In agriculture, plant protection products (i.e. pesticides) protect crops from pests, weeds and diseases. However, pesticides introduced into our environment can also contaminate the air, partly due to volatilisation after pesticide application. Measuring volatilisation in field crops requires trapping techniques, which are costly and time-consuming. There is therefore a strong need for metrological developments to implement (1) analysers that can measure pesticide concentrations continuously over a short period of time, (2) the monitoring of pesticide emissions over a sufficiently long period to capture the entire volatilisation period and (3) the acquisition of data sets in little-explored situations, particularly in wine-growing practices.
The aim of the Online-PTR4-Pest project was to develop the measurement of concentration and volatilisation for three pesticides using proton transfer mass spectrometry (PTR-MS). This technique should eventually enable real-time measurement of pesticide concentrations in the air, as well as field measurement of pesticide volatilisation (using inverse modelling or possibly turbulent covariance methods).
Three pesticides were selected: Prosulfocarb, Pendimethalin (two herbicides used in field crops) and Cyflufenamide (a vine fungicide). Several analysers were used: gas chromatography with thermodesorption mass spectrometry (TD-GC-MS) and PTR-MS. Measurement of the two herbicides was validated using the highly sensitive PTR-Qi-TOF-MS (a time-of-flight mass spectrometer with a proton transfer ionisation source and quad used as an ion guide). Gas-phase calibration is a key stage in the metrological development of PTR-MS measurements. A permeation calibration system was developed and successfully tested, enabling the PTR-MS to be calibrated over a concentration range of 3 ppt to 10 ppb for prosulfocarb and 1 ppt to 3 ppb for pendimethalin.
A three-week field campaign was carried out at the ‘BioEcoAgro’ cross-border joint research unit of INRAE in Mons, with measurements on wheat plots. Air concentrations of Proculfocarb and Pendimethalin were quantified (using both analytical chains and a time step of 5 minutes). These air concentrations varied between 0 and 15 µg m 3 for Prosulfocarb and 0 to 3 µg m 3 for Pendimethalin. Volatilisation fluxes for these two herbicides were estimated using two different methods (aerodynamic gradient and inverse modelling). Over the first few days of field measurements, volatilization of Prosulfocarb was around ten times higher than that of Pendimethalin, regardless of the method used. However, the two methods gave different volatilisation values, as the inverse modelling method was made more uncertain by the applications of these pesticides in the surrounding fields. Finally, the Volt'air-Veg model of pesticide volatilisation was tested on the two datasets. The feasibility of measuring gaseous pesticides in the air in real time using a PTR-MS has been demonstrated.
How to cite: Loubet, B., Lafouge, F., Decuq, C., Ciuraru, R., Buysse, P., Esnault, B., and Gros, V.: Online fluxes of pesticides over bare soil in France with a PTRMS: results from French the Online-PTR4-Pest study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17390, https://doi.org/10.5194/egusphere-egu25-17390, 2025.
Agriculture is a major source of volatile organic compounds (VOCs), key precursors of secondary air pollutants such as ozone and aerosols. These VOCs react with atmospheric oxidants (e.g., hydroxyl radicals, ozone, nitrate radicals) to form more oxidized compounds with a low volatility that can condense to the particulate phase, driving the formation of secondary organic aerosols (SOA). SOA, a major component of atmospheric aerosols, significantly impacts air quality, climate, and human health. However, estimating SOA production remains highly uncertain due to the complexity of these processes and the diversity of precursors. The shift toward sustainable agriculture has increased the use of organic fertilizers, such as sewage sludge, compost, and animal waste. Given the vast agricultural land area, the spreading of organic fertilizers represents a potentially significant source of VOC emissions. However, their impact on the atmosphere remains poorly understood, mainly due to a lack of studies. The general aim of this work is to improve our knowledge on the impact of spreading these organic fertilizers on air quality, as a source of VOCs. Laboratory study was carried out to analyze VOC emissions from organic fertilizers (sewage sludge, compost and methanization digestate) and to assess the impact of temperature on these emissions. An experimental set-up combining a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS), an emission chamber and a multi-valve system was employed to assess VOC emission from three different organic fertilizers, at three temperatures (10°C, 20°C and 30°C).The analysis revealed a total emission of 118 VOCs from digestate, 99 from sewage sludge and 200 from compost. One notable observation is the perceptible diversity in the chemical composition of these three organic fertilizers. Specifically, each fertilizer presents hydrocarbon, oxygenated and nitrogenated compounds, with hydrocarbons and oxygenated compounds dominating in all three fertilizers. On the other hand, sulfur compounds are only present in sludge and compost, while digestate had a significantly higher prevalence of nitrogenated compounds. Acetone (C3H6O) is the most emitted compound from digestate and sewage sludge, while methanol (CH4O) predominates in compost emissions. In addition, compounds such as monoterpenes (C10H16), cresols (C7H8O) and phenols (C6H6O), known SOA precursors, were among the most emitted compounds. Secondly, most compounds showed a positive response to temperature, with some increasing linearly and others exhibiting exponential response. Conversely, very few VOCs, such as acetic acid, unexpectedly decreased with rising temperatures. The impact of temperature variations on VOC emissions and the mechanisms driving these patterns will be discussed. Lastly, the potential of organic fertilizers to form ozone through VOC emissions has been estimated for each emitted molecule. Compost had the highest ozone-forming potential followed by sewage sludge and digestate. For digestates and composts, the primary species responsible for ozone formation were hydrocarbons (63% and 60%, respectively), even though oxygenated compounds dominated their emissions. In contrast, for sewage sludge, 56% of the ozone were produced by oxygenates. The results suggested that, from the perspective of air quality, digestate may be a preferable organic fertilizer compared to compost and sewage sludge.
