Articles | Volume 18, issue 23
https://doi.org/10.5194/amt-18-7297-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-7297-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Laser-Induced Fluorescence coupled with Machine Learning as an effective approach for real-time identification of bacteria in bioaerosols
Alejandro Fontal
CORRESPONDING AUTHOR
AIRLAB, Climate and Health Group, CANU Program, Barcelona Institute for Global Health, Barcelona, 08003, Spain
Sílvia Borràs
AIRLAB, Climate and Health Group, CANU Program, Barcelona Institute for Global Health, Barcelona, 08003, Spain
Lídia Cañas
AIRLAB, Climate and Health Group, CANU Program, Barcelona Institute for Global Health, Barcelona, 08003, Spain
Department of Evolutionary Biology, Ecology and Environmental Sciences, Universitat de Barcelona, Barcelona, 08028, Spain
Sofya Pozdniakova
AIRLAB, Climate and Health Group, CANU Program, Barcelona Institute for Global Health, Barcelona, 08003, Spain
Xavier Rodó
AIRLAB, Climate and Health Group, CANU Program, Barcelona Institute for Global Health, Barcelona, 08003, Spain
ICREA, Barcelona, 08010, Spain
Related authors
No articles found.
Roger Curcoll, Alba Àgueda, Josep-Anton Morguí, Lídia Cañas, Sílvia Borràs, Arturo Vargas, and Claudia Grossi
Atmos. Chem. Phys., 25, 6299–6323, https://doi.org/10.5194/acp-25-6299-2025, https://doi.org/10.5194/acp-25-6299-2025, 2025
Short summary
Short summary
In this work, the methane emissions from the rice crops of the Ebro Delta were estimated with the Radon Tracer Method, using back trajectories and radon and methane observations. Estimated fluxes show a strong seasonality with maximums in October, corresponding with the period of harvest and straw incorporation. The estimated annual methane emission was about 262.8 kg CH4 ha‑1. Results were compared with fluxes obtained with static chambers showing strong agreement between both methodologies.
Patricia Tarín-Carrasco, Desislava Petrova, Laura Chica-Castells, Jelena Lukovic, Xavier Rodó, and Ivana Cvijanovic
EGUsphere, https://doi.org/10.5194/egusphere-2023-3057, https://doi.org/10.5194/egusphere-2023-3057, 2024
Preprint archived
Short summary
Short summary
Future precipitation in Mediterranean climate regions are associated with a high uncertainty. Using CMIP6 ensemble, this study examines future precipitation projections under two climate change scenarios. Overall, annual precipitation decreases across these regions, except northern California. Despite improvements in CMIP6, significant intermodel differences persist, emphasizing the need for impact studies that consider the entire range of projected precipitation changes to address uncertainties
Cited articles
Amato, P., Mathonat, F., Nuñez Lopez, L., Péguilhan, R., Bourhane, Z., Rossi, F., Vyskocil, J., Joly, M., and Ervens, B.: The aeromicrobiome: the selective and dynamic outer-layer of the Earth's microbiome, Front. Microbiol., 14, https://doi.org/10.3389/fmicb.2023.1186847, 2023.
Banerjee, S. and van der Heijden, M. G. A.: Soil microbiomes and one health, Nat. Rev. Microbiol., 21, 6–20, https://doi.org/10.1038/s41579-022-00779-w, 2023.
Behzad, H., Gojobori, T., and Mineta, K.: Challenges and Opportunities of Airborne Metagenomics, Genome Biol. Evol., 7, 1216–1226, https://doi.org/10.1093/gbe/evv064, 2015.
Berezin, M. Y. and Achilefu, S.: Fluorescence Lifetime Measurements and Biological Imaging, Chem. Rev., 110, 2641–2684, https://doi.org/10.1021/cr900343z, 2010.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brodie, E. L., DeSantis, T. Z., Parker, J. P. M., Zubietta, I. X., Piceno, Y. M., and Andersen, G. L.: Urban aerosols harbor diverse and dynamic bacterial populations, Proc. Natl. Acad. Sci. USA, 104, 299–304, https://doi.org/10.1073/pnas.0608255104, 2007.
Calvo, A. I., Baumgardner, D., Castro, A., Fernández-González, D., Vega-Maray, A. M., Valencia-Barrera, R. M., Oduber, F., Blanco-Alegre, C., and Fraile, R.: Daily behavior of urban Fluorescing Aerosol Particles in northwest Spain, Atmos. Environ., 184, 262–277, https://doi.org/10.1016/j.atmosenv.2018.04.027, 2018.
