MétaCan
Menu
← Back to cohort
Record W4242669276 · doi:10.5194/acp-2018-898

Relative Humidity Effect on the Formation of Highly Oxidized Molecules and New Particles during Monoterpene Oxidation

2018· preprint· en· W4242669276 on OpenAlexaff
Xiaoxiao Li, Sabrina Chee, Jiming Hao, Jonathan P. D. Abbatt, Jingkun Jiang, James N. Smith

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilU.S. Department of EnergyBiological and Environmental ResearchDivision of Atmospheric and Geospace SciencesNational Science Foundation
KeywordsScanning mobility particle sizerChemistryRadicalRelative humidityParticle (ecology)OzonolysisPhotochemistryAutoxidationMoleculeIonizationMonomerMonoterpeneReactivity (psychology)Analytical Chemistry (journal)IonParticle sizeOrganic chemistryParticle-size distributionPhysical chemistryMeteorology

Abstract

fetched live from OpenAlex

Abstract. It has been widely observed around the world that the frequency and intensity of new particle formation (NPF) events are reduced during periods of high relative humidity (RH). The current study focuses on how RH affects the formation of highly oxidized molecules (HOMs), which are key components of NPF and initial growth caused by oxidized organics. The ozonolysis of α-pinene, limonene, and △3-carene, with and without OH-scavenger, were carried out under low NOx conditions under a range of RH (from ~3 % to ~90 %) in a temperature-controlled flow tube. A Scanning Mobility Particle Sizer (SMPS) was used to measure the size distribution of generated particles and a novel transverse-ionization chemical ionization inlet with a high-resolution time-of-fight mass spectrometer detected HOMs. A major finding from this work is that neither the detected HOMs nor their abundance changed significantly with RH, which indicates that the detected HOMs must be formed from water-independent pathways. In fact, the distinguished OH- and O3-derived peroxy radicals (RO2), HOM monomers, and HOM dimers could mostly be explained by the autoxidation of RO2 followed by bimolecular reactions with other RO2 or hydroperoxy radicals (HO2), rather than from a water-influenced pathway like through the formation of a stabilized Criegee intermediate (sCI). However, as RH changed from 3 to 90 % the particle number concentrations decreased by a factor of 2~3 while particle mass concentrations increased or decreased slightly within a factor of 2. These observations show that, while high RH appears to inhibit NPF as evident by the decreasing number concentration, this reduction is not caused by a decrease in RO2-derived HOMs formation. One possible explanation is the existence of other extremely low volatility compounds (ELVOCs), like gas phase formed sCI-included accretion products, which are responsible for the very first steps of NPF but are not detected by nitrate-based chemical ionization mass spectrometry. These ELVOCs may be preferentially reduced at high RH compared to more volatile compounds, the latter of which mainly determine the final mass concentration of particles. Another possibility is that a fraction of HOMs cluster with water (but detected as the declustered molecules) at high RH in such a way that they may no longer be able to participate in cluster formation, thereby suppressing NPF.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.221
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicAtmospheric chemistry and aerosols→French-language works237,207→