A model-based analysis of foliar NO <sub> <i>x</i> </sub> deposition
Bibliographic record
Abstract
Abstract. Foliar deposition of NO2 removes a large fraction of the global soil-emitted NOx. Understanding the mechanisms of NOx foliar loss is important for constraining surface ozone, constraining NOx mixing ratios, and assessing the impacts of nitrogen inputs to ecosystems. We have constructed a 1-D multibox model with representations of chemistry and vertical transport to evaluate the impact of leaf-level processes on canopy-scale concentrations, lifetimes, and canopy fluxes of NOx. Our model is able to closely replicate canopy fluxes and above-canopy NOx daytime mixing ratios observed during two field campaigns, one in a western Sierra Nevada pine forest (BEARPEX-2009) and the other in a northern Michigan mixed hardwood forest (UMBS-2012). We present a conceptual argument for the importance of NO2 dry deposition and demonstrate that NO2 deposition can provide a mechanistic explanation for the canopy reduction of NOx. We show that foliar deposition can explain observations suggesting as much as ∼60 % of soil-emitted NOx is removed within forest canopies. Stomatal conductances greater than 0.1 cm s−1 result in modeled canopy reduction factors in the range of those used in global models, reconciling inferences of canopy NOx reduction with leaf-level deposition processes. We show that incorporating parameterizations for vapor pressure deficit and soil water potential has a substantial impact on predicted NO2 deposition in our model, with the percent of soil NOx removed within one canopy increasing by ∼15 % in wet conditions compared to dry conditions. NO2 foliar deposition was also found to have a significant impact on ozone and nitrogen budgets under both high- and low-NOx conditions.
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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 | 0.001 |
| 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 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".