Determination of atmospherically deposited microplastics in moss samples

Determination of atmospherically deposited microplastics in moss samples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determination of atmospherically deposited microplastics in moss samples Mike Wenzel, Björn Fischer, Carmen Wolf, Christine Kube, Stefan Nickel, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4887548/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Standardized methods for sampling and detection of atmospherically deposited microplastics are lacking. Contrary to that, the use of moss as a biomonitoring system was established concerning other atmospheric pollutants, such as heavy metals and persistent organic pollutants. Only a few research groups actually focus on detecting atmospherically deposited microplastics in moss. In general, the determination of microplastics in environmental samples is commonly performed using a particle-based or mass-based analytical approach. However, a dearth of mass-based investigations is noticeable, especially for atmospherically deposited microplastics. Given this background, this study shows the determination of atmospherically deposited microplastics in moss utilizing thermal extraction desorption gas chromatography-mass spectrometry (TED-GC-MS) and Raman microspectroscopy (µRaman) to acquire both information. The moss samples analyzed were collected as part of the German moss survey 2020/2021, supported by the German Environment Agency. Three distinct sampling sites were investigated, which could be categorized based on their distances from potential emission sources. Results Concerning µRaman analysis, most microplastic particles could be determined within a 10 to 100 µm size range. Further, most microplastic aspect ratios were determined in a range of 0.25 to 1.00, indicating a fragmental shape. Additionally, a correlation between the number of microplastic particles determined and the distance of the potential emission source was observable. It was determined to be 688, 474, and 248 particles per sampling site with a distance of 150 m, 225 m, and 360 m. Both analytical approaches (TED-GC-MS & µRaman) concurred in identifying the polymer types (polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET)) in the moss samples. Concerning TED-GC-MS, 7 to 111 µg/g could be determined, depending on the polymer types and distance to the potential emission source. Conclusion µRaman and TED-GC-MS investigations demonstrated correlations between microplastic particle numbers, size, types, and mass concentrations with the distance of the potential emission source. The investigation corroborates the mutual complementarity of both analytical approaches, enabling more comprehensive information on samples. Biomonitoring Microplastics Atmospheric deposition TED-GC-MS µRaman Figures Figure 1 1. Introduction According to Plastics Europe (2022), a strong and continuing demand for plastic products is observable worldwide [ 35 ]. However, plastic waste follows a similar trend, and plastic pollution growth was predicted [ 7 ]. Due to the escalating environmental pollution with plastic, thousands of researches have focused on investigations concerning microplastics [ 37 ]. Plastic particles and fibers in a dimension of 1 µm − 1 mm are defined as microplastics [ 9 ], while plastics in a range of 1 mm – 5 mm are defined as large microplastics [ 22 ]. The potential ecotoxicological effects of microplastics and their impact on human health were discussed in the literature [ 46 ] since microplastic particles have been identified in all environmental compartments such as water [ 3 , 25 ], soil [ 29 , 34 ], and air [ 4 , 20 ]. However, these environmental compartments were investigated to a different extent. In this context, especially investigations of microplastics in the atmosphere are lacking in comparison to e.g., the marine environment [ 2 ]. This can be critical since human microplastics exposure occurs in different pathways, including inhalation, and potential effects on human health can be expected in this regard [ 10 , 36 ]. Moreover, the atmospheric transport and deposition can be a major pathway for microplastics to reach remote regions [ 16 ] and their occurrence in the atmosphere could have local climate impacts in future scenarios [ 1 ]. Therefore, the identification and quantification of atmospheric microplastic is crucial to achieve a better understanding of its sources and behavior in the atmosphere. In general, the identification and quantification of microplastics in microplastic research is commonly combined with the micro-spectroscopical (particle-based) and thermoanalytical (mass-based) methods [ 22 ]. Microspectroscopic methods, such as Fourier Transform Infrared (µFTIR) and Raman microspectroscopy (µRaman), provide information about the microplastics particle size distribution, particle morphology (shape), and identity [ 6 , 24 ]. To investigate those parameters regarding atmospheric microplastics is necessary, as the long-range transport of microplastics can be dependent on their shape and sizes [ 41 ]. Thermoanalytical methods, like pyrolysis-gas chromatography-mass spectrometry (py-GC/MS) and thermal extraction desorption-gas chromatography-mass spectrometry (TED-GC-MS), can be used to obtain information about the integral polymer mass and identity [ 13 – 15 , 19 ]. Using both approaches can be beneficial to gain more complete information on the samples investigated [ 3 ]. With regard to atmospheric microplastics, a lack of mass-based information is observable, and most investigations published were about particle sizes and numbers [ 21 , 32 , 47 ]. Further, standardized sampling strategies for atmospheric microplastics are not established yet. Concerning this issue, empirical evidence supporting the combination of a biomonitoring sampling system utilizing spider webs with subsequent mass-based analysis was provided [ 19 ]. Further, within the domain of biomonitoring systems, a few research groups presently focus on utilizing moss as a biomonitoring system to assess the fate of atmospherically deposited microplastics in the environment [ 8 , 23 , 38 ]. The use of moss was already established for other atmospheric pollutants such as heavy metals, nitrogen, and persistent organic components and is frequently used to investigate spatial and temporal trends, for instance in the framework of the international Moss Surveys conducted every five years since 1990 in Europe [ 12 , 17 , 30 , 31 , 40 ]. However, only visual and spectroscopical investigations have been performed yet for the determination of atmospheric microplastics in moss [ 23 , 38 ]. Hence, a new sample preparation method called µPEEL (microplastics extraction through exfoliation) was recently developed, which is suitable for TED-GC-MS and µRaman analysis to provide mass-based and particle-based information [ 45 ]. It was observed that the µPEEL method is appropriate for TED-GC-MS analysis since suitable and robust recoveries of microplastics could be achieved and matrix effects originating from organic and inorganic matrix components could be minimized. Furthermore, µPEEL provides a high separation quality proven by the possibility of contrast-based particle identification. To that end, this study shows mass-based and particle-based investigations concerning atmospherically deposited microplastics in environmental moss samples for the first time. The samples investigated were collected at three different sampling sites in Germany as part of the International Moss Survey 2020/2021 [ 43 ], funded and scientifically supported by the German Environment Agency. The µPEEL method was used for sample preparation, while the analysis was carried out utilizing µRaman and TED-GC-MS. 2. Materials and Methods 2.1 Test material, moss sampling and sampling site specifications TED-GC-MS calibration was performed using polymer test material provided by the Federal Institute for Materials Research and Testing (Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany). Therefore, microparticles consisting of polyethylene terephthalate (PET) (X 50 : 62 µm), polystyrene (PS) (X 50 : 206 µm), polypropylene (PP) (X 50 : 174 µm), polyethylene (PE) (X 50 : 20 µm), and styrene-butadiene-rubber (SBR) were used. SBR was used to identify and quantify tire wear (TW) as a source of microplastic pollution. Currently, SBR is unavailable in defined particle sizes and, therefore, not documented. In this study it was hypothesized that the particle number and polymer mass in a sample from a specific sampling site could depend on the distance from the potential emission source and the prevailing wind direction. Therefore, the moss samples investigated were collected at different sampling sites in Germany, which were determined by the application of a methodology combining decision modeling and inferential statistics [ 39 ]. The sampling was performed within the German Moss Survey 2020/2021 framework, supported by the German Environment Agency. The samples were collected as a composite sample of the specific sampling site. Nonetheless, all sampling specifications and requirements proposed in the Moss Survey Manual [ 43 ] were considered. This manual provides information and recommendations regarding methodological aspects, such as the minimum distances to be kept from trees or potential emission sources during the sampling. In this context, emission source-specific distance thresholds are defined in the manual [ 43 ]. It is assumed that if the distance of the emission source is higher than the defined threshold, no influence can be claimed or that regional deposition patterns overlay the respective effect. The threshold distances used rely on [ 43 ]. Distances to potential emission sources were determined using satellite images. The main wind direction regarding the specific sampling site was determined at the nearest available environmental monitoring station equipped with the respective measuring devices [ 39 ]. Table 1 summarizes the information about the distances to potential emission sources for the three investigated sampling sites. Hereby, the closest potential emission sources that are aligned with the main wind direction were considered [ 39 ]. More detailed sample information, such as the sampling site coordinates, distance thresholds for the respective type of emission source, the distance to the next monitoring station, and the moss species, are described in the supporting information (Section SI 1, Table 1 – 1 ). Table 1 Overview of potential emission sources, their distances to the respective sampling site, and information concerning the main wind direction. Sampling site [No.] Potential emission source Distance [m] / Cardinal direction to the sampling site Main wind direction 1 Agricultural area 150 / SW SW 2 Agricultural area 360 / W W 3 Single houses 225 / SW SW Town 225 / SW 2.2 Sample preparation Overall, three samples per sampling site were prepared (and analyzed) to increase the sample mass investigated which could have an impact on the representativeness of the sampling site. The moss sample preparation procedure was performed according to Wenzel et al. (2023) [ 45 ]. Dead material, attached litter, and brown parts were removed from the moss sample. The prepared moss sample represented the last two to three years of growth [ 43 ]. After this, the moss sample was dried in a stainless-steel oven (Memmert GmbH + C. KG, Schwabach, Germany) at 40°C for 24 hours. After that, the µPEEL method was applied. This method includes three main steps: exfoliation, sieving, and flotation. The exfoliation was carried out using 1 g of dry weighted (dw) moss and 20 g of glass beads (d = 0.5 mm, BioSpec Products, Inc. USA), placed in a round bottom flask (V = 200 mL), which was shaken in an overhead shaker (Heidolph Instruments GmbH & Co.KG, Schwabach, Germany) for 10 minutes. The sample size used was