Predicting seasonal and spatial patterns of long-term nitrogen oxides concentration in Tehran, Iran using land use regression
Bibliographic record
Abstract
Background: Tehran, the capital city of Iran in the Middle East, experiences extreme air pollution concentrations. Aims: Long-term spatial and seasonal patterns of nitrogen oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations were estimated by land use regression (LUR). Methods: Hourly measurements of NO, NO2 and NOx were obtained from 23 automatic air pollution monitoring stations spread across the metropolitan area of Tehran. In addition, 210 variables were compiled using a Geographic Information System. Finally, annual and seasonal models (cooler and warmer season) were built using multiple linear regression with a novel step-by-step algorithm. Results: The annual mean concentrations of NO, NO2 and NOx were 88.1, 53.1, and 141.8 ppb, respectively. The cooler season mean concentrations were 117, 20, and 180.2 ppb, respectively and the warmer season mean concentrations were 60, 44.6, and 104.7 ppb, respectively. The leave-one-out cross-validation (LOOCV) R2 values for the LUR models ranged from 0.50 to 0.84 for NO, from 0.59 to 0.69 for NO2, and from 0.70 to 0.77 for NOx. The most predictive variables for NO included measures of distance to traffic, while those for NO2 and NOx were more influenced by industrial sources. Conclusions: Resulting models and maps show that patterns were consistent for the annual and cooler season models for NO, NO2 and NOx, but there were clear differences for warmer seasons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".