Characteristics, Accumulation, and Potential Health Risks of Antimony in Atmospheric Particulate Matter
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Antimony (Sb), a priority pollutant listed by the U.S. Environmental Protection Agency (USEPA), can cause adverse effects on human health, with particular impacts on skin, eyes, gastrointestinal tract, and respiratory system. In this study, a database of Sb concentrations in the global atmosphere was developed through a survey of measurements published in more than 600 articles, which was then used to assess the health risks of Sb exposure based on a USEPA assessment model. Most measurements showed Sb concentrations of less than ∼10 ng m –3, but those at several contaminated sites exhibited Sb concentrations of more than 100 ng m –3 . For measurements conducted in urban environments, Sb concentrations in the total suspended particles (TSP) and particles of less than 10 (PM 10 ) or 2.5 μm (PM 2.5 ) were the highest in Asia, followed by Europe, South America, and North America. Sb concentrations were generally higher in winter and fall than during other seasons in TSP and PM 10 samples. A significant correlation was observed between Sb and As in TSP and PM 2.5 on a global scale. Sb was mainly derived from anthropogenic sources, especially traffic emission, industrial emission, and fossil combustion. Hazard quotients (HQ) of Sb in TSP, PM 10, and PM 2.5 were higher for children than adults because of their lighter body weight, inferior physical resistance, and higher ingestion probability. The global database for atmospheric Sb concentrations demonstrates a relatively low noncarcinogenic risk in most regions. Long-term monitoring is still required to identify the sources and growth potentials of Sb so that effective control policies can be established.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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".