Articles | Volume 26, issue 16
https://doi.org/10.5194/acp-26-12111-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-12111-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2026
Research article |
| 26 Aug 2026
| 26 Aug 2026
Seasonal and interannual variability of atmospheric ammonia over Guatemala driven by land use, biomass burning, and meteorological circulation
Department of Atmospheric Process, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, Burger Chaussee 2, LG 4/3 Campus Nord, 03044, Cottbus, Germany
Pedro Saravia
Universidad de San Carlos de Guatemala, Escuela Regional de Ingeniería Sanitaria (ERIS), Ciudad Universitaria, 11° avenida Zona 12, Ciudad de Guatemala, Guatemala
Katja Trachte
Department of Atmospheric Process, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, Burger Chaussee 2, LG 4/3 Campus Nord, 03044, Cottbus, Germany
Related authors
No articles found.
Christoph Braun, Florian Ehmele, Christian Beier, Edgar Fabián Espitia-Sarmiento, Hendrik Feldmann, Thomas Frisius, Beate Geyer, Marie Hundhausen, Klaus Keuler, Jürg Luterbacher, Kevin Sieck, Katja Trachte, Elena Xoplaki, and Joaquim G. Pinto
EGUsphere, https://doi.org/10.5194/egusphere-2026-2517, https://doi.org/10.5194/egusphere-2026-2517, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Adaptation to future climatic changes requires robust climate information. Here, a newly produced set of high-resolution climate simulations using multiple regional climate models over Germany is compared to observation-based datasets over the period 1961 to 1990. Overall, the simulations represent the observed climate well and, together with their future counterparts considering global warming, expand our knowledge about projected climatic changes to inform climate adaptation measures.
Kevin Sieck, Joaquim G. Pinto, Beate Geyer, Klaus Keuler, Christian Beier, Christoph Braun, Florian Ehmele, Hendrik Feldmann, Thomas Frisius, Philipp Heinrich, Marie Hundhausen, Ronny Petrik, and Katja Trachte
EGUsphere, https://doi.org/10.5194/egusphere-2026-1024, https://doi.org/10.5194/egusphere-2026-1024, 2026
Short summary
Short summary
In this study we present the evaluation of a new generation of user informed climate simulations at scales of typical weather forecasts. It provides detailed information that are vital for climate impact studies at municipal level. Besides typical short comings of climate models in terms of deviations compared to reference data, the simulations show good agreement with observed climate especially in terms of regional patterns.
Charuta Murkute, Franz Pucha-Cofrep, Galo Carrillo-Rojas, Jürgen Homeier, Oliver Limberger, Andreas Fries, Jörg Bendix, and Katja Trachte
EGUsphere, https://doi.org/10.5194/egusphere-2026-222, https://doi.org/10.5194/egusphere-2026-222, 2026
Short summary
Short summary
We explored how a tropical dry forest in southern Ecuador absorbs and releases carbon over three years using field measurements. The forest acted as a carbon sink, taking up more carbon than it released, especially during the rainy season. Carbon uptake followed plant growth and light availability, while carbon release responded to heat and moisture. The results show these forests are sensitive to climate extremes and play an important role in climate regulation.
Veronika Ettrichrätz, Christian Beier, Klaus Keuler, and Katja Trachte
EGUsphere, https://doi.org/10.5194/egusphere-2023-552, https://doi.org/10.5194/egusphere-2023-552, 2023
Preprint archived
Short summary
Short summary
Will heavy precipitation increase under climate change by the end of this century? The analyses of 40 regional climate simulations for two climate scenarios show that large parts of northern, central, and eastern Europe will be affected by a robust increase in heavy and extreme precipitation, while southwestern Europe will rather experience a slight decrease. Both the increase and the affected areas can be up to twice as large in an extreme than in a more moderate greenhouse gas scenario.