based on published studies concerning the determination of microplastics in moss [ 38 ]. Important to note is that the application of the µPEEL method does not involve homogenization procedures of the composite sample (e.g., cryo-grinding), as exfoliation becomes inapplicable. This is due to the abrasion mechanism itself and to avoid an impact on the microplastics shape and size. After exfoliation, the sample was sieved for one minute using a vibratory sieve shaker (Retsch® AS 200 control; Retsch GmbH, Haan, Germany). During the sieving, the collection pan was wetted with 20 mL of pure water. As a result, turbulence distributions of the glass beads used, the exfoliated matter (microplastics and moss abrasion) are reduced, and the green shots of moss remain on the sieve. Further, the exfoliated microplastics were separated from the moss abrasion by using the improved hydrophobicity-water/air-based enrichment cell for microplastics (µSEP) [ 44 ]. In this regard, the volume flow through the diffusor was set to 7 L/min, and the separation was performed for 30 minutes [ 45 ]. The aqueous sample suspension was collected in a round-bottom flask during the separation. For TED-GC-MS analysis, the sample suspension was vacuum filtered through a Whatman ™ cellulose nitrate membrane filter (diameter: 45 mm; pore size: 0.45 µm; GE Healthcare Life Sciences; Chalfont ST Giles, GB). The remaining sample on the filter was transferred to a 600-µL alumina crucible (Mettler Toledo GmbH, Gießen, Germany), which is directly usable for TED-GC-MS measurements. Concerning µRaman analysis, the sample suspension was vacuum filtered through a 25-mm diameter, gold-coated membrane filter with a pore size of 0.8 µm (APC GmbH, Eschborn, Germany). After filtration, the gold-coated filter was attached to a microscope slide using double-sided adhesive foil, and the underside of the slide was heated to 40°C for 10 seconds to remove tiny air bubbles. 2.3 Analytical Methods 2.3.1 TED-GC-MS The TED-GC-MS analytical method is well-established [ 13 , 18 , 44 ] and was also applied during the evaluation of the µPEEL method [ 45 ]. The sample was pyrolyzed using a thermogravimetric furnace (TGA 2, Mettler Toledo GmbH, Gießen, Germany). During pyrolysis, the sample analyzed was placed in a 600-µL alumina crucible (Mettler Toledo GmbH, Gießen, Germany). The pyrolysis products were transferred through an interface to the adsorption unit by applying a nitrogen flow and were sorbed on a solid-phase stir bar adsorber consisting of polydimethylsiloxane. Using an autosampler (MultiPurposeSampler), the adsorber was transferred to the thermal desorption unit. Interface, adsorber, adsorption-, desorption unit, and the MultiPurposeSampler used are commercially available from Gerstel GmbH & Co KG, Mülheim an der Ruhr, Germany. Afterwards, the pyrolysis products were desorbed, cryofocused (CIS4, Gerstel GmbH & Co KG, Mülheim an der Ruhr, Germany), and injected into the gas chromatograph (GC7890, Agilent, Santa Clara, California, USA). After the chromatographic separation, the pyrolysis products were analyzed using a mass spectrometer (5977B MSD, Agilent). Detailed setup parameters can be found in the supporting information (Section SI 2, Table 2 1, Table 2 2 ). 2.3.2 µRaman Within the particle-based µRaman analysis, an alpha300 R Confocal Raman microscope (WITec, Ulm, Germany) was used, and three main steps - capturing a light microscopic dark field image, image processing, and Raman microscopic analysis - were included. The Zeiss EC Epiplan HD 20 × / 0.5 NA microscope objective was used to cover a light microscopic dark field image with an area of 608.61 mm 2 (24.67 mm × 24.67 mm). 81× 81 images were stitched on the X- and Y-axis, and 13 images per position on the Z-axis were captured. In the dark field, all particles appear bright against the background, originating from the gold-coated filter. Using the software WITec ParticleScout (version 5.3.18.110), the image captured was processed, and the software can detect the bright particles automatically by calculating a brightness threshold (bright particles against a dark background). Further, all particles were masked by the software. As a result, information about the particle size can be obtained, as described in Section 2.4 in detail. The Raman microscopic identification was performed using a WITec UHTS 300 spectrometer in combination with an Andor iDus Deep Depletion charge-coupled device (CCD) camera (cooled to − 60°C), which were both attached to the alpha300R Confocal Raman microscope. A reflection grating with 600 lines/mm could achieve an average spectral resolution of 3.1 cm − 1 /pixel. Particle measurements were performed using a single-mode laser with an excitation wavelength of 532 nm, a laser power of 2 mW, and a Zeiss EC Epiplan-Neofluar DIC 50 × /0.8 NA microscope objective. The particles were automatically centered in the laser focus using the particle masking coordinates. For each particle, spectral autofocus was used for focus adjustments, and Raman spectra were recorded by accumulating 5 × 1 second of exposure time. 2.4 Data evaluation Regarding TED-GC-MS analysis, the focus was on the investigation of the polymers PS, PP, PET, and SBR (a marker for tire wear (TW)). The identification and quantification were performed according to the previous study of the authors [ 45 ]. Polymer-specific pyrolysis products (qualifier products) at their retention times and their respective mass spectra were considered for identification. The area of a specific fragment ion (quantifier ion) of one characteristic pyrolysis product (quantifier product) was determined for quantification. In this context, polymer masses in a sample were calculated using an external calibration. All TED-GC-MS data were evaluated using the MSD ChemStation data analysis software (Version: F.01.03.2357, Agilent Technologies, Inc.). Details about the data evaluation concerning TED-GC-MS analysis, such as the utilized pyrolysis products, can be found in the supporting information (Section SI 3, Table 3 − 1). Concerning µRaman analysis, the particle sizes and shapes were determined with the use of the maximum and minimum Feret diameters (Fmax and Fmin). Given the number of pixels necessary for masking a particle and the area per pixel, the software (WITec ParticleScout) can calculate the particle area, circular equivalent diameter, and spherical equivalent volume. The minimum and maximum Feret diameter were determined by measuring the maximum and minimum distances observed when the particle is rotated between two parallel tangents at any angle. In order to represent the particle size, the determined Fmax was used in this study, while the aspect ratio (Feret min / Feret max) was used to provide information about the particle shape. With regard to the identification of the particles, a minimum and maximum size threshold of Fmax were set within the analysis to reduce the measurement-time effort. In this context, a minimum size threshold of 10 µm was set. The maximum size threshold depended on the particle load, and particles up to 1000 µm were investigated. However, the µRaman results shown in this study only refer to 10–200 µm particle sizes. This is based on the observation that no microplastics could be identified at larger dimensions within each sampling location, even when a higher maximum size threshold was applied. Moreover, microplastics with an aspect ratio of 0.25 were classified as fragments in this study. Moreover, approximately 2–10 % f the whole filter surface was investigated due to the particle homogeneity assessment demonstrated in a previous study [ 45 ]. A slight variation in the area observed ensured that almost a consistent particle number could be analyzed for each sample. The determined numbers of microplastics (n) mentioned for each sampling site are extrapolated considering the whole filter surface area and the triplicate measurements were merged together. Actual findings for each sampling site are shown in the supporting information (Section SI 3.5, Table 3 – 3 ). Overall, the polymer identification was carried out using a commercially available database (S.T. Japan-Europe GmbH, Microplastics & Related Compounds Database, L60035). It contains 1475 polymer spectra for comparison purposes. Due to the high variability of reported particle sizes and microplastic types concerning atmospheric microplastics [ 47 ], the chosen analytical method seemed to be a well-suited compromise between an acceptable sample throughput (ecological and economic) and compatibility purposes with other studies. 2.5 Quality assurance All devices were made of glass (sampling glass bottle, round bottom flask, glass beads, µSEP) or stainless steel (oven, vibratory sieve shaker) for quality assurance. The removal of dead material, attached litter, and brown parts was carried out in a laminar flow box (Karl Bleymehl Reinraumtechnik GmbH, ASW-UD, Inden, Germany), and all experiments were performed in a laboratory stainless steel fume hood to avoid plastic cross-contamination. Furthermore, several blank values, such as process and system blanks and quality control samples, were analyzed to prevent misinterpretations. A description of the blank values and quality control sample can be found in the supporting information (Section SI 3.2). 3. Results & Discussion 3.1 Polymer particle sizes, shapes, and types Table 2 shows the sizes (Feret max) of microplastics found (n%) within moss of the three sampling sites investigated. It can be seen that microplastics with particle sizes in the range of 10 µm -100 µm are dominant, while larger particle sizes (100 µm – 200 µm) are less present and could only be found at sampling sites 1 and 3. This result hints that smaller microplastic particles (10 µm -100 µm) are more likely to be atmospherically transported and deposited. In addition, Table 3 shows the aspect ratio of the determined microplastics (n%) at the three sampling sites. Aspect ratios in a range of 0.25–1.00 are dominating, which indicates a fragmental shape. These observed shapes and sizes are comparable with published atmospheric microplastics data [ 4 , 5 , 27 ]. Additionally, a correlation based on distance concerning the potential emission source is evident in the determined number of microplastics (n) at the different sampling sites (sampling site 1: n = 688; sampling site 2: n = 248; sampling site 3: n = 474). The determined number of microplastics is decreasing, while the distance from the potential emission source is increasing (sample 1: dist. = 150 m; sample 2: dist. = 360 m; sample 3: dist. = 225 m). Notably, this trend is not expected to be linear since microplastics could also be determined in more remote areas, such as the Pyrenees Mountains [ 4 , 5 ]. Table 2 Determined polymer sizes (Feret max) within the three sampling sites investigated. The results for each size range are shown in percentages in relation to the determined number of microplastics (n). The sizes were determined by capturing a light microscopic dark field image of the occupied filter combined with further image processing. Sampling site [No.] Particle load concerning various size classes (Fmax) [n % of specific sampling site] Number of microplastics [n] 10–25 µm 25–50 µm 50–100 µm 100–150 µm 150–200 µm 1 18 48 18 9 7 688 2 60 17 23 0 0 248 3 63 16 7 14 0 474 Table 3 Calculated aspect ratio of the microplastics within the investigated sampling sites. The results for each aspect ratio range are shown in percentages in relation to the determined number of microplastics (n). The Feret min and Ferret max diameters were determined by capturing a light microscopic dark field image of the occupied filter and further image processing. Sampling site [No.] Particle load concerning various aspect ratios [n % of specific sampling site] Number of microplastics [n] 0–0.25 0.25–0.50 0.50–0.75 0.75–1.00 1 0 30 29 41 688 2 4 13 28 55 248 3 0 26 