Uncovering global-scale risks from commercial chemicals in air
Bibliographic record
Abstract
Abstract Commercial chemicals are used extensively across global urban centers, posing a potential exposure risk to 4.2 billion people, which accounts for 55% of the global population. Harmful chemicals are often assessed and regulated based on their environmental persistence, bioaccumulation, and toxic properties, under international and national initiatives such as the Stockholm Convention. However, current regulatory frameworks largely rely upon knowledge of the properties of the parent chemicals, with minimal consideration given to their atmospheric transformation products. This is mainly due to a significant lack of experimental data, as identifying transformation products in complex mixtures of airborne chemicals is an immense analytical challenge,hence making a comprehensive and reliable risk assessment for harmful chemicals currently unachievable. Here, we develop a novel framework, combining laboratory and field experiments, non-target analysis techniques, and in-silico modelling, to identify and assess the hazards of airborne chemicals, which takes into account atmospheric chemical reactions. By applying this framework to organophosphate flame retardants, as representative chemicals of emerging concern, we find that their transformation products are globally distributed across 18 megacities, representing a previously unrecognized exposure risk for the world’s urban populations. Furthermore, the transformation products can be up to an order of magnitude more persistent, environmentally mobile, and toxic than the parent chemicals. The results indicate that the overall human and environmental risks associated with flame retardants could be significantly underestimated, while highlighting a strong need to include atmospheric transformations in the development of regulations for all harmful chemicals moving forward.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".