Cited articles
Abeed, R., Viatte, C., Porter, W. C., Evangeliou, N., Clerbaux, C., Clarisse, L., Van Damme, M., Coheur, P.-F., and Safieddine, S.: A roadmap to estimating agricultural ammonia volatilization over Europe using satellite observations and simulation data, Atmos. Chem. Phys., 23, 12505–12523, https://doi.org/10.5194/acp-23-12505-2023, 2023.
Ahmad, A. and Dey, L.: A k-mean clustering algorithm for mixed numeric and categorical data, Data Knowl. Eng., 63, 503–527, https://doi.org/10.1016/j.datak.2007.03.016, 2007.
Alfaro Marroquín, G. and Gómez, R.: Antecedentes y contexto del cambio climático en Guatemala, in: Primer reporte de evaluación del conocimiento sobre cambio climático en Guatemala, edited by: Castellanos, E. J., Paiz-Estévez, A., Escribá, J., Rosales-Alconero, M., and Santizo, A., Editorial Universitaria UVG, Guatemala, 2–19, https://sgccc.org.gt/wp-content/uploads/2019/07/1RepCCGuaCap1.pdf (last access: 22 May 2025), 2019.
Anderson, T. G., McKinnon, K. A., Pons, D., and Anchukaitis, K. J.: How Exceptional Was the 2015–2019 Central American Drought?, Geophys. Res. Lett., 50, https://doi.org/10.1029/2023GL105391, 2023.
Andreae, M. O.: Emission of trace gases and aerosols from biomass burning – an updated assessment, Atmos. Chem. Phys., 19, 8523–8546, https://doi.org/10.5194/acp-19-8523-2019, 2019.
Bardales Espinoza, W. A., Castañón, C., and Herrera Herrera, J. L.: Clima de Guatemala, tendencias observadas e índices de cambio climático, in: Primer reporte de evaluación del conocimiento sobre cambio climático en Guatemala, vol. 1, edited by: Castellanos, E. J., Paiz-Estévez, A., Escribá J., Rosales-Alconero M., and Santizo A., Editorial Universitaria UVG, Guatemala, 20–39, https://sgccc.org.gt/wp-content/uploads/2019/07/1RepCCGuaCap2.pdf (last access: 18 April 2025), 2019.
Chaluleu, C. A.: Fototrampeo en bosques nubosos y latifoliados de la Reserva de la Biósfera Sierra de las Minas, Revista Mesoamericana de Biodiversidad y Cambio Climático–Yu'am, 4, 44–65, 2020.
Chang, Y., Zhang, Y.-L., Kawichai, S., Wang, Q., Van Damme, M., Clarisse, L., Prapamontol, T., and Lehmann, M. F.: Convergent evidence for the pervasive but limited contribution of biomass burning to atmospheric ammonia in peninsular Southeast Asia, Atmos. Chem. Phys., 21, 7187–7198, https://doi.org/10.5194/acp-21-7187-2021, 2021.
Chen, P. and Wang, Q.: Underestimated industrial ammonia emission in China uncovered by material flow analysis, Environ. Pollut., 368, 125740, https://doi.org/10.1016/j.envpol.2025.125740, 2025.
Chen, P., Wang, Q., Shao, M., and Liu, R.: Significantly underestimated traffic-related ammonia emissions in Chinese megacities: Evidence from satellite observations during COVID-19 lockdowns, Chemosphere, 361, 142497, https://doi.org/10.1016/j.chemosphere.2024.142497, 2024.
Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., and Giglio, L.: Historical background and current developments for mapping burned area from satellite Earth observation, Remote Sens. Environ., 225, 45–64, https://doi.org/10.1016/j.rse.2019.02.013, 2019.
Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D., and Coheur, P.-F.: Global ammonia distribution derived from infrared satellite observations, Nat. Geosci., 2, 479–483, https://doi.org/10.1038/ngeo551, 2009.
