Quantitative Detection of Microplastics in Water through Fluorescence Signal Analysis

Microplastics (MPs) have recently been acknowledged as a new major and ubiquitous environmental pollutant with still unclear, yet potentially high, risks for different ecosystems and human health. Nevertheless, quantitative identification protocols rely on long and subjective visual counting necessarily performed on microscopes by well-trained operators. In this study, an automatic, fast, portable, and inexpensive method for the quantitative detection of MPs in water is proposed. The system is based on the typical optical setup of a fluorescence confocal microscope specifically adapted to automatically count dye-stained MPs in flowing liquids using a low-power laser beam. The fluorescence pulses emitted by flowing MPs are revealed and processed by a specific software using a pattern recognition algorithm to discriminate and count real fluorescence pulses out of noise fluctuations. The system was calibrated with commercial orange fluorescent 10 mu m and 1 mu m polystyrene microspheres, and remarkable agreement with theoretical predictions was obtained regarding different parameters. Tests were also performed with laboratory-prepared MPs dispersed in different types of real water samples. In this case, the agreement with theory was slightly worse and differences found in the quantitative results require further investigation. However, the present study demonstrated the proof of concept of a method for quick automated MP counting in water.

Automated summary

Microplastics (MPs) are a new and widespread environmental pollutant with potential risks to ecosystems and human health. Existing identification methods rely on manual visual counting by trained operators, which is time-consuming and subjective. This study presents an automatic, fast, portable, and inexpensive method for quantitative detection of MPs in water using a fluorescence confocal microscope. The system uses a low-power laser to count dye-stained MPs in flowing liquid based on fluorescence pulses, which are processed using a pattern recognition algorithm. The method was calibrated and showed agreement with theoretical predictions, although further investigation is needed for quantitative results in real water samples.