Urban particulate matter pollution: a tale of five cities
Bibliographic record
Abstract
Five case studies (Athens and Paris in Europe, Pittsburgh and Los Angeles in the United States, and Mexico City in Central America) are used to gain insights into the changing levels, sources, and role of atmospheric chemical processes in air quality in large urban areas as they develop technologically. Fine particulate matter is the focus of our analysis. In all cases reductions of emissions by industrial and transportation sources have resulted in significant improvements in air quality during the last few decades. However, these changes have resulted in the increasing importance of secondary particulate matter (PM) which dominates over primary in most cases. At the same time, long range transport of secondary PM from sources located hundreds of kilometres from the cities is becoming a bigger contributor to the urban PM levels in all seasons. "Non-traditional" sources including cooking, and residential and agricultural biomass burning contribute an increasing fraction of the now reduced fine PM levels. Atmospheric chemistry is found to change the chemical signatures of a number of these sources relatively fast both during the day and night, complicating the corresponding source apportionment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".