Impacts of additional HONO sources on O<sub>3</sub> and PM<sub>2.5</sub> chemical coupling and control strategies in the Beijing–Tianjin–Hebei region of China
Bibliographic record
Abstract
The objective of this work is to examine the impacts of additional HONO sources on the chemical interaction between ozone (O3) and particulate matter with a diameter ≤ 2.5 µm (PM2.5). Three additional HONO sources, i.e. HONO emissions, the reaction of photo-excited nitrogen dioxide (NO2*) with water vapour (H2O), and NO2 heterogeneous reaction on aerosol surfaces, were inserted into the fully coupled Weather Research and Forecasting-Chemistry model to evaluate O3 and PM2.5 concentration enhancements in the Beijing–Tianjin–Hebei (BTH) region during August 2007. Results show that the additional HONO sources significantly increase O3 and PM2.5 concentrations during daytime. Up to 9 ppb enhancements of O3 and 32 µg m−3 increases in PM2.5 are found at seven urban sites over the BTH region. O3 increases are closely connected to PM2.5 increases over urban areas during daytime when the additional HONO sources are taken into account. PM2.5 inorganic components of SO42–, NO3– and NH4+ are increased by 5–18, 10–58 and 10–40%, respectively, over urban areas during daytime. The simultaneous increment of O3 and PM2.5 during daytime due to the additional HONO sources is related to the increasing oxidants (OH, H2O2 and O3) that enhance the atmospheric oxidising capacity. The concentration variations of O3 and PM2.5 under a variety of NOx, volatile organic compound and ammonia (NH3) emission control scenarios show that the additional HONO sources increase the sensitivity of O3 and PM2.5 concentrations to the changes of NOx emissions. An increase of the PM2.5 sensitivity to changes in NH3 emissions is also found. This indicates that without considering the additional HONO sources, the effectiveness of emission control strategies in reducing O3 and PM2.5 concentrations would be significantly underestimated.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".