54 20 474 Figure 1 shows the polymer types found (n%) at each sampling site. At sampling sites 1 and 3, the widest variety of polymers could be found. Concerning sampling site 1, seven different polymers (polycarbonate (PC), polydimethylsiloxane (PDMS), polyethylene (PE), polyvinyl chloride (PVC), polypropylene (PP), polytetrafluoroethylene (PTFE), and polyethylene terephthalate (PET)) could be identified. At sampling site 2, four polymers (PE, PVC, PET, and PP) were determined, while at sampling site 3, seven polymer types (PC, PDMS, PTFE, PE, PVC, PET, and polyamide (PA)) could be identified. The polymer types found, as well as the observation that PE constitutes one dominant type of atmospheric microplastics (sampling site 1: 54%, sampling site 2: 67%, sampling site 3: 23%), are comparable with published data on atmospheric microplastics [ 21 , 26 ]. Further, the variety of polymers found could also be related to the distance of the potential emission source and the main wind direction. Concerning sampling sites 1 and 2 (highest PE contents), the potential emission sources are probably agricultural fields. In this case, agricultural mulch films, mainly PE [ 28 ], could be a possible source of the determined contents. However, that is somewhat speculative since the agricultural management of these fields is not known. In these cases, the distance threshold reported in the Moss manual [ 43 ] is higher than the distance to the potential emission source, which could be the origin of these results. This could also be the reason for the lower number of microplastics found at sampling site 2. Surprisingly, at sampling site 3, no PET could be identified. This sampling site is close to a town (high population density), and PET is often used for synthetic textiles and clothes [ 33 ]. Nevertheless, concerning the high demand for products consisting of PE, PP, PVC, PET, PC, and PA [ 35 ], it is plausible that these polymers were found in our study. The findings of PTFE and PDMS are complex to discuss, even though they could also be identified in other studies [ 26 , 42 ]. 3.2 Polymer mass Mass concentrations (µg/g (dw) (dry weight)) of identified polymers determined in the investigated moss samples are shown in Table 4 . The identification and quantification were carried out using TED-GC-MS. Not-mentioned polymers within a specific subsample were determined to be below the limit of detection (LOD). Negative polymer findings in the subsample are marked as < LOD (all investigated polymers). Details concerning the LOD and limit of quantification (LOQ) for each polymer investigated can be seen in the supporting information (Section SI 3.4, Table 3 − 2). Determined concentrations vary from LOQ by analyzing different subsamples, even in subsamples from the same sampling site. However, it was observed that PE and PP could be identified and quantified at all sampling sites following the µRaman investigations. Nonetheless, PET could only be quantified at sampling site 2. Not-identified PET at sampling site 1 could be attributed to inhomogeneous atmospheric deposition or technical limitations such as the LOD. By comparing the results of the three sampling sites, only slight differences in concentrations concerning sampling sites 1 and 2 (sampling site 1: 22–46 µg/g; sampling site 2: 22–48 µg/g) are observable. Concerning sampling site 3, higher concentrations, up to 111 µg/g, can be determined. Additionally, tire wear could be identified and quantified (7 µg/g), which can be allocated to the potential emission source. The sampling site is located near a town, and a higher traffic-related exposure can be assumed. The concentration found for TW is comparable to the existing literature. Thereby, at a distance of 300 m, a concentration of approximately 13 mg/kg was determined [ 29 ]. However, since tire abrasion is not detectable with spectroscopic methods, a comparison with the µRaman data is impossible. Importantly, sampling a defined area (e.g., 1 m 2 ) of moss is hardly possible at each sampling site, and it is assumed in this study, that the composite sample represents the whole sampling site. This contrasts with other microplastic deposition sampling approaches, where, e.g., funnels with a defined area were used [ 32 ]. Therefore, similar to other environmental compartments such as soil, the required representative elementary mass or volume [ 11 ] of moss which need to be sampled and analyzed for representative statements is not known yet. Therefore, we recommend investigating the issue of representative sampling techniques for the determination of microplastics in moss in future studies. Table 4 Determined mass concentrations (µg/g (dw)) of identified polymers concerning each sampling site and subsample analyzed. All the polymers not mentionedwithin a specific subsample were determined to be below the limit of detection (LOD). Negative polymer findings in the subsample are marked as < LOD (all investigated polymers). Sampling site [No.] Subsample Concentration [µg/g (dw)] 1 1 22 (PP); 46 (PE) 2 < LOD (all investigated polymers) 3 32 (PP); 43 (PE) 2 1 22 (PP); 27 (PE) 2 48 (PET); 42 (PE) 3 < LOD (all investigated polymers) 3 1 71 (PP); 111 (PE) 2 27 (PP); 82 (PE); 7 (SBR) 3 < LOD (all investigated polymers) 4. Conclusion Microplastics could be determined in environmental moss samples using a mass-based (TED-GC-MS) and particle-based (µRaman) analytical approach. Moreover, the mutual complementarity of both analytical approaches could be corroborated, emphasizing the potential of combining these methods to obtain more comprehensive information about samples. For instance, it is demonstrated that complementary analytical approaches can be beneficial concerning identified polymers. Furthermore, the determined polymer masses, particle numbers, and polymer diversity could be linked to the distance and type of the potential emission source. Nonetheless, to determine correlations in this regard, more sampling sites need to be investigated. Nevertheless, due to the findings in this study, moss can be assumed to be a suitable biomonitoring system for atmospheric microplastics. On the basis of our observations, we suggest potential improvements for moss monitoring programs pertaining to atmospheric microplastics. 1. Sample collection The area of the moss sampled should be clearly defined. This would increase the comparability of different studies concerning atmospherically deposited microplastics. Further, a grid sampling approach could be used in future studies. In this case, the sample should not be taken as a composite sample but instead from each grid spot. These samples should be separately prepared and analyzed, while the results can be summarized. This could lead to more representative statements and a better assessment of a specific sampling site. Further, this approach can avoid false statements from high- or low-contaminated local sampling spots. 2. Sample preparation In this study moss subsamples of 1 g (dw) were prepared and analyzed. However, due to technical limitations such as the LOD and LOQ of the TED-GC-MS, higher sample masses could be necessary to investigate trends of microplastic concentrations in future studies. 3. Analysis and data evaluation A mass-based and particle-based analysis method should be used to gain information as complete as possible. However, the lack of standardization can hamper a direct comparison of different studies. E.g., the definition of shapes is often not well-defined and can be subjective when performing visual inspections. The present study distinguished fibers and fragments by determining an aspect ratio. This strategy can be helpful to achieve more objective statements and increase the comparability of different studies, and it is therefore recommended for future investigations. Abbreviations Abbreviations TED-GC-MS Thermal extraction desorption gas chromatography-mass spectrometry µRaman Raman microspectroscopy µPEEL Microplastics extraction through exfoliation µSEP Hydrophobicity-water/air-based enrichment cell for microplastics µFTIR Fourier Transform Infrared microscopy py-GC–MS Pyrolysis-gas chromatography-mass spectrometry µPEEL Microplastics extraction through exfoliation PET Polyethylene terephthalate PS Polystyrene PP Polypropylene PE Polyethylene SBR Styrene-butadiene-rubber TW Tire wear PC Polycarbonate PDMS Polydimethylsiloxane PVC Polyvinyl chloride PTFE Polytetrafluoroethylene PA Polyamide W West SW South-West dw Dry weight CCD Charge-coupled device Fmax Maximum Feret diameter Fmin Minimum Feret diameter t R Retention time m/z Mass to charge ratio LOD Limit of detection LOQ Limit of quantification Sampling site [No.] Particle load concerning various size classes (Fmax) [n % of specific sampling site] Number of microplastics [n] 10–25 µm 25–50 µm 50–100 µm 100–150 µm 150–200 µm 1 18 48 18 9 7 688 2 60 17 23 0 0 248 3 63 16 7 14 0 474 Sampling site [No.] Potential emission source Distance [m] / Cardinal direction to the sampling site Main wind direction 1 Agricultural area 150 / SW SW 2 Agricultural area 360 / W W 3 Single houses 225 / SW SW Town 225 / SW Sampling site [No.] Particle load concerning various aspect ratios [n % of specific sampling site] Number of microplastics [n] 0–0.25 0.25–0.50 0.50–0.75 0.75–1.00 1 0 30 29 41 688 2 4 13 28 55 248 3 0 26 54 20 474 Sampling site [No.] Subsample Concentration [µg/g (dw)] 1 1 22 (PP); 46 (PE) 2 < LOD (all investigated polymers) 3 32 (PP); 43 (PE) 2 1 22 (PP); 27 (PE) 2 48 (PET); 42 (PE) 3 < LOD (all investigated polymers) 3 1 71 (PP); 111 (PE) 2 27 (PP); 82 (PE); 7 (SBR) 3 < LOD (all investigated polymers) Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material The datasets of the current study are available from the corresponding author on reasonable request. Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This investigation took place in the research project 3720632010 "Pilot studies on the suitability of bioindication with mosses for the detection of atmospheric deposition of persistent organic pollutants as well as microplastics", the German contribution to the European Moss Survey 2020/2021, which was funded by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consume Protection and scientifically accompanied by the German Federal Environment Agency. The Responsibility for the content of this publication lies with the authors. The instrumentation used in this study (TED-GC-MS and µRaman) was funded by the European Regional Development Fund (EFRE) "Investments for Growth and Employment" within the framework of the iMulch project (EFRE-0801177). We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen. Authors' contributions MW performed the laboratory work, analyzed the samples using TED-GC-MS, interpreted the TED-GC-MS and µRaman data, and wrote the manuscript. BF conducted the µRaman analysis. CW was involved in the funding acquisition, while both CW and CK contributed to the formulation of overarching goals. SN, AD, BV, and WS contributed to conceptualization and methodology of the associated project including the determination of sampling site specifications. GR contributed to the methodology and conceptualization of the sample preparation method applied. JS, TCS, and JT contributed to the methodology and conceptualization of the sample preparation method applied and were responsible for supervision. JT was further responsible for funding acquisition. All authors read and approved the final manuscript. Acknowledgments This investigation took place in the research project 3720632010 "Pilot studies on the suitability of bioindication with mosses for the detection of atmospheric deposition of persistent organic pollutants as well as microplastics", the German contribution to the European Moss Survey 2020/2021, which was funded by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consume Protection and scientifically accompanied by the German Federal Environment Agency. The instrumentation used in this study (TED-GC-MS and µRaman) was funded by the European Regional Development Fund (EFRE) "Investments for Growth and Employment" within the framework of the iMulch project (EFRE-0801177). We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen. Author details 1 Institut für Umwelt & Energie, Technik & Analytik e. V. (IUTA), Duisburg, Germany 2 Instrumental Analytical Chemistry (IAC), University of Duisburg-Essen, Essen, Germany 3 FISCHER GmbH, Raman Spectroscopic Services, Necklenbroicher Str. 22, 40667 Meerbusch, Germany 4 Planwerk, Büro für ökologische Fachplanungen, Nidda, Germany 5 ANECO, Institut für Umweltschutz GmbH & Co, Mönchengladbach, Germany 6 Instrumental and Environmental Analytics, Niederrhein University of Applied Sciences, Krefeld, Germany 7 Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany 8 Cooperation laboratory (KL) of Ruhrverband and Emschergenossenschaft/Lippeverband (EGLV), Essen, Germany * Corresponding author: Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany. E-mail address: [email protected] References Aeschlimann M, Li G, Kanji ZA et al. (2022) Microplastics and nanoplastics in the atmosphere: the potential impacts on cloud formation processes. 