Geographic and Temporal Variability of atmospheric surface Ammonia (NH3) in France, Belgium, and the Netherlands (2015 – 2023) across different land-use types: Insights from Ground-Based and combined Satellite Observations.
Notice bibliographique
Résumé
This research study analyzes atmospheric ammonia (NH₃) surface concentrations in France, Belgium (Flanders region), and the Netherlands, highlighting their geographic, temporal, and diel variability from 2015 to 2023 using ground-based measurements (31 sites) and combined satellite data (IASI and CrIS). NH3 is the major alkaline gas in the atmosphere, affects air quality and aerosol formation, and degrades ecosystems, making its monitoring essential. The highest annual average NH₃ levels were observed in the Netherlands (7.4 ± 4.1 µg/m³), followed by Belgium (4.5±3.4 µg/m³) and France (3.7±2.1 µg/m³) as per in-situ observations. Rural areas characterized by agricultural practices showed higher levels than other land-use types, peaking in spring and summer due to fertilizer application and manure volatilization. Rural sites reached 8.5 ± 4.0 µg/m³ and 5.4 ± 3.9 µg/m³, in the Netherlands and Belgium (Flanders region) respectively. Urban areas recorded noticeable NH₃ concentrations either across Belgium (Flanders region) (3.5±2.0 µg/m³) and France (4.4 ± 2.0 µg/m³) which may be attributable to vehicular traffic, wastewater management, industrial operations, and the geographical dispersion of agricultural emissions. Seasonal variations observed notable NH₃ peaks in spring and summer, due to agricultural intensification and increased temperatures, while winter had the lowest concentrations due to decreased emissions. Diel patterns showed midday peaks in rural areas due to increased volatilization, while urban areas showed morning peaks related to traffic emissions. Satellite-derived NH₃ data from combined IASI and CrIS sensing showed moderate to strong correlations with ground-based measurements (R = 0.32–0.8), while satellites tended to underestimate local concentrations. Unlike surface measurements, satellite data revealed NH₃ concentrations across land-use types were little different, with means and standard deviations as follows: Crops (2.35 ± 2.08 µg/m³), High-Density Urban (2.39 ± 2.20 µg/m³), Low-Density Urban (2.35 ± 2.14 µg/m³), and Rural (2.26 ± 2.00 µg/m³). Comparable trends were noted in entire Belgium (+0.023 µg/m³ per year) and the Netherlands (+0.043 µg/m³ per year), where NH₃ concentrations were higher in 2020 and decreased in the following years possibly due to improved air dispersion and increased precipitation. The results highlight the key role of agriculture, which is the dominant source of NH₃ emissions, but urban regions also contribute significantly through vehicular and industrial activities. Effective mitigation techniques are crucial, including optimal fertilizer application, sophisticated manure management, and stringent urban emission regulations. These plans are in line with regional and national regulations, such as France’s PREPA plan, which aims to reduce NH₃ emissions by 13% by 2030 (Chatain et al., 2022). The integration of satellite and ground-based data offers a thorough understanding of NH₃ dynamics, facilitating the formulation of specific regulatory frameworks to reduce emissions, protect ecosystems, and improve air quality in these areas.ReferencesChatain, M., Chretien, E., Crunaire, S., & Jantzem, E. (2022). Road Traffic and Its Influence on Urban Ammonia Concentrations (France). Atmosphere, 13(7), Article 7. https://doi.org/10.3390/atmos13071032 
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».