Quantifying Nitrous Acid Formation Mechanisms Using Measured Vertical Profiles During the CalNex 2010 Campaign and 1D Column Modeling
Bibliographic record
Abstract
Abstract Nitrous acid (HONO) is an important radical precursor that can impact secondary pollutant levels, especially in urban environments. Due to uncertainties in its heterogeneous formation mechanisms, models often under predict HONO concentrations. A number of heterogeneous sources at the ground have been proposed but there is no consensus about which play a significant role in the urban boundary layer. We present a new one‐dimensional chemistry and transport model which performs surface chemistry based on molecular collisions and chemical conversion, allowing us to add detailed HONO formation chemistry at the ground. We conducted model runs for the 2010 CalNex campaign, finding good agreement with observations for key species such as O3, NOx, and HOx. With the ground sources implemented, the model captures the diurnal and vertical profile of the HONO observations. Primary HOx production from HONO photolysis is 2–3 times more important than O3 or HCHO photolysis at mid‐day, below 10 m. The HONO concentration, and its contribution to HOx, decreases quickly with altitude. Heterogeneous chemistry at the ground provided a HONO source of 2.5 × 1011 molecules cm−2 s−1 during the day and 5 × 1010 molecules cm−2 s−1 at night. The night time source was dominated by NO2 hydrolysis. During the day, photolysis of surface HNO3/nitrate contributed 45%–60% and photo‐enhanced conversion of NO2 contributed 20%–45%. Sensitivity studies addressing the uncertainties in both photolytic mechanisms show that, while the relative contribution of either source can vary, HNO3/nitrate is required to produce a surface HONO source that is strong enough to explain observations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".