Characterising the temporal variations of ground-level ozone and its relationship with traffic-related air pollutants in the united kingdom: a quantile regression approach
Bibliographic record
Abstract
Ground-level ozone is a secondary air pollutant and is photochemically produced by solar radiation from the reaction of volatile organic compounds (VOCs) and nitrogen oxides (NOx). Ground-level ozone is considered a harmful pollutant due to its adverse impact on human health, agricultural crops and materials. The concern-ing factor is that in spite of decreasing trends in some other air pollutants (e.g. NOx), ozone concentrations are still increasing. This paper describes the temporal variations of ozone at four air quality monitoring sites (Harwell, Leeds, Marylebone and Strath Vaich) in the United Kingdom for the year of 2008. The association of ozone with some traffi c-related air pollutants has been explored applying a quantile regression model (QRM). The traffi c-related air pollutants considered as predictors for this study are hydrocarbons (HC), nitric oxides (NO), nitrogen dioxides (NO2), carbon monoxides (CO) and particulate matter (PM2.5). QRM can handle the non-linearities in the relationship of ozone and its predictors and is applicable to non-normal air quality data distribution. The behaviour and interaction of ozone with its predictors vary at different regimes of ozone distributions, which remains hidden when applying an ordinary least square regression model. QRM explains signifi cantly more variations in ozone concentrations (global goodness of fi t R1 = 0.88) as compared to ordinary least square regression (coeffi cient of determination R2 = 0.32) and is therefore better suited for ozone data analysis and prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".