Cole, J. J.: Aquatic Microbiology for Ecosystem Scientists: New and Recycled Paradigms in Ecological Microbiology, Ecosystems, 2, 215–225, https://doi.org/10.1007/s100219900069, 1999.
Crawford, I., Bower, K., Topping, D., Di Piazza, S., Massabò, D., Vernocchi, V., and Gallagher, M.: Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification, Atmosphere, 14, 1214, https://doi.org/10.3390/atmos14081214, 2023.
Datta, R., Heaster, T. M., Sharick, J. T., Gillette, A. A., and Skala, M. C.: Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications, J. Biomed. Opt., 25, 071203, https://doi.org/10.1117/1.JBO.25.7.071203, 2020.
Després, V. R., Huffman, J. A., Burrows, S. M., Hoose, C., Safatov, A. S., Buryak, G., Fröhlich-Nowoisky, J., Elbert, W., Andreae, M. O., Pöschl, U., and Jaenicke, R.: Primary biological aerosol particles in the atmosphere: a review, Tellus B Chem. Phys. Meteorol., 64, https://doi.org/10.3402/tellusb.v64i0.15598, 2012.
Eduard, W., Heederik, D., Duchaine, C., and Green, B. J.: Bioaerosol exposure assessment in the workplace: the past, present and recent advances, J. Environ. Monit., 14, 334–339, https://doi.org/10.1039/C2EM10717A, 2012.
Fierer, N.: Embracing the unknown: disentangling the complexities of the soil microbiome, Nat. Rev. Microbiol., 15, 579–590, https://doi.org/10.1038/nrmicro.2017.87, 2017.
Fontal, A.: Rapid-E output for aerosolized fluorophores and Bacteria, Zenodo [data set], https://doi.org/10.5281/zenodo.15485702, 2025a.
Fontal, A.: lif-bacteria-aerosols-ms: AMT paper code companion, Zenodo [code], https://doi.org/10.5281/zenodo.17753297, 2025b.
Fröhlich-Nowoisky, J., Kampf, C. J., Weber, B., Huffman, J. A., Pöhlker, C., Andreae, M. O., Lang-Yona, N., Burrows, S. M., Gunthe, S. S., Elbert, W., Su, H., Hoor, P., Thines, E., Hoffmann, T., Després, V. R., and Pöschl, U.: Bioaerosols in the Earth system: Climate, health, and ecosystem interactions, Atmospheric Res., 182, 346–376, https://doi.org/10.1016/j.atmosres.2016.07.018, 2016.
Gabbarini, V., Rossi, R., Ciparisse, J.-F., Malizia, A., Divizia, A., De Filippis, P., Anselmi, M., Carestia, M., Palombi, L., Divizia, M., and Gaudio, P.: Laser-induced fluorescence (LIF) as a smart method for fast environmental virological analyses: validation on Picornaviruses, Sci. Rep., 9, 12598, https://doi.org/10.1038/s41598-019-49005-3, 2019.
Gilbert, J. A. and Stephens, B.: Microbiology of the built environment, Nat. Rev. Microbiol., 16, 661–670, https://doi.org/10.1038/s41579-018-0065-5, 2018.
Griffin, D. W.: Atmospheric Movement of Microorganisms in Clouds of Desert Dust and Implications for Human Health, Clin. Microbiol. Rev., 20, 459–477, https://doi.org/10.1128/CMR.00039-06, 2007.
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data?, Adv. Neural Inf. Process. Syst., 35, 507–520, 2022.
Gusareva, E. S., Acerbi, E., Lau, K. J. X., Luhung, I., Premkrishnan, B. N. V., Kolundžija, S., Purbojati, R. W., Wong, A., Houghton, J. N. I., Miller, D., Gaultier, N. E., Heinle, C. E., Clare, M. E., Vettath, V. K., Kee, C., Lim, S. B. Y., Chénard, C., Phung, W. J., Kushwaha, K. K., Nee, A. P., Putra, A., Panicker, D., Yanqing, K., Hwee, Y. Z., Lohar, S. R., Kuwata, M., Kim, H. L., Yang, L., Uchida, A., Drautz-Moses, D. I., Junqueira, A. C. M., and Schuster, S. C.: Microbial communities in the tropical air ecosystem follow a precise diel cycle, Proc. Natl. Acad. Sci. USA, 116, 23299–23308, https://doi.org/10.1073/pnas.1908493116, 2019.
