Assessing Correlation between PM2.5 and Meteorological Variables and Projecting the Impact of Climate Change on PM2.5
Bibliographic record
Abstract
This study builds the Statistical Downscaling Model (SDSM), coupled with the Artificial Neural Network (ANN), to identify meteorological variables that show strong influence on the concentrations of fine particulate matter (PM2.5) and to project future PM2.5 concentrations using global climate model in IPCC Fifth Assessment Report (AR5). Toronto and Sarnia, Canada are chosen to study the effects of meteorological influence and climate change on PM2.5, as a comparison of metropolitan and industrial cities. Higher PM2.5 in Summer are detected which is affected by the long range transport of pollutants. Seasonal models are built using ANN in both cities to study the influential predictors in each season, which perform better than annual models with a 10-15% increase in R2 value. The SDSM model projects future PM2.5 under the assumption of constant emissions. Results show that the impact of climate change on PM2.5 is relatively small due to the cancellation of opposite changes caused by predictors.
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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) | 0.001 | 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".