Elucidating the origins of ultrafine particles in a major city using long-term datasets: Evidence of a new midday process
Bibliographic record
Abstract
Ultrafine particles (UFPs) are both directly emitted from human activities and produced through atmospheric processes. The origins of ultrafine particles were explored in an urban area by analyzing 6 to 520 nm particle size data collected from 2006 to 2021 near a busy roadway in downtown Toronto, Canada. Days were classified into five categories: Strong Nucleation, Midday Pollution, Traffic Pollution, Baseline, and Mixed. Strong Nucleation days, which comprised about 6% of the days, showed long nucleation events (¿ 3 hr) with an average particle number concentration of 3.1±0.1 × 1 0 4 #/ cm 3 around noon (10 am to 2 pm). Midday Pollution days also exhibited higher particle concentrations around noon with an average of 3.3±0.06 × 1 0 4 #/ cm 3 . The higher number concentrations on these days appeared to arise from locally emitted UFP and be associated with enhanced production of UFP within vehicle exhaust plumes. The Traffic Pollution days showed morning traffic emissions, with no midday rise. The average total UFP concentration around the morning rush hour (6 am to 9 am) on these days was 2.1±0.2 × 1 0 4 #/ cm 3 . About 27% of the days had lower particle number concentrations (daily average: 1.2±0.2 × 1 0 4 #/ cm 3 ) throughout the day. The number concentrations were lower on these “Baseline days” and the influence of traffic emissions was also lower but still observable in the diurnal pattern. Lastly, Mixed days were the days that showed higher than Baseline concentrations of UFP around the morning rush hour (2.0±0.06 × 1 0 4 #/ cm 3 ) or midday (2.3±0.1 × 1 0 4 #/ cm 3 ); UFP on these days came from a mix of traffic pollution, nucleation event, or a midday process, with no one of these sources clearly dominant. These days could not be categorized into any of these categories with confidence. Analysis of the organic and inorganic speciation, trace elements, and traffic-related air pollutants suggested that the UFP on Midday Pollution days came from vehicle emissions enhanced by reactions within their exhaust plumes. Moreover, the time series analysis of these categories showed that the frequency of Midday Pollution days has decreased over the years with the number of Baseline days correspondingly increasing. Meteorological analysis showed that Midday and Traffic Pollution days happened more often in winter while Strong Nucleation days were more frequent in summer. This study has shown that higher midday UFP concentrations do not arise only due to nucleation events and that a previously unrecognized Midday Pollution process can be a large contributor. • Particle size distribution was collected in an urban area from 2006 to 2021. • Based on particle size distribution days were classified into five categories. • A previously unrecognized ”Midday Pollution” process was observed after classifying. • Noon UFP levels on Midday Pollution days were like those on Strong Nucleation days. • UFP on Midday Pollution days had larger spikes associated with vehicle exhaust.
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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".