Effect of photochemical ageing on the ice nucleation properties of diesel and wood burning particles
Bibliographic record
Abstract
Abstract. A measurement campaign (IMBALANCE) conducted in 2009 was aimed at characterizing the physical and chemical properties of freshly emitted and photochemically aged combustion particles emitted from a log wood burner and diesel vehicles: a EURO3 Opel Astra with a diesel oxidation catalyst (DOC) but no particle filter and a EURO2 Volkswagen Transporter TDI Syncro without emission aftertreatment. Ice nucleation experiments in the deposition and condensation freezing modes were conducted with the Portable Ice Nucleation Chamber (PINC) at three nominal temperatures, −30 °C, −35 °C and −40 °C. Freshly emitted diesel particles showed ice formation only at −40 °C in the deposition mode at 137% relative humidity with respect to ice (RHi) and 92% relative humidity with respect to water (RHw), and photochemical ageing did not play a role in modifying their ice nucleation behaviour. Only one diesel experiment where α-pinene was added for the ageing process, showed an ice nucleation enhancement at −35 °C. Wood burning particles also act as ice nuclei (IN) at −40 °C in the deposition mode at the same conditions as for diesel particles and photochemical ageing also did not alter the ice formation properties of the wood burning particles. Unlike diesel particles, wood burning particles form ice via condensation freezing at −35 °C whereas no ice nucleation was observed at −30 °C. Photochemical ageing did not affect the ice nucleation ability of the diesel and wood burning particles at the three different temperatures investigated but a broader range of temperatures below −40 °C need to be investigated in order to draw an overall conclusion on the effect of photochemical ageing on deposition/condensation ice nucleation across the entire temperature range relevant to cold clouds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".