Bibliographic record
Abstract
Abstract. Long-term PM2.5 data are needed to study the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at 4011 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR). PM2.5 monitoring is the target of machine learning, and atmospheric visibility and other related variables are the inputs. The training results show that the slope between the estimated PM2.5 concentration and the monitored PM2.5 concentration is 0.946± 0.0002 within the 95 % confidence interval (CI), the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.0 μg/m3, and the mean absolute error (MAE) is 3.1 μg/m3. The test results show that the slope between the predicted PM2.5 concentration and the monitored PM2.5 concentration is 0.862 ± 0.0010 within a 95 % CI, the R2 is 0.80, the RMSE is 13.5 μg/m3, and the MAE is 6.9 μg/m3. The multiyear mean PM2.5 concentrations from 1959 to 2022 in the United States, Canada, Europe, China, and India are 11.2 μg/m3, 8.2 μg/m3, 20.1 μg/m3, 51.3 μg/m3 and 88.6 μg/m3, respectively. PM2.5 is low and continues to decrease from 1959 to 2022. PM2.5 in the United States increases slightly at a rate of 0.38 μg/m3/decade from 1959 to 1990 and decreases at a rate of -1.32 μg/m3/decade from 1991 to 2022. Trends in Europe are positive (5.69 μg/m3/decade) from 1959 to 1972 and negative (-1.91 μg/m3/decade) from 1973 to 2022. Trends in China and India are increasing (3.04 and 3.35 μg/m3/decade, respectively) from 1959 to 2012 and decreasing (-38.82 and -42.84 μg/m3/decade, respectively) from 2013 to 2022. The dataset is available at National Tibetan Plateau / Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024).
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.011 |
| Insufficient payload (model declined to judge) | 0.337 | 0.317 |
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".