Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling

Abstract

The study of microplastics (MPs) in soils is impeded by similarities between plastic and non-plastic particles and the misidentification of MP by current analytical methods such as visual microscopic examination. Soil MPs pose serious ecological and public health risks because of their abundance, persistence, and ubiquity. Thus, reliable identification methods are badly needed for scientific study. One possible solution is UV–Vis–NIR spectroscopy, which has the ability to rapidly identify and quantify concentrations of soil microplastics. In this study, a full-range, field portable spectrometer (350–2500 nm) with ultra-high spectral resolution (1.5 nm, 3.0 nm, and 3.8 nm) identified three types of common plastics: low-density polyethylene (LDPE), polyvinyl chloride (PVC), and polypropylene (PP). Three sets of artificially MP-treated vermiculite soil samples were prepared for model prediction testing and validation: 150 samples for model calibration and 50 samples for model validation. A partial least square regression model using the spectral signatures for quantification of soil and MP mixtures was built with all three plastic polymers. Prediction R2 values of all three polymers showed promising results: polypropylene R2 = 0.943, polyvinyl chloride R2 = 0.983, and polyethylene R2 = 0.957. Our study supports previous work showing that combining ultra-high-resolution UV–Vis–NIR spectrometry with quantitative modeling can improve the accuracy and speed of MP identification and quantification in soil.

Keywords

• vermiculite
• plastic
• spectral
• quantitative
• polymer
• regression