Insect Infestation Increases Viscosity of Biogenic Secondary Organic Aerosol
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plant stress alters emissions of volatile organic compounds. However, little is known about how this could influence climate-relevant properties of secondary organic aerosol (SOA), particularly from complex mixtures such as real plant emissions. In this study, the chemical composition and viscosity were examined for SOA generated from real healthy and aphid-stressed Canary Island pine ( Pinus canariensis ) trees, which are commonly used for landscaping in Southern California. Healthy Canary Island pine (HCIP) and stressed Canary Island pine (SCIP) aerosols were generated in a 5 m 3 environmental chamber at 35–84% relative humidity and room temperature via OH-initiated oxidation. Viscosities of the collected particles were measured using an offline poke-flow method, after conditioning the particles in a humidified air flow. SCIP particles were consistently more viscous than HCIP particles. The largest differences in particle viscosity were observed in particles conditioned at 50% relative humidity where the viscosity of SCIP particles was an order of magnitude larger than that of HCIP particles. The increased viscosity for the aphid-stressed pine tree SOA was attributed to the increased fraction of sesquiterpenes in the emission profile. The real pine SOA particles, both healthy and aphid-stressed, were more viscous than α-pinene SOA particles, demonstrating the limitation of using a single monoterpene as a model compound to predict the physicochemical properties of real biogenic SOA. However, synthetic mixtures composed of only a few major compounds present in emissions (<10 compounds) can reproduce the viscosities of SOA observed from the more complex real plant emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".