctly. To examine issue, previously compiled database containing 1603 observations 208 concentrations globe between 1971 2020 was analysed. Reported span nine orders magnitude. Investigating relationship sampling methods reported concentrations, striking correlation smaller unit volumes higher observed. Many studies scored poorly quality scoring protocols according size taken. It critical that sufficient particles measured reduce errors random chance. Given inverse with particle abundance, volume required for representative should be calculated case-by-case, what microplastics under investigation where being measured. Here we have developed Representative Sample Volume Predictor (RSVP) tool, which standardises statistical prediction ensure detected given level confidence. Reviewing reports freshwater, found ~12% would false negative error rate >5%. Such run risk wrongly concluding absent samples quantitative. The RSVP tool also provides harmonised Poisson point process estimation confidence intervals test whether two likely significantly different, even replication. this way, demonstrate application evaluate historic but assist new study designs environmental relevant reliable. can applied other randomly dispersed events space or time, has potential transdisciplinary tool.

Language: Английский

The importance of ensuring representative sample volumes in microplastic monitoring - A predictive methodology DOI Creative Commons
Richard K. Cross, S. Craig Roberts, Monika D. Jürgens

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Abstract A large body of literature is available quantifying microplastic contamination in freshwater and marine systems across the globe. “Microplastics” do not represent a single analyte. Rather, they are usually operationally defined based on their size, polymer shape, dependent sample collection method analytical range measurement technique. In absence standardised methods, significant variability uncertainty remains as to how compare data from different sources so consider exposure correctly. To examine issue, previously compiled database containing 1603 observations 208 concentrations globe between 1971 2020 was analysed. Reported span nine orders magnitude. Investigating relationship sampling methods reported concentrations, striking correlation smaller unit volumes higher observed. Many studies scored poorly quality scoring protocols according size taken. It critical that sufficient particles measured reduce errors random chance. Given inverse with particle abundance, volume required for representative should be calculated case-by-case, what microplastics under investigation where being measured. Here we have developed Representative Sample Volume Predictor (RSVP) tool, which standardises statistical prediction ensure detected given level confidence. Reviewing reports freshwater, found ~12% would false negative error rate >5%. Such run risk wrongly concluding absent samples quantitative. The RSVP tool also provides harmonised Poisson point process estimation confidence intervals test whether two likely significantly different, even replication. this way, demonstrate application evaluate historic but assist new study designs environmental relevant reliable. can applied other randomly dispersed events space or time, has potential transdisciplinary tool.

Language: Английский

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