Impacts of Soil NO<sub><i>x</i></sub> Emission\non O<sub>3</sub> Air Quality in Rural California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nitrogen oxides (NO<sub><i>x</i></sub>) are a key precursor\nin O<sub>3</sub> formation. Although stringent anthropogenic NO<sub><i>x</i></sub> emission controls have been implemented\nsince the early 2000s in the United States, several rural regions\nof California still suffer from O<sub>3</sub> pollution. Previous\nfindings suggest that soils are a dominant source of NO<sub><i>x</i></sub> emissions in California; however, a statewide assessment\nof the impacts of soil NO<sub><i>x</i></sub> emission (SNO<sub><i>x</i></sub>) on air quality is still lacking. Here we\nquantified the contribution of SNO<sub><i>x</i></sub> to\nthe NO<sub><i>x</i></sub> budget and the effects of SNO<sub><i>x</i></sub> on surface O<sub>3</sub> in California during\nsummer by using WRF-Chem with an updated SNO<sub><i>x</i></sub> scheme, the Berkeley Dalhousie Iowa Soil NO Parameterization\n(BDISNP). The model with BDISNP shows a better agreement with TROPOMI\nNO<sub>2</sub> columns, giving confidence in the SNO<sub><i>x</i></sub> estimates. We estimate that 40.1% of the state’s total\nNO<sub><i>x</i></sub> emissions in July 2018 are from soils,\nand SNO<sub><i>x</i></sub> could exceed anthropogenic sources\nover croplands, which accounts for 50.7% of NO<sub><i>x</i></sub> emissions. Such considerable amounts of SNO<sub><i>x</i></sub> enhance the monthly mean NO<sub>2</sub> columns by 34.7% (53.3%)\nand surface NO<sub>2</sub> concentrations by 176.5% (114.0%), leading\nto an additional 23.0% (23.2%) of surface O<sub>3</sub> concentration\nin California (cropland). Our results highlight the cobenefits of\nlimiting SNO<sub><i>x</i></sub> to help improve air quality\nand human health in rural California.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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 it