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.
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".