Clarisse, L., Shephard, M. W., Dentener, F., Hurtmans, D., Cady‐Pereira, K., Karagulian, F., Van Damme, M., Clerbaux, C., and Coheur, P.: Satellite monitoring of ammonia: A case study of the San Joaquin Valley, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013291, 2010.
Clarisse, L., Van Damme, M., Clerbaux, C., and Coheur, P.-F.: Tracking down global NH3 point sources with wind-adjusted superresolution, Atmos. Meas. Tech., 12, 5457–5473, https://doi.org/10.5194/amt-12-5457-2019, 2019.
Clarisse, L., Franco, B., Van Damme, M., Di Gioacchino, T., Hadji-Lazaro, J., Whitburn, S., Noppen, L., Hurtmans, D., Clerbaux, C., and Coheur, P.: The IASI NH3 version 4 product: averaging kernels and improved consistency, Atmos. Meas. Tech., 16, 5009–5028, https://doi.org/10.5194/amt-16-5009-2023, 2023.
Coheur, P.-F., Clarisse, L., Turquety, S., Hurtmans, D., and Clerbaux, C.: IASI measurements of reactive trace species in biomass burning plumes, Atmos. Chem. Phys., 9, 5655–5667, https://doi.org/10.5194/acp-9-5655-2009, 2009.
CONAP: Ley de Áreas Protegidas y su Reglamento, Decreto No. 4-89 y sus Reformas, Decretos No. 18-89, 110-96 y 111-97 del Congreso de la República de Guatemala, 114, https://sip.conap.gob.gt/wp-content/uploads/2022/02/Ley-de-Areas-Protegidas-y-su-Reglamento.-Decreto-4-89.pdf (last access: 2 June 2025), 2016.
CONRED: Protocolo Nacional Temporada de Incendios Forestales y No Forestales, Guatemala, 3–83, https://wp-conred.conred.gob.gt/wp-content/uploads/2025/11/Protocolo-Nacional-Temporada-de-Incendios-Forestales-y-No-Forestales.pdf (last access: 19 April 2025), 2025.
Copernicus Atmosphere Monitoring Service: CAMS global reanalysis (EAC4), Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store [data set], https://doi.org/10.24381/d58bbf47, 2020.
Copernicus Climate Change Service: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023.
Corona‐Núñez, R. O. and Campo, J. E.: Climate and socioeconomic drivers of biomass burning and carbon emissions from fires in tropical dry forests: A Pantropical analysis, Glob. Change Biol., 29, 1062–1079, https://doi.org/10.1111/gcb.16516, 2023.
Dammers, E., Palm, M., Van Damme, M., Vigouroux, C., Smale, D., Conway, S., Toon, G. C., Jones, N., Nussbaumer, E., Warneke, T., Petri, C., Clarisse, L., Clerbaux, C., Hermans, C., Lutsch, E., Strong, K., Hannigan, J. W., Nakajima, H., Morino, I., Herrera, B., Stremme, W., Grutter, M., Schaap, M., Wichink Kruit, R. J., Notholt, J., Coheur, P.-F., and Erisman, J. W.: An evaluation of IASI-NH3 with ground-based Fourier transform infrared spectroscopy measurements, Atmos. Chem. Phys., 16, 10351–10368, https://doi.org/10.5194/acp-16-10351-2016, 2016.
Dong, Z. P., Yu, X., Li, X. M., and Dai, J.: Analysis of variation trends and causes of aerosol optical depth in Shaanxi Province using MODIS data, Chinese Sci. Bull., 58, 4486–4496, https://doi.org/10.1007/S11434-013-5991-Z, 2013.
Dragosits, U., Theobald, M. R., Place, C. J., Lord, E., Webb, J., Hill, J., ApSimon, H. M., and Sutton, M. A.: Ammonia emission, deposition and impact assessment at the field scale: a case study of sub-grid spatial variability, Environ. Pollut., 117, 147–158, https://doi.org/10.1016/S0269-7491(01)00147-6, 2002.