How to cite: khazma, M., Wortham, H., Kammer, J., and Temime-roussel, B.: FERTIPAS: Emissions of organic FERTIlizers as Secondary Organic Aerosol Precursors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8992, https://doi.org/10.5194/egusphere-egu25-8992, 2025.
Intensive agricultural systems are a main source of greenhouse gas (GHG) emissions. The nitrogen (N) fertilizers that are applied to crops to increase crop productions during growing season can lose approximately half of the applied N to the atmosphere as nitrous oxide (N2O) and ammonia (NH3).This results in growers’ financial losses and can cause environmental pollutions. Quantification of gas emissions not only helps to develop inventories of regional and national emissions but also to improve management practices to mitigate the emissions. However, accurate quantification of the gas emissions at farm scale is challenging as the natural reactive ammonia gas is a “sticky” gas, and N2O has spatialand temporal variability. There is a need of proper techniques to continually measure a suite of gases including N2O and NH3simultaneously to reduce the complexity of using multiple gas sensors for measurements.
A trial was conducted in July 2024 to measure N2O, NH3, and CH4emissions following the fertilizer and fertilizer inhibitor applications at a commercial sugarcane farm in Queensland, Australia. Two separate plots were chosen, one plot was for a control plot with urea fertilizer and the second one was for the treatment plot applying urea and urea inhibitor. At each plot, a slant-path Fourier transform infrared spectrometer (slant-path FTIR) was deployed to measure a suite of gas concentrations for three weeks, including N2O, NH3, and CH4, simultaneously.Thirty-min averaged wind statistics and the coordinates of locations of equipment and experimental plots were collected. These measurements of gas concentration and wind statistics were used to calculate gas fluxes using a micrometeorological technique. The fluxes of N2O, NH3, and CH4from control and treatment plots showed that the effects of inhibitor on reduction of N2O and CH4 emissions were significant over the measurement period but NH3 flux reduction was only triggered by the irrigation event.
How to cite: Sukdanont, P., Bai, M., Kee Lam, S., Suter, H., and Chen, D.: The use of open-path FTIR techniques to measure nitrous oxide, ammonia, and methane emissions from a sugarcane farm in Australia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20286, https://doi.org/10.5194/egusphere-egu25-20286, 2025.
The impact of climate change on agricultural emissions becomes important, and strongly affects pollutant levels such as NH3 in the atmosphere. Atmospheric NH3 levels and emission rates are very sensitive to meteorology factors such as temperature and precipitation. Being one of the major pollutants emitted from agriculture, NH3 becoming increasingly important, both because it is a pollutant itself and it contributes to the formation of secondary particulate matter. Long-term assessment of IASI NH3 retrievals showed localized consistent hotspots in some regions of Türkiye, often associated with agricultural activities. Although, the reported emission levels do not chance, there was significant temporal variation observed in the NH3 retrievals in those regions.
In this study, twelve years of NH3 retrievals were spatially processed to obtain annual, seasonal and monthly NH3 distribution maps with a 1x1 km2 gridded domain covering whole Türkiye. The results indicated significant temporal variability which also changes according to different regions. The temporal changes of NH3 for three localized hotspots with significant agricultural activity among the highest NH3 levels in Türkiye were selected and evaluated as; Igdir (Cold semi-arid), Izmir (Hot summer Mediterranean) and Samsun (Humid sub-tropical). The selection was performed to identify the different climatic conditions, crop and fertilizer types. Level-2 IASI NH3 retrievals, yearly agricultural statistics, and meteorological measurements were utilized for understanding the changes in NH3 levels. Among these hotspots, Igdir has the highest seasonal variation with maximum late spring to summer, and Samsun is with least seasonal variation. Within the years investigated, 2015, 2018-2019 and 2022-2023 showed highest number of extreme NH3 retrievals. This study aims to provide a new approach to the assessment of agricultural NH3 variability by the long-term assessment to obtain region-specific temporal profiles which the regional air quality models strongly depend on.
Keywords: ammonia, meteorological factors, temporal variation
Acknowledgements: IASI is a joint mission of EUMETSAT and the Centre National d'Etudes Spatiales (CNES, France). The authors acknowledge the AERIS data infrastructure for providing access to the IASI data in this study and ULB-LATMOS for the development of the retrieval algorithms. This study was supported by the Scientific and Technological Research Council of Türkiye under the grant number 123Y364.
