110 citations as recorded by crossref.

1. Measurement report: Atmospheric fluorescent bioaerosol concentrations measured during 18 months in a coniferous forest in the south of Sweden M. Petersson Sjögren et al.
2. Association between short-term pollen exposure and blood pressure in adults: A repeated-measures study A. Bürgler et al.
3. Global Climate Change and Pollen Aeroallergens J. Davies et al.
4. Operational pollen classification using digital holography and fluorescence B. Crouzy et al.
5. Impact of Fungal Spores on Asthma Prevalence and Hospitalization K. Hughes et al.
6. Efficient and scalable training set generation for automated pollen monitoring with Hirst-type samplers A. Biricz et al.
7. Lessons from automatic airborne pollen monitoring by holography at three Australian sites A. Milic et al.
8. Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis A. Chaves et al.
9. Air Sampling and Analysis of Aeroallergens: Current and Future Approaches E. Levetin et al.
10. Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector G. Daunys et al.
11. Why should we care about high temporal resolution monitoring of bioaerosols in ambient air? M. Smith et al.
12. Quantification of pollen viability in Lantana camara by digital holographic microscopy V. Kumar et al.
13. Applying wind patterns and land use to estimate the concentrations of airborne pollen of herbaceous taxa in a statistical framework A. Picornell et al.
14. Emerging scientific and industrial applications of digital holography: an overview R. Kumar & G. Dwivedi
15. Real-time field measurements of bioaerosols in the agricultural environment: Concentrations, components and environmental impacts Z. Chen et al.
16. Assessment of real-time bioaerosol particle counters using reference chamber experiments G. Lieberherr et al.
17. Low-Cost Pollen and Allergy Symptoms Monitoring with Beenose Sampler and Livepollen App: The Case Study of the Metz City, France, During Spring 2023 J. Renard et al.
18. CALIPSO Overpasses During Three Atmospheric Pollen Events Detected by Hirst-Type Volumetric Samplers in Two Urban Cities in Greece A. Karageorgopoulou et al.
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.
20. Fast Atmospheric Aerosol Size and Shape Imaging Instrument: Design, Calibration, and Intelligent Interaction L. Dong et al.
21. Detection and characterization of bioaerosol emissions from wastewater treatment plants: Challenges and opportunities J. Tian et al.
22. Spectral dependence of birch and pine pollen optical properties using a synergy of lidar instruments M. Filioglou et al.
23. Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment N. Suzuki et al.
24. Development and application of a method to classify airborne pollen taxa concentration using light scattering data K. Miki et al.
25. What do spore particles look like - use of real-time measurements and holography imaging to view spore particles from four bioaerosol generators C. Pogner et al.
26. Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations P. Matavulj et al.
27. Calibration of optical particle size spectrometers against a primary standard: Counting efficiency profile of the TSI Model 3330 OPS and Grimm 11-D monitor in the particle size range from 300 nm to 10 μm K. Vasilatou et al.
28. Biodiversity, abundance, seasonal and diurnal airborne pollen distribution patterns at two different heights in Augsburg, Germany F. Kolek et al.
29. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al.
30. The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level I. Ullah et al.
31. Methoden und Standards des Pollenmonitorings - Aussagekraft von Pollenmessungen in unterschiedlichen Höhen M. Bastl et al.
32. Optical measurement instrument for detection of powdery mildew and grey mould in protected crops G. Bouquet et al.
33. Optimisation of bioaerosol sampling using an ultralight aircraft: A novel approach in determining the 3-D atmospheric biodiversity M. Plaza et al.
34. Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers S. Horender et al.
35. 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.
36. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al.
37. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al.
38. Advances in automatic airborne fungal spore monitoring: detection-efficiency test of the BAA500 M. Žilka et al.
39. Tutorial: Aerosol characterization with digital in-line holography M. Berg
40. Holographic Air‐Quality Monitor (HAM) N. Bravo-Frank et al.
41. 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
42. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al.
43. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al.
44. The role of automatic pollen and fungal spore monitoring across major end-user domains F. Tummon et al.
45. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al.
46. Toward Accurate Real-Time Bioaerosol Monitoring in the Particle Size Range 1 μm–70 μm K. Vasilatou et al.
47. Visual classification of allergenic pollen in iteratively reconstructed lens-less DIHM images B. Cugmas et al.
48. A portable flow tube homogenizer for aerosol mixing in the sub-micrometre and lower micrometre particle size range S. Horender et al.
49. Particle generation and dispersion from high-speed dental drilling M. Kumar et al.
50. Methods and standards of pollen monitoring—significance of pollen measurements at different altitudes M. Bastl et al.
51. Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al.
52. Pollen and spores as proxies for palaeoenvironment reconstruction: A review of sediment-based research M. ZARGAR et al.
53. Neural networks for increased accuracy of allergenic pollen monitoring M. Polling et al.
54. Backscatter multiple wavelength digital holography for color micro-particle imaging R. Giri & M. Berg
55. Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy S. Brdar et al.
56. Advancing automated identification of airborne fungal spores: guidelines for cultivation and reference dataset creation N. Bruffaerts et al.
57. Traceable methods for calibrating condensation particle counters at concentrations down to 1 cm−3 H. Sakurai et al.
58. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines F. Tummon et al.
59. Aeroallergen Monitoring by the National Allergy Bureau: A Review of the Past and a Look Into the Future E. Levetin et al.
60. Review article: how emerging technologies could reshape pollen monitoring for epidemic thunderstorm asthma K. Hanoun et al.
61. Laboratory evaluation of the (VIS, IR) scattering matrix of complex-shaped ragweed pollen particles D. Cholleton et al.
62. A first evaluation of multiple automatic pollen monitors run in parallel F. Tummon et al.
63. The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe B. Clot et al.
64. Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al.
65. High-order spatial phase shift method realizes modulation analysis through a single-frame image Y. Long et al.
66. Pollen image manipulation and projection using latent space B. Mills et al.
67. In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation B. Thiede et al.
68. 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.
69. Comparison of computer vision models in application to pollen classification using light scattering G. Daunys et al.
70. Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics N. Beres et al.
71. Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps J. Maya-Manzano et al.
72. Advancing in the pollen frontier: a comprehensive evaluation and meta-analysis of automatic pollen monitoring systems Q. Farooq et al.
73. Verifying the viable particle counts of biofluorescent particle counters by using inkjet aerosol generators K. Iida et al.
74. Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro
75. Laboratory evaluation of the scattering matrix of ragweed, ash, birch and pine pollen towards pollen classification D. Cholleton et al.
76. The color of aerosol particles R. Giri & M. Berg
77. Real-time pollen identification using holographic imaging and fluorescence measurements S. Erb et al.
78. An exploration of temporal coherence of light through holography A. Escarguel & C. Martin
79. Imaging atmospheric aerosol particles from a UAV with digital holography O. Kemppinen et al.
80. Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al.
81. Pollen holographic images and light-induced fluorescence measurements at the species level S. Erb et al.
82. DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring M. Polling et al.
83. Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation P. Matavulj et al.
84. Aerosol physical characterization: A review on the current state of aerosol documentary standards and calibration strategies K. Vasilatou et al.
85. Analysis of quality control outcomes of grass pollen identification and enumeration: experience matters A. Milic et al.
86. In-flight sensing of pollen grains via laser scattering and deep learning J. Grant-Jacob et al.
87. In situ biological particle analyzer based on digital inline holography D. Sanborn et al.
88. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment M. Triviño et al.
89. 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.
90. Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach J. Schaefer et al.
91. Intercomparison of holographic imaging and single-particle forward light scattering in situ measurements of liquid clouds in changing atmospheric conditions P. Tiitta et al.
92. Profiling pollen and biomass burning particles over Payerne, Switzerland using laser-induced fluorescence lidar and in situ techniques during the 2023 PERICLES campaign M. Gidarakou et al.
93. Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija
94. Monitoring techniques for pollen allergy risk assessment C. Suanno et al.
95. Automatic detection of airborne pollen: an overview J. Buters et al.
96. Airborne DNA: State of the art – Established methods and missing pieces in the molecular genetic detection of airborne microorganisms, viruses and plant particles C. Pogner et al.
97. 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.
98. Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign M. Sofiev et al.
99. Domain adaptation for improving automatic airborne pollen classification with expert-verified measurements P. Matavulj et al.
100. Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al.
101. Sensors and Analytical Technologies for Air Quality: Particulate Matters and Bioaerosols X. Su et al.
102. Isolating the species element in grass pollen allergy: A review C. Frisk et al.
103. Recent developments in monitoring and modelling airborne pollen, a review J. Maya-Manzano et al.
104. Self-supervised and few-shot learning for robust bioaerosol monitoring A. Willi et al.
105. Flow cytometric analysis of pollen and spores: An overview of applications and methodology P. Kron et al.
106. Automatic real-time monitoring of fungal spores: the case of Alternaria spp. S. Erb et al.
107. Counting pollen instead of sheep? Investigating the relationship between pollen exposure and sleep A. Alonso Hellweg et al.
108. RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al.
109. Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al.
110. Manual and automatic quantification of airborne fungal spores during wheat harvest period I. Simović et al.