Modelling hygroscopicity-induced gas–aerosol partitioning, organic surface enrichment and cloud droplet formation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".