A Unified Cellular Toxic Potency Dataset of PM2.5 across Chemicals, Emission Sources, and Regions in China
Bibliographic record
Abstract
Exposure to fine particulate matter (PM 2.5 ) is a leading global health risk factor. Effective mitigation demands a multidimensional understanding that integrates chemical, source and region-level differences in PM 2.5 toxicities relevant to human health. We present a standardized in vitro cellular assay dataset characterizing PM 2.5 toxic potencies across emission sources, chemical constituents and atmospheric environments. Real-world PM 2.5 samples from 23 major anthropogenic sources, covering industrial, transportation and residential sectors, were evaluated for cytotoxicity and oxidative stress potency, identifying key sources driving PM 2.5 -induced health risks. Toxic potency-adjusted concentrations of bioactive PM 2.5 components, including polycyclic aromatic hydrocarbons, elemental carbon, metals, and non-metal species, were quantified to attribute overall PM 2.5 toxicity to specific chemicals. Furthermore, the toxic potencies of ambient PM 2.5 collected from selected urban and rural areas in China were identified, enabling the development of evaluation metrics for quantifying regional inequalities in PM 2.5 health risks. This dataset establishes a universal toxicity benchmark for standardized comparisons of PM 2.5 health impacts, providing a valuable resource for exposure assessment, source prioritization, and air quality risk evaluation.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".