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Chemosphere 294:133653. doi: 10.1016/j.chemosphere.2022.133653 Nickel S, Schröder W, Völksen B et al. (2022) Influence of the Canopy Drip Effect on the Accumulation of Atmospheric Metal and Nitrogen Deposition in Mosses. Forests 13:605. doi: 10.3390/f13040605 Nickel S, Schröder W, Dreyer A et al. (2023) Mapping spatial and temporal trends of atmospheric deposition of nitrogen at the landscape level in Germany 2005, 2015 and 2020 and their comparison with emission data. Science of The Total Environment 891:164478. doi: 10.1016/j.scitotenv.2023.164478 O'Brien S, Rauert C, Ribeiro F et al. (2023) There's something in the air: A review of sources, prevalence and behaviour of microplastics in the atmosphere. Science of The Total Environment 874:162193. doi: 10.1016/j.scitotenv.2023.162193 Oliveira CRS de, Da Silva Júnior AH, Mulinari J et al. (2023) Fibrous microplastics released from textiles: Occurrence, fate, and remediation strategies. Journal of Contaminant Hydrology 256:104169. doi: 10.1016/j.jconhyd.2023.104169 Park SY, Kim CG (2022) A comparative study on the distribution behavior of microplastics through FT-IR analysis on different land uses in agricultural soils. Environmental Research 215:114404. doi: 10.1016/j.envres.2022.114404 PlasticsEurope, Plastics - the Facts 2022, Tech, rep., https://plasticseurope.org/knowledge-hub/plastics-the-facts-2022/ Prata JC (2018) Airborne microplastics: Consequences to human health? Environ Pollut 234:115–126. doi: 10.1016/j.envpol.2017.11.043 Renner G, Schmidt TC, Schram J (2018) Analytical methodologies for monitoring micro(nano)plastics: Which are fit for purpose? Current Opinion in Environmental Science & Health 1:55–61. doi: 10.1016/j.coesh.2017.11.001 Roblin B, Aherne J (2020) Moss as a biomonitor for the atmospheric deposition of anthropogenic microfibres. Sci Total Environ 715:136973. doi: 10.1016/j.scitotenv.2020.136973 Schröder W, Dreyer A, Nickel S et al. (2024) Pilotstudien zur Eignung der Bioindikation mit Moosen zur Erfassung der atmosphärischen Deposition persistenter organischer Schadstoffe sowie Mikroplastik. Schröder W, Nickel S, Dreyer A et al. (2024) Spatial patterns and temporal trends of trace elements in mosses from 1990 to 2020 in Germany. Environ Sci Eur 36:1–28. doi: 10.1186/s12302-023-00827-z Tatsii D, Bucci S, Bhowmick T et al. (2024) Shape Matters: Long-Range Transport of Microplastic Fibers in the Atmosphere. Environmental Science & Technology 58:671–682. doi: 10.1021/acs.est.3c08209 Trainic M, Flores JM, Pinkas I et al. (2020) Airborne microplastic particles detected in the remote marine atmosphere. Commun Earth Environ 1:1–9. doi: 10.1038/s43247-020-00061-y United Nations Economic Commission for Europe Convention on long-range transboundary air pollution / International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (2020): Monitoring of atmospheric deposition of heavy metals, nitrogen and POPs in Europe using bryophytes. Monitoring manual 2020 survey. ( https://icpvegetation.ceh.ac.uk/ ) Wenzel M, Fischer B, Renner G et al. (2022) Efficient and sustainable microplastics analysis for environmental samples using flotation for sample pre-treatment. Green Analytical Chemistry 3:100044. doi: 10.1016/j.greeac.2022.100044 Wenzel M, Schoettl J, Pruin L et al. (2023) Determination of atmospherically deposited microplastics in moss: Method development and performance evaluation. Green Analytical Chemistry 7:100078. doi: 10.1016/j.greeac.2023.100078 Wu M, Yang C, Du C et al. (2020) Microplastics in waters and soils: Occurrence, analytical methods and ecotoxicological effects. Ecotoxicology and Environmental Safety 202:110910. doi: 10.1016/j.ecoenv.2020.110910 Zhang Y, Kang S, Allen S et al. (2020) Atmospheric microplastics: A review on current status and perspectives. Earth-Science Reviews 203:103118. doi: 10.1016/j.earscirev.2020.103118 Additional Declarations No competing interests reported. Supplementary Files 240808SupportingInformationWenzeletal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4887548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349713363,"identity":"46ff20fc-decb-4688-98ca-51fbb1606a24","order_by":0,"name":"Mike Wenzel","email":"","orcid":"","institution":"Institut für Umwelt \u0026 Energie, Technik \u0026 Analytik e. V. 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Schmidt","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Torsten","middleName":"C.","lastName":"Schmidt","suffix":""},{"id":349713379,"identity":"defdde73-1b52-43e9-8568-b37b0e79fe34","order_by":11,"name":"Jochen Tuerk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxgYGNhibmeEDAwMPA0MCkM1GWIsESAvjDGK0wGTBWph5wAIEtDC3Nz978HEHQ53B8d7HxrZtd2QY2HMMHzCU2eB2WM8xc8OZZxgkDM4cN07ObXvGw8DzxtiA4Vwabi0zctikeduAWm6kMR/ObTvMY38jx0yCse0wfi1/QVruP2M+bAnUwiCRY/6Dse0/fi2MYFvYmJMZIVrMGBjbDuDzi5lkb5uE5MwzacyGPeeAWnieFUsknEvGqcUQGGISP9ts+PmOH2OW+FF22J6BPXnjhw9ldri1NIApCTThBJwaGBjk8ciNglEwCkbBKIAAAJnASqvg5Y0kAAAAAElFTkSuQmCC","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":true,"prefix":"","firstName":"Jochen","middleName":"","lastName":"Tuerk","suffix":""}],"badges":[],"createdAt":"2024-08-09 13:48:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4887548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4887548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66118482,"identity":"7f26cd95-8fee-4f85-8a7e-7bc54b0e616c","added_by":"auto","created_at":"2024-10-08 01:05:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentified polymer types within the three sampling sites investigated in %. The percentages are related to the determined microplastic particle number at the specific sampling site (sampling site 1: n = 688; sampling site 2: n = 248; sampling site 3: n = 474). The polymers were identified using µRaman analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4887548/v1/990177ac720338aa45640103.png"},{"id":72430871,"identity":"d13e0450-d34b-442c-873f-a5fd82100d95","added_by":"auto","created_at":"2024-12-27 03:53:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4887548/v1/951c53e6-3d48-4aba-843f-04214be57da6.pdf"},{"id":66118480,"identity":"c6f3eaa2-9577-44bf-a8dd-6733193e5123","added_by":"auto","created_at":"2024-10-08 01:05:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22623,"visible":true,"origin":"","legend":"","description":"","filename":"240808SupportingInformationWenzeletal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4887548/v1/80671d4ff7919d3c237fa58b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determination of atmospherically deposited microplastics in moss samples","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to Plastics Europe (2022), a strong and continuing demand for plastic products is observable worldwide [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, plastic waste follows a similar trend, and plastic pollution growth was predicted [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Due to the escalating environmental pollution with plastic, thousands of researches have focused on investigations concerning microplastics [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Plastic particles and fibers in a dimension of 1 \u0026micro;m \u0026minus;\u0026thinsp;1 mm are defined as microplastics [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while plastics in a range of 1 mm \u0026ndash; 5 mm are defined as large microplastics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The potential ecotoxicological effects of microplastics and their impact on human health were discussed in the literature [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] since microplastic particles have been identified in all environmental compartments such as water [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], soil [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and air [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, these environmental compartments were investigated to a different extent. In this context, especially investigations of microplastics in the atmosphere are lacking in comparison to e.g., the marine environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This can be critical since human microplastics exposure occurs in different pathways, including inhalation, and potential effects on human health can be expected in this regard [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, the atmospheric transport and deposition can be a major pathway for microplastics to reach remote regions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and their occurrence in the atmosphere could have local climate impacts in future scenarios [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, the identification and quantification of atmospheric microplastic is crucial to achieve a better understanding of its sources and behavior in the atmosphere.\u003c/p\u003e \u003cp\u003eIn general, the identification and quantification of microplastics in microplastic research is commonly combined with the micro-spectroscopical (particle-based) and thermoanalytical (mass-based) methods [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Microspectroscopic methods, such as Fourier Transform Infrared (\u0026micro;FTIR) and Raman microspectroscopy (\u0026micro;Raman), provide information about the microplastics particle size distribution, particle morphology (shape), and identity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To investigate those parameters regarding atmospheric microplastics is necessary, as the long-range transport of microplastics can be dependent on their shape and sizes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThermoanalytical methods, like pyrolysis-gas chromatography-mass spectrometry (py-GC/MS) and thermal extraction desorption-gas chromatography-mass spectrometry (TED-GC-MS), can be used to obtain information about the integral polymer mass and identity [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Using both approaches can be beneficial to gain more complete information on the samples investigated [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith regard to atmospheric microplastics, a lack of mass-based information is observable, and most investigations published were about particle sizes and numbers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Further, standardized sampling strategies for atmospheric microplastics are not established yet. Concerning this issue, empirical evidence supporting the combination of a biomonitoring sampling system utilizing spider webs with subsequent mass-based analysis was provided [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Further, within the domain of biomonitoring systems, a few research groups presently focus on utilizing moss as a biomonitoring system to assess the fate of atmospherically deposited microplastics in the environment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The use of moss was already established for other atmospheric pollutants such as heavy metals, nitrogen, and persistent organic components and is frequently used to investigate spatial and temporal trends, for instance in the framework of the international Moss Surveys conducted every five years since 1990 in Europe [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, only visual and spectroscopical investigations have been performed yet for the determination of atmospheric microplastics in moss [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hence, a new sample preparation method called \u0026micro;PEEL (microplastics extraction through exfoliation) was recently developed, which is suitable for TED-GC-MS and \u0026micro;Raman analysis to provide mass-based and particle-based information [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. It was observed that the \u0026micro;PEEL method is appropriate for TED-GC-MS analysis since suitable and robust recoveries of microplastics could be achieved and matrix effects originating from organic and inorganic matrix components could be minimized. Furthermore, \u0026micro;PEEL provides a high separation quality proven by the possibility of contrast-based particle identification.