CoChat
Sign Up
Cited by 1,359

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

White and wonderful? Microplastics prevail in snow from the Alps to the Arctic

Bergmann, Melanie, Mützel, Sophia, Primpke, Sebastian, Tekman, Mine B., Trachsel, Jürg, Gerdts, Gunnar·2019·Science Advances
DOI 10.1126/sciadv.aax1157PMC PMC6693909
Open this paper in CoChat — free account

Abstract

We detect microplastics in European and Arctic snow, highlighting the importance of atmospheric transport as a pathway.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1126/sciadv.aax1157

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Map of sampling locations for snow. (A) Sampling sites in the Arctic (ICE, ice floes; SV, Svalbard) and (B) in Europe (HL, Heligoland; BR, Bremen; BV, Bavaria; TSCH, Tschuggen; DV, Davos). (C) Overview of all locations. Size of circles reflects MP particle quantities at log scale.
Fig. 1 Map of sampling locations for snow. (A) Sampling sites in the Arctic (ICE, ice floes; SV, Svalbard) and (B) in Europe (HL, Heligoland; BR, Bremen; BV, Bavaria; TSCH, Tschuggen; DV, Davos). (C) Overview of all locations. Size of circles reflects MP particle quantities at log scale.CC BY-NC · PubMed Central
Fig. 2 Particles detected in snow samples collected at different locations from Europe to the Arctic. (A) MP particle quantities recorded by FTIR at different locations. (B) Concentrations of microfibers detected at different locations (note that no polymers were identified). (C) Box-and-whiskers plot of proportions of MP numbers in different size classes from all snow samples. The upper and lower boundaries of the box indicate the 75th and 25th percentiles, respectively. The line within the box marks the median, error bars (in µm) indicate the 90th and 10th percentiles, and black diamonds represent outliers. (D) Relative composition of polymers identified by FTIR at different locations.
Fig. 2 Particles detected in snow samples collected at different locations from Europe to the Arctic. (A) MP particle quantities recorded by FTIR at different locations. (B) Concentrations of microfibers detected at different locations (note that no polymers were identified). (C) Box-and-whiskers plot of proportions of MP numbers in different size classes from all snow samples. The upper and lower boundaries of the box indicate the 75th and 25th percentiles, respectively. The line within the box marks the median, error bars (in µm) indicate the 90th and 10th percentiles, and black diamonds represent outliers. (D) Relative composition of polymers identified by FTIR at different locations.CC BY-NC · PubMed Central
Fig. 3 Photographs of MPs detected in snow. (A) Polystyrene fiber from Svalbard 4 (length, 1101 μm); (B) polypropylene particle from Heligoland (diameter, 256 μm); (C) polyvinyl chloride fiber from Ice Floe 8 (length, 956 μm). Note that these particular two fibers could be analyzed by FTIR as they happened to lay plane on the filter. (D to F) Aluminum oxide filter with enriched snow sample. (G to I) Corresponding polymer-dependent false-color image after FTIR measurement and automated analysis. (D and G) Bavaria 2: The sample with most MPs; (E and H) Ice Floe 9: the sample with the third most MPs; (F and I) Bremen: The sample with intermediate MP numbers but many fibers (photo credit: S. Mützel, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung).
Fig. 3 Photographs of MPs detected in snow. (A) Polystyrene fiber from Svalbard 4 (length, 1101 μm); (B) polypropylene particle from Heligoland (diameter, 256 μm); (C) polyvinyl chloride fiber from Ice Floe 8 (length, 956 μm). Note that these particular two fibers could be analyzed by FTIR as they happened to lay plane on the filter. (D to F) Aluminum oxide filter with enriched snow sample. (G to I) Corresponding polymer-dependent false-color image after FTIR measurement and automated analysis. (D and G) Bavaria 2: The sample with most MPs; (E and H) Ice Floe 9: the sample with the third most MPs; (F and I) Bremen: The sample with intermediate MP numbers but many fibers (photo credit: S. Mützel, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung).CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 41

