AutoPollen special issue
Tummon, F., Clot, B. Editorial: AutoPollen special issue. Aerobiologia 40, 1–2 (2024).
Tummon, F., Adams-Groom, B., Antunes, C.M. et al. The role of automatic pollen and fungal spore monitoring across major end-user domains. Aerobiologia 40, 57–75 (2024).
Adamov, S., Pauling, A. A real-time calibration method for the numerical pollen forecast model COSMO-ART. Aerobiologia 39, 327–344 (2023).
Triviño, M.M., Maya-Manzano, J.M., Tummon, F. et al. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment. Aerobiologia 39, 257–273 (2023).
Erb, S., Berne, A., Burgdorfer, N. et al. Automatic real-time monitoring of fungal spores: the case of Alternaria spp.. Aerobiologia 40, 123–127 (2023).
Daunys, G., Šukienė, L., Vaitkevičius, L. et al. Comparison of computer vision models in application to pollen classification using light scattering. Aerobiologia 40, 109–121 (2022).
Tummon, F., Bruffaerts, N., Celenk, S. et al. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines. Aerobiologia 40, 39–55 (2022).
Buters, J., Clot, B., Galán, C. et al. Automatic detection of airborne pollen: an overview. Aerobiologia 40, 13–37 (2022).
Tummon, F., Adamov, S., Clot, B. et al. A first evaluation of multiple automatic pollen monitors run in parallel. Aerobiologia 40, 93–108 (2021).
Adamov, S., Lemonis, N., Clot, B. et al. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers. Aerobiologia 40, 77–91 (2021).
Clot, B., Gilge, S., Hajkova, L. et al. The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe. Aerobiologia 40, 3–11 (2020).
Other AutoPollen publications
González-Alonso, M., Oteros, J., Widmann, M., Maya-Manzano, J. M., Skjøth, C., Grewling, L., O’Connor, D., Sofiev, M., Tummon, F., Crouzy, B., Clot, B., Buters, J., Kadantsev, E., Palamarchuk, Y., Martinez-Bracero, M., Pope, F. D., Mills, S., Šikoparija, B., Matavulj, P., and Schmidt-Weber, C. B. Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices. Science of the Total Environment, 172913–172913 (2024).
Maya-Manzano, J. M., Tummon, F., Abt, R., Allan, N., et al. Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps. Science of the Total Environment, 866, 161220 (2023).
Sofiev, M., Buters, J., Tummon, F., Fatahi, Y., Sozinova, O., Adams-Groom, B., Bergmann, K. C., Dahl, Å., Gehrig, R., Gilge, S., Seliger, A. K., Kouznetsov, R., Lieberherr, G., O’Connor, D., Oteros, J., Palamarchuk, J., Ribeiro, H., Werchan, B., Werchan, M., & Clot, B. Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields. Science of the Total Environment, 900, 165800 (2023).
Tummon, F., Alados Arboledas, L., Bonini, M., Guinot, B., Hicke, M., Jacob, J., Kendrovski, V., McCairns, W., Petermann, E., Peuch, V.-H., Pfaar, O., Siacrd, M., Sikoparija, B., and Clot, B. The need for Pan-European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper. Clinical Translational Allergy (2021).
Related scientific publications
Erb, S., Graf, E., Zeder, Y., Lionetti, S., Berne, A., Clot, B., Lieberherr, G., Tummon, F., Wullschleger, P., & Crouzy, B. Real-time pollen identification using holographic imaging and fluorescence measurements. Atmospheric Measurement Techniques, 17(2), 441–451 (2024).
Suarez-Suarez, M., Maya-Manzano, J.M., Clot, B. et al. Accuracy of a hand-held resistance-free flowmeters for flow adjustments of Hirst-Type pollen traps. Aerobiologia 39, 143–148 (2023).
Beggs, P. J., Clot, B., Sofiev, M., & Johnston, F. H. Climate change, airborne allergens, and three translational mitigation approaches. EBioMedicine, 104478 (2023).
Crouzy, B., Lieberherr, G., Tummon, F., and Clot, B. False positives: handling them operationally for automatic pollen monitoring. Aerobiologia, 38, 429-432 (2022).
Schaefer, J., Milling, M., Schuller, B.W., Bauer, B., Brunner, J.O., Traidl-Hoffmann, C., and Damialis, A. Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach. Science of The Total Environment, 796, 148932 (2021).
Lieberherr, G., Auderset, K., Calpini, B., Clot, B., Crouzy, B., Gysel-Beer, M., Konzelmann, T., Manzano, J., Mihajlovic, A., Moallemi, A., O’Connor, D., Sikoparija, B., Sauvageat, E., Tummon, F., and Vasilatou, K., Assessment of Real-time Bioaerosol Particle Counters using Reference Chamber Experiments. Atmospheric Measurement Techniques, 14, 7693-7706 (2021).