ESRI: ESRI: Sentinel-2 2020 Global Land Use/Land Cover (LULC) Map, 12 pp., https://www.esri.com/about/newsroom/wp-content/uploads/2021/08/global.pdf (last access: 9 April 2025), 2021.
European Space Agency: Copernicus Global Digital Elevation Model, European Space Agency [data set], https://doi.org/10.5069/G9028PQB, 2024.
Evangeliou, N., Tichý, O., Svendby Otervik, M., Eckhardt, S., Balkanski, Y., and Hauglustaine, D. A.: Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia, Aerosol Research, 3, 155–174, https://doi.org/10.5194/ar-3-155-2025, 2025.
FAO: Fire Management – Global Assessment 2006, 135 pp., http://www.fao.org/docrep/009/a0969e/a0969e00.htm (last access: 28 September 2025), 2007.
FAO: FAOSTAT. Countries by commodity, https://www.fao.org/faostat/en/ (last access: 5 May 2025), 2019.
Farren, N. J., Davison, J., Rose, R. A., Wagner, R. L., and Carslaw, D. C.: Underestimated Ammonia Emissions from Road Vehicles, Environ. Sci. Technol., 54, 15689–15697, https://doi.org/10.1021/acs.est.0c05839, 2020.
Flemming, J., Benedetti, A., Inness, A., Engelen, R. J., Jones, L., Huijnen, V., Remy, S., Parrington, M., Suttie, M., Bozzo, A., Peuch, V.-H., Akritidis, D., and Katragkou, E.: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015, Atmos. Chem. Phys., 17, 1945–1983, https://doi.org/10.5194/acp-17-1945-2017, 2017.
Fouilloux, A.: annefou/metos_python: Working with Spatio-temporal data in Python (v2018.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.1165281, 2018.
Franco, B., Clarisse, L., Stavrakou, T., Müller, J. ‐F, Van Damme, M., Whitburn, S., Hadji‐Lazaro, J., Hurtmans, D., Taraborrelli, D., Clerbaux, C., and Coheur, P. ‐F: A General Framework for Global Retrievals of Trace Gases From IASI: Application to Methanol, Formic Acid, and PAN, J. Geophys. Res.-Atmos., 123, https://doi.org/10.1029/2018JD029633, 2018.
Freeborn, P. H., Wooster, M. J., and Roberts, G.: Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa, Remote Sens. Environ., 115, 475–489, https://doi.org/10.1016/j.rse.2010.09.017, 2011.
Fu, Y., Li, R., Wang, X., Bergeron, Y., Valeria, O., Chavardès, R. D., Wang, Y., and Hu, J.: Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products, Remote Sens. (Basel)., 12, 2870, https://doi.org/10.3390/rs12182870, 2020.
Gašparović, M., Zrinjski, M., and Gudelj, M.: Automatic cost-effective method for land cover classification (ALCC), Comput. Environ. Urban Syst., 76, 1–10, https://doi.org/10.1016/j.compenvurbsys.2019.03.001, 2019.
Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An Enhanced Contextual Fire Detection Algorithm for MODIS, Remote Sens. Environ., 87, 273–282, https://doi.org/10.1016/S0034-4257(03)00184-6, 2003.
Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., and Kasibhatla, P.: Global estimation of burned area using MODIS active fire observations, Atmos. Chem. Phys., 6, 957–974, https://doi.org/10.5194/acp-6-957-2006, 2006.
Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sens. Environ., 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018.
Giglio, L., Schroeder, W., Hall, J. V., and Justice, C. O.: MODIS Collection 6 and Collection 6.1 Active Fire Product User's Guide, https://modis-land.gsfc.nasa.gov/pdf/MODIS_C6_C6.1_Fire_User_Guide_1.0.pdf (last access: 15 May 2025), 2021a.
Giglio, L., Justice, C., Boschetti, L., and Roy, D.: MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD64A1.061, 2021b.
