Using optimized particle imaging of micro-Raman to characterize microplastics in water samples

Abstract

Characterizing the chemical properties, morphologies, size, and quantities of microplastics (MPs) in water samples with high precision is critically important for understanding the environmental behaviors of MPs. Traditional detection methods, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy point-by-point detection, provide worthy reference techniques but are time- and labor-consuming. We established a super time-saving and high-precision technique to characterize MPs using micro-Raman automatic particle identification (MR-API). Based on the identification of PS spheres, screen magnification, exposure time, and the number of scans are selected as crucial detection parameters for MR-API analysis, which highly affect the precision of the results. Detecting particles down to 1 μm requires magnification of the mosaic until the scale showed 200 μm. The recommended setting parameters were 83.33 or 100 ms exposure time, 20 scans, 7 mW laser power, and 1 μm image pixel size, suitable for polystyrene (PS), polypropylene (PP), polyethylene terephthalate (PET), polyethylene (PE), polyvinyl chloride (PVC), and polyamide (PA) particles detection. With the complete procedure of MR-API measurements, the recovery of MPs was 61.67–90.00 %. To validate the feasibility of the MR-API, the method was used to detect samples of known plastic types (mask leachates) and unknown plastic types (urban lake). A total of 4540 particles in the sample of mask leachates consuming 35 h 50 min 43 s, and 0.92 ± 0.49 % of particles were identified as MPs. The urban river sample efficiently identified PP, PET, PE, PVC, PS, EVA, and VC/VAC MPs using this method. The detected MPs size ranged from 8.3 to 5000 μm, saving 75.03 % and 58.38 % of the time compared to the conventional micro-FTIR and micro-Raman point-by-point methods, respectively. Therefore, this method is effective for detecting MPs in the environmental samples and has excellent prospects.