Influence of Traffic and Meteorological Conditions on Ozone Pollution in Kharagpur, India
Bibliographic record
Abstract
This study targets on ozone (O3) pollution resulting from road traffic in India (special focus on National Highway 16 or NH-16 and its nearby areas), where diesel and petrol are major fuels used for transportation system which are major contributors to O3 forming precursors such as NOx and VOC emissions.Ozone concentration was measured by using a Serinus 10 ozone analyser and weather parameters was measured by a portable weather station Kestrel 5500.Analysis revealed that the higher traffic volume correlates negatively (r = -0.87)with lower O3 levels during morning and evening whereas lower traffic volume is associated with higher O3 levels during afternoon.Traffic was manually counted and classified.Using Multiple Linear Regression (MLR), O3 concentration levels are predicted along NH-16 in Kharagpur, West Bengal, India.The MLR model performance is assessed by R-squared, and Ftest, along with AIC and BIC tests which are evidencing that MLR is the most suitable model, accurately predicting O3 pollution levels.The study explores that the 8-h average O3 concentrations (117.25 µg m -3 ) measured along the NH-16 has exceed NAAQS 2009 (100 µg m -3 ) and WHO 2021 (100 µg m -3 ) prescribed standards.South-east (SE) winds with moderate speeds (0.5 -1.5 ms -1 ) were elevating O3 levels in the study regions.As the direction of wind change, transport of pollutants was occurring away from the traffic area to nearby rural areas along the NH-16.O3 levels for 8-h period were also high in nearby rural areas (112.56 µg m -3 ).Study tells that an urgent action is needed, including comprehensive O3 pollution assessment on all India's national highways and implementation of new policies to mitigate O3 pollution across NHs all over India.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".