Analysis of Microplastics in Roadside Debris by Py-GC-MS

Importance of the Topic

Microplastics are ubiquitous pollutants originating from the fragmentation of larger plastic items. Their small size and widespread distribution pose risks to ecosystems and human health. Reliable methods for identifying and quantifying microplastics in complex environmental matrices are essential for monitoring pollution sources, assessing remediation efforts, and informing regulatory strategies.

Study Objectives and Overview

This study demonstrates a streamlined workflow for the individual qualitative and quantitative analysis of microplastics collected from roadside debris. By combining pyrolysis-GC-MS with F-Search MPs 2.0 software, the authors aim to eliminate laborious pretreatment steps and achieve accurate profiling of multiple polymer types in a single run.

Methodology

The analytical procedure uses a calibration reference containing twelve high-production plastics. Known masses (0.4, 2.0, 4.0 mg) of this reference are loaded into sample cups with quartz wool and pyrolyzed at 600 °C. Roadside debris samples (approximately 4.1 mg) are spiked with CaCO3, packed with quartz wool, and analyzed under identical conditions. The GC-MS method employs a temperature program from 40 °C to 320 °C and electron ionization. Calibration curves for each polymer are generated via F-Search MPs 2.0, using characteristic pyrolysis fragments and selected quantitative ions.

Instrumentation

• Pyrolyzer EGA/PY-3030D Multi-Shot with AS-1020E Auto-Shot Sampler
• Gas Chromatograph-Mass Spectrometer GCMS-QP 2020 NX
• UAMP column kit for separation

Main Results and Discussion

All twelve polymers exhibited strong linearity (R2 ≥ 0.995). Analysis of the roadside sample identified six polymers with similarity scores above 90 percent: polyethylene (PE), polypropylene (PP), polystyrene (PS), styrene-butadiene rubber (SBR), polyethylene terephthalate (PET), polymethyl methacrylate (PMMA), nylon-6 (N-6), and nylon-6,6 (N-66). Quantitative results indicated PE as the dominant component (44 percent of total), reflecting contributions from packaging films and containers. SBR represented the second largest fraction (37 percent), consistent with tire wear debris. Comparison of selected ion monitoring chromatograms and mass spectra against standards confirmed the reliability of identifications.

Benefits and Practical Applications

• Eliminates time-consuming manual sorting of microplastics from debris.
• Provides simultaneous qualitative and quantitative data for multiple polymers.
• Reduces operator workload and improves throughput in environmental monitoring labs.
• Supports quality assurance workflows in pollution assessment and remediation studies.

Future Trends and Potential Applications

Advances may include high-throughput automation, integration with online data platforms for real-time monitoring, and extension to airborne and aquatic microplastic analysis. Coupling with complementary spectroscopic techniques could further enhance polymer identification and trace additive detection.

Conclusion

The combined Py-GC-MS and F-Search MPs 2.0 approach offers a robust, pretreatment-free solution for comprehensive microplastic analysis in complex environmental samples. Calibration with a multi-polymer reference ensures accurate quantitation, while software-assisted spectral matching streamlines identification. This method holds promise for routine monitoring of microplastic pollution along roadways, in soils, and in aquatic systems.