How to cite: Tokgoz, S., Alban, A. M., Saracoglu, S., and Kaynak, B.: Long-Term NH3 Assesment with Meteorological Parameters to Obtain Temporal Profiles in Agricultural Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17924, https://doi.org/10.5194/egusphere-egu25-17924, 2025.
The Colorado Front Range urban corridor and the plains to the east are important source regions of ammonia (NH3), an unregulated pollutant primarily emitted from agricultural activities. Upslope flows driven by the mountain-plains circulation and synoptic scale storm circulations periodically transport these emissions into Rocky Mountain National Park located 50 km west, where excess reactive nitrogen (N) deposition is a historical problem with well documented impacts on the ecosystem. A combination of low-cost Radiello passive sampler NH3 measurements and NH3 total column retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess temporal and spatial variability in NH3 across three distinct land use categories in the region: agricultural, urban, and rural. The NH3 mixing ratio from passive measurements was strongly correlated with the number of confined animal feedlot operations (CAFOs) within a 12 km radius, confirming the importance of that emission source category. Ground-level passive NH3 measurements have a strong correlation with monthly gridded IASI satellite retrievals. Using satellite retrievals, we find an increasing NH3 trend of approximately 3% per year in agricultural and urban sub-regions. We attribute less than 0.2% of the increasing NH3 trend to reductions in particle sulfate. The absolute trend follows the spatial distribution of CAFOs. In the agricultural sub-region, the absolute NH3 trend is on average greater than 2 times larger than that observed in the urban sub-region. The ground-based observations do not have a trend. The lack of ground-based trend is attributed to increasing boundary layer height and dilution of concentrations, through analysis of ERA5 reanalysis data. Lofting NH3 higher into the atmosphere can increase atmospheric lifetime, associated with transport and deposition further from source regions and increased particle formation. Elevated NH3 from wildfire smoke was observed in August 2020, a period of active wildfire activity in northern Colorado, from IASI satellite retrievals. This elevation was less apparent in surface measurements, likely also due to the lofting of the smoke plume. Modeled smoke plumes from the Hazard Mapping System were used to assess the potential impacts of wildfires on observed NH3 trends.
How to cite: Naimie, L., Pan, D., Sullivan, A. P., Corwin, K. A., Benedict, K., Low, L., and Collett, J. L.: Leveraging In Situ and Satellite Data to understand Changing Ammonia above an Agricultural Hotspot, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13536, https://doi.org/10.5194/egusphere-egu25-13536, 2025.
Over the past century, ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, and a reduction in biodiversity, and they affect human health. Up-to-date and spatially and temporally resolved information on NH3 emissions is essential to better quantify their impact. In this study we applied the existing Daily Emissions Constrained by Satellite Observations (DECSO) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by nitrogen oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2°×0.2° using daily observations from both CrIS and the TROPOspheric Monitoring Instrument (TROPOMI; on the Sentinel-5 Precursor (S5P) satellite). Due to the limited number of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg yr−1, with a precision of about 5 %–17 % per grid cell per year over the European domain (35–55° N, 10° W–30° E). The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in situ observations indicates a consistency in terms of magnitude on the country totals, with the results also being comparable regarding the temporal and spatial distributions. The validation of DECSO over Europe implies that we can use DECSO to quickly derive fairly accurate monthly emissions of NH3 over regions with limited local information on NH3 emissions
How to cite: Ding, J., van der A, R., Eskes, H., Dammers, E., Shephard, M., Wichink Kruit, R., Guevara, M., and Tarrason, L.: Ammonia emission estimates using CrIS satellite observations over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6101, https://doi.org/10.5194/egusphere-egu25-6101, 2025.
Excess ammonia (NH3) emissions from human activities pose critical risks to global ecosystems and human health. Despite the urgent need for NH3 emission controls, a comprehensive evaluation of the cost-effectiveness of mitigation strategies remains underdeveloped. In this study, we adopt a multi-model framework to assess the cost and impact of 32 mitigation measures across seven key sectors in 185 countries. Our results indicate that targeted implementation of these measures, particularly in the agricultural sector, could reduce global NH3 emissions by 49% (36–57%). The estimated implementation cost of $279±69 billion outweighs the projected environmental, health, and resource benefits of $568±182 billion. China and India emerge as critical regions for prioritizing NH3 mitigation, offering the highest societal returns, while Sub-Saharan Africa shows limited economic viability. Future scenario analysis reveals that sustainable policy pathways could reduce NH3 emissions by 55% by 2050. Conversely, weak climate action and inadequate nitrogen regulations may result in a 19% increase in emissions, exacerbating environmental degradation and hindering progress toward sustainable development goals. Our findings underscore the urgent need for coordinated global efforts and region-specific policies to establish and achieve effective NH3 mitigation targets.
How to cite: Zhang, X., Gu, B., Winiwarter, W., van Grinsven, H., Sutton, M., and Zhang, S.: Global ammonia emission could be halved with cost-effective measures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2618, https://doi.org/10.5194/egusphere-egu25-2618, 2025.
