NH3 spatio-temporal variability over Paris, Mexico and Toronto and its link to PM2.5 during pollution events
Bibliographic record
Abstract
Large cities can experience high levels of fine particulate matter (PM2.5) pollution linked to ammonia (NH3) mainly emitted from agricultural activities. Using a combination of PM2.5 and NH3 measurements from in situ instruments, satellite infrared spectrometers, and atmospheric model simulations, we demonstrate the role of atmospheric NH3 and meteorological conditions in pollution events occurring in Paris, Toronto, and Mexico City.Ten years of measurements from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the spatio-temporal NH3 variability over and around the three cities. The three regions are subject to long range transport of NH3, as shown using HYSPLIT cluster back-trajectories. The results show that the NH3 variability is mainly driven by meteorology, and interestingly, we can detect the fertilizers application period by looking at the NH3 – temperature relationship. To check how well chemistry transport models perform during pollution events, we evaluate simulations made using the GEOS-Chem model for March 2011. In these simulations we find that NH3 concentrations are overall underestimated, though day-to-day variability is well represented. PM2.5 is generally underestimated over Paris and Mexico, but overestimated over Toronto.We use complementary information derived from IASI, and ground-based open-path measurements over Paris. We, therefore, assess the NH3 temporal variabilities at different timescales (diurnal, seasonal, and interannual), to unravel NH3 sources (agriculture and traffic) in Paris.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".