\u003c/p\u003e \u003cp\u003eTo that end, this study shows mass-based and particle-based investigations concerning atmospherically deposited microplastics in environmental moss samples for the first time. The samples investigated were collected at three different sampling sites in Germany as part of the International Moss Survey 2020/2021 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], funded and scientifically supported by the German Environment Agency. The \u0026micro;PEEL method was used for sample preparation, while the analysis was carried out utilizing \u0026micro;Raman and TED-GC-MS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Test material, moss sampling and sampling site specifications\u003c/h2\u003e \u003cp\u003eTED-GC-MS calibration was performed using polymer test material provided by the Federal Institute for Materials Research and Testing (Bundesanstalt f\u0026uuml;r Materialforschung und -pr\u0026uuml;fung, Berlin, Germany). Therefore, microparticles consisting of polyethylene terephthalate (PET) (X\u003csub\u003e50\u003c/sub\u003e: 62 \u0026micro;m), polystyrene (PS) (X\u003csub\u003e50\u003c/sub\u003e: 206 \u0026micro;m), polypropylene (PP) (X\u003csub\u003e50\u003c/sub\u003e: 174 \u0026micro;m), polyethylene (PE) (X\u003csub\u003e50\u003c/sub\u003e: 20 \u0026micro;m), and styrene-butadiene-rubber (SBR) were used. SBR was used to identify and quantify tire wear (TW) as a source of microplastic pollution. Currently, SBR is unavailable in defined particle sizes and, therefore, not documented. In this study it was hypothesized that the particle number and polymer mass in a sample from a specific sampling site could depend on the distance from the potential emission source and the prevailing wind direction.\u003c/p\u003e \u003cp\u003eTherefore, the moss samples investigated were collected at different sampling sites in Germany, which were determined by the application of a methodology combining decision modeling and inferential statistics [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The sampling was performed within the German Moss Survey 2020/2021 framework, supported by the German Environment Agency. The samples were collected as a composite sample of the specific sampling site.\u003c/p\u003e \u003cp\u003eNonetheless, all sampling specifications and requirements proposed in the Moss Survey Manual [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] were considered. This manual provides information and recommendations regarding methodological aspects, such as the minimum distances to be kept from trees or potential emission sources during the sampling. In this context, emission source-specific distance thresholds are defined in the manual [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It is assumed that if the distance of the emission source is higher than the defined threshold, no influence can be claimed or that regional deposition patterns overlay the respective effect. The threshold distances used rely on [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Distances to potential emission sources were determined using satellite images. The main wind direction regarding the specific sampling site was determined at the nearest available environmental monitoring station equipped with the respective measuring devices [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the information about the distances to potential emission sources for the three investigated sampling sites. Hereby, the closest potential emission sources that are aligned with the main wind direction were considered [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. More detailed sample information, such as the sampling site coordinates, distance thresholds for the respective type of emission source, the distance to the next monitoring station, and the moss species, are described in the supporting information (Section SI 1, Table\u0026nbsp;\u0026lt;link rid=\"tb1\"\u0026gt;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u0026lt;/link\u0026gt;\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of potential emission sources, their distances to the respective sampling site, and information concerning the main wind direction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling site [No.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential emission source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance [m] / Cardinal direction to the sampling site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain wind direction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 / SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 / W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle houses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 / SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 / SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample preparation\u003c/h2\u003e \u003cp\u003eOverall, three samples per sampling site were prepared (and analyzed) to increase the sample mass investigated which could have an impact on the representativeness of the sampling site. The moss sample preparation procedure was performed according to Wenzel et al. (2023) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Dead material, attached litter, and brown parts were removed from the moss sample. The prepared moss sample represented the last two to three years of growth [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. After this, the moss sample was dried in a stainless-steel oven (Memmert GmbH\u0026thinsp;+\u0026thinsp;C. KG, Schwabach, Germany) at 40\u0026deg;C for 24 hours. After that, the \u0026micro;PEEL method was applied. This method includes three main steps: exfoliation, sieving, and flotation. The exfoliation was carried out using 1 g of dry weighted (dw) moss and 20 g of glass beads (d\u0026thinsp;=\u0026thinsp;0.5 mm, BioSpec Products, Inc. USA), placed in a round bottom flask (V\u0026thinsp;=\u0026thinsp;200 mL), which was shaken in an overhead shaker (Heidolph Instruments GmbH \u0026amp; Co.KG, Schwabach, Germany) for 10 minutes. The sample size used was based on published studies concerning the determination of microplastics in moss [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Important to note is that the application of the \u0026micro;PEEL method does not involve homogenization procedures of the composite sample (e.g., cryo-grinding), as exfoliation becomes inapplicable. This is due to the abrasion mechanism itself and to avoid an impact on the microplastics shape and size. After exfoliation, the sample was sieved for one minute using a vibratory sieve shaker (Retsch\u0026reg; AS 200 control; Retsch GmbH, Haan, Germany). During the sieving, the collection pan was wetted with 20 mL of pure water. As a result, turbulence distributions of the glass beads used, the exfoliated matter (microplastics and moss abrasion) are reduced, and the green shots of moss remain on the sieve. Further, the exfoliated microplastics were separated from the moss abrasion by using the improved hydrophobicity-water/air-based enrichment cell for microplastics (\u0026micro;SEP) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this regard, the volume flow through the diffusor was set to 7 L/min, and the separation was performed for 30 minutes [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The aqueous sample suspension was collected in a round-bottom flask during the separation. For TED-GC-MS analysis, the sample suspension was vacuum filtered through a Whatman \u0026trade; cellulose nitrate membrane filter (diameter: 45 mm; pore size: 0.45 \u0026micro;m; GE Healthcare Life Sciences; Chalfont ST Giles, GB). The remaining sample on the filter was transferred to a 600-\u0026micro;L alumina crucible (Mettler Toledo GmbH, Gie\u0026szlig;en, Germany), which is directly usable for TED-GC-MS measurements. Concerning \u0026micro;Raman analysis, the sample suspension was vacuum filtered through a 25-mm diameter, gold-coated membrane filter with a pore size of 0.8 \u0026micro;m (APC GmbH, Eschborn, Germany). After filtration, the gold-coated filter was attached to a microscope slide using double-sided adhesive foil, and the underside of the slide was heated to 40\u0026deg;C for 10 seconds to remove tiny air bubbles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analytical Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 TED-GC-MS\u003c/h2\u003e \u003cp\u003eThe TED-GC-MS analytical method is well-established [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and was also applied during the evaluation of the \u0026micro;PEEL method [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The sample was pyrolyzed using a thermogravimetric furnace (TGA 2, Mettler Toledo GmbH, Gie\u0026szlig;en, Germany). During pyrolysis, the sample analyzed was placed in a 600-\u0026micro;L alumina crucible (Mettler Toledo GmbH, Gie\u0026szlig;en, Germany). The pyrolysis products were transferred through an interface to the adsorption unit by applying a nitrogen flow and were sorbed on a solid-phase stir bar adsorber consisting of polydimethylsiloxane. Using an autosampler (MultiPurposeSampler), the adsorber was transferred to the thermal desorption unit. Interface, adsorber, adsorption-, desorption unit, and the MultiPurposeSampler used are commercially available from Gerstel GmbH \u0026amp; Co KG, M\u0026uuml;lheim an der Ruhr, Germany. Afterwards, the pyrolysis products were desorbed, cryofocused (CIS4, Gerstel GmbH \u0026amp; Co KG, M\u0026uuml;lheim an der Ruhr, Germany), and injected into the gas chromatograph (GC7890, Agilent, Santa Clara, California, USA). After the chromatographic separation, the pyrolysis products were analyzed using a mass spectrometer (5977B MSD, Agilent). Detailed setup parameters can be found in the supporting information (Section SI 2, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e 1, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 \u0026micro;Raman\u003c/h2\u003e \u003cp\u003eWithin the particle-based \u0026micro;Raman analysis, an alpha300 R Confocal Raman microscope (WITec, Ulm, Germany) was used, and three main steps - capturing a light microscopic dark field image, image processing, and Raman microscopic analysis - were included. The Zeiss EC Epiplan HD 20 \u0026times; / 0.5 NA microscope objective was used to cover a light microscopic dark field image with an area of 608.61 mm\u003csup\u003e2\u003c/sup\u003e (24.67 mm \u0026times; 24.67 mm). 