Non‐Negligible Uptake of Nitrous Acid in Present‐Day Clouds
Bibliographic record
Abstract
Abstract Cloudwater acidity has continuously decreased over the eastern United States due to anthropogenic emission control. Cloud uptake of weak acids, which is more effective under less acidity conditions, has attracted increasing interest. This study assessed how and to what extent nitrous acid (HONO) uptake impacted present‐day cloud chemistry. A gas‐cloudwater partitioning scheme with subsequent oxidation reactions for HONO was developed and implemented, and HONO heterogeneous chemistry was updated in the Community Multiscale Air Quality Modeling System (CMAQ). The modified CMAQ was employed to quantify the effect of HONO uptake on cloudwater acidity and acidity‐dependent chemical processes during the June 2021 Michigan‐Ontario Ozone Source Experiment campaign. Our model results indicated that HONO uptake could lower cloudwater pH, especially in clouds with pH > 5, by 0.1 or larger and produce non‐negligible changes in both total ion concentrations and ion composition. These results were corroborated for a cloud event by available measurements at the Whiteface Mountain (WFM) monitoring site in New York State. Simulations indicated cloud uptake of 10% of gaseous HONO (HONO(g)) during the event, suggesting a potential sink for HONO(g) and likely resulting in a 0.1 unit decrease in cloudwater pH. Subsequently, ammonium (NH4+) volatilization was suppressed, which reduced NH4+ underestimation by up to 10%. In addition, simulated nitrite concentrations were averaged 10 μeq/L, in agreement with previous measurement studies. This study highlighted the importance of accurately representing weak acids in chemical transport models to improve understanding of present‐day cloud chemistry.
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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".