Gu, C., Wang, S., Zhu, J., Dai, W., Liu, J., Xue, R., Che, X., Lin, Y., Duan, Y., Wenig, M. O., and Zhou, B.: Underestimated ammonia vehicular emissions in metropolitan city revealed by on-road mobile measurement, Environ. Res. Lett., 18, 104040, https://doi.org/10.1088/1748-9326/acf94a, 2023.
Hantson, S., Padilla, M., Corti, D., and Chuvieco, E.: Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence, Remote Sens. Environ., 131, 152–159, https://doi.org/10.1016/j.rse.2012.12.004, 2013.
Herrera, B., Bezanilla, A., Blumenstock, T., Dammers, E., Hase, F., Clarisse, L., Magaldi, A., Rivera, C., Stremme, W., Strong, K., Viatte, C., Van Damme, M., and Grutter, M.: Measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements, Atmos. Chem. Phys., 22, 14119–14132, https://doi.org/10.5194/acp-22-14119-2022, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hervas, A.: Land, development and contract farming on the Guatemalan oil palm frontier, J. Peasant Stud., 46, 115–141, https://doi.org/10.1080/03066150.2017.1351435, 2019.
Holder, C. D.: Fog precipitation in the Sierra de las Minas Biosphere Reserve, Guatemala, Hydrol. Process., 17, 2001–2010, https://doi.org/10.1002/hyp.1224, 2003.
Holder, C. D.: The hydrological significance of cloud forests in the Sierra de las Minas Biosphere Reserve, Guatemala, Geoforum, 37, 82–93, https://doi.org/10.1016/j.geoforum.2004.06.008, 2006.
IGM: Mapa hipsométrico de la República de Guatemala, Instituto Geográfico Nacional, https://www.ign.es/web/catalogo-cartoteca/resources/html/031123.html (last access: 20 May 2025), 2002
INE: Encuesta Nacional Agropecuaria con enfoque en granos básicos y cultivos permanentes, año agrícola 2019–2020, Instituto Nacional de Estadística, Guatemala, https://www.ine.gob.gt/sistema/uploads/2021/01/22/20210122164213QDinUvuRa9GjopyXaTuNMXc3gd6Jq1Q1.pdf (last access: 12 June 2025), 2020.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
INSIVUMEH: Variabilidad y Cambio Climático en Guatemala, Instituto Nacional de Sismología, Vulcanología, Meteorología e Hidrología, Guatemala, https://insivumeh.gob.gt/seccion-cambio-climatico/ (last access: 10 May 2025), 2018.
INSIVUMEH: Estado del Clima en Guatemala, Instituto Nacional de Sismología, Vulcanología, Meteorología e Hidrología, Guatemala, https://insivumeh.gob.gt/wp-content/uploads/2026/02/Estado_del_clima.pdf (last access: 25 October 2025), 2023.
Justice, C. O., Giglio, L., Roy, D., Boschetti, L., Csiszar, I., Davies, D., Korontzi, S., Schroeder, W., O'Neal, K., and Morisette, J.: MODIS-Derived Global Fire Products, in: Land Remote Sensing and Global Environmental Change. Remote Sensing and Digital Image Processing, vol. 11, edited by: Ramachandran, B., Justice, C., and Abrams, M., Springer, New York, 661–679, https://doi.org/10.1007/978-1-4419-6749-7_29, 2010.
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., and Brumby, S. P.: Global land use/land cover with Sentinel 2 and deep learning, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707, https://doi.org/10.1109/IGARSS47720.2021.9553499, 2021.
Kucieńska, B., Raga, G. B., and Rodríguez, O.: Cloud-to-ground lightning over Mexico and adjacent oceanic regions: a preliminary climatology using the WWLLN dataset, Ann. Geophys., 28, 2047–2057, https://doi.org/10.5194/angeo-28-2047-2010, 2010.
Li, F., Zhang, X., and Kondragunta, S.: Biomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire Product, Remote Sens. (Basel)., 12, 1561, https://doi.org/10.3390/rs12101561, 2020.