81\u0026times; 81 images were stitched on the X- and Y-axis, and 13 images per position on the Z-axis were captured. In the dark field, all particles appear bright against the background, originating from the gold-coated filter. Using the software WITec ParticleScout (version 5.3.18.110), the image captured was processed, and the software can detect the bright particles automatically by calculating a brightness threshold (bright particles against a dark background). Further, all particles were masked by the software. As a result, information about the particle size can be obtained, as described in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e in detail. The Raman microscopic identification was performed using a WITec UHTS 300 spectrometer in combination with an Andor iDus Deep Depletion charge-coupled device (CCD) camera (cooled to \u0026minus;\u0026thinsp;60\u0026deg;C), which were both attached to the alpha300R Confocal Raman microscope. A reflection grating with 600 lines/mm could achieve an average spectral resolution of 3.1 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e/pixel. Particle measurements were performed using a single-mode laser with an excitation wavelength of 532 nm, a laser power of 2 mW, and a Zeiss EC Epiplan-Neofluar DIC 50 \u0026times; /0.8 NA microscope objective. The particles were automatically centered in the laser focus using the particle masking coordinates. For each particle, spectral autofocus was used for focus adjustments, and Raman spectra were recorded by accumulating 5 \u0026times; 1 second of exposure time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data evaluation\u003c/h2\u003e \u003cp\u003eRegarding TED-GC-MS analysis, the focus was on the investigation of the polymers PS, PP, PET, and SBR (a marker for tire wear (TW)). The identification and quantification were performed according to the previous study of the authors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Polymer-specific pyrolysis products (qualifier products) at their retention times and their respective mass spectra were considered for identification. The area of a specific fragment ion (quantifier ion) of one characteristic pyrolysis product (quantifier product) was determined for quantification. In this context, polymer masses in a sample were calculated using an external calibration. All TED-GC-MS data were evaluated using the MSD ChemStation data analysis software (Version: F.01.03.2357, Agilent Technologies, Inc.). Details about the data evaluation concerning TED-GC-MS analysis, such as the utilized pyrolysis products, can be found in the supporting information (Section SI 3, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eConcerning \u0026micro;Raman analysis, the particle sizes and shapes were determined with the use of the maximum and minimum Feret diameters (Fmax and Fmin). Given the number of pixels necessary for masking a particle and the area per pixel, the software (WITec ParticleScout) can calculate the particle area, circular equivalent diameter, and spherical equivalent volume. The minimum and maximum Feret diameter were determined by measuring the maximum and minimum distances observed when the particle is rotated between two parallel tangents at any angle. In order to represent the particle size, the determined Fmax was used in this study, while the aspect ratio (Feret min / Feret max) was used to provide information about the particle shape. With regard to the identification of the particles, a minimum and maximum size threshold of Fmax were set within the analysis to reduce the measurement-time effort. In this context, a minimum size threshold of 10 \u0026micro;m was set. The maximum size threshold depended on the particle load, and particles up to 1000 \u0026micro;m were investigated. However, the \u0026micro;Raman results shown in this study only refer to 10\u0026ndash;200 \u0026micro;m particle sizes. This is based on the observation that no microplastics could be identified at larger dimensions within each sampling location, even when a higher maximum size threshold was applied. Moreover, microplastics with an aspect ratio of \u0026lt;\u0026thinsp;0.25 can be classified as fibers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and microplastics with an aspect ratio of \u0026gt;\u0026thinsp;0.25 were classified as fragments in this study. Moreover, approximately 2\u0026ndash;10 % f the whole filter surface was investigated due to the particle homogeneity assessment demonstrated in a previous study [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A slight variation in the area observed ensured that almost a consistent particle number could be analyzed for each sample. The determined numbers of microplastics (n) mentioned for each sampling site are extrapolated considering the whole filter surface area and the triplicate measurements were merged together. Actual findings for each sampling site are shown in the supporting information (Section SI 3.5, Table\u0026nbsp;\u0026lt;link rid=\"tb3\"\u0026gt;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u0026lt;/link\u0026gt;\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, the polymer identification was carried out using a commercially available database (S.T. Japan-Europe GmbH, Microplastics \u0026amp; Related Compounds Database, L60035). It contains 1475 polymer spectra for comparison purposes. Due to the high variability of reported particle sizes and microplastic types concerning atmospheric microplastics [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], the chosen analytical method seemed to be a well-suited compromise between an acceptable sample throughput (ecological and economic) and compatibility purposes with other studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Quality assurance\u003c/h2\u003e \u003cp\u003eAll devices were made of glass (sampling glass bottle, round bottom flask, glass beads, \u0026micro;SEP) or stainless steel (oven, vibratory sieve shaker) for quality assurance. The removal of dead material, attached litter, and brown parts was carried out in a laminar flow box (Karl Bleymehl Reinraumtechnik GmbH, ASW-UD, Inden, Germany), and all experiments were performed in a laboratory stainless steel fume hood to avoid plastic cross-contamination. Furthermore, several blank values, such as process and system blanks and quality control samples, were analyzed to prevent misinterpretations. A description of the blank values and quality control sample can be found in the supporting information (Section SI 3.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results \u0026 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Polymer particle sizes, shapes, and types\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the sizes (Feret max) of microplastics found (n%) within moss of the three sampling sites investigated. It can be seen that microplastics with particle sizes in the range of 10 \u0026micro;m -100 \u0026micro;m are dominant, while larger particle sizes (100 \u0026micro;m \u0026ndash; 200 \u0026micro;m) are less present and could only be found at sampling sites 1 and 3. This result hints that smaller microplastic particles (10 \u0026micro;m -100 \u0026micro;m) are more likely to be atmospherically transported and deposited. In addition, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the aspect ratio of the determined microplastics (n%) at the three sampling sites. Aspect ratios in a range of 0.25\u0026ndash;1.00 are dominating, which indicates a fragmental shape.\u003c/p\u003e \u003cp\u003eThese observed shapes and sizes are comparable with published atmospheric microplastics data [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, a correlation based on distance concerning the potential emission source is evident in the determined number of microplastics (n) at the different sampling sites (sampling site 1: n\u0026thinsp;=\u0026thinsp;688; sampling site 2: n\u0026thinsp;=\u0026thinsp;248; sampling site 3: n\u0026thinsp;=\u0026thinsp;474). The determined number of microplastics is decreasing, while the distance from the potential emission source is increasing (sample 1: dist. = 150 m; sample 2: dist. = 360 m; sample 3: dist. = 225 m). Notably, this trend is not expected to be linear since microplastics could also be determined in more remote areas, such as the Pyrenees Mountains [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetermined polymer sizes (Feret max) within the three sampling sites investigated. The results for each size range are shown in percentages in relation to the determined number of microplastics (n). The sizes were determined by capturing a light microscopic dark field image of the occupied filter combined with further image processing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSampling site\u003c/p\u003e \u003cp\u003e[No.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eParticle load concerning various size classes (Fmax)\u003c/p\u003e \u003cp\u003e[n\u003csub\u003e%\u003c/sub\u003e of specific sampling site]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of microplastics\u003c/p\u003e \u003cp\u003e[n]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;25\u003c/p\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;50\u003c/p\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u0026ndash;150 \u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150\u0026ndash;200 \u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculated aspect ratio of the microplastics within the investigated sampling sites. The results for each aspect ratio range are shown in percentages in relation to the determined number of microplastics (n). The Feret min and Ferret max diameters were determined by capturing a light microscopic dark field image of the occupied filter and further image processing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSampling site\u003c/p\u003e \u003cp\u003e[No.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eParticle load concerning various aspect ratios\u003c/p\u003e \u003cp\u003e[n\u003csub\u003e%\u003c/sub\u003e of specific sampling site]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of microplastics [n]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;0.25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;0.50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u0026ndash;0.75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u0026ndash;1.00\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the polymer types found (n%) at each sampling site. At sampling sites 1 and 3, the widest variety of polymers could be found. Concerning sampling site 1, seven different polymers (polycarbonate (PC), polydimethylsiloxane (PDMS), polyethylene (PE), polyvinyl chloride (PVC), polypropylene (PP), polytetrafluoroethylene (PTFE), and polyethylene terephthalate (PET)) could be identified. At sampling site 2, four polymers (PE, PVC, PET, and PP) were determined, while at sampling site 3, seven polymer types (PC, PDMS, PTFE, PE, PVC, PET, and polyamide (PA)) could be identified. The polymer types found, as well as the observation that PE constitutes one dominant type of atmospheric microplastics (sampling site 1: 54%, sampling site 2: 67%, sampling site 3: 23%), are comparable with published data on atmospheric microplastics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther, the variety of polymers found could also be related to the distance of the potential emission source and the main wind direction. Concerning sampling sites 1 and 2 (highest PE contents), the potential emission sources are probably agricultural fields. In this case, agricultural mulch films, mainly PE [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], could be a possible source of the determined contents. However, that is somewhat speculative since the agricultural management of these fields is not known. In these