Healy, D. A., Huffman, J. A., O'Connor, D. J., Pöhlker, C., Pöschl, U., and Sodeau, J. R.: Ambient measurements of biological aerosol particles near Killarney, Ireland: a comparison between real-time fluorescence and microscopy techniques, Atmos. Chem. Phys., 14, 8055–8069, https://doi.org/10.5194/acp-14-8055-2014, 2014.
Hill, S. C., Pinnick, R. G., Niles, S., Fell, N. F., Pan, Y. L., Bottiger, J., Bronk, B. V., Holler, S., and Chang, R. K.: Fluorescence from airborne microparticles: dependence on size, concentration of fluorophores, and illumination intensity, Appl. Optics, 40, 3005–3013, https://doi.org/10.1364/ao.40.003005, 2001.
Huson, D. H., Auch, A. F., Qi, J., and Schuster, S. C.: MEGAN analysis of metagenomic data, Genome Res., 17, 377–386, https://doi.org/10.1101/gr.5969107, 2007.
Kiselev, D., Bonacina, L., and Wolf, J.-P.: Individual bioaerosol particle discrimination by multi-photon excited fluorescence, Opt. Express, 19, 24516–24521, https://doi.org/10.1364/OE.19.024516, 2011.
Kiselev, D., Bonacina, L., and Wolf, J.-P.: A flash-lamp based device for fluorescence detection and identification of individual pollen grains, Rev. Sci. Instrum., 84, 033302, https://doi.org/10.1063/1.4793792, 2013.
Kwaśny, M., Bombalska, A., Kaliszewski, M., Włodarski, M., and Kopczyński, K.: Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications, Sensors, 23, 3339, https://doi.org/10.3390/s23063339, 2023.
LeCun, Y. and Bengio, Y.: Convolutional networks for images, speech, and time series, in: The Handbook of Brain Theory and Neural Networks, edited by: Arbib, M. A., MIT Press, Cambridge, MA, USA, 255–258, 1995.
Luhung, I., Uchida, A., Lim, S. B. Y., Gaultier, N. E., Kee, C., Lau, K. J. X., Gusareva, E. S., Heinle, C. E., Wong, A., Premkrishnan, B. N. V., Purbojati, R. W., Acerbi, E., Kim, H. L., Junqueira, A. C. M., Longford, S., Lohar, S. R., Yap, Z. H., Panicker, D., Koh, Y., Kushwaha, K. K., Ang, P. N., Putra, A., Drautz-Moses, D. I., and Schuster, S. C.: Experimental parameters defining ultra-low biomass bioaerosol analysis, Npj Biofilms Microbiomes, 7, 37, https://doi.org/10.1038/s41522-021-00209-4, 2021a.
Luhung, I., Uchida, A., Lim, S. B. Y., Gaultier, N. E., Kee, C., Lau, K. J. X., Gusareva, E. S., Heinle, C. E., Wong, A., Premkrishnan, B. N. V., Purbojati, R. W., Acerbi, E., Kim, H. L., Junqueira, A. C. M., Longford, S., Lohar, S. R., Yap, Z. H., Panicker, D., Koh, Y., Kushwaha, K. K., Ang, P. N., Putra, A., Drautz-Moses, D. I., and Schuster, S. C.: Experimental parameters defining ultra-low biomass bioaerosol analysis, Npj Biofilms Microbiomes, 7, 1–11, https://doi.org/10.1038/s41522-021-00209-4, 2021b.
Matavulj, P., Panić, M., Šikoparija, B., Tešendić, D., Radovanović, M., and Brdar, S.: Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation, Appl. Artif. Intell., 37, 2157593, https://doi.org/10.1080/08839514.2022.2157593, 2023.
