Variations in Vertical O3 and SO2 Concentrations During Rainfall in a Boreal-temperate Forest
Bibliographic record
Abstract
Ozone (O 3 ) and sulfur dioxide (SO 2 ) in the atmospheric boundary layer are air pollutants that pose significant threats to forest health. Rainfall can significantly modulate the concentrations of these trace gases. In forest ecosystems, it is essential to assess the extent to which rainfall impacts gas concentrations and to understand the underlying mechanisms driving such changes. Here, we explore how rainfall alters the vertical distribution of O 3 and SO 2 mixing ratios within and above a boreal-temperate forest canopy, drawing upon multi-year measurements (2009–2013) from a 42-meter tower. Based on 194 persistent rainfall events, comparisons of vertical O 3 and SO 2 mixing ratios between rain and non-rain conditions are made at matched height, hour, and month to minimize biases introduced by spatial and temporal background variations. In contrast to tropical forest observations where O 3 increases during rain, our results indicate a consistent decline in O 3 by 2–6 ppb (10–20%) during rainfall across all heights, from near the surface to above the canopy, except for during evening rainfall. Reductions in SO 2 are even more substantial, reaching 0.3–0.5 ppb (40–50%) throughout the profile. We further assess the role of four key processes of washout, photochemistry, deposition, and vertical transport in modulating O 3 and SO 2 vertical profiles during rainfall. The substantial reduction in SO 2 is mainly attributable to its high solubility, while O 3 reductions during the day are largely due to declined photochemical production. Rain-induced surface wetness modestly enhances non-stomatal deposition of both gases, observably at night, whereas the overall daytime O 3 deposition remains relatively stable, as the enhanced non-stomatal deposition is offset by the decreased stomatal deposition. Additionally, upward transport following the rainfall onset could contribute to the reduction of O 3 in the boundary layer, while downward transport after the rainfall peak could contribute to O 3 enhancement. This effect is most obvious at night, when photochemistry is absent. These findings provide new insights into how rainfall impacts air pollutants and underscore the role of the four processes in shaping O 3 and SO 2 vertical distributions in boreal-temperate forests, thereby advancing our understanding of atmosphere-biosphere interactions.
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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 | 0.000 |
| 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 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".