Recirculated transport mechanism aggravates ozone pollution over the mountainous coastal region: Increased contribution from vertical mixing
Bibliographic record
Abstract
In coastal areas where many highly developed metropolis and city agglomeration are located, high ozone (O3) concentrations are one of the most severe environmental problems, which is gravely impacted by the sea-land breeze (SLB) circulation. The complex terrain, such as coastal mountain, complicates SLB circulation and its impact on coastal O3 pollution. Previous studies have shown that coastal mountain intensifies SLB circulation in various regions worldwide, such as the southern California coastal region, the Mediterranean coastal region, and the Beijing-Tianjin-Hebei region. However, the specific impacts of the underlying mechanism on coastal O3 levels, along with a quantified contribution analysis, remain insufficiently explored. Here we present that the coastal mountain terrain in the Western Taiwan Strait of South China induces a three-dimensional O3 transport involving SLB circulation through integrating observational data from 2015 to 2022 and employing WRF-Chem modeling, specifically focusing on the thermally driven sea breeze during the day combined with the up-slope flow generated by the coastal mountain. This flow carries O3-rich air mass, which are caused by both photochemical reactions and cross-boundary transport originating from land and offshore sources. As the air moves inland and up the mountainside, it ultimately forming a layer of high-level O3 aloft. O3 trapped in the layer acts as a reservoir of elevated O3 for the coastal plain and aggravates O3 pollution on the following day via vertical mixing by over 40 μg m−3 h−1. Based on the sensitivity simulation, this work quantifies that the coastal mountain contributes 23.4 % of surface O3 concentrations increase in the coastal urban area. This recirculated transport of O3 has important implications for understanding meteorological drivers on atmospheric environment in complex terrain.
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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) | 0.005 | 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".