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Record W4409415037 · doi:10.1021/acs.est.5c03264

An Atmospheric Chemistry Perspective on Airborne Micro- and Nanoplastic Particles

2025· review· en· W4409415037 on OpenAlexafffund
Yue Zhang, Jonathan H. Slade, Andrew P. Ault, Arthur W. H. Chan

Bibliographic record

VenueEnvironmental Science & Technology · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
FundersDivision of Atmospheric and Geospace SciencesUniversity of MichiganCanada Research ChairsCollege of Literature, Science, and the ArtsTexas A and M UniversityU.S. Environmental Protection AgencyNational Science Foundation
KeywordsPerspective (graphical)Atmospheric chemistryAtmospheric compositionEnvironmental scienceAir pollutionEnvironmental chemistryMeteorologyChemistryComputer scienceGeographyOzoneAtmosphere (unit)

Abstract

fetched live from OpenAlex

Micro- and nanoplastic particles (MNPPs) are emerging pollutants with significant environmental impacts due to their persistence, increasing concentrations, and potential health risks. Most MNPP studies have focused on identifying, quantifying, and assessing their ecotoxicological impacts in water or soil. However, the atmosphere is crucial in transporting and chemically transforming MNPPs. Further, well-established aerosol particle characterization techniques are underutilized and inconsistently applied in existing atmospheric MNPP studies. This perspective synthesizes the existing literature and addresses future research needs unique to atmospheric MNPPs, highlighting the need to bridge the microplastics and atmospheric aerosol communities to better understand their sources, chemical transformations, transport mechanisms, as well as their health effects and ecological impacts, which differ from those in soil and water. Advancing research in these areas requires standardized methods and a multidisciplinary approach to comprehensively assess MNPP interactions across environmental compartments, providing essential insights into their environmental fate and risks.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.232
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2025
Admission routes2
Has abstractyes

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