79 citations as recorded by crossref.

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24. Detection and characterization of bioaerosol emissions from wastewater treatment plants: Challenges and opportunities J. Tian et al. 10.3389/fmicb.2022.958514
25. 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
26. 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
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29. Imaging atmospheric aerosol particles from a UAV with digital holography O. Kemppinen et al. 10.1038/s41598-020-72411-x
30. 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
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32. 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
33. Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability S. Mills et al. 10.1016/j.scitotenv.2023.165853
34. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
35. Methoden und Standards des Pollenmonitorings - Aussagekraft von Pollenmessungen in unterschiedlichen Höhen M. Bastl et al. 10.1007/s15007-023-5792-x
36. DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring M. Polling et al. 10.1016/j.scitotenv.2021.150468
37. Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation P. Matavulj et al. 10.1080/08839514.2022.2157593
38. 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
39. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al. 10.1007/s10453-022-09757-4
40. Analysis of quality control outcomes of grass pollen identification and enumeration: experience matters A. Milic et al. 10.1007/s10453-021-09723-6
41. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al. 10.3390/s23062964
42. 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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45. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment M. Triviño et al. 10.1007/s10453-023-09790-x
46. 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
47. 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
48. 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
49. 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
50. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
51. Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija 10.1016/j.aeolia.2020.100636
52. Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
53. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. 10.1007/s10453-021-09724-5
54. Automatic detection of airborne pollen: an overview J. Buters et al. 10.1007/s10453-022-09750-x
55. The role of automatic pollen and fungal spore monitoring across major end-user domains F. Tummon et al. 10.1007/s10453-024-09820-2
56. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al. 10.3390/s22134984
57. 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
58. Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al. 10.15835/nbha49312444
59. 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
60. Particle generation and dispersion from high-speed dental drilling M. Kumar et al. 10.1007/s00784-023-05163-3
61. Sensors and Analytical Technologies for Air Quality: Particulate Matters and Bioaerosols X. Su et al. 10.1002/asia.202001051
62. Isolating the species element in grass pollen allergy: A review C. Frisk et al. 10.1016/j.scitotenv.2023.163661
63. Methods and standards of pollen monitoring—significance of pollen measurements at different altitudes M. Bastl et al. 10.1007/s40629-023-00268-3
64. Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al. 10.1007/s10453-020-09669-1
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66. Neural networks for increased accuracy of allergenic pollen monitoring M. Polling et al. 10.1038/s41598-021-90433-x
67. Flow cytometric analysis of pollen and spores: An overview of applications and methodology P. Kron et al. 10.1002/cyto.a.24330
68. Backscatter multiple wavelength digital holography for color micro-particle imaging R. Giri & M. Berg 10.1364/AO.441509
69. Automatic real-time monitoring of fungal spores: the case of Alternaria spp. S. Erb et al. 10.1007/s10453-023-09780-z
70. 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
71. RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
72. Traceable methods for calibrating condensation particle counters at concentrations down to 1 cm−3 H. Sakurai et al. 10.1088/1681-7575/acf5f1
73. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines F. Tummon et al. 10.1007/s10453-022-09755-6
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78. 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
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