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Py–GC–MS analysis for microplastics: Unlocking matrix challenges and sample recovery when analyzing wastewater for polypropylene and polystyrene

Water Research 2024 42 citations

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This study tested a common lab method for detecting microplastics in wastewater and found that while the analytical technique itself was reliable, the sample preparation steps caused significant loss of plastic material. This means some microplastic contamination studies may be underestimating the true levels in wastewater. Accurate measurement methods are important because wastewater is a major pathway through which microplastics reach drinking water and the environment.

Polymers
Study Type Environmental

Matrix interference and recovery when using pyrolysis gas chromatography (Py-GC-MS) to analyze wastewater for polystyrene (PS) and polypropylene (PP) microplastics (MP) was studied. Raw wastewater underwent a sample preparation train commonly applied for such matrix. The train consisted of six discrete steps to reduce the organic matter content without affecting MP in the sample. One large wastewater sample was collected, homogenized, and subdivided into 21 subsamples. Three samples were analyzed without further sample preparation. The remaining samples were divided in sets of three, and each set underwent an increasing number of steps of the procedure, up to the last set, which underwent the full treatment. The matrix effect on the determination of PS and PP was statistically evaluated by comparing in-matrix and external calibration curves at each step. Recovery of MP was assessed after each step by adding deuterated PS to the samples. A main finding was that there was no significant matrix effect for these polymers throughout the preparation train, suggesting that matrix components did not interfere with the analytical method. However, a significant loss of polymer mass was found between the steps, which may result in MPs falling below detection limits. Therefore, Py-GC-MS could be used for MP quantification before analysis by other techniques which require more extensive matrix removal. A downside of this approach is that analyzing such samples without matrix reduction will increase the need for instrumental maintenance.

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