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Aerosol physical characterization: A review on the current state of aerosol documentary standards and calibration strategies

2024· review· en· W4403962541 on OpenAlexafffund
Konstantina Vasilatou, Kenjiro Iida, Mohsen Kazemimanesh, Jason S. Olfert, Hiromu Sakurai, Timothy A. Sipkens, Gregory J. Smallwood

Bibliographic record

VenueJournal of Aerosol Science · 2024
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsNational Research Council CanadaUniversity of Alberta
FundersHorizon 2020Institut "Jožef Stefan"Transport CanadaConsiglio Nazionale delle RicercheUniverza v Novi Gorici
KeywordsAerosolCalibrationEnvironmental scienceCurrent (fluid)Characterization (materials science)Remote sensingMeteorologyNanotechnologyMaterials scienceGeographyPhysics

Abstract

fetched live from OpenAlex

Aerosols have a wide-ranging impact on the climate, air quality, human health, and agriculture. Despite the ongoing advances in aerosol measurement science and technology, the uncertainties in quantifying aerosol physical properties remain significant in many applications. The accurate characterization of airborne particles - including number and mass concentration, size distribution and light absorption - is critical for understanding their behavior in the atmosphere and environmental fate. We delve into the physical characterization of aerosols, highlighting the measurement and documentary standards that underpin measurement traceability and enable comparison of data collected by instruments based on measurement principles at different times or locations. In particle metrology, recent advances have led to sophisticated primary measurement standards, with relative expanded measurement uncertainties down to 1.1 % (coverage factor k = 2; 95 % confidence interval). These standards enable time- and cost-effective instrument calibration to support research, industry, and legislation. We discuss documentary standards and regulations related to air quality and control of particle emissions from vehicles, aviation, shipping, and stationary sources, with the aim to increase awareness of these documents and underline differences in measurement protocols in different sub-fields of aerosol sciences. Importantly, we emphasize the need for further harmonization of measurement procedures, providing specific examples and making suggestions towards this goal. This review, with its comprehensive coverage of aerosol measurement and documentary standards across different sub-disciplines, can serve as a reliable guide for scientists and regulators interested in improving the accuracy of their measurements. • Comprehensive coverage of aerosol documentary and measurement standards across different sub-disciplines of aerosol science. • Paths to measurement traceability for different aerosol metrics. • Overview of state-of-the-art laboratory set-ups for aerosol physical characterization. • Summary of best measurement uncertainties attained in particle metrology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.027
GPT teacher head0.319
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2024
Admission routes2
Has abstractyes

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