81 citations as recorded by crossref.

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27. Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps J. Maya-Manzano et al. 10.1016/j.scitotenv.2022.161220
28. Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning M. DaOrazio et al. 10.1109/TBME.2021.3109384
29. Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
30. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al. 10.1007/s10453-022-09757-4
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32. Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose J. Renard et al. 10.3390/s24123731
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34. CALIPSO Overpasses During Three Atmospheric Pollen Events Detected by Hirst-Type Volumetric Samplers in Two Urban Cities in Greece A. Karageorgopoulou et al. 10.3390/atmos16030317
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41. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
42. High-Resolution Fluorescence Spectra of Airborne Biogenic Secondary Organic Aerosols: Comparisons to Primary Biological Aerosol Particles and Implications for Single-Particle Measurements M. Zhang et al. 10.1021/acs.est.1c02536
43. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al. 10.3390/s22134984
44. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment M. Triviño et al. 10.1007/s10453-023-09790-x
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51. Automatisches Pollenmonitoring in Deutschland J. Buters et al. 10.1007/s15007-020-2527-0
52. Manual and automatic quantification of airborne fungal spores during wheat harvest period I. Simović et al. 10.1007/s10453-023-09788-5
53. Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
54. How to select the optimal monitoring locations for an aerobiological network: A case of study in central northwest of Spain A. Rodríguez-Fernández et al. 10.1016/j.scitotenv.2022.154370
55. Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors M. González-Alonso et al. 10.1016/j.scitotenv.2022.160180
56. Real-time automatic detection of starch particles in ambient air B. Šikoparija et al. 10.1016/j.agrformet.2022.109034
57. Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy S. Brdar et al. 10.1038/s41598-023-30064-6
58. Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system) K. Miki & S. Kawashima 10.5194/amt-14-685-2021
59. Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach J. Schaefer et al. 10.1016/j.scitotenv.2021.148932
60. Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen A. Mondol et al. 10.3390/s19204428
61. Total Bioaerosol Detection by a Succinimidyl-Ester-Functionalized Plasmonic Biosensor To Reveal Different Characteristics at Three Locations in Switzerland G. Qiu et al. 10.1021/acs.est.9b05184
62. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al. 10.3390/s23062964
63. Air Sampling and Analysis of Aeroallergens: Current and Future Approaches E. Levetin et al. 10.1007/s11882-023-01073-2
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67. Aerosol physical characterization: A review on the current state of aerosol documentary standards and calibration strategies K. Vasilatou et al. 10.1016/j.jaerosci.2024.106483
68. Laboratory evaluation of the scattering matrix of ragweed, ash, birch and pine pollen towards pollen classification D. Cholleton et al. 10.5194/amt-15-1021-2022
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78. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
79. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
80. Multi-point analysis of airborne Japanese cedar (Cryptomeria japonica D. Don) pollen by Pollen Robo and the relationship between pollen count and the severity of symptoms Y. Takahashi et al. 10.1007/s10453-019-09603-0
81. Extension of WRF-Chem for birch pollen modelling—a case study for Poland M. Werner et al. 10.1007/s00484-020-02045-1