cases, the distance threshold reported in the Moss manual [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] is higher than the distance to the potential emission source, which could be the origin of these results. This could also be the reason for the lower number of microplastics found at sampling site 2. Surprisingly, at sampling site 3, no PET could be identified. This sampling site is close to a town (high population density), and PET is often used for synthetic textiles and clothes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nevertheless, concerning the high demand for products consisting of PE, PP, PVC, PET, PC, and PA [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], it is plausible that these polymers were found in our study. The findings of PTFE and PDMS are complex to discuss, even though they could also be identified in other studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Polymer mass\u003c/h2\u003e \u003cp\u003eMass concentrations (\u0026micro;g/g (dw) (dry weight)) of identified polymers determined in the investigated moss samples are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The identification and quantification were carried out using TED-GC-MS. Not-mentioned polymers within a specific subsample were determined to be below the limit of detection (LOD). Negative polymer findings in the subsample are marked as \u0026lt;\u0026thinsp;LOD (all investigated polymers). Details concerning the LOD and limit of quantification (LOQ) for each polymer investigated can be seen in the supporting information (Section SI 3.4, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2). Determined concentrations vary from \u0026lt;\u0026thinsp;LOD to \u0026gt;\u0026thinsp;LOQ by analyzing different subsamples, even in subsamples from the same sampling site. However, it was observed that PE and PP could be identified and quantified at all sampling sites following the \u0026micro;Raman investigations. Nonetheless, PET could only be quantified at sampling site 2. Not-identified PET at sampling site 1 could be attributed to inhomogeneous atmospheric deposition or technical limitations such as the LOD. By comparing the results of the three sampling sites, only slight differences in concentrations concerning sampling sites 1 and 2 (sampling site 1: 22\u0026ndash;46 \u0026micro;g/g; sampling site 2: 22\u0026ndash;48 \u0026micro;g/g) are observable. Concerning sampling site 3, higher concentrations, up to 111 \u0026micro;g/g, can be determined. Additionally, tire wear could be identified and quantified (7 \u0026micro;g/g), which can be allocated to the potential emission source. The sampling site is located near a town, and a higher traffic-related exposure can be assumed. The concentration found for TW is comparable to the existing literature. Thereby, at a distance of 300 m, a concentration of approximately 13 mg/kg was determined [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, since tire abrasion is not detectable with spectroscopic methods, a comparison with the \u0026micro;Raman data is impossible.\u003c/p\u003e \u003cp\u003eImportantly, sampling a defined area (e.g., 1 m\u003csup\u003e2\u003c/sup\u003e) of moss is hardly possible at each sampling site, and it is assumed in this study, that the composite sample represents the whole sampling site. This contrasts with other microplastic deposition sampling approaches, where, e.g., funnels with a defined area were used [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, similar to other environmental compartments such as soil, the required representative elementary mass or volume [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] of moss which need to be sampled and analyzed for representative statements is not known yet. Therefore, we recommend investigating the issue of representative sampling techniques for the determination of microplastics in moss in future studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetermined mass concentrations (\u0026micro;g/g (dw)) of identified polymers concerning each sampling site and subsample analyzed. All the polymers not mentionedwithin a specific subsample were determined to be below the limit of detection (LOD). Negative polymer findings in the subsample are marked as \u0026lt;\u0026thinsp;LOD (all investigated polymers).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling site [No.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubsample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003cp\u003e[\u0026micro;g/g (dw)]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (PP); 46 (PE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; LOD (all investigated polymers)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (PP); 43 (PE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (PP); 27 (PE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (PET); 42 (PE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; LOD (all investigated polymers)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (PP); 111 (PE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (PP); 82 (PE); 7 (SBR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; LOD (all investigated polymers)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eMicroplastics could be determined in environmental moss samples using a mass-based (TED-GC-MS) and particle-based (\u0026micro;Raman) analytical approach. Moreover, the mutual complementarity of both analytical approaches could be corroborated, emphasizing the potential of combining these methods to obtain more comprehensive information about samples. For instance, it is demonstrated that complementary analytical approaches can be beneficial concerning identified polymers. Furthermore, the determined polymer masses, particle numbers, and polymer diversity could be linked to the distance and type of the potential emission source. Nonetheless, to determine correlations in this regard, more sampling sites need to be investigated. Nevertheless, due to the findings in this study, moss can be assumed to be a suitable biomonitoring system for atmospheric microplastics. On the basis of our observations, we suggest potential improvements for moss monitoring programs pertaining to atmospheric microplastics.\u003c/p\u003e\n\u003ch3\u003e1. Sample collection\u003c/h3\u003e\n\u003cp\u003eThe area of the moss sampled should be clearly defined. This would increase the comparability of different studies concerning atmospherically deposited microplastics.\u003c/p\u003e \u003cp\u003eFurther, a grid sampling approach could be used in future studies. In this case, the sample should not be taken as a composite sample but instead from each grid spot. These samples should be separately prepared and analyzed, while the results can be summarized. This could lead to more representative statements and a better assessment of a specific sampling site. Further, this approach can avoid false statements from high- or low-contaminated local sampling spots.\u003c/p\u003e\n\u003ch3\u003e2. Sample preparation\u003c/h3\u003e\n\u003cp\u003eIn this study moss subsamples of 1 g (dw) were prepared and analyzed. However, due to technical limitations such as the LOD and LOQ of the TED-GC-MS, higher sample masses could be necessary to investigate trends of microplastic concentrations in future studies.\u003c/p\u003e\n\u003ch3\u003e3. Analysis and data evaluation\u003c/h3\u003e\n\u003cp\u003eA mass-based and particle-based analysis method should be used to gain information as complete as possible. However, the lack of standardization can hamper a direct comparison of different studies. E.g., the definition of shapes is often not well-defined and can be subjective when performing visual inspections. The present study distinguished fibers and fragments by determining an aspect ratio. This strategy can be helpful to achieve more objective statements and increase the comparability of different studies, and it is therefore recommended for future investigations.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAbbreviations\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTED-GC-MS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eThermal extraction desorption gas chromatography-mass spectrometry\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;Raman\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRaman microspectroscopy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;PEEL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMicroplastics extraction through exfoliation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;SEP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHydrophobicity-water/air-based enrichment cell for microplastics\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;FTIR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFourier Transform Infrared microscopy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003epy-GC\u0026ndash;MS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePyrolysis-gas chromatography-mass spectrometry\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;PEEL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMicroplastics extraction through exfoliation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePET\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolyethylene terephthalate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolystyrene\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolypropylene\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolyethylene\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSBR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStyrene-butadiene-rubber\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTire wear\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolycarbonate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePDMS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolydimethylsiloxane\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePVC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolyvinyl chloride\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePTFE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolytetrafluoroethylene\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolyamide\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eWest\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSouth-West\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003edw\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDry weight\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCCD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharge-coupled device\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFmax\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMaximum Feret diameter\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFmin\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinimum Feret diameter\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003et\u003csub\u003eR\u003c/sub\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRetention time\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003em/z\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMass to charge ratio\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLOD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLimit of detection\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLOQ\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLimit of quantification\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSampling site\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[No.]