The use of mineral fertilizers in Sub-Saharan Africa (SSA) is crucial for enhancing agricultural productivity but also raises concerns about emissions of nitrous oxide (N₂O), a potent greenhouse gas. Despite their importance for agriculture, N₂O emissions remain poorly understood in SSA, limiting the development of accurate emissions inventories and the adoption of climate-smart agricultural practices.
In the N2O-SSA project, we quantified N₂O emissions from maize and potato cropping systems under nitrogen application rates of 50 kg N/ha and 100 kg N/ha, compared to control plots, using automated static chamber methods. Fertilizer treatments included urea and triple superphosphate (TSP), and control plots received no nitrogen. Preliminary results showed significant temporal and treatment-specific variability in N₂O emissions, with peaks following fertilizer applications and rainfall events, highlighting the interaction between nitrogen availability and soil moisture. Cumulative annual N₂O emissions were found to vary widely depending on nitrogen application rates and crop types, with fertilizer treatments driving the majority of emissions. Emission factors (EFs) were within ranges consistent with previous studies, highlighting differences between crops such as maize and potatoes. Control plots consistently showed negligible emissions, underlining the critical role of nitrogen inputs in driving N₂O fluxes.
These findings underline the importance of crop-specific nitrogen dynamics in shaping N₂O emissions, and the need for tailored nitrogen management strategies to balance agricultural productivity with environmental sustainability. In the next phase of the project, we will analyze soil samples for N₂O isotopic composition, measuring δ¹⁵N-NH₄ and δ¹⁵N-NO₃, in addition to analyzing gas samples to provide further insights into the sources of N₂O emissions. This will inform more efficient nitrogen management practices for sustainable agricultural systems in Sub-Saharan Africa.
How to cite: Ouma, T., Agredazywczuk, P., Barthel, M., Otinga, A., Njoroge, R., Leitner, S., Zhu, Y., Oduor, C., Oluoch, K. C., Obozinski, G., Six, J., and Harris, E.: Improving nitrous oxide (N₂O) emissions accounting in Kenya: Insights and measurement results relating to fertilizer practices, environmental drivers, and N isotopic composition , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-452, https://doi.org/10.5194/egusphere-egu25-452, 2025.
Two key forms of reactive nitrogen (Nr) in the atmosphere are nitrogen oxides (NO+NO2) and ammonia (NH3). Both species are abundantly emitted from anthropogenic sources (fossil fuel combustion, agriculture) with devastating consequences on the environment, human health and climate. Complementing sparse ground-based measurements, current satellite sounders provide daily coverage of their global distribution. However, the spatial resolution of these instruments (>20 km² for NO2 and >100km² for NH3) is orders of magnitudes greater than the typical size of the main Nr sources (industries, farms, roads), which makes identification of the emitters, and corresponding quantification of their emission strengths particularly challenging.
To understand and address the impacts of a perturbed nitrogen cycle, and in response to the current observational gap, a dedicated satellite for the monitoring of NO2 and NH3 at high spatial resolution has been conceptualised, called Nitrosat. Its main objective is to quantify simultaneously the emission sources of NH3 and NOx at the landscape scale (<0.25 km²) and to characterize seasonal patterns (<1 month) in their emissions. The two imaging spectrometers onboard Nitrosat will operate respectively in the infrared for NH3 and the visible for NO2, offering gapless coverage in a single swath.
Starting from representative examples of measurement techniques that are presently used to derive emission fluxes from NH3 and NO2 satellite observations, we discuss the limitations of current sounders. We introduce the Nitrosat measurement concept and, exploiting both model simulations and aircraft campaign data, provide examples from the EE11 Phase 0 studies of how Nitrosat will enable retrieval of emission fluxes from local and diffuse sources in a way that will not be possible with other current or planned missions.
How to cite: Levelt, P. and Coheur, P. and the Nitrosat Team: Nitrosat, a satellite mission concept for mapping reactive nitrogen at the landscape scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11422, https://doi.org/10.5194/egusphere-egu25-11422, 2025.
Crop production is a major source of agricultural carbon emissions, yet the life cycle carbon footprints (LCCFs) of key global staple crops remain underexplored. This study quantifies the LCCFs of three major grain crops—maize, rice, and wheat—using a hybrid approach that integrates machine learning (ML) models and life cycle assessment (LCA) for the period from 2006 to 2019. We systematically calculated the cradle-to-farm-gate carbon footprint (CF), accounting for emissions from upstream inputs, transportation, and field operations. Emission factors (EFs) and CF compositions were assessed over different time periods. Additionally, we developed a novel Supply-Demand Balanced Carbon Allocation Model (SD-CAM) to trace the sources and flows of upstream CF. Our results reveal a steady increase in the CF of these crops over time, with significant regional variations in both EFs and CF composition. The primary carbon footprint of global rice production is mainly attributed to field carbon emissions, with nitrogen fertilizers as the secondary carbon source. In contrast, nitrogen fertilizers are the dominant carbon source for maize and wheat. Interestingly, while maize's total field emissions are a net carbon source, wheat production acts as a carbon sink. The majority of the CF is concentrated in a few key grain-producing countries, such as China, India, and the United States. Regarding the upstream carbon footprint (IUCCF), major producing countries like China and Canada have consistently been the primary sources of upstream carbon inputs throughout the study period. However, with the rise of global economic initiatives like the Belt and Road, emerging upstream contributors such as Morocco and Vietnam have increasingly become significant contributors in upstream carbon emissions. This study provides valuable insights into the environmental impacts of agricultural production over time, offering guidance for sustainable agricultural policies, carbon responsibility allocation, and international low-carbon cooperation.
