Aerosol Toxicokinetics: A Framework for Unraveling Toxicological Dynamics from Air to the Body
Bibliographic record
Abstract
Exposure to atmospheric aerosols threatens human health and is yet to be effectively addressed globally. Aerosol toxicity strongly depends upon components whose chemical profiles and concentrations can constantly evolve throughout atmospheric transformation, inhalation, distribution, metabolism, and excretion. Despite the abundant studies on aerosol components and their toxic effects, the dynamics in component concentrations and related biological effects from air to the body remain unclear. Here, we propose a conceptual toxicokinetic framework to mathematically deduce the bioavailable concentration from the changing bulk concentration of aerosol constituents in the atmosphere. The biological effects of single or multiple components are further predicted via toxicodynamic modeling according to their bioavailable concentrations. Atmospheric concentrations of toxic composition can in turn be regulated by risk-based guidelines, aiming to alleviate in vivo toxic effects. This perspective demonstrates how serial toxicokinetic–toxicodynamic equations bridge the knowledge gap between ambient aerosols and associated toxic effects in human bodies. The prediction from an inhalation perspective also allows connecting with the exposomes from aggregate exposure pathways. We call for the development of the model validity and integrate quantitative adverse outcome pathways to apply for exposure–disease modeling, providing novel insights into air quality policymaking and public health management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".