Microplastic Mass Estimation Using Two-Dimensional Chemical Images from Quantum-Cascade Laser-Based Infrared...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Spectroscopic chemical imaging provides particle count and characterization but lacks mass information.
• Particle mass is critical for toxicological assessments and environmental impact studies.
• Existing methods for plastic particle analysis are limited in providing mass data.

Purpose of the Study:

• To develop and validate a novel hybrid model for estimating the mass of plastic particles.
• To integrate bidimensional parameters from infrared imaging spectroscopy into mass estimation.
• To create a user-friendly tool for routine mass estimation of microplastics.

Main Methods:

• Utilized tunable quantum-cascade laser-based infrared imaging spectroscopy to obtain bidimensional particle parameters (height, width, area, perimeter).
• Developed a hybrid model employing geometric approximations: cylinders for fibers, parallelepipeds/spheroids for fragments based on circularity.
• Tested and hybridized existing models, validating against known plastic particle sets (20-1500 μm, 190-9400 μg).

Main Results:

• The hybrid model successfully estimated plastic particle mass with relative errors below 20%.
• The approach demonstrated effectiveness across a wide range of particle sizes and weights.
• The developed Excel-based spreadsheet (NOMME) facilitates streamlined application of the mass estimation model.

Conclusions:

• The novel hybrid model offers a reliable method for estimating plastic particle mass from spectroscopic imaging data.
• This technique enhances the analytical capabilities of current spectroscopic methods, particularly for toxicological applications.
• The NOMME tool provides a practical solution for researchers needing to quantify microplastic mass.