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Modelling hygroscopicity-induced gas–aerosol partitioning, organic surface enrichment and cloud droplet formation

2020· article· en· W3090871388 on OpenAlexaff
Andreas Zuend, Kyle Gorkowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMcGill University
Fundersnot available
KeywordsAerosolContext (archaeology)Particle (ecology)ChemistrySurface tensionInorganic ionsRelative humidityPhase (matter)HumidityIonAmmoniaChemical engineeringChemical physicsEnvironmental chemistryMeteorologyThermodynamicsOrganic chemistry

Abstract

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The interactions among low and semi-volatile organic compounds, water and other inorganic components within fine-mode aerosols are complex. We show that understanding several features of this complexity can be important in the context of phase separation, a particle surface composition enriched by organics and for related cloud droplet activation modeling. Context: oxidized organic compounds contribute to the particle hygroscopicity, yet typically to a lesser extent than dissolved inorganic ions. The overall hygroscopicity of aerosols in turn greatly affects their water uptake in an air parcel experiencing increasing relative humidity. The mechanism acts directly in terms of adding water mass, as well as indirectly via a hygroscopicity-induced feedback leading to enhanced gas–to–particle partitioning of semi-volatile organic components alongside a re-equilibration of the aerosol with inorganic acids, ammonia and further water uptake. Furthermore, non-ideal mixing may induce liquid–liquid phase separation, often leading to an organic-rich phase of relatively low surface tension surrounding an inorganic-rich particle core. This phase separation effect and related surface enhancements of organic component concentrations affect not only the morphology but also the potential for near-surface chemical reactions, as well as the thermodynamics controlling an aerosol particle’s activation into a cloud droplet at realistic water vapour supersaturations. New experimental techniques and field observations over the past few years have encouraged model development for an improved representation of these processes on a detailed level (see, e.g., discussion in Davies et al., 2019). This has led to a better understanding of the potential role of organic aerosol compounds spanning a range of polarities and an associated evolution of surface tension prior to the cloud condensation nucleus (CCN) activation point. While detailed process models still lack finer details to fully capture these composition and phase effects reliably and predictively, important challenges exist in translating these mechanisms into computationally efficient and feasible reduced-complexity models of use for air quality and chemistry-climate modelling. In this presentation, we will outline the current state of a relatively complete process-level aerosol thermodynamics model based on AIOMFAC and introduce key features of a recently developed reduced-complexity organic aerosol model that accounts for water content and hygroscopicity-induced feedbacks on composition (Gorkowski et al., 2019). A key advantage of the reduced-complexity model is its ability to use only input typically known from field measurements or data available in large-scale air quality models. Our approach is compatible with a volatility basis set approach and allows for extending it by adding a realistic humidity dependence. In addition, we account for phase separation and related effects on surface tension in a simplified, computationally efficient manner. This approach and its results for aerosol hygroscopicity and cloud droplet activation will be discussed. References: Davies, J. F., Zuend, A., and Wilson, K. R.: Technical note: The role of evolving surface tension in the formation of cloud droplets, Atmos. Chem. Phys., 19, 2933–2946, doi:10.5194/acp-19-2933-2019, 2019. Gorkowski, K., Preston, T. C., and Zuend, A.: Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model, Atmos. Chem. Phys., 19, 13383–13407, 10.5194/acp-19-13383-2019, 2019.

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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.215
Teacher spread0.181 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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