Maya-Manzano, J. M., Tummon, F., Abt, R., Allan, N., Bunderson, L., Clot, B., Crouzy, B., Daunys, G., Erb, S., Gonzalez-Alonso, M., Graf, E., Grewling, Ł., Haus, J., Kadantsev, E., Kawashima, S., Martinez-Bracero, M., Matavulj, P., Mills, S., Niederberger, E., Lieberherr, G., Lucas, R. W., O'Connor, D. J., Oteros, J., Palamarchuk, J., Pope, F. D., Rojo, J., Šaulienė, I., Schäfer, S., Schmidt-Weber, C. B., Schnitzler, M., Šikoparija, B., Skjøth, C. A., Sofiev, M., Stemmler, T., Triviño, M., Zeder, Y., and Buters, J.: Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps, Sci. Total Environ., 866, 161220, https://doi.org/10.1016/j.scitotenv.2022.161220, 2023.
Miki, K., Fujita, T., and Sahashi, N.: Development and application of a method to classify airborne pollen taxa concentration using light scattering data, Sci. Rep., 11, 22371, https://doi.org/10.1038/s41598-021-01919-7, 2021.
Morris, C. E., Sands, D. C., Bardin, M., Jaenicke, R., Vogel, B., Leyronas, C., Ariya, P. A., and Psenner, R.: Microbiology and atmospheric processes: research challenges concerning the impact of airborne micro-organisms on the atmosphere and climate, Biogeosciences, 8, 17–25, https://doi.org/10.5194/bg-8-17-2011, 2011.
Oteros, J., Weber, A., Kutzora, S., Rojo, J., Heinze, S., Herr, C., Gebauer, R., Schmidt-Weber, C. B., and Buters, J. T. M.: An operational robotic pollen monitoring network based on automatic image recognition, Environ. Res., 191, 110031, https://doi.org/10.1016/j.envres.2020.110031, 2020.
Pan, Y.-L.: Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence, J. Quant. Spectrosc. Radiat. Transf., 150, 12–35, https://doi.org/10.1016/j.jqsrt.2014.06.007, 2015.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Pöhlker, C., Huffman, J. A., and Pöschl, U.: Autofluorescence of atmospheric bioaerosols – fluorescent biomolecules and potential interferences, Atmos. Meas. Tech., 5, 37–71, https://doi.org/10.5194/amt-5-37-2012, 2012.
Rodó, X., Pozdniakova, S., Borràs, S., Matsuki, A., Tanimoto, H., Armengol, M.-P., Pey, I., Vila, J., Muñoz, L., Santamaria, S., Cañas, L., Morguí, J.-A., Fontal, A., and Curcoll, R.: Microbial richness and air chemistry in aerosols above the PBL confirm 2,000-km long-distance transport of potential human pathogens, Proc. Natl. Acad. Sci. USA, 121, e2404191121, https://doi.org/10.1073/pnas.2404191121, 2024.
Šaulienė, I., Šukienė, L., Daunys, G., Valiulis, G., Vaitkevičius, L., Matavulj, P., Brdar, S., Panic, M., Sikoparija, B., Clot, B., Crouzy, B., and Sofiev, M.: Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps, Atmos. Meas. Tech., 12, 3435–3452, https://doi.org/10.5194/amt-12-3435-2019, 2019.
Savage, N. J., Krentz, C. E., Könemann, T., Han, T. T., Mainelis, G., Pöhlker, C., and Huffman, J. A.: Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles, Atmos. Meas. Tech., 10, 4279–4302, https://doi.org/10.5194/amt-10-4279-2017, 2017.
Tastassa, A. C., Sharaby, Y., and Lang-Yona, N.: Aeromicrobiology: A global review of the cycling and relationships of bioaerosols with the atmosphere, Sci. Total Environ., 912, 168478, https://doi.org/10.1016/j.scitotenv.2023.168478, 2024.
Tellier, R., Li, Y., Cowling, B. J., and Tang, J. W.: Recognition of aerosol transmission of infectious agents: a commentary, BMC Infect. Dis., 19, 101, https://doi.org/10.1186/s12879-019-3707-y, 2019.
Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R.: Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy, Appl. Environ. Microbiol., 73, 5261–5267, https://doi.org/10.1128/AEM.00062-07, 2007.
Short summary
Monitoring airborne microbes is crucial for health and ecosystems, but often slow and expensive. We adapted an existing instrument, Rapid-E, using Laser-Induced Fluorescence and machine learning, for rapid, field-deployable bacterial identification. Our system successfully detected bacteria and showed promise in distinguishing various species. This faster approach improves environmental monitoring and helps safeguard public health by quickly spotting potential microbial threats in the air.
Monitoring airborne microbes is crucial for health and ecosystems, but often slow and expensive....