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Record W4319304743 · doi:10.1016/j.gsf.2023.101566

Cryosphere as a temporal sink and source of microplastics in the Arctic region

2023· article· en· W4319304743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeoscience Frontiers · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
FundersHorizon 2020 Framework ProgrammeCanada First Research Excellence FundOcean Frontier InstituteLeverhulme TrustMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesEuropean CommissionState Key Laboratory of Cryospheric ScienceH2020 Marie Skłodowska-Curie ActionsNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaHorizon 2020
KeywordsArcticEnvironmental scienceCryosphereSea iceMicroplasticsSnowPermafrostArctic ice packOceanographyArctic geoengineeringSink (geography)SeawaterMeltwaterPhysical geographyClimatologyAtmospheric sciencesGeologySea ice thicknessGeographyGeomorphology

Abstract

fetched live from OpenAlex

Microplastics (MPs) pollution has become a serious environmental issue of growing global concern due to the increasing plastic production and usage. Under climate warming, the cryosphere, defined as the part of Earth’s layer characterized by the low temperatures and the presence of frozen water, has been experiencing significant changes. The Arctic cryosphere (e.g., sea ice, snow cover, Greenland ice sheet, permafrost) can store and release pollutants into environments, making Arctic an important temporal sink and source of MPs. Here, we summarized the distributions of MPs in Arctic snow, sea ice, seawater, rivers, and sediments, to illustrate their potential sources, transport pathways, storage and release, and possible effects in this sentinel region. Items concentrations of MPs in snow and ice varied about 1- 6 orders of magnitude in different regions, which were mostly attributed to the different sampling and measurement methods, and potential sources of MPs. MPs concentrations from Arctic seawater, river/lake water, and sediments also fluctuated largely, ranging from several items of per unit to more than 40,000 items m-3, 100 items m-3, and 10,000 items kg-1 dw, respectively. Arctic land snow cover can be a temporal storage of MPs, with MPs deposition flux of about (4.9 - 14.26) × 108 items km-2 yr-1. MPs transported by rivers to Arctic ocean was estimated to be approximately 8 - 48 ton yr-1, with discharge flux of MPs at about (1.65 - 9.35) × 108 items/s. Average storage of MPs in sea ice was estimated to be about 6.1×1018 items, with annual release of about 5.1×1018 items. Atmospheric transport of MPs from long-distance terrestrial sources contributed significantly to MPs deposition in Arctic land snow cover, sea ice and oceanic surface waters. Arctic Great Rivers can flow MPs into the Arctic Ocean. Sea ice can temporally store, transport and then release MPs in the surrounded environment. Ocean currents from the Atlantic brought high concentrations of MPs into the Arctic. However, there existed large uncertainties of estimation on the storage and release of MPs in Arctic cryosphere owing to the hypothesis of average MPs concentrations. Meanwhile, representatives of MPs data across the large Arctic region should be mutually verified with in situ observations and modeling. Therefore, we suggested that systematic monitoring MPs in the Arctic cryosphere, potential threats on Arctic ecosystems, and the carbon cycle under increasing Arctic warming, are urgently needed to be studied in future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it