Technical Note: Particulate reactive oxygen species concentrations and their association with environmental conditions in an urban, subtropical climate
Bibliographic record
Abstract
Abstract. Reactions between hydrocarbons and ozone or hydroxyl radicals lead to the formation of oxidized species, including reactive oxygen species (ROS), and secondary organic aerosol (SOA) in the troposphere. ROS can be carried deep into the lungs by small aerodynamic particles where they can cause oxidative stress and cell damage. While environmental studies have focused on ROS in the gas phase and rainwater, it is also important to determine concentrations of ROS on respirable particles. Samples of PM2.5 collected over 3 h at midday on 40 days during November 2011 and September 2012 show that the particulate ROS concentration in Austin, Texas, ranged from a minimum value of 0.02 nmoles H2O2 m−3 air in December to 3.81 nmoles H2O2 m−3 air in September. Results from correlation tests and linear regression analysis on particulate ROS concentrations and environmental conditions (which included ozone and PM2.5 concentrations, temperature, relative humidity, precipitation and solar radiation) indicate that ambient particulate ROS is significantly influenced by the ambient ozone concentration, temperature and incident solar radiation. Particulate ROS concentrations measured in this study were in the range reported by other studies in the US, Taiwan and Singapore. This study is one of the first to assess seasonal variations in particulate ROS concentrations and helps explain the influence of environmental conditions on particulate ROS concentrations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".