Ten-year Trends in Ambient PM2.5 and PM2.5-bound Element Concentrations in an Industrialized Border City
Bibliographic record
Abstract
In this study, linear regression was adopted to characterize trends in ambient fine particulate matter (PM2.5) and PM2.5bounded element concentrations using data collected at Windsor West monitoring station in Windsor, Ontario, Canada, during 2003-2012.Twenty-two PM2.5-buonded elements were reported, and 10 of them had concentrations below method detection limits over 70% of the time.Therefore, they were excluded from further analysis.The 12 retained elements are Al, Br, Ca, Fe, K, Mn, Ni, Pb, S, Si, Ti and Zn.These 12 elements combined contributed 13% of total PM2.5 concentration, with S being the largest contributor (8.4%), followed by Fe (1.4%).The annual mean PM2.5 concentrations ranged from 7.3 µg/m 3 in 2009 to 10.4 µg/m 3 in 2005, are lower than the Canadian Ambient Air Quality Standards of 10 µg/m 3 for PM2.5 except for in 2005.The ten-year mean was 8.8 µg/m 3 .Significant decreasing trends were found for PM2.5 (25%), Fe (19%), Mn (47%), S (44%), Si (72%), Ti (92%), and Zn (71%), while no statistically significant change was observed for Al, Br, Ca, K, Ni, and Pb.Our findings suggest that the emission control strategies implemented during the study period were effective in reducing concentrations of PM2.5 and six out of 12 PM2.5-boundedelements in Windsor.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".