Li, J., Hendricks, J., Righi, M., and Beer, C. G.: An aerosol classification scheme for global simulations using the k-means machine learning method, Geosci. Model Dev., 15, 509–533, https://doi.org/10.5194/gmd-15-509-2022, 2022.
Liu, Y., Qian, Y., Rasch, P. J., Zhang, K., Leung, L.-R., Wang, Y., Wang, M., Wang, H., Huang, X., and Yang, X.-Q.: Fire–precipitation interactions amplify the quasi-biennial variability in fires over southern Mexico and Central America, Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024, 2024a.
Liu, Y., Chen, J., Shi, Y., Zheng, W., Shan, T., and Wang, G.: Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data, Earth Syst. Sci. Data, 16, 3495–3515, https://doi.org/10.5194/essd-16-3495-2024, 2024b.
Lopez-Ridaura, S., Barba-Escoto, L., Reyna, C., Hellin, J., Gerard, B., and van Wijk, M.: Food security and agriculture in the Western Highlands of Guatemala, Food Secur., 11, 817–833, https://doi.org/10.1007/s12571-019-00940-z, 2019.
Lovarelli, D., Fugazza, D., Costantini, M., Conti, C., Diolaiuti, G., and Guarino, M.: Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data, Atmos. Environ., 259, 118534, https://doi.org/10.1016/j.atmosenv.2021.118534, 2021.
Luo, Z., Zhang, Y., Chen, W., Van Damme, M., Coheur, P.-F., and Clarisse, L.: Estimating global ammonia (NH3) emissions based on IASI observations from 2008 to 2018, Atmos. Chem. Phys., 22, 10375–10388, https://doi.org/10.5194/acp-22-10375-2022, 2022.
Mahata, K., Das, R., Das, S., and Sarkar, A.: Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India, Journal of Intelligent Systems, 30, 273–286, https://doi.org/10.1515/jisys-2019-0155, 2020.
McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020.
Monzón-Alvarado, C., Cortina-Villar, S., Schmook, B., Flamenco-Sandoval, A., Christman, Z., and Arriola, L.: Land-use decision-making after large-scale forest fires: Analyzing fires as a driver of deforestation in Laguna del Tigre National Park, Guatemala, Appl. Geogr., 35, 43–52, https://doi.org/10.1016/j.apgeog.2012.04.008, 2012.
Orrego León, E. O., Hernández Quevedo, M. P., and Gómez Jordán, R. C.: Variabilidad del inicio, final y duración de la época lluviosa en Guatemala y su tendencia, Revista Mesoamericana de Biodiversidad y Cambio Climático–Yu'am, 5, 4–24, 2021.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., and Duchesnay, É.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Pham, D. T., Dimov, S. S., and Nguyen, C. D.: Selection of K in k-means clustering, Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci., 219, 103–119, https://doi.org/10.1243/095440605X8298, 2005.
Pohl, V., Gilmer, A., Hellebust, S., McGovern, E., Cassidy, J., Byers, V., McGillicuddy, E. J., Neeson, F., and O'Connor, D. J.: Ammonia Cycling and Emerging Secondary Aerosols from Arable Agriculture: A European and Irish Perspective, Air, 1, 37–54, https://doi.org/10.3390/air1010003, 2022.
Remy, S. and Kaiser, J. W.: Daily global fire radiative power fields estimation from one or two MODIS instruments, Atmos. Chem. Phys., 14, 13377–13390, https://doi.org/10.5194/acp-14-13377-2014, 2014.
Ríos, B. and Raga, G. B.: Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America, Int. J. Remote Sens., 39, 949–970, https://doi.org/10.1080/01431161.2017.1392641, 2018.
Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Radu, R., Schepers, D., Villaume, S., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.: The ERA5 global reanalysis from 1940 to 2022, Q. J. Roy. Meteor. Soc., 150, 4014–4048, https://doi.org/10.1002/qj.4803, 2024.