  1. 10.1126/science.1260352 · DOI · Paper page
  2. M. Bergmann, M. B. Tekman, L. Gutow, Marine litter: Sea change for plastic pollution. Nature 544, 297 (2017). · DOI
  3. M. Bergmann, B. Lutz, M. B. Tekman, L. Gutow, Citizen scientists reveal: Marine litter pollutes Arctic beaches and affects wild life. Mar. Pollut. Bull. 125, 535–540 (2017). · DOI
  4. 10.1002/2014EF000240 · DOI
  5. B. E. Grøsvik, T. Prokhorova, E. Eriksen, P. Krivosheya, P. A. Horneland, D. Prozorkevich, Assessment of marine litter in the Barents Sea, a part of the Joint Norwegian–Russian Ecosystem Survey. Front. Mar. Sci. 5, 72 (2018). · DOI
  6. 10.1016/j.marpolbul.2018.03.011 · DOI
  7. M. Bergmann, N. Sandhop, I. Schewe, D. D’Hert, Observations of floating anthropogenic litter in the Barents Sea and Fram Strait, Arctic. Polar Biol. 39, 553–560 (2016). · DOI
  8. 10.1126/sciadv.1600582 · DOI
  9. M. B. Tekman, T. Krumpen, M. Bergmann, Marine litter on deep Arctic seafloor continues to increase and spreads to the North at the HAUSGARTEN observatory. Deep Sea Res. Part I 120, 88–99 (2017). · DOI
  10. 10.1016/j.envpol.2018.02.062 · DOI
  11. 10.1021/acs.est.7b03331 · DOI
  12. 10.1038/s41467-018-03825-5 · DOI · Paper page
  13. S. Dehghani, F. Moore, R. Akhbarizadeh, Microplastic pollution in deposited urban dust, Tehran metropolis, Iran. Environ. Sci. Pollut. Res. 24, 20360–20371 (2017). · DOI
  14. L. Cai, J. Wang, J. Peng, Z. Tan, Z. Zhan, X. Tan, Q. Chen, Characteristic of microplastics in the atmospheric fallout from Dongguan city, China: Preliminary research and first evidence. Environ. Sci. Pollut. Res. 24, 24928–24935 (2017). · DOI · Paper page
  15. 10.1071/EN14167 · DOI · Paper page
  16. L. C. Woodall, C. Gwinnett, M. Packer, R. C. Thompson, L. F. Robinson, G. L. J. Paterson, Using a forensic science approach to minimize environmental contamination and to identify microfibres in marine sediments. Mar. Pollut. Bull. 95, 40–46 (2015). · DOI
  17. J. Gasperi, S. L. Wright, R. Dris, F. Collard, C. Mandin, M. Guerrouache, V. Langlois, F. J. Kelly, B. Tassin, Microplastics in air: Are we breathing it in? Curr. Opin. Environ. Sci. Health 1, 1–5 (2018). · DOI · Paper page
  18. S. Zhao, Y. Yu, J. He, D. Yin, B. Wang, Below-cloud scavenging of aerosol particles by precipitation in a typical valley city, northwestern China. Atmos. Environ. 102, 70–78 (2015). · DOI
  19. J. Heintzenberg, M. Rummukainen, Airborne particles in snow. J. Glaciol. 39, 239–244 (1993). · DOI
  20. S. Primpke, C. Lorenz, R. Rascher-Friesenhausen, G. Gerdts, An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis. Anal. Methods 9, 1499–1511 (2017). · DOI
  21. R. Dris, J. Gasperi, M. Saad, C. Mirande, B. Tassin, Synthetic fibers in atmospheric fallout: A source of microplastics in the environment? Mar. Pollut. Bull. 104, 290–293 (2016). · DOI
  22. M. Uematsu, R. A. Duce, J. M. Prospero, Deposition of atmospheric mineral particles in the North Pacific Ocean. J. Atmos. Chem. 3, 123–138 (1985). · DOI
  23. G. A. Sehmel, Particle resuspension from an asphalt road caused by car and truck traffic. Atmos. Environ. 7, 291–309 (1973). · DOI
  24. G. Lonati, M. Giugliano, S. Cernuschi, The role of traffic emissions from weekends’ and weekdays’ fine PM data in Milan. Atmos. Environ. 40, 5998–6011 (2006). · DOI
  25. K. S. Golokhvast, Airborne biogenic particles in the snow of the cities of the russian far east as potential allergic compounds. J Immunol Res 2014, 7 (2014). · DOI