Tešendić, D., Boberić Krstićev, D., Matavulj, P., Brdar, S., Panić, M., Minić, V., & Šikoparija, B. RealForAll: real-time system for automatic detection of airborne pollen. Enterprise Information Systems, 1–17 (2020).
Sauvageat, E., Zeder, Y., Auderset, K., Calpini, B., Clot, B., Crouzy, B., Kozelmann, T., Lieberherr, G., and Tummon, F. Real-time pollen monitoring using digital holography. Atmospheric Measurement Techniques, 13, 1539–1550 (2020).
Chappuis, C., Tummon, F., Clot, B., Konzelmann, T., Calpini, B., and Crouzy, B. Automatic pollen monitoring: first insights from hourly data. Aerobiologia, 36, 159-170 (2020).
Huffman, J.A., Perring, A.E., Savage, N.J., Clot, B., Crouzy, B., Tummon, F., Shoshanim, O., Damit, B., Schneider, J., Sivaprakasam, V., Zawadowicz, M.A., Crawford, I., Gallagher, M., Topping, D., Doughty, D.C., Hill, S.C., and Pan, Y. Real-time sensing of bioaerosols: Review and current perspectives. Aerosol Science and Technology, 54, 465-495 (2020).
Addison-Smith, B., Wraith, D. & Davies, J.M. Standardising pollen monitoring: quantifying confidence intervals for measurements of airborne pollen concentration. Aerobiologia 36, 605–615 (2020).
Oteros, J., Weber, A., Kutzora, S., Rojo, J., Heinze, S., Herr, C., Gebauer, R., Schmidt-Weber, C.B., Buters, J.T.M. An operational robotic pollen monitoring network based on automatic image recognition, Environmental Research, 191, 110031 (2020).
Sofiev, M. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models. Aerobiologia, 35, 523-531 (2019).
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. Atmospheric Measurement Techniques, 12, 3435-3452 (2019).
Šantl-Temkiv, T., Sikoparija, B., Maki, T., Carotenuto, F., Amato, P., Yao, M., Morris, C.E., Schnell, R., Jaenicke, R., Pöhlker, C., DeMott, P.J., Hill, T.C.J., Huffman, A. Bioaerosol Field Measurements: Challenges and Perspectives in Outdoor Studies. Aerosol Science and Technology, 1-27 (2019).
Oteros, J., Sofiev, M., Smith, M., Damialis, A., Prank, M., Werchan, M., Wachter, R., Weber, A., Kutzora, S., Heinze, S., Herr, C.E.W., Menzel, A., Bergmann, K.C., Traidl-Hoffmann, C., Schmidt-Weber, C.B., and Buters J.T.M. Building an automatic Pollen Monitoring Network (ePIN): Selection of optimal stations by clustering pollen zones. Science of the Total Environment, 688, 1263-1274 (2019).
Fernández-Rodríguez, S., Tormo-Molina, R., Lemonis, N., Clot, B., O’Connor, D.J., and Sodeau, J. R. Comparison of fungal spores concentrations measured with wideband integrated bioaerosol sensor and Hirst methodology. Atmospheric Environment, 175, 1–14 (2018).
Kawashima, S., Thibaudon, M., Matsuda, S., Fujita, T., Lemonis, N., Clot, B., and Oliver, G. Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen. Aerobiologia, 33, 351–362 (2017).
Crouzy, B., Stella, M., Konzelmann, T., Calpini, B., and Clot, B. All-optical automatic pollen identification: Towards an operational system. Atmospheric Environment, 140, 202-212 (2016).
Sofiev, M., Vira, J., Kouznetsov, R., Prak, M., Soares, J., and Genikhovich, E. Construction of the SILAM Eulerian atmospheric dispersion model based on the advection algorithm of Michael Galperin. Geoscientific Model Development, 8, 3497–3522 (2015).
Oteros, J., García-Mozo, H., Alcázar, P., Belmonte, J., Bermejo, D., Boi, M., Cariñanos, P., Díaz de la Guardia, C., Bernández-González, D., González-Minero, F., Gutiérrez-Bustillo, A.M., Moreno-Grau, S., Pérez-Badía, R., Rodríguez–Rajo, F.J., Ruíz-Valenzuela, L., Suárez-Pérez, J., Trigo, M.M., Domínguez-Vilches, E., and Galán, C. A new method for determining the sources of airborne particles. Journal of Environmental Management, 155, 212–218 (2015).
Kawashima, S., Clot, B., Fujita, T., Takahashi, Y., & Nakamura, K. An algorithm and a device for counting airborne pollen automatically using laser optics. Atmospheric Environment, 41(36), 7987–7993 (2007).
WMO 2023 State of Climate Services: Health. WMO-No. 1335. ISBN 978-92-63-11335-1. https://library.wmo.int/records/item/68500-2023-state-of-climate-services-health.