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eParticle load concerning various size classes (Fmax)\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[n\u003csub\u003e%\u003c/sub\u003e of specific sampling site]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of microplastics\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[n]\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u0026ndash;25\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;m\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u0026ndash;50\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;m\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e50\u0026ndash;100\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026micro;m\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e100\u0026ndash;150 \u0026micro;m\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e150\u0026ndash;200 \u0026micro;m\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e48\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e688\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e248\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e63\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e474\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSampling site [No.]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePotential emission source\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eDistance [m] / Cardinal direction to the sampling site\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eMain wind direction\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgricultural area\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e150 / SW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eSW\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgricultural area\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e360 / W\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eW\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSingle houses\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e225 / SW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eSW\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTown\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e225 / SW\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSampling site\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[No.]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eParticle load concerning various aspect ratios\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[n\u003csub\u003e%\u003c/sub\u003e of specific sampling site]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of microplastics [n]\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u0026ndash;0.25\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.25\u0026ndash;0.50\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.50\u0026ndash;0.75\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.75\u0026ndash;1.00\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e41\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e688\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e248\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e54\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e474\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSampling site [No.]\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSubsample\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eConcentration\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e[\u0026micro;g/g (dw)]\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22 (PP); 46 (PE)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt; LOD (all investigated polymers)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (PP); 43 (PE)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22 (PP); 27 (PE)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e48 (PET); 42 (PE)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt; LOD (all investigated polymers)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e71 (PP); 111 (PE)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e27 (PP); 82 (PE); 7 (SBR)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt; LOD (all investigated polymers)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe datasets of the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis investigation took place in the research project 3720632010 \u0026quot;Pilot studies on the suitability of bioindication with mosses for the detection of atmospheric deposition of persistent organic pollutants as well as microplastics\u0026quot;, the German contribution to the European Moss Survey 2020/2021, which was funded by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consume Protection and \u0026nbsp;scientifically accompanied by the German Federal Environment Agency. The Responsibility for the content of this publication lies with the authors.\u003c/p\u003e\n\u003cp\u003eThe instrumentation used in this study (TED-GC-MS and \u0026micro;Raman) was funded by the European Regional Development Fund (EFRE) \u0026quot;Investments for Growth and Employment\u0026quot; within the framework of the iMulch project (EFRE-0801177).\u003c/p\u003e\n\u003cp\u003eWe acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eMW performed the laboratory work, analyzed the samples using TED-GC-MS, interpreted the TED-GC-MS and \u0026micro;Raman data, and wrote the manuscript. BF conducted the \u0026micro;Raman analysis. CW was involved in the funding acquisition, while both CW and CK contributed to the formulation of overarching goals. SN, AD, BV, and WS contributed to conceptualization and methodology of the associated project including the determination of sampling site specifications. GR contributed to the methodology and conceptualization of the sample preparation method applied. JS, TCS, and JT contributed to the methodology and conceptualization of the sample preparation method applied and were responsible for supervision. JT was further responsible for funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis investigation took place in the research project 3720632010 \u0026quot;Pilot studies on the suitability of bioindication with mosses for the detection of atmospheric deposition of persistent organic pollutants as well as microplastics\u0026quot;, the German contribution to the European Moss Survey 2020/2021, which was funded by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consume Protection and scientifically accompanied by the German Federal Environment Agency.\u003c/p\u003e\n\u003cp\u003eThe instrumentation used in this study (TED-GC-MS and \u0026micro;Raman) was funded by the European Regional Development Fund (EFRE) \u0026quot;Investments for Growth and Employment\u0026quot; within the framework of the iMulch project (EFRE-0801177).\u003c/p\u003e\n\u003cp\u003eWe acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003e1 Institut f\u0026uuml;r Umwelt \u0026amp; Energie, Technik \u0026amp; Analytik e. V. \u0026nbsp;(IUTA), Duisburg, Germany\u003c/p\u003e\n\u003cp\u003e2 Instrumental Analytical Chemistry (IAC), University of Duisburg-Essen, Essen, Germany\u003c/p\u003e\n\u003cp\u003e3 FISCHER GmbH, Raman Spectroscopic Services, Necklenbroicher Str. 22, 40667 Meerbusch, Germany\u003c/p\u003e\n\u003cp\u003e4 Planwerk, B\u0026uuml;ro f\u0026uuml;r \u0026ouml;kologische Fachplanungen, Nidda, Germany\u003c/p\u003e\n\u003cp\u003e5 ANECO, Institut f\u0026uuml;r Umweltschutz GmbH \u0026amp; Co, M\u0026ouml;nchengladbach, Germany\u003c/p\u003e\n\u003cp\u003e6 Instrumental and Environmental Analytics, Niederrhein University of Applied Sciences, Krefeld, Germany\u003c/p\u003e\n\u003cp\u003e7 Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany\u003c/p\u003e\n\u003cp\u003e8 Cooperation laboratory (KL) of Ruhrverband and Emschergenossenschaft/Lippeverband (EGLV), Essen, Germany\u003c/p\u003e\n\u003cp\u003e* Corresponding author: Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universit\u0026auml;tsstr. 2, 45141 Essen, Germany. 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(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icpvegetation.ceh.ac.uk/\u003c/span\u003e\u003cspan address=\"https://icpvegetation.ceh.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenzel M, Fischer B, Renner G et al. (2022) Efficient and sustainable microplastics analysis for environmental samples using flotation for sample pre-treatment. Green Analytical Chemistry 3:100044. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.greeac.2022.100044\u003c/span\u003e\u003cspan address=\"10.1016/j.greeac.2022.100044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenzel M, Schoettl J, Pruin L et al. (2023) Determination of atmospherically deposited microplastics in moss: Method development and performance evaluation. 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Earth-Science Reviews 203:103118. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.earscirev.2020.103118\u003c/span\u003e\u003cspan address=\"10.1016/j.earscirev.2020.103118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biomonitoring, Microplastics, Atmospheric deposition, TED-GC-MS, µRaman","lastPublishedDoi":"10.21203/rs.3.rs-4887548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4887548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStandardized methods for sampling and detection of atmospherically deposited microplastics are lacking. Contrary to that, the use of moss as a biomonitoring system was established concerning other atmospheric pollutants, such as heavy metals and persistent organic pollutants. Only a few research groups actually focus on detecting atmospherically deposited microplastics in moss. In general, the determination of microplastics in environmental samples is commonly performed using a particle-based or mass-based analytical approach. However, a dearth of mass-based investigations is noticeable, especially for atmospherically deposited microplastics. Given this background, this study shows the determination of atmospherically deposited microplastics in moss utilizing thermal extraction desorption gas chromatography-mass spectrometry (TED-GC-MS) and Raman microspectroscopy (\u0026micro;Raman) to acquire both information. The moss samples analyzed were collected as part of the German moss survey 2020/2021, supported by the German Environment Agency. Three distinct sampling sites were investigated, which could be categorized based on their distances from potential emission sources.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eConcerning \u0026micro;Raman analysis, most microplastic particles could be determined within a 10 to 100 \u0026micro;m size range. Further, most microplastic aspect ratios were determined in a range of 0.25 to 1.00, indicating a fragmental shape. Additionally, a correlation between the number of microplastic particles determined and the distance of the potential emission source was observable. It was determined to be 688, 474, and 248 particles per sampling site with a distance of 150 m, 225 m, and 360 m. Both analytical approaches (TED-GC-MS \u0026amp; \u0026micro;Raman) concurred in identifying the polymer types (polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET)) in the moss samples. Concerning TED-GC-MS, 7 to 111 \u0026micro;g/g could be determined, depending on the polymer types and distance to the potential emission source.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e\u0026micro;Raman and TED-GC-MS investigations demonstrated correlations between microplastic particle numbers, size, types, and mass concentrations with the distance of the potential emission source. The investigation corroborates the mutual complementarity of both analytical approaches, enabling more comprehensive information on samples.\u003c/p\u003e","manuscriptTitle":"Determination of atmospherically deposited microplastics in moss samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 01:05:44","doi":"10.21203/rs.3.rs-4887548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9acdc732-ba3d-4302-af7a-93778a982381","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-27T03:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 01:05:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4887548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4887548","identity":"rs-4887548","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}