MétaCan
Menu
Back to cohort
Record W2120211912 · doi:10.5194/acp-11-7027-2011

Quantification of aerosol chemical composition using continuous single particle measurements

2011· article· en· W2120211912 on OpenAlexafffundabout
Cheol–Heon Jeong, M. McGuire, Krystal J. Godri Pollitt, Jay G. Slowik, Peter Rehbein, Greg J. Evans

Bibliographic record

VenueAtmospheric chemistry and physics · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersOntario Innovation Trust
KeywordsAerosolChemical compositionChemistryMass spectrometryParticle (ecology)Ammonium nitrateParticulatesAnalytical Chemistry (journal)NitrateScanning mobility particle sizerParticle sizeParticle-size distributionEnvironmental chemistryChromatography

Abstract

fetched live from OpenAlex

Abstract. Mass concentrations of sulphate, nitrate, ammonium, organic carbon (OC), elemental carbon (EC) were determined from real time single particle data in the size range 0.1–3.0 μm measured by an Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) at urban and rural sites in Canada. To quantify chemical species within individual particles measured by an ATOFMS, ion peak intensity of m/z −97 for sulphate, −62 for nitrate, +18 for ammonium, +43 for OC, and +36 for EC were scaled using the number and size distribution data by an Aerodynamic Particle Sizer (APS) and a Fast Mobility Particle Sizer (FMPS). Hourly quantified chemical species from ATOFMS single-particle analysis were compared with collocated fine particulate matter (aerodynamic diameter < 2.5 μm, PM2.5) chemical composition measurements by an Aerosol Mass Spectrometer (AMS) at a rural site, a Gas-Particle Ion Chromatograph (GPIC) at an urban site, and a Sunset Lab field OCEC analyzer at both sites. The highest correlation was found for nitrate, with correlation coefficients (Pearson r) of 0.89 (ATOFMS vs. GPIC) and 0.85 (ATOFMS vs. AMS). ATOFMS mass calibration factors, determined for the urban site, were used to calculate mass concentrations of the major PM2.5 chemical components at the rural site near the US border in southern Ontario. Mass reconstruction using the ATOFMS mass calibration factors agreed very well with the PM2.5 mass concentrations measured by a Tapered Element Oscillating Microbalance (TEOM, r = 0.86) at the urban site and a light scattering monitor (DustTrak, r = 0.87) at the rural site. In the urban area nitrate was the largest contributor to PM2.5 mass in the winter, while organics and sulphate contributed ~64 % of the summer PM2.5 in the rural area, suggesting a strong influence of regional/trans-boundary pollution. The mass concentrations of five major species in ten size-resolved particle-types and aerosol acidity of each particle-type were determined for the rural site. On a mass basis sulphate and OC rich particle-types (OC-S and OC-S-N) accounted for up to 59 % of the particles characterized and aerosols were weakly acidic in the rural area. This is the first study to estimate hourly quantitative data of sulphate, nitrate, ammonium, OC and EC in ambient particles from scaled ATOFMS single particle analysis; these were closely comparable with collocated high time resolution data of sulphate, nitrate and ammonium detected by AMS and GPIC.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.223
Teacher spread0.170 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations80
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueAtmospheric chemistry and physicsSame topicAtmospheric chemistry and aerosolsFrench-language works237,207