Influence of Atmospheric Dispersion and New Particle Formation Events on Ambient Particle Number Concentration in Rochester, United States, and Toronto, Canada
Bibliographic record
Abstract
Continuous measurements of particle number concentrations were performed in Rochester, NY, and Toronto, Ontario, Canada during the 2003 calendar year. Strong seasonal dependency in particle number concentration was observed at two sites. The average number concentration of ambient particles was 9670 +/- 6960 cm(-3) in Rochester, whereas in Toronto the average number of particles was 28,010 +/- 13,350 cm(-3). The particle number concentrations were higher in winter months than in summer months by a factor of 1.5 in Rochester and 1.6 in Toronto. In general, there were also distinct diurnal variations of aerosol number concentration. The highest weekdays/weekends ratio of number concentration was typically observed during the rush-hour period in winter months with a ratio of 2.1 in Rochester and 2.0 in Toronto. The correlation in the total particle number concentrations between the two urban sites was stronger in winter because of the common urban traffic patterns, but weaker in summer because of local sulfur dioxide (SO2)-related particle formation events in Rochester in the summer. Strong morning particle formation events were frequently observed during colder winter months. Good correlations between particle number and carbon monoxide (CO) as well as temperature suggested that motorvehicle emissions lead to the formation of new particles as the exhaust mixes with the cold air. Regional nucleation and growth events frequently occurred in April. Local SO2-related particle formation events most frequently occurred in August. SO2 and UV-B were highly correlated with particle concentration, suggesting a high association of photochemical processes with these local events. A high directionality in a northerly direction was observed for particle number and SO2, indicating the influence of point sources located north of Rochester.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".