Analysis of Relationship between O3, NO, and NO2 in Riyadh, Saudi Arabia
Bibliographic record
Abstract
For the first time in the city of Riyadh, the formation of O 3 and its relationship with NO and NO 2 (NO x ) was investigated. Throughout the summer O 3 , NO, and NO 2 were collected from three locations: residential, industrial, and rural areas. During the sampling period O 3 concentrations exceeded 1-hour local standards a few times yet remained consistent with the standards most of the time. The O 3 concentrations were observed highest in the rural location and lowest in the industrial area. The diurnal variation of NO followed a double peak: one in the morning and the other in the evening, representing the traffic pattern. Early morning NO peaks were observed in the rural location, which were attributed to the movement of NO from other locations. The O 3 concentrations depicted typical pattern, increasing after sunrise and reaching its maximum during midday. The highest O 3 concentrations were observed in the rural location followed by the residential and industrial. NO 2 photolysis rates were 3–4 times higher compared to other similar investigations, potentially due to intense solar radiation. A strong negative correlation was observed between NO x and O 3 values in the industrial location indicating photochemical activities around the industrial area were higher, likely due to additional NO x emissions from industries. Regression analysis of NO x and OX (O 3 +NO 2 ) indicated that in residential and industrial locations at nighttime there were large NO x independent regional contributions which is attributed to VOCs. The Weekend Effect was observed in the city potentially due to the production of the OH radical and subsequent reactions with VOCs implying that the area is VOC-sensitive.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".