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Laser Ablation-Proton Transfer Reaction-Mass Spectrometry (LA-PTR-MS): A Gas-Phase Detection Method for Microplastics Content in Soil Based on Laser Ablation Mass Spectrometry under Small Sample Sizes
Summary
A new laser ablation–proton transfer reaction mass spectrometry method was developed to rapidly detect and quantify PBAT and polystyrene microplastics in soil using diagnostic volatile markers, achieving R² values above 0.994 with small sample sizes and outperforming full-spectrum machine learning models. This approach addresses a critical gap in soil microplastic monitoring by enabling real-time, sensitive detection without the labor-intensive sample preparation required by conventional techniques.
Contemporary research on microplastics primarily focuses on aquatic and atmospheric systems, thereby leaving the understanding of microplastic pollution in soils relatively underdeveloped. Conventional detection techniques often require labor-intensive preparation, costly instrumentation, and lack real-time capability. To address these limitations, we develop a laser ablation–proton-transfer-reaction mass spectrometry (LA-PTR-MS) method for rapid, sensitive detection of microplastics in soil. Polybutylene adipate terephthalate (PBAT) and polystyrene (PS) serve as representative biodegradable and conventional microplastics. Thermal degradation analysis identifies diagnostic volatile markers: PBAT generates propene (C₃H₆) and nonanal (C₉H₁₈O), while PS produces styrene (C₈H₈). These ions are accurately detected and show less interference from soil background. Multiple linear regression (MLR) models using these targeted markers achieve superior small-sample accuracy (n = 21) — PS: R2 = 0.994, RMSE = 0.0729‰; PBAT: R2 = 0.997, RMSE = 0.0527‰ — outperforming machine learning models built on full-spectrum signals because they reduce noise, focus on chemically specific features, and avoid overfitting. Field validation finds PBAT and PS concentrations of ~ 1 ‰ in plots with prior application, while other areas remain below detection limits. These results indicate that targeted-feature analysis not only enhances robustness and interpretability but also minimizes noise and overfitting in small-sample conditions. Nevertheless, full-spectrum data mining retains importance for leveraging microplastic fingerprint information. Together, these insights demonstrate, for the first time, that a laser-based PTR-MS platform integrated with chemo selective targeted regression can achieve reliable, preparation-efficient quantification, establishing a new paradigm for rapid soil microplastic monitoring.