Spencer, N. H.: Squared Euclidean Distances, in: Essentials of Multivariate Data Analysis, 95, CRC Press, ISBN 9781466584785, 2013.
Sutton, M. A., Nemitz, E., Erisman, J. W., Beier, C., Bahl, K. B., Cellier, P., de Vries, W., Cotrufo, F., Skiba, U., Di Marco, C., Jones, S., Laville, P., Soussana, J. F., Loubet, B., Twigg, M., Famulari, D., Whitehead, J., Gallagher, M. W., Neftel, A., Flechard, C. R., Herrmann, B., Calanca, P. L., Schjoerring, J. K., Daemmgen, U., Horvath, L., Tang, Y. S., Emmett, B. A., Tietema, A., Peñuelas, J., Kesik, M., Brueggemann, N., Pilegaard, K., Vesala, T., Campbell, C. L., Olesen, J. E., Dragosits, U., Theobald, M. R., Levy, P., Mobbs, D. C., Milne, R., Viovy, N., Vuichard, N., Smith, J. U., Smith, P., Bergamaschi, P., Fowler, D., and Reis, S.: Challenges in quantifying biosphere–atmosphere exchange of nitrogen species, Environ. Pollut., 150, 125–139, https://doi.org/10.1016/j.envpol.2007.04.014, 2007.
Sutton, M. A., Reis, S., Riddick, S. N., Dragosits, U., Nemitz, E., Theobald, M. R., Tang, Y. S., Braban, C. F., Vieno, M., Dore, A. J., Mitchell, R. F., Wanless, S., Daunt, F., Fowler, D., Blackall, T. D., Milford, C., Flechard, C. R., Loubet, B., Massad, R., Cellier, P., Personne, E., Coheur, P. F., Clarisse, L., Van Damme, M., Ngadi, Y., Clerbaux, C., Skjøth, C. A., Geels, C., Hertel, O., Wichink Kruit, R. J., Pinder, R. W., Bash, J. O., Walker, J. T., Simpson, D., Horváth, L., Misselbrook, T. H., Bleeker, A., Dentener, F., and de Vries, W.: Towards a climate-dependent paradigm of ammonia emission and deposition, Philos. T. R. Soc. B, 368, 20130166, https://doi.org/10.1098/rstb.2013.0166, 2013.
Umargono, E., Suseno, J. E., and Vincensius Gunawan, S. K.: K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median, in: Proceedings of the International Conferences on Information System and Technology, 234–240, https://doi.org/10.5220/0009908402340240, 2019.
Vermote, E., Ellicott, E., Dubovik, O., Lapyonok, T., Chin, M., Giglio, L., and Roberts, G. J.: An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD011188, 2009.
Viatte, C., Petit, J.-E., Yamanouchi, S., Van Damme, M., Doucerain, C., Germain-Piaulenne, E., Gros, V., Favez, O., Clarisse, L., Coheur, P.-F., Strong, K., and Clerbaux, C.: Ammonia and PM2.5 Air Pollution in Paris during the 2020 COVID Lockdown, Atmosphere (Basel), 12, 160, https://doi.org/10.3390/atmos12020160, 2021.
Van Damme, M., Clarisse, L., Heald, C. L., Hurtmans, D., Ngadi, Y., Clerbaux, C., Dolman, A. J., Erisman, J. W., and Coheur, P. F.: Global distributions, time series and error characterization of atmospheric ammonia (NH3) from IASI satellite observations, Atmos. Chem. Phys., 14, 2905–2922, https://doi.org/10.5194/acp-14-2905-2014, 2014.
Van Damme, M., Whitburn, S., Clarisse, L., Clerbaux, C., Hurtmans, D., and Coheur, P.-F.: Version 2 of the IASI NH3 neural network retrieval algorithm: near-real-time and reanalysed datasets, Atmos. Meas. Tech., 10, 4905–4914, https://doi.org/10.5194/amt-10-4905-2017, 2017.
