Urban particulate matter pollution: a tale of five cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.016 | 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 it