Spatiotemporal Distributions and Source Apportionment of PM<sub>2.5</sub>‐Bound Antimony in Beijing, China
Bibliographic record
Abstract
Abstract Antimony (Sb) is a toxic heavy metal, and PM2.5‐bound Sb (SbPM2.5) in the air impacts human health via inhalation pathways. In this study, we analyzed multiyear measurements of ambient SbPM2.5 in Beijing to characterize its spatiotemporal distributions, identify main sources, and predict future trends. The results show that ambient SbPM2.5 has been decreasing with fluctuations from 2005 to 2012 and decreased rapidly after 2013, which was likely a result of the government's enhanced air pollution control plan that targeted main sources of industrial Sb. Across the city, average SbPM2.5 concentrations were relatively higher at nonemission locomotive traffic sampling sites and in densely populated areas (9.45–19.45 ng/m3) and lower in background areas (0.6–0.9 ng/m3). Regional‐scale emissions and local human activities both affected the spatial distributions of SbPM2.5. Notably, SbPM2.5 concentration increased by 58.3% from 2006 to 2013 in one suburban background area, indicating the changing emission distributions and intensities over the study period. A neural network model was developed and tested to predict future SbPM2.5 levels, results from which showed that with simulated massive reductions in coal supplies and a rapid boom in the waste incineration industry, SbPM2.5 concentration would vary in a smaller range (from 4.08 to 4.38 ng/m3) over the next decade as compared to the observed range during 2011–2018 (19.0–5.44 ng/m3). The impact of the continued expansion of the waste incineration industry on SbPM2.5 pollution needs to be considered in future emission control policies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".