The influence of gas-particle partitioning and surface-atmosphere exchange on ammonia during BAQS-Met
Bibliographic record
Abstract
Abstract. The Border Air Quality and Meteorology study (BAQS-Met) was an intensive field campaign conducted in Southwestern Ontario during the summer of 2007. The focus of BAQS-Met was determining the causes of the formation of ozone and fine particulate matter (PM2.5), and of the regional significance of trans-boundary transport and lake breeze circulations on that formation. Fast (1 Hz) measurements of ammonia were acquired using a Quantum Cascade Laser Tunable Infrared Differential Absorption Spectrometer (QC-TILDAS) at the Harrow supersite. Measurements of PM2.5 ammonium, sulfate and nitrate were made using an Ambient Ion Monitor Ion Chromatograph (AIM-IC) with hourly time resolution. The median mixing ratio of ammonia was 2.5 ppb, with occasional high spikes at night resulting from local emissions. Measurements were used to assess major local emissions of NH3, diurnal profiles and gas-particle partitioning. The measurements were compared with results from A Unified Regional Air-quality Modelling System (AURAMS). While the fraction of total ammonia (NHx≡NH3 + NH4+) observed in the gas phase peaks between 0.1 and 0.8, AURAMS tended to predict fractions of either less than 0.05 or greater than 0.8. The model frequently predicted acidic aerosol, in contrast with observations wherein NHx almost always exceeded the observed equivalents of sulfate. One explanation for our observations is that the net flux of ammonia from the land surface to the atmosphere increases when aerosol sulfate is present, effectively buffering the mixing ratio of gas phase ammonia, a process not included in the model. A simple representation of an offline bi-directional flux parameterization using the ISORROPIA thermodynamic model was successful at reducing the population of zero gas fraction points, but not the higher gas fraction points.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".