The Influence of Selected Meteorological Parameters on the Concentration of Surface Ozone in the Central Region of Poland
Bibliographic record
Abstract
This paper presents the results of measurements of the concentration of surface ozone and concurrent standard meteorological parameters: total solar radiation, temperature, relative humidity, pressure, wind speed, and vertical and horizontal components of the wind. The data were collected from 2005 to 2010 at stations located in central Poland (Mazowieckie voivodeship): Warszawa (urban), Legionowo (suburban), Granica and Belsk (rural). Furthermore, Granica is situated in the forested area of Kampinoski National Park. Continuously measured surface ozone concentrations demonstrated the well-known diurnal cycle of surface ozone concentration with a maximum in the afternoon and a minimum in the early morning hours. The averaged diurnal variations over six years reveal that the highest concentrations appear at rural stations (Belsk: 55 µg m−3 and Granica: 50 µg m−3) and the lowest at the urban station (Warszawa: 41 µg m−3). The threshold for high levels of surface ozone (120 µg m−3 per 8 h) was exceeded most often at Granica and Belsk. The occurrence of the ozone "weekend effect," especially at urban stations, has been identified. The difference between weekend and weekday surface ozone concentrations at urban and rural stations was as high as 6.5 µg m−3 and approximately 2 µg m−3, respectively. Using appropriate statistical tools, it has been shown that meteorological conditions have a significant influence on ozone concentration. High correlation coefficients were found between ozone concentration and solar radiation, temperature, relative humidity, and wind speed. The forward stepwise regression model explains up to 75% of the variations in daily surface ozone concentration in terms of meteorological variability in summer and up to 70% in winter. At the same time, a multilayer perceptron neural network model was used to reconstruct the concentration of surface ozone. High correlation coefficients (up to 0.89) indicate that, on the basis of standard meteorological parameters and NO2 concentration, we can determine ozone concentration with high accuracy.
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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 | 0.000 | 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".