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Record W2603981308 · doi:10.5194/acp-17-13037-2017

Mixing times of organic molecules within secondary organic aerosol particles: a global planetary boundary layer perspective

2017· article· en· W2603981308 on OpenAlexaff
Adrian M. Maclean, C. L. Butenhoff, James W. Grayson, Kelley C. Barsanti, J. L. Jiménez, Allan K. Bertram

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersPortland State UniversityM.J. Murdock Charitable TrustU.S. Department of Energy
KeywordsAerosolMixing (physics)Boundary layerViscosityChemistryPineneAtmosphere (unit)IsopreneMixing ratioPlanetary boundary layerRelative humidityAtmospheric sciencesMeteorologyThermodynamicsOrganic chemistryGeologyPhysicsPhysical chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract. When simulating the formation and life cycle of secondary organic aerosol (SOA) with chemical transport models, it is often assumed that organic molecules are well mixed within SOA particles on the timescale of 1 h. While this assumption has been debated vigorously in the literature, the issue remains unresolved in part due to a lack of information on the mixing times within SOA particles as a function of both temperature and relative humidity. Using laboratory data, meteorological fields, and a chemical transport model, we estimated how often mixing times are < 1 h within SOA in the planetary boundary layer (PBL), the region of the atmosphere where SOA concentrations are on average the highest. First, a parameterization for viscosity as a function of temperature and RH was developed for α-pinene SOA using room-temperature and low-temperature viscosity data for α-pinene SOA generated in the laboratory using mass concentrations of ∼ 1000 µg m−3. Based on this parameterization, the mixing times within α-pinene SOA are < 1 h for 98.5 % and 99.9 % of the occurrences in the PBL during January and July, respectively, when concentrations are significant (total organic aerosol concentrations are > 0.5 µg m−3 at the surface). Next, as a starting point to quantify how often mixing times of organic molecules are < 1 h within α-pinene SOA generated using low, atmospherically relevant mass concentrations, we developed a temperature-independent parameterization for viscosity using the room-temperature viscosity data for α-pinene SOA generated in the laboratory using a mass concentration of ∼ 70 µg m−3. Based on this temperature-independent parameterization, mixing times within α-pinene SOA are < 1 h for 27 and 19.5 % of the occurrences in the PBL during January and July, respectively, when concentrations are significant. However, associated with these conclusions are several caveats, and due to these caveats we are unable to make strong conclusions about how often mixing times of organic molecules are < 1 h within α-pinene SOA generated using low, atmospherically relevant mass concentrations. Finally, a parameterization for viscosity of anthropogenic SOA as a function of temperature and RH was developed using sucrose–water data. Based on this parameterization, and assuming sucrose is a good proxy for anthropogenic SOA, 70 and 83 % of the mixing times within anthropogenic SOA in the PBL are < 1 h for January and July, respectively, when concentrations are significant. These percentages are likely lower limits due to the assumptions used to calculate mixing times.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations51
Published2017
Admission routes1
Has abstractyes

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