Correlation between PM2.5 and Particle Number Concentrations in Four Major Cities: Toronto, Los Angeles, Helsinki and London
Bibliographic record
Abstract
Gaining more knowledge on how different particulate metrics are related would help in successfully controlling particulate matter (PM) concentrations in the ambient air. This study focused on the mass and number concentrations, particularly PM2.5 or mass concentration of particles with diameter of <2.5 µm in order to illustrate that mitigating PM2.5 would not necessarily reduce the ultrafine particles (UFP) concentration. Particles with diameter <0.1 µm are best quantified by the particle number concentration (PNC). The two parameters, PM2.5 and PNC are affected by different drivers, therefore may vary spatially and temporally between cities. PM2.5 is relatively more homogenous within an air shed while PNC is more variable depending mainly on the distribution of the combustion emission sources. To better understand these two important metrics and demonstrate their similarities and differences, this study aims to provide quantitative information on the relationship between ambient PM2.5 and PNC in four cities: Toronto, Canada; Los Angeles, USA; Helsinki, Finland; and London, UK. All these cities are located in the temperate region though Helsinki and Toronto are classified under Moist Continental Mid-Latitude Climate while London and Los Angeles are classified under Moist Subtropical Mid-Latitude climate based on the Köppen-Geiger system. Urban areas are particularly interesting because high population density implies that considerable amount of anthropogenic pollutants are produced in any city.
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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) | 0.002 | 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 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".