  26. P. M. Outridge, R. W. Macdonald, F. Wang, G. A. Stern, A. P. Dastoor, A mass balance inventory of mercury in the Arctic Ocean. Environ. Chem. 5, 89–111 (2008). · DOI
  27. S. Eckhardt, A. Stohl, S. Beirle, N. Spichtinger, P. James, C. Forster, C. Junker, T. Wagner, U. Platt, S. G. Jennings, The North Atlantic Oscillation controls air pollution transport to the Arctic. Atmos. Chem. Phys. 3, 1769–1778 (2003). · DOI
  28. 10.1126/sciadv.aau2768 · DOI
  29. D.-D. Rousseau, D. Duzer, J.-L. Etienne, G. Cambon, D. Jolly, J. Ferrier, P. Schevin, Pollen record of rapidly changing air trajectories to the North Pole. J. Geophys. Res. Atmos. 109, D06116 (2004).
  30. 10.1016/j.envpol.2016.12.013 · DOI
  31. L. Yuyan, F. Bo, S. Yaxing, W. Ting, Z. H. L. Zhizhong, Scavenging of atmospheric particulates by snow in Changji, China. Global NEST J. 20, 471–476 (2018). · DOI
  32. K. Enders, R. Lenz, C. A. Stedmon, T. G. Nielsen, Abundance, size and polymer composition of marine microplastics ≥ 10 μm in the Atlantic Ocean and their modelled vertical distribution. Mar. Pollut. Bull. 100, 70–81 (2015). · DOI · Paper page
  33. A. I. Catarino, V. Macchia, W. G. Sanderson, R. C. Thompson, T. B. Henry, Low levels of microplastics (MP) in wild mussels indicate that MP ingestion by humans is minimal compared to exposure via household fibres fallout during a meal. Environ. Pollut. 237, 675–684 (2018). · DOI
  34. J. L. Pauly, S. J. Stegmeier, H. A. Allaart, R. T. Cheney, P. J. Zhang, A. G. Mayer, R. J. Streck, Inhaled cellulosic and plastic fibers found in human lung tissue. Cancer Epidemiol. Biomark. Prev. 7, 419–428 (1998).
  35. J. C. Pimentel, R. Avila, A. G. Lourenço, Respiratory disease caused by synthetic fibres: a new occupational disease. Thorax 30, 204–219 (1975). · DOI · Paper page
  36. Y. Nazarenko, S. Fournier, U. Kurien, R. B. Rangel-Alvarado, O. Nepotchatykh, P. Seers, P. A. Ariya, Role of snow in the fate of gaseous and particulate exhaust pollutants from gasoline-powered vehicles. Environ. Pollut. 223, 665–675 (2017). · DOI
  37. M. Haave, C. Lorenz, S. Primpke, G. Gerdts, Different stories told by small and large microplastics in sediment - first report of microplastic concentrations in an urban recipient in Norway. Mar. Pollut. Bull. 141, 501–513 (2019). · DOI
  38. M. J. Anderson, D. C. I. Walsh, PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecol. Monogr. 83, 557–574 (2013). · DOI
  39. J.-C. Gallet, I. Merkouriadi, G. E. Liston, C. Polashenski, S. Hudson, A. Rösel, S. Gerland, Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N-ICE2015) expedition. J. Geophys. Res. Atmos. 122, 10820–10836 (2017).
  40. J.-G. Winther, O. Bruland, K. Sand, S. Gerland, D. Marechal, B. Ivanov, P. Gøowacki, M. König, Snow research in Svalbard—An overview. Polar Res. 22, 125–144 (2003).
  41. C. Marty GCOS SWE data from 11 stations in Switzerland. WSL Institute for Snow and Avalanche Research SLF. doi:10.16904/15 (2017). · DOI

Cited by

Sorted by citations

See all 1,359

Metadata

Authors
Bergmann, M., Mützel, S., Primpke, S., Tekman, M. B., Trachsel, J., & Gerdts, G.
Affiliations
  • Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, Bremerhaven 27570, Germany.
  • WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland.
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2019-08-02
Volume
5
Issue
8
ISSN
2375-2548
DOI
10.1126/sciadv.aax1157
PMC
PMC6693909
Cited by
1,359
References
5
Language
en
Funders
Helmholtz Association (FRAM)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review