How to cite: Liu, S., He, Y., Liu, Y., and Jiang, Q.: Tracing the life cycle carbon footprint of global staple crops: an integrated approach combining machine learning and life cycle assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4965, https://doi.org/10.5194/egusphere-egu25-4965, 2025.
Agricultural soils are the primary source of nitrous oxide (N2O) emissions into the atmosphere, contributing 78% of total N2O emissions. These emissions are influenced by different tillage practices and types of plant cover that are left after harvest. Cover crops (CC) are widely used in agriculture to take up excess nitrogen from the fields, thereby reducing nitrate leaching and increasing soil carbon accrual. However, despite these benefits, agricultural soils containing cover crops have often resulted in high N2O emissions.
A two-year field study with measurement of N2O emissions was conducted in Denmark using a long-term conservation agriculture experiment, including cover cropping, no tillage and crop rotation. The study focused on the influence of soil tillage and cover cropping on soil physical properties and N2O emissions. The tillage systems included no tillage (direct seeding) and conventional ploughing; CC management included paired subplots with oil-seed radish (Raphanus sativus L.), where (i) the cover crop residues were terminated and removed in autumn (CC-rem), and (ii) the cover crop residues were killed by the frost and left in the field (CC-left). Bare soil treatment (i.e., without CC) is included as a reference. Spring oats (Avena sativa L.) grew as the main crop during the first year followed by spring barley (Hordeum vulgare L.) in the second year.
The first-year results for N2O fluxes showed that there were no significant differences in N2O emissions between the tillage practices. However, emissions varied significantly among CC treatments. Compared to the reference without CC, peak emissions (up to 74 µg N2O-N m-2 h-1) were observed for both cover crop treatments. During the cropping season, most of the emissions occurred after fertilization. Especially, +CC-left emitted more N2O than CC-rem during the main cropping season. Before establishing the main crop, CC-rem emitted more N2O than CC-left. Volume-effective porosity, air permeability, bulk density and gas diffusivity are critical soil physical properties that influenced N2O emissions among the cover crop treatments.
How to cite: Bommisetty, H., Elsgaard, L., and Juhl Munkholm, L.: The effect of post-harvest cover crop management on N2O emissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3228, https://doi.org/10.5194/egusphere-egu25-3228, 2025.
Ammonia (NH3) emissions play a significant role in air quality degradation, biodiversity loss, and human health risks by forming secondary pollutants such as fine particulate matter (PM2.5). This study presents a decadal (2013–2022) spatiotemporal analysis of NH3 emissions in the lowlands of eastern Germany, using data from the Infrared Atmospheric Sounding Interferometer (IASI-B) onboard the MetOp-B satellite. The region, characterized by predominantly agricultural land use (54.71%), offers a valuable case for understanding NH3 emission dynamics across diverse landscapes. Integrating satellite remote sensing, machine learning, and atmospheric modeling, this analysis reveals pronounced seasonal and spatial variations, with agricultural activities identified as the primary source of emissions. K-means clustering highlights the influence of cropland, grassland, and urban areas on NH3 emission patterns, identifying significant agricultural hotspots. Additionally, advanced geospatial analysis establishes significant correlations between NH3 concentrations and meteorological variables. NH3 emissions were positively associated with surface solar radiation, temperature, atmospheric boundary layer height, and convective available potential energy, while precipitation, moisture flux, and wind speed exhibited negative correlations. Backward trajectory dispersion modeling employing the HYSPLIT model provided insights into NH3 transport pathways. The results confirmed the influence of both, local sources and non-local contributions. These findings show the major role of meteorological conditions in NH3 dispersion and underscore the importance of sustainable agricultural practices in mitigating emissions.
How to cite: Saravia, C. and Trachte, K.: Decadal analysis of ammonia emission levels in the lowlands of eastern Germany using remote sensing data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4260, https://doi.org/10.5194/egusphere-egu25-4260, 2025.
Atmospheric ammonia (NH3) is a significant pollutant that rapidly reacts with atmospheric acids like sulfuric acid (H2SO4) and nitric acid (HNO3) to form fine particulate matter (PM2.5), which has negative effects on both the environment and public health. NH3 has several kinds of sources but main emitter is agriculture, which originates from crop production and livestock managements. Besides conventional emissions from agriculture, agricultural waste burning is also significant in some regions although prohibited.
