Multi-scale impacts of Indochina biomass burnings on tropospheric ozone in coastal South China: Insights from long-term (2000–2024) observations
Bibliographic record
Abstract
Biomass burning is an important source of tropospheric ozone (O 3 ). This study explored the impacts of Indochina springtime (March–April) biomass burnings on the variability and trend of tropospheric O 3 in coastal South China using long-term (2000–2024) ozonesondes in Hong Kong and satellite fire retrievals in the Indochina Peninsula (ICP), complemented with EAC4 reanalysis data. We find that the lower-free-tropospheric O 3 (LFTO 3 ) concentrations in Hong Kong are significantly correlated with the Indochina biomass burnings, particularly with the two-day-ago biomass burnings in northern Laos ( r = 0.57, p < 0.01). While Indochina biomass burning contributes more than 30 ppbv enhancements in LFTO 3 concentrations over coastal South China, their impacts on surface O 3 concentrations are insignificant. During the study period, there is a significant increasing trend in springtime LFTO 3 concentrations in Hong Kong (0.37 ppbv/year), despite decreasing quantity and intension of Indochina biomass burnings. This long-term LFTO 3 increasing trend is mainly driven by the eastward migration of Indochina biomass burnings (mainly due to the increase in biomass burnings in the central ICP region), which reduces transport distance to Hong Kong by ∼300 km and thereby improves the transport efficiency, ultimately contributing ∼90 % of the long-term LFTO 3 increase in Hong Kong. These findings advance understanding of Indochina biomass burning transport impacts on multi-scale tropospheric O 3 variability in coastal South China.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".