Bibliographic record
Abstract
The objective of the present study was to better understand the impacts of the additional sources of nitrous acid(HONO)on visibility, which is an aspect not considered in current air quality models. Simulations of HONO contributions to visibility over the North China Plain(NCP) during August 2007 using the fully coupled Weather Research and Forecasting/Chemistry(WRF/Chem) model were performed, including three additional HONO sources:(1) the reaction of photo-excited nitrogen dioxide(NO*2) with water vapor;(2) the NO2 heterogeneous reaction on aerosol surfaces; and(3) HONO emissions. The model generally reproduced the spatial patterns and diurnal variations of visibility over the NCP well. When the additional HONO sources were included in the simulations, the visibility was occasionally decreased by 20%–30%(3–4 km) in local urban areas of the NCP. Monthly-mean concentrations of NO-3, NH+4, SO2-4and PM2.5were increased by 20%–52%(3–11μg m-3), 10%–38%, 6%–10%, and 6%–11%(9–17 μg m-3), respectively; and in urban areas, monthly-mean accumulationmode number concentrations(AMNC) and surface concentrations of aerosols were enhanced by 15%–20% and 10%–20%,respectively. Overall, the results suggest that increases in concentrations of PM2.5, its hydrophilic components, and AMNC,are key factors for visibility degradation. A proposed conceptual model for the impacts of additional HONO sources on visibility also suggests that visibility estimation should consider the heterogeneous reaction on aerosol surfaces and the enhanced atmospheric oxidation capacity due to additional HONO sources, especially in areas with high mass concentrations of NOxand aerosols.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".