Türkiye is an agricultural producing country, and the largest agricultural areas and livestock farms are located in South-Central Anatolia. This study aims to investigate the possible causes of high NH3 and PM levels in this region, focusing on agricultural activities such as crop production, livestock farming, and agricultural residue burning. Using IASI Level-2 NH3 retrievals, the spatio-temporal changes in NH3 levels was investigated over the region. Annual and seasonal changes in NH3 levels were evaluated together with meteorological parameters and ground-based PM10 and PM2.5 measurements. In order to understand the effect of agricultural burning on high NH3 and PM levels in fall season, biomass burning regions were determined with VIIRS S-NPP Fire Radiative Power (FRP) product and aerosol types were examined with CALIOP Level-1 and Level-2 VFM product. High NH3 levels were detected in the study area which has the highest agricultural activity in Türkiye. Seasonal distributions of the region showed that significant levels in fall season, unlike all other regions in Türkiye indicating highest summer NH3 levels. These findings indicated a different source causing high NH3 levels in the fall season other than the agricultural activities usually having highest impact in spring and summer seasons. In the fall seasons (2019-2023), the highest FRP values were observed with values three times or higher than of other seasons. Especially, the highest number of fires occurred in fall of 2020 and 2023, when higher NH3 levels were also observed. Additional to regional high values, hotspots of NH3 were identified in Konya–Eregli, Nigde–Bor, and Aksaray–Merkez. NH3 levels were also observed higher during winter seasons in these hotspots where livestock farms are frequently located. Therefore, effects of livestock farming and residual burning as a NH3 source stood out in this region rather than conventional fertilizer applications. It is important that these lesser known and investigated emission sources of NH3 need to be evaluated to understand their role in secondary particulate matter formation and their impact on public health in the region.
Keywords: Ammonia, Agricultural residue burning, Livestock management
Acknowledgements: IASI is a joint mission of EUMETSAT and the Centre National d'Etudes Spatiales (CNES, France). The authors acknowledge the AERIS data infrastructure for providing access to the IASI data in this study and ULB-LATMOS for the development of the retrieval algorithms. This study was supported by the Scientific and Technological Research Council of Türkiye under the grant number 123Y364.
How to cite: Alban, A. M., Tokgoz, S., Saracoglu, S., and Kaynak, B.: Agricultural sources impact on NH3 and PM levels in the South-Central Anatolia , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17465, https://doi.org/10.5194/egusphere-egu25-17465, 2025.
As an agrarian country, India heavily depends on fertilizers for food production to meet consumption demands, which contributes to a significant portion of global ammonia emissions. Ammonia is an essential precursor gas to form secondary PM2.5 by reacting with SO2 and NO2 and degrades air quality significantly. Thus, it is imperative to implement mitigation strategies to reduce ammonia emissions from the agricultural sector for air quality improvement. In this study, we have updated the sub-sectoral agriculture activity data for each state of India, using 2022 as the base year. Ammonia emissions from each sub-sectoral activity for each state were estimated in the GAINS model for baseline and future scenarios under the current policy framework. We estimated the mitigation potential for ammonia emissions in agriculture by applying different alternate control scenarios. Under the current baseline scenario, the ammonia emissions (in Kilotons) from urea application are the highest among all the states, followed by other livestock such as sheep and horses, other cattle (Beef), dairy cattle, poultry, nitrogenous fertilizer use and production, and agricultural waste burning. The major contributor states to annual ammonia emissions (in Kt/yr) from urea application are Uttar Pradesh (625 ), followed by Andhra Pradesh (290.67) and Madhya Pradesh (271.32). The major contributor states to NH3 emissions from livestock sectoral activities (other cattle, dairy cattle, sheep and horses, poultry, etc.) are Uttar Pradesh (827.73) followed by Andhra Pradesh (478.65) and Rajasthan (491.13). The NH3 emissions (kt/y) from nitrogenous fertilizer production and consumption was highest from Uttar Pradesh (23.28), followed by Gujarat (10.86) and Maharashtra (10.44), while the contribution from agriculture waste burning was estimated largely from Uttar Pradesh (61.10), followed by Andhra Pradesh (32.91) and Tamil Nadu (30.04). We consider several strategies, such as deep manure placement, low nitrogen feed, scrubber for livestock housing, urea substitution, neem-coated urea, and biochar additives to reduce NH3 emissions and estimate their mitigation potentials in this work. To date, there are no specific regulations in India targeting agricultural ammonia emissions at the same level as those of other sector pollutants. Therefore, our results will be useful for policymakers for developing state-specific sub-sectoral mitigation strategies to address this critical issue.
Keywords: Ammonia, fertilizer, livestock, emissions, control scenarios
How to cite: Chaudhary, R., Dey, S., Habib, G., and Purohit, P.: Assessing the ammonia mitigation potential from the Indian agriculture sector for improving air quality in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15997, https://doi.org/10.5194/egusphere-egu25-15997, 2025.
Ammonia (NH3) emitted into the atmosphere contributes to increase in fine particulate matter concentrations through secondary formation and affects human comfort through unpleasant odors. Pig farms are a significant source of ammonia, but the actual emissions are highly variable depending on facility types, meteorological conditions, and operational practices, causing high uncertainty in estimating emissions. In this study, hourly atmospheric ammonia concentrations were measured in Yongji, Gimje, South Korea, a region well known for its large-scale old pig farming, over all four seasons from September 2023 to July 2024. Using the data, seasonal ammonia emissions from pig farms were simulated with the WindTrax Backward Lagrangian Stochastic model. Our findings will be presented. This can provide a foundation for validating bottom-up estimates of ammonia emissions and valuable insights on reducing uncertainties associated with ammonia emissions from pig farms.
