Atmospheric ammonia variability and link with particulate matter formation: a case study over the Paris area
Bibliographic record
Abstract
The Paris megacity experiences frequent particulate matter (i.e.PM 2.5 , particulate matter with a diameter less than 2.5 µm) pollution episodes in spring (March–April). At this time of the year, large numbers of the particles consist of ammonium sulfate and nitrate which are formed from ammonia (NH 3 ) released during fertilizer spreading practices and transported from the surrounding areas to Paris. There is still limited knowledge of the emission sources around Paris, their magnitude, and their seasonality. Using space-borne NH 3 observation records of 10 years (2008–2017) and 5 years (2013–2017) provided by the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) instrument, regional patterns of NH 3 variabilities (seasonal and interannual) are derived. Observations reveal identical high seasonal variability with three major NH 3 hotspots found from March to August. The high interannual variability is discussed with respect to atmospheric total precipitation and temperature. A detailed analysis of the seasonal cycle is performed using both IASI and CrIS instrument data, together with outputs from the CHIMERE atmospheric model. For 2014 and 2015, the CHIMERE model shows coefficients of determination of 0.58 and 0.18 when compared to IASI and CrIS, respectively. With respect to spatial variability, the CHIMERE monthly NH 3 concentrations in spring show a slight underrepresentation over Belgium and the United Kingdom and an overrepresentation in agricultural areas in the French Brittany–Pays de la Loire and Plateau du Jura region, as well as in northern Switzerland. In addition, PM 2.5 concentrations derived from the CHIMERE model have been evaluated against surface measurements from the Airparif network over Paris, with which agreement was found ( r 2 = 0.56) with however an underestimation during spring pollution events. Using HYSPLIT cluster analysis of back trajectories, we show that NH 3 total columns measured in spring over Paris are enhanced when air masses originate from the north-east (e.g. the Netherlands and Belgium), highlighting the importance of long-range transport in the NH 3 budget over Paris. Variability in NH 3 in the north-east region is likely to impact NH 3 concentrations in the Parisian region since the cross-correlation function is above 0.3 (at lag = 0 and 1 d). Finally, we quantify the key meteorological parameters driving the specific conditions important for the formation of PM 2.5 from NH 3 in the Île-de-France region in spring. Data-driven results based on surface PM 2.5 measurements from the Airparif network and IASI NH 3 measurements show that a combination of the factors such as a low boundary layer of ∼500 m, a relatively low temperature of 5 ∘ C, a high relative humidity of 70 %, and wind from the north-east contributes to a positive PM 2.5 and NH 3 correlation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".