Samples from the secondary clarifier effluent of a waste water treatment plant (serving 98500 inhabitants) were analyzed to determine the microplastics (MP) emission. The samples were collected using a stainless steel centrifugal pump and filtered through a 10 μm stainless steel cartridge filter. Microplastics particles (MPPs) and microplastics fibers (MPFs) were recovered by chemical and physical sample purification. To remove natural organic matter, the samples were first subjected to oxidative treatment with H 2 O 2 and NaClO. Inorganic materials were subsequently removed by density separation in ZnCl 2 (ρ = 1.9 g/cm 3 ) using a centrifuge. Special centrifuge tubes were developed for this purpose. Sample analysis was performed on a Si filter by Raman micro-spectroscopy. Particles with a diameter (d p ) ≥ 10 μm were analyzed. The results were differentiated by dry and wet weather samples. On average, 5900 MPPs m −3 were identified in the effluent on wet weather days compared to 3000 MPPs m −3 on dry weather days. Most of the MPPs detected were in the 30 μm < d p < 100 μm size range. The MPFs ranged between 100 μm and 1000 μm in length. While most of the MPFs were of PET origin, the MPPs consisted mainly of PET, PP, PE and PS.

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The elimination of microplastic particles (MPP) and microplastic fibers (MPF) was investigated in the final treatment stage (sand filtration) in two municipal wastewater treatment plants (WWTP) and the final treatment stage (sand filtration) at a process WWTP of a manufacturer of polyvinyl chloride (PVC). At each sampling site, three samples on three different days were taken (before/after sand filtration). The samples were filtered through a 10 μm stainless steel cartridge filter utilizing a stainless steel centrifugal pump. Microplastics (MP) were separated from the wastewater matrix by oxidative treatment and density separation and analyzed by Raman microspectroscopy. Due to precautionary measures, procedural blanks were very low with a mean number of 4.3 ± 2.7 MPP and 0.88 ± 0.56 MPF within eight blank samples. The municipal WWTPs were able to eliminate 99.2% ± 0.29% and 99.4% ± 0.15% of MP in the sand filtration stage. The sand filtration of a PVC manufacturer eliminated 99.2%–99.9%.

This study is one of the first to investigate the recovery rate of high- and low-density microplastic particles (polyvinyl chloride and polypropylene) from wastewater treatment plant effluents or comparable technical facilities under nearly realistic experimental conditions. For this purpose, a method of continuous dosing of microplastic particles into an experimental flume for open-channel flow was developed. Subsequently, 12 samples were taken using volume-reduced sampling and the entire sample purification process including oxidative treatment (with hydrogen peroxide and sodium hypochlorite), density separation (with sodium polytungstate), and subsampling was carried out. Detection was conducted using automatic particle recognition and µ-Ramanspectroscopy. An average recovery rate of 27 ± 10% was determined for polypropylene microplastic particles (d = 53 ± 29 µm), decreasing with the particle size, and 78 ± 14% for polyvinyl chloride microplastic particles (d = 151 ± 37 µm). The results suggest that microplastic emissions from wastewater treatment plants are underestimated, particularly because the recovery rate of small microplastic particles < 50 µm is only 9%.

The packaging material of bottled water is suspected to be a source of microplastics (MP). In this preliminary study, the screw cap system was examined for its MP release potential. Therefore, the concentration of MP (≥10 μm) was determined in reusable polyethylene terephthalate (PET) bottles with polypropylene (PP) caps and polyethylene (PE) seals after opening and closing once and 11 times. The entire contents of the bottles were filtered onto a silicon filter after opening. The inside of the bottles and caps was rinsed with filtered water. The total Si filter area was analyzed by Raman microspectroscopy. After one opening, 131 ± 25 microplastic particles (MPP) per liter were detected. After 11 openings and closings, 242 ± 64 MPP/L were detected. The increase is caused by a significant increase in the number of PP particles from 100 ± 27 to 185 ± 52 MPP/L. The concentration of PE and PET particles did not change significantly. Regardless of polymer type and the number of openings and closings, 80% of the MPP were identified in the smallest size class investigated (10−50 μm). Using a specially developed method, the concentration of MPP was determined before the screw cap system was opened and did not differ from the procedural blank.

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This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (one single model and one model per class) with the Rectified Linear Unit function (ReLU) (hidden layer) as the activation function and the sigmoid function as the output layer were evaluated and compared to human-only annotation. Based on the one-model-per-class algorithm, an approach for human–machine teaming was developed. This method makes it possible to analyze microplastic (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) spectra with high recall (≥ 99.4%) and precision (≥ 97.1%). Compared to human-only spectra annotation, the human–machine teaming reduces the researchers’ time required per sample from several hours to less than one hour.

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