How to cite: Kim, Y., Choi, J., Kim, J., Song, H., Yoo, C., and Song, M.: Top-down estimation of ammonia emissions from pig farm area using Backward Lagrangian Stochastic model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1203, https://doi.org/10.5194/egusphere-egu25-1203, 2025.
Atmospheric volatile organic compounds (VOCs) were measured in this study during four seasons (winter, summer, spring, and autumn) between 2020 to 2022, using gas chromatography equipped with a photoionization detector (PID), at Seosan, South Korea. The mean concentration of ∑34VOCs was 21.2 ± 26.6 µg/m3, with the highest levels measured in autumn (33.6 ± 40.4 µg/m3). The toluene/benzene ratio indicated industrial activities dominated in winter and spring, while solvent use and agriculture were key in autumn, with biomass burning common in both seasons. The secondary organic aerosol formation potential (SOAFP) was highest during autumn and summer, significantly contributing to PM2.5 levels. The Monte Carlo simulation revealed benzene concentrations frequently exceeded the permissible carcinogenic risk threshold (1 × 10-6), suggesting potential health hazards. Meanwhile, the non-carcinogenic risks of seven selected VOCs remained within acceptable limits (hazard quotient [HQ] < 1). The outcomes of the study emphasized the importance of understanding VOC characteristics, sources, and implications for public health.
How to cite: Shastri, A., Nirmalkar, J., Kim, S., Oh, S., Lee, K., and Song, M.: Seasonality and health risk assessment of anthropogenic volatile organic compounds (VOCs) in a rural Seosan, South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1240, https://doi.org/10.5194/egusphere-egu25-1240, 2025.
According to the Global Burden of Diseases, PM2.5 (particles with a diameter under 2.5 µm) is the leading cause of diseases and deaths in 2021 (Brauer et al., 2024). Along with decreased emissions, removal through deposition is used to reduce exposure to particulate matter (PM). With a leaf area index (m² of leaf per m² of land) usually higher than 1, plants allow for a higher deposition surface, hence more particle removal from ambient air. Thus, understanding and estimating PM deposition on vegetation is necessary to assess the impact of vegetation on air quality. In situ measurements above vegetation have shown that PM (vertical) deposition velocity can be positive and negative (Pellerin et al., 2017). No 1-dimensional PM deposition model can predict such values. The objective of this study is to implement a working bi-directional PM exchange scheme in the Surfatm exchange model (Personne et al., 2024), a 1-dimensional SVAT model, using a resistive scheme. The bi-directional fluxes are introduced using a compensation point approach, which can be interpreted as the PM surface concentration. This allows the concentration gradient to change signs depending on the difference of concentration between ambient air and the surface. Two PM exchange datasets above a grassland in Lusignan (France) are used to calibrate and validate the model respectively (Pellerin et al., 2017). The Surfatm-PM model can predict positive and negative deposition velocities, with notable differences attributable to the formation mechanism of the particles, such as the process of coagulation or nucleation or condensation between ambient air and (vegetated) interfaces.
How to cite: Courty, A., Stella, P., Bhoonah, R., Hébert, D., Laguionie, P., Maro, D., Rouspard, P., Lamaud, E., Quelo, D., Personne, E., and Saint-Jean, S.: Surfatm-PM: a model of bi-directional particulate matter exchanges over a grassland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10491, https://doi.org/10.5194/egusphere-egu25-10491, 2025.
In recent years, there has been a growing concern about the impacts of ozone pollution on crop
production, particularly in peri-urban cropping areas. As an oxidant, ozone affects plant
biochemical and physiological processes, which in turn disrupt crop development and result in
yield losses. Wheat, a staple crop that sustains billions of people worldwide, is particularly
susceptible to ozone pollution. Quantifying the effects of ozone on wheat yields is crucial for
shaping agronomic and environmental policies at both national and European levels, not only
for the present but also for future scenarios involving climate change, air quality, and
agricultural land management. Another key element to consider is the effect of ozone on soil
organic carbon sequestration in croplands. Crop models play a vital role in quantifying the
combined effects of ozone and management practices on crop growth, yield, biomass
accumulation, and soil carbon dynamics.
The CERES-O3 model developed in 2005 which extends from the CERES-EGC crop model
by integrating Farquhar’s photosynthesis model, efficiently fulfills these requirements.
CERES-O3 simulates the effects of elevated ozone concentrations on photosynthetic rates,
including Rubisco carboxylation efficiency, and consequently on biomass production and
yields.
We use new sets of experimental data obtained at the Grignon ICOS (Integrated Carbon
Observation System) site under varying pedoclimatic conditions against experimental data from
the literature to evaluate the model’s performance. Model simulations reveal that elevated
ozone concentrations reduce photosynthetic rates, stomatal conductance, and Rubisco
carboxylation efficiency, culminating in diminished biomass and grain yield. Furthermore,
parameterizations for two wheat cultivars (Premio and Soissons) show similar ozone effects on
both cultivars.
Although developed more than 20 years ago, CERES-O3 remains a promising tool to quantify
current and predict future ozone impacts at local and global scales. It has strong potential to
enable the exploration of mitigation strategies, including cultivar development, improved
agronomic practices, and policy interventions to curb ozone pollution. It can be used to better
understand the combined effects of ozone pollution and climate stress, which are essential for
ensuring food security in changing global environments. Future steps regarding the model
involve assessing the potential impacts of ozone on soil carbon sequestration in croplands,
which remains a little-known factor in nature-based solutions to mitigate climate change.
Keywords: Ozone, wheat, crop yield, photosynthesis, modeling, stomatal conductance,
CERES-O3
How to cite: Njiki, M. H., Lauerwald, R., Castell, J.-F., and Massad, R.-S.: Modeling the impacts of ozone deposition on wheat yields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9903, https://doi.org/10.5194/egusphere-egu25-9903, 2025.
This study evaluates the potential of deep learning (DL) models to predict enteric methane (CH₄) emissions in dairy cows using data from automated milking and feeding systems, behavioral sensors, and public weather databases. Methane emissions were recorded for 52 cows from October 2022 to December 2023 using the sniffer technology at Gladbacherhof, an organic research farm run by the Justus Liebig University Giessen, Germany. Among the tested models, Long Short-Term Memory (LSTM) networks outperformed Convolutional Neural Networks (CNNs) and hybrid CNN-LSTM models given that data from all sources were available (Scenario S1), achieving an R² of 0.88 and a mean bias error (MBE) of 13.55 ppm CH₄. To assess model applicability under varying data scenarios, features were categorized as "rare," "moderate," or "public" based on their ease of acquisition. Using only public weather data (Scenario S2) resulted in poor predictions, while incorporating moderate-effort farm data (Scenario S3) improved accuracy (R² = 0.45, MBE = 17.60). Adding three rarely available feed-related features, namely feed efficiency, concentrate intake, and total dry matter intake considerably enhanced performance (Scenario S4: R² = 0.74, MBE = 14.36). Random forest analysis highlighted feed-related data as critical for improving prediction performance. These findings demonstrate the capability of DL models to accurately predict CH₄ emissions using readily accessible farm data integrated with a small set of high-impact feed-related features. This approach provides a valuable tool for developing targeted strategies to mitigate methane emissions in dairy farming.
How to cite: Sahraei, A., Knob, D., Lambertz, C., Gattinger, A., and Breuer, L.: Predicting Enteric Methane Emissions in Dairy Cows Using Deep Learning Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8327, https://doi.org/10.5194/egusphere-egu25-8327, 2025.
With the acceleration of climate change, there is an increasing focus on the management of greenhouse gases. Although carbon dioxide is a primary concern, methane and nitrous oxide significantly contribute to the overall greenhouse gas concentration in the atmosphere, necessitating research on their monitoring and quantification. More than 50% of methane emissions originate from sources including natural gas and oil processing, enteric fermentation, and landfills, making those industries the focus of intensive monitoring attempts, encompassing satellite-based observations for extensive and periodic assessment. Further, methane plumes can be detected and emission rates assessed using wind field data for high-concentration sources.
In agriculture, rice paddies are a major source of methane emissions. Nonetheless, a low emission rate per unit area frequently produces undetectable plumes, resulting in dependence on inventory-based simulations instead of measurement-based monitoring. Despite the low emission rate, the extensive expanse of rice fields implies that alterations in fertilizer application or agricultural methodologies can result in substantial changes in overall emissions, thereby requiring prompt monitoring. Moreover, rice cultivation is predominantly concentrated in Asia, which could significantly affect emissions if disrupted by climatic and meteorological changes in the region.
This research develops an algorithm to identify changes in methane emissions utilizing satellite-derived methane concentration data from TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5p, TANSO-FTS-2 (Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer-2) onboard GOSAT (Greenhous gases Observing SATellite), and AIRS (Atmospheric Infrared Sounder) onboard Aqua. Through the analysis of over three years of aggregated data and its comparison with crop calendars, reference datasets named baseline data specifically designed for the growth and agricultural cycles of rice were developed with the valid value ranges. These were employed to identify increases or decreases in greenhouse gas emissions or alterations in emission timing by contrasting current observations with baseline data. The algorithm was implemented in principal rice cultivation regions of South Korea, effectively detecting substantial methane emissions during the irrigation phase causing anaerobic fermentations to soil under the water. This method illustrates the capability of satellite data to improve the comprehension and regulation of agricultural methane emissions. Additionally, guidelines for sustainable agricultural practices and the management of greenhouse gas emissions in agriculture will be feasible.
How to cite: Sim, S., Yun, Y.-S., Cho, S., Park, S., Seong, B., Kim, Y., Lee, M., and Cho, K.: Development of a Satellite-Based Algorithm for Detecting Methane Emission Changes from Rice Paddies , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9186, https://doi.org/10.5194/egusphere-egu25-9186, 2025.
