77 citations as recorded by crossref.

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18. Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al. 10.15835/nbha49312444
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21. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
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23. A global survey addressing sustainability of pollen monitoring D. Dwarakanath et al. 10.1016/j.waojou.2024.100997
24. Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers I. Crawford et al. 10.3390/atmos11101039
25. 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
26. Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning M. DaOrazio et al. 10.1109/TBME.2021.3109384
27. 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
28. False positives: handling them operationally for automatic pollen monitoring B. Crouzy et al. 10.1007/s10453-022-09757-4
29. The role of automatic pollen and fungal spore monitoring across major end-user domains F. Tummon et al. 10.1007/s10453-024-09820-2
30. 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