Van Damme, M., Clarisse, L., Whitburn, S., Hadji-Lazaro, J., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: Industrial and agricultural ammonia point sources exposed, Nature, 564, 99–103, https://doi.org/10.1038/s41586-018-0747-1, 2018.
Van Damme, M., Clarisse, L., Franco, B., Sutton, M. A., Erisman, J. W., Wichink Kruit, R., van Zanten, M., Whitburn, S., Hadji-Lazaro, J., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: Global, regional and national trends of atmospheric ammonia derived from a decadal (2008–2018) satellite record, Environ. Res. Lett., 16, 055017, https://doi.org/10.1088/1748-9326/abd5e0, 2021.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Whitburn, S., Van Damme, M., Clarisse, L., Turquety, S., Clerbaux, C., and Coheur, P.-F.: Doubling of annual ammonia emissions from the peat fires in Indonesia during the 2015 El Niño, Geophys. Res. Lett., 43, 11007–11014, https://doi.org/10.1002/2016GL070620, 2016.
Whitburn, S., Van Damme, M., Clarisse, L., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: IASI-derived NH3 enhancement ratios relative to CO for the tropical biomass burning regions, Atmos. Chem. Phys., 17, 12239–12252, https://doi.org/10.5194/acp-17-12239-2017, 2017.
Wooster, M., Zhukov, B., and Oertel, D.: Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products, Remote Sens. Environ., 86, 83–107, https://doi.org/10.1016/S0034-4257(03)00070-1, 2003.
Wyer, K. E., Kelleghan, D. B., Blanes-Vidal, V., Schauberger, G., and Curran, T. P.: Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health, J. Environ. Manage., 323, 116285, https://doi.org/10.1016/j.jenvman.2022.116285, 2022.
Xu, W., Zhao, Y., Wen, Z., Chang, Y., Pan, Y., Sun, Y., Ma, X., Sha, Z., Li, Z., Kang, J., Liu, L., Tang, A., Wang, K., Zhang, Y., Guo, Y., Zhang, L., Sheng, L., Zhang, X., Gu, B., Song, Y., Van Damme, M., Clarisse, L., Coheur, P.-F., Collett, J. L., Goulding, K., Zhang, F., He, K., and Liu, X.: Increasing importance of ammonia emission abatement in PM2.5 pollution control, Sci. Bull. (Beijing), 67, 1745–1749, https://doi.org/10.1016/j.scib.2022.07.021, 2022.
Zhou, C., Zhou, H., Holsen, T. M., Hopke, P. K., Edgerton, E. S., and Schwab, J. J.: Ambient Ammonia Concentrations Across New York State, J. Geophys. Res.-Atmos., 124, 8287–8302, https://doi.org/10.1029/2019JD030380, 2019.
Zhou, Y., Zhao, Y., Mao, P., Zhang, Q., Zhang, J., Qiu, L., and Yang, Y.: Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China, Atmos. Chem. Phys., 17, 211–233, https://doi.org/10.5194/acp-17-211-2017, 2017.
Zhu, L., Henze, D. K., Bash, J. O., Cady-Pereira, K. E., Shephard, M. W., Luo, M., and Capps, S. L.: Sources and Impacts of Atmospheric NH3: Current Understanding and Frontiers for Modeling, Measurements, and Remote Sensing in North America, Curr. Pollut. Rep., 1, 95–116, https://doi.org/10.1007/s40726-015-0010-4, 2015.
Short summary
This study analyzes atmospheric ammonia in Guatemala using satellite data, fire records, land-use maps, and weather data from 2015–2023. Results show that agricultural activities contribute to persistent background ammonia levels, while seasonal fires trigger short-term increases intensified by warm, dry conditions. These findings identify pollution hotspots and improve understanding of air quality and nitrogen pollution in Central America.
This study analyzes atmospheric ammonia in Guatemala using satellite data, fire records,...
Altmetrics
Final-revised paper
Preprint