116 citations as recorded by crossref.

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5. Impact of Fungal Spores on Asthma Prevalence and Hospitalization K. Hughes et al. https://doi.org/10.3390/ijms23084313
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7. Lessons from automatic airborne pollen monitoring by holography at three Australian sites A. Milic et al. https://doi.org/10.1007/s10453-026-09907-y
8. Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis A. Chaves et al. https://doi.org/10.1007/s12145-023-01189-z
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12. Quantification of pollen viability in Lantana camara by digital holographic microscopy V. Kumar et al. https://doi.org/10.1017/qpb.2023.5
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18. CALIPSO Overpasses During Three Atmospheric Pollen Events Detected by Hirst-Type Volumetric Samplers in Two Urban Cities in Greece A. Karageorgopoulou et al. https://doi.org/10.3390/atmos16030317
19. On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen J. Gómez Martín et al. https://doi.org/10.1016/j.jqsrt.2021.107761
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22. Spectral dependence of birch and pine pollen optical properties using a synergy of lidar instruments M. Filioglou et al. https://doi.org/10.5194/acp-23-9009-2023
23. Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment N. Suzuki et al. https://doi.org/10.3389/falgy.2026.1805985
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28. Biodiversity, abundance, seasonal and diurnal airborne pollen distribution patterns at two different heights in Augsburg, Germany F. Kolek et al. https://doi.org/10.1016/j.atmosenv.2021.118774
29. A High-Resolution Multifocal RGB Pollen Grain Image Dataset for Deep Learning Computer Vision Tasks from Biobío Region, Chile I. Sanhueza et al. https://doi.org/10.1038/s41597-026-07495-7
30. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. https://doi.org/10.1002/clt2.12015
31. The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level I. Ullah et al. https://doi.org/10.1007/s10453-026-09902-3
32. Methoden und Standards des Pollenmonitorings - Aussagekraft von Pollenmessungen in unterschiedlichen Höhen M. Bastl et al. https://doi.org/10.1007/s15007-023-5792-x
33. Optical measurement instrument for detection of powdery mildew and grey mould in protected crops G. Bouquet et al. https://doi.org/10.1051/jeos/2024024
34. Optimisation of bioaerosol sampling using an ultralight aircraft: A novel approach in determining the 3-D atmospheric biodiversity M. Plaza et al. https://doi.org/10.1016/j.heliyon.2024.e38924
35. Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers S. Horender et al. https://doi.org/10.1007/s10453-025-09889-3
36. 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. https://doi.org/10.1016/j.scitotenv.2022.154370
37. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al. https://doi.org/10.1007/s10453-022-09757-4
38. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al. https://doi.org/10.3390/s23062964
39. Advances in automatic airborne fungal spore monitoring: detection-efficiency test of the BAA500 M. Žilka et al. https://doi.org/10.1016/j.envpol.2026.128148
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41. Holographic Air‐Quality Monitor (HAM) N. Bravo-Frank et al. https://doi.org/10.1155/2024/2210837
42. 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 https://doi.org/10.5194/amt-14-685-2021
43. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. https://doi.org/10.1080/02786826.2022.2142510
44. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. https://doi.org/10.1007/s10453-021-09724-5
45. The role of automatic pollen and fungal spore monitoring across major end-user domains F. Tummon et al. https://doi.org/10.1007/s10453-024-09820-2
46. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al. https://doi.org/10.3390/s22134984
47. Toward Accurate Real-Time Bioaerosol Monitoring in the Particle Size Range 1 μm–70 μm K. Vasilatou et al. https://doi.org/10.1021/acsestair.5c00282
48. Visual classification of allergenic pollen in iteratively reconstructed lens-less DIHM images B. Cugmas et al. https://doi.org/10.1038/s41598-026-36618-8
49. A portable flow tube homogenizer for aerosol mixing in the sub-micrometre and lower micrometre particle size range S. Horender et al. https://doi.org/10.1088/1361-6501/ac81a1
50. Particle generation and dispersion from high-speed dental drilling M. Kumar et al. https://doi.org/10.1007/s00784-023-05163-3
51. Methods and standards of pollen monitoring—significance of pollen measurements at different altitudes M. Bastl et al. https://doi.org/10.1007/s40629-023-00268-3
52. Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al. https://doi.org/10.1007/s10453-020-09669-1
53. A methodological framework for evaluating real-time bioaerosol classification algorithms M. Meurville et al. https://doi.org/10.5194/amt-19-3427-2026
54. Regularized LSTM models for cleaning multivariate time series from low-cost sensors for pollen detection in the air M. Oudrane et al. https://doi.org/10.1007/s00477-026-03270-7
55. Pollen and spores as proxies for palaeoenvironment reconstruction: A review of sediment-based research M. ZARGAR et al. https://doi.org/10.1177/05529360251318072
56. Neural networks for increased accuracy of allergenic pollen monitoring M. Polling et al. https://doi.org/10.1038/s41598-021-90433-x
57. Backscatter multiple wavelength digital holography for color micro-particle imaging R. Giri & M. Berg https://doi.org/10.1364/AO.441509
58. Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy S. Brdar et al. https://doi.org/10.1038/s41598-023-30064-6
59. Advancing automated identification of airborne fungal spores: guidelines for cultivation and reference dataset creation N. Bruffaerts et al. https://doi.org/10.1007/s10453-025-09864-y
60. Traceable methods for calibrating condensation particle counters at concentrations down to 1 cm−3 H. Sakurai et al. https://doi.org/10.1088/1681-7575/acf5f1
61. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines F. Tummon et al. https://doi.org/10.1007/s10453-022-09755-6
62. Aeroallergen Monitoring by the National Allergy Bureau: A Review of the Past and a Look Into the Future E. Levetin et al. https://doi.org/10.1016/j.jaip.2022.11.026
63. Review article: how emerging technologies could reshape pollen monitoring for epidemic thunderstorm asthma K. Hanoun et al. https://doi.org/10.1007/s10453-025-09874-w
64. Laboratory evaluation of the (VIS, IR) scattering matrix of complex-shaped ragweed pollen particles D. Cholleton et al. https://doi.org/10.1016/j.jqsrt.2020.107223
65. A first evaluation of multiple automatic pollen monitors run in parallel F. Tummon et al. https://doi.org/10.1007/s10453-021-09729-0
66. The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe B. Clot et al. https://doi.org/10.1007/s10453-020-09666-4
67. Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. https://doi.org/10.1016/j.scitotenv.2023.161969
68. High-order spatial phase shift method realizes modulation analysis through a single-frame image Y. Long et al. https://doi.org/10.1364/AO.488041
69. Pollen image manipulation and projection using latent space B. Mills et al. https://doi.org/10.3389/fpls.2025.1539128
70. In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation B. Thiede et al. https://doi.org/10.5194/amt-18-6291-2025
71. Extending traceability in airborne particle size distribution measurements beyond 10 µm: Counting efficiency and unit-to-unit variability of four aerodynamic particle size spectrometers K. Vasilatou et al. https://doi.org/10.1080/02786826.2022.2139659
72. Comparison of computer vision models in application to pollen classification using light scattering G. Daunys et al. https://doi.org/10.1007/s10453-022-09769-0
73. Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics N. Beres et al. https://doi.org/10.5194/amt-17-6945-2024
74. Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps J. Maya-Manzano et al. https://doi.org/10.1016/j.scitotenv.2022.161220
75. Advancing in the pollen frontier: a comprehensive evaluation and meta-analysis of automatic pollen monitoring systems Q. Farooq et al. https://doi.org/10.1007/s10453-025-09865-x
76. Verifying the viable particle counts of biofluorescent particle counters by using inkjet aerosol generators K. Iida et al. https://doi.org/10.1080/02786826.2024.2316836
77. Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro https://doi.org/10.1016/j.measen.2021.100365
78. Laboratory evaluation of the scattering matrix of ragweed, ash, birch and pine pollen towards pollen classification D. Cholleton et al. https://doi.org/10.5194/amt-15-1021-2022
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81. An exploration of temporal coherence of light through holography A. Escarguel & C. Martin https://doi.org/10.1088/1361-6404/ad7a41
82. A Two-Stage, Semi-Supervised Deep Learning Framework for the Detection and Classification of Ambient Pollen using Evanescent Wave Scattering S. Dhawan et al. https://doi.org/10.1021/acs.est.6c06126
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