88 citations as recorded by crossref.

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20. Comparison of computer vision models in application to pollen classification using light scattering G. Daunys et al. 10.1007/s10453-022-09769-0
21. 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
22. Verifying the viable particle counts of biofluorescent particle counters by using inkjet aerosol generators K. Iida et al. 10.1080/02786826.2024.2316836
23. Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring G. Tancev & F. Toro 10.1016/j.measen.2021.100365
24. 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
25. Detection and characterization of bioaerosol emissions from wastewater treatment plants: Challenges and opportunities J. Tian et al. 10.3389/fmicb.2022.958514
26. Spectral dependence of birch and pine pollen optical properties using a synergy of lidar instruments M. Filioglou et al. 10.5194/acp-23-9009-2023
27. Development and application of a method to classify airborne pollen taxa concentration using light scattering data K. Miki et al. 10.1038/s41598-021-01919-7
28. 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. 10.1080/02786826.2024.2338544
29. The color of aerosol particles R. Giri & M. Berg 10.1038/s41598-023-28823-6
30. Real-time pollen identification using holographic imaging and fluorescence measurements S. Erb et al. 10.5194/amt-17-441-2024
31. An exploration of temporal coherence of light through holography A. Escarguel & C. Martin 10.1088/1361-6404/ad7a41
32. Imaging atmospheric aerosol particles from a UAV with digital holography O. Kemppinen et al. 10.1038/s41598-020-72411-x
33. Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations P. Matavulj et al. 10.1016/j.scitotenv.2022.158234
34. 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. 10.1016/j.jaerosci.2021.105818
35. Biodiversity, abundance, seasonal and diurnal airborne pollen distribution patterns at two different heights in Augsburg, Germany F. Kolek et al. 10.1016/j.atmosenv.2021.118774
36. Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al. 10.1016/j.scitotenv.2023.165853
37. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
38. Methoden und Standards des Pollenmonitorings - Aussagekraft von Pollenmessungen in unterschiedlichen Höhen M. Bastl et al. 10.1007/s15007-023-5792-x
39. Optical measurement instrument for detection of powdery mildew and grey mould in protected crops G. Bouquet et al. 10.1051/jeos/2024024
40. DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring M. Polling et al. 10.1016/j.scitotenv.2021.150468
41. Optimisation of bioaerosol sampling using an ultralight aircraft: A novel approach in determining the 3-D atmospheric biodiversity M. Plaza et al. 10.1016/j.heliyon.2024.e38924
42. Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation P. Matavulj et al. 10.1080/08839514.2022.2157593
43. 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
44. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al. 10.1007/s10453-022-09757-4
45. 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
46. Analysis of quality control outcomes of grass pollen identification and enumeration: experience matters A. Milic et al. 10.1007/s10453-021-09723-6
47. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al. 10.3390/s23062964
48. In-flight sensing of pollen grains via laser scattering and deep learning J. Grant-Jacob et al. 10.1088/2631-8695/abfdf8
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50. In situ biological particle analyzer based on digital inline holography D. Sanborn et al. 10.1002/bit.28338
51. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment M. Triviño et al. 10.1007/s10453-023-09790-x
52. 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
53. 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
54. Intercomparison of holographic imaging and single-particle forward light scattering in situ measurements of liquid clouds in changing atmospheric conditions P. Tiitta et al. 10.5194/amt-15-2993-2022
55. Holographic Air‐Quality Monitor (HAM) N. Bravo-Frank et al. 10.1155/2024/2210837
56. 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
57. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
58. Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija 10.1016/j.aeolia.2020.100636
59. Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
60. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. 10.1007/s10453-021-09724-5
61. Automatic detection of airborne pollen: an overview J. Buters et al. 10.1007/s10453-022-09750-x
62. The role of automatic pollen and fungal spore monitoring across major end-user domains F. Tummon et al. 10.1007/s10453-024-09820-2
63. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al. 10.3390/s22134984
64. 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. 10.1016/j.scitotenv.2024.177439
65. 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
66. Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign M. Sofiev et al. 10.1016/j.envres.2022.113798
67. Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al. 10.15835/nbha49312444
68. A portable flow tube homogenizer for aerosol mixing in the sub-micrometre and lower micrometre particle size range S. Horender et al. 10.1088/1361-6501/ac81a1
69. Particle generation and dispersion from high-speed dental drilling M. Kumar et al. 10.1007/s00784-023-05163-3
70. Sensors and Analytical Technologies for Air Quality: Particulate Matters and Bioaerosols X. Su et al. 10.1002/asia.202001051
71. Isolating the species element in grass pollen allergy: A review C. Frisk et al. 10.1016/j.scitotenv.2023.163661
72. Methods and standards of pollen monitoring—significance of pollen measurements at different altitudes M. Bastl et al. 10.1007/s40629-023-00268-3
73. Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al. 10.1007/s10453-020-09669-1
74. Recent developments in monitoring and modelling airborne pollen, a review J. Maya-Manzano et al. 10.1080/00173134.2020.1769176
75. Neural networks for increased accuracy of allergenic pollen monitoring M. Polling et al. 10.1038/s41598-021-90433-x
76. Flow cytometric analysis of pollen and spores: An overview of applications and methodology P. Kron et al. 10.1002/cyto.a.24330
77. Backscatter multiple wavelength digital holography for color micro-particle imaging R. Giri & M. Berg 10.1364/AO.441509
78. Automatic real-time monitoring of fungal spores: the case of Alternaria spp. S. Erb et al. 10.1007/s10453-023-09780-z
79. 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
80. RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
81. Traceable methods for calibrating condensation particle counters at concentrations down to 1 cm−3 H. Sakurai et al. 10.1088/1681-7575/acf5f1
82. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines F. Tummon et al. 10.1007/s10453-022-09755-6
83. Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al. 10.5194/acp-22-3931-2022
84. Aeroallergen Monitoring by the National Allergy Bureau: A Review of the Past and a Look Into the Future E. Levetin et al. 10.1016/j.jaip.2022.11.026
85. Manual and automatic quantification of airborne fungal spores during wheat harvest period I. Simović et al. 10.1007/s10453-023-09788-5
86. Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
87. 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
88. Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724