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An integrated, tiered microplastic workflow, supporting rapid broadscale detection options
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Researchers developed an integrated, tiered microplastic analytical workflow supporting rapid broadscale detection options, designed to enable coordinated and harmonized large-scale monitoring initiatives that address current limitations in assessing microplastic presence, distribution, and environmental impacts.
With growing concerns regarding microplastic pollution, there is an urgent need to improve understanding of their presence, distribution, and environmental impacts. This necessitates more coordinated and harmonised large-scale microplastic monitoring initiatives. However, such assessments are traditionally expensive, labour-intensive, and hindered by a lack of standardised sampling and analytical protocols, which impede rapid, yet accurate identification of microplastic sources and ecological risks. To improve environmental microplastic contamination estimates, this study proposes a rapid, cost-effective, and bulk-processing approach within a criteria-driven Tiered Microplastics Workflow (TMW). This approach enables the efficient quantification of microplastic contamination in estuarine surface waters, offering adaptable levels of analytical resolution, that is scalable for environmental monitoring. Key features of the TMW include:•: sieving, digestion, density separation, vacuum degassing, size-classed filtration, Nile Red staining, and automated fluorescent particle counts via a Python script, enabling 24 samples to be processed in five days.• Enabling microplastic identification in broadscale monitoring within a 20 % error margin. Script-based microplastic counts align with FTIR results (R² = 0.83).• Sample processing can be paused and switched to other analytical methods while maintaining data comparability ensuring data harmonisation.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.