Assessment of chemical extracts of urban air for endocrine disruption using a serie of in vitro bioassays: a case study in Montreal, Canada
Bibliographic record
Abstract
Recent data indicate that urban air harbors a complex mixture of industrial chemicals, pesticides, and combustion by-products, many of which may act as endocrine-disrupting chemicals (EDCs). By interfering with the endocrine system, EDCs can affect human health. Hence, there is an urgent need to better characterize outdoor airborne chemical mixtures and their health impact. This study assessed the bioactivity on key targets of EDCs for chemical extracts of the atmospheric vapour phase (i.e., excluding particulate matter) collected from 40 sites across Montreal, Canada, using passive air samplers deployed over 82 summer days. Seven validated bioassays were used to test the ability of each extract to alter the estrogen, androgen, thyroid, and steroidogenesis pathways. Of the 42 urban air extracts tested, none induced alteration of the human thyroid peroxidase activity or activated the human androgen receptor (hAR) but three induced estrogen receptor activation and five inhibited the sodium/iodide symporter by 40-60 %. More than 20 extracts antagonized hESR1 and/or hAR, a few with very strong potency. Yet, blanks used as controls also induced signals in the antagonist mode of the transactivation assays and affected testosterone production in the H295R steroidogenic assay, challenging data interpretations. Overall, our data indicate the potential for thyroid, estrogenic, antiestrogenic and antiandrogenic disruption caused by the chemical mixtures present in the outdoor air of a major metropolis. This work provides one of the first integrated assessments of endocrine activity from atmospheric vapour phase chemical extracts, underscoring the importance of incorporating bioanalytical tools into air-quality and health-risk evaluation frameworks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".