NH <sub>3</sub> spatio-temporal variability over Paris, Mexico and Toronto and its link to PM <sub>2.5</sub> during pollution events
Bibliographic record
Abstract
Abstract. Megacities can experience high levels of fine particulate matter (PM2.5) pollution linked to ammonia (NH3) mainly emitted from agricultural activities. Here, we investigate such pollution in the cities of Paris, Mexico and Toronto, each of which have distinct emission sources, agricultural regulations, and topography. 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. In Europe and North America, we determine that temperature is associated with the increase in NH3 atmospheric concentrations with coefficient of determination (r2) of 0.8 over agricultural areas. The variety of the NH3 sources (industry and agricultural) and the weaker temperature seasonal cycle in southern North America induce a lower correlation factor (r2 = 0.5). The three regions are subject to long range transport of NH3, as shown using HYSPLIT cluster back-trajectories. The highest NH3 concentrations measured at the city scales are associated with air masses coming from the surrounding and north-northeast regions of Paris, the south-southwest areas of Toronto, and the southeast/southwest zones of Mexico City. Using NH3 and PM2.5 measurements derived from IASI and surface observations from 2008 to 2017, annually frequent pollution events are identified in the 3 cities. Wind roses reveal statistical patterns during these pollution events with dominant northeast-southwest directions in Paris and Mexico cities, and the transboundary transport of pollutants from the United-States in Toronto. 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.
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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 | 0.001 | 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".