Impacts of transportation emissions on horizontal and vertical distributions of air pollutants in Shanghai: Insights from emission reduction in COVID-19 lockdown
Bibliographic record
Abstract
Transportation is a major sector of anthropogenic emissions in urban areas and deteriorates air quality. The surface and vertical observational data were combined with the model results to reveal its impact on the horizontal and vertical variations of pollutants during the COVID-19 lockdown period. The evident reductions in ambient PM 2.5 (∼30%) and NO 2 (∼50%) concentrations but a ∼25% increase in O 3 concentration were observed at the transportation sites. On the vertical scale, a uniform decrease of ∼28% in PM 2.5 concentrations was observed within 600 m. However, the vertical profiles of NO 2 and O 3 exhibited increasing vertical variation rates with concentrations varying significantly within 400 m. Meanwhile, O x shared a similar pattern of vertical profile with O 3 , with a uniform increase (∼5%) within 600 m in the urban area. The WRF-CMAQ model reproduced the variations, and revealed that the reduction of transportation emissions was the key factor contributing to the increase of urban O 3 and O x due to the weakened NO titration effect. The simulated vertical profile of NO 2 was featured by a decreasing curve, while that of O 3 exhibited the opposite trend. We find that the transportation emissions impact vertical concentrations of NO 2 and O 3 within at most 400 m. The process analysis revealed that the vertical transport and horizontal transport from bay areas contributed to O 3 in the urban area, while chemical processes mainly consumed it. The reduction in transportation emissions weakened the consumption and resulted in O 3 accumulation during rush hours and at night. The variation of planetary boundary layer height also favored the rise of urban O 3 by promoting vertical transport at daytime and trapping it at night. The reduction in NO x emissions from the transportation enhanced O 3 pollution, suggesting that collaborative reductions in VOCs from multiple sectors should be conducted. This study also indicated that regional collaborations in emission reductions were necessary for comprehensive air pollution prevention. • Changes in air pollutant concentrations caused by lockdown differentiated evidently at transportation and industrial sites. • Vertical profiles of pollutants within 400 m height responded obviously to the reduction of transportation emissions. • Transportation emissions reduction and vertical transport are key factors of ozone accumulation near the surface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".