Ground-level ozone’s impact on human health in terms of respiratory diseases using Reactive Oxygen Species as an inflammatory marker
Bibliographic record
Abstract
Ozone accumulation over time in the human body due to climate change exacerbates existing respiratory diseases and magnifies its negative consequences on human health. Ground-level ozone is artificially produced by catalyzing nitrogen oxides (NOx) and volatile organic compounds (VOCs). Usually, antioxidants in the human body are sufficient to neutralize the particles, but that ability is reduced when an individual has pre-existing respiratory diseases. Under these circumstances, ozone interacts with various proteins and lipids in the lower respiratory tract, damaging epithelial cells, producing an excess of Reactive Oxygen Species (ROS) and causing symptoms such as inflammation and airway constriction. This study explored the impact of ozone on human health through a representative simulation model that captured the cumulative impact of ground-level ozone on the human lung, including considerations of existing respiratory diseases with ROS as an inflammatory marker. Within the simulation, agents were used to represent oxygen and carbon dioxide, ozone, and physiological saline, with a controlled spread of disease between the agents suggestive of the creation of ROS. Used in conjunction with stoichiometric equations, the model provided a quantitative prediction of the theoretical lifespan of a person under varying ozone concentrations. The simulation found a negative correlation between ozone concentration and average lifespan, of 114 and 52 years at ozone concentrations of 0 and 100 ppb respectively, representing a linear decrease of 0.6 years for every 1 ppb increase in ground-level ozone concentration. More accurately, the simulation results fit an exponential regression with the equation y=112.08e−0.008x with R2 > 0.99. This study provided quantitative evidence from simulation, consistent with empirical studies, of the detrimental effects of ozone on exacerbating pre-existing respiratory diseases by causing an increase in ROS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".