Simulation of particle diversity and mixing state over Greater Paris: a model–measurement inter-comparison
Bibliographic record
Abstract
Air quality models are used to simulate and forecast pollutant concentrations, from continental scales to regional and urban scales. These models usually assume that particles are internally mixed, i.e. particles of the same size have the same chemical composition, which may vary in space and time. Although this assumption may be realistic for continental-scale simulations, where particles originating from different sources have undergone sufficient mixing to achieve a common chemical composition for a given model grid cell and time, it may not be valid for urban-scale simulations, where particles from different sources interact on shorter time scales. To investigate the role of the mixing state assumption on the formation of particles, a size-composition resolved aerosol model (SCRAM) was developed and coupled to the Polyphemus air quality platform. Two simulations, one with the internal mixing hypothesis and another with the external mixing hypothesis, have been carried out for the period 15 January to 11 February 2010, when the MEGAPOLI winter field measurement campaign took place in Paris. The simulated bulk concentrations of chemical species and the concentrations of individual particle classes are compared with the observations of Healy et al. (Atmos. Chem. Phys., 2013, 13, 9479-9496) for the same period. The single particle diversity and the mixing-state index are computed based on the approach developed by Riemer et al. (Atmos. Chem. Phys., 2013, 13, 11423-11439), and they are compared to the measurement-based analyses of Healy et al. (Atmos. Chem. Phys., 2014, 14, 6289-6299). The average value of the single particle diversity, which represents the average number of species within each particle, is consistent between simulation and measurement (2.91 and 2.79 respectively). Furthermore, the average value of the mixing-state index is also well represented in the simulation (69% against 59% from the measurements). The spatial distribution of the mixing-state index shows that the particles are not mixed in urban areas, while they are well mixed in rural areas. This indicates that the assumption of internal mixing traditionally used in transport chemistry models is well suited to rural areas, but this assumption is less realistic for urban areas close to emission sources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".