# An integrated, tiered microplastic workflow, supporting rapid broadscale detection options

## TL;DR

This paper introduces a new workflow for efficiently detecting microplastics in estuarine waters, enabling large-scale monitoring with cost-effective methods.

## Contribution

The study presents a tiered microplastic workflow that streamlines processing and enables scalable, accurate microplastic detection.

## Key findings

- The workflow processes 24 samples in five days using automated fluorescent particle counts and Python scripts.
- The Rapid Count Method achieves a 20% error margin and aligns well with FTIR results (R² = 0.83).
- Flexible resolution allows switching between analytical methods while maintaining data comparability.

## Abstract

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:•Streamlined processing: 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.•Rapid Count Method: Enabling microplastic identification in broadscale monitoring within a 20 % error margin. Script-based microplastic counts align with FTIR results (R² = 0.83).•Flexible resolution: Sample processing can be paused and switched to other analytical methods while maintaining data comparability ensuring data harmonisation.

Streamlined processing: 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.

Rapid Count Method: Enabling microplastic identification in broadscale monitoring within a 20 % error margin. Script-based microplastic counts align with FTIR results (R² = 0.83).

Flexible resolution: Sample processing can be paused and switched to other analytical methods while maintaining data comparability ensuring data harmonisation.

Image, graphical abstract