Impacts of Soil NO<sub><i>x</i></sub> Emission\non O<sub>3</sub> Air Quality in Rural California
Bibliographic record
Abstract
Nitrogen oxides (NOx) are a key precursor\nin O3 formation. Although stringent anthropogenic NOx emission controls have been implemented\nsince the early 2000s in the United States, several rural regions\nof California still suffer from O3 pollution. Previous\nfindings suggest that soils are a dominant source of NOx emissions in California; however, a statewide assessment\nof the impacts of soil NOx emission (SNOx) on air quality is still lacking. Here we\nquantified the contribution of SNOx to\nthe NOx budget and the effects of SNOx on surface O3 in California during\nsummer by using WRF-Chem with an updated SNOx scheme, the Berkeley Dalhousie Iowa Soil NO Parameterization\n(BDISNP). The model with BDISNP shows a better agreement with TROPOMI\nNO2 columns, giving confidence in the SNOx estimates. We estimate that 40.1% of the state’s total\nNOx emissions in July 2018 are from soils,\nand SNOx could exceed anthropogenic sources\nover croplands, which accounts for 50.7% of NOx emissions. Such considerable amounts of SNOx enhance the monthly mean NO2 columns by 34.7% (53.3%)\nand surface NO2 concentrations by 176.5% (114.0%), leading\nto an additional 23.0% (23.2%) of surface O3 concentration\nin California (cropland). Our results highlight the cobenefits of\nlimiting SNOx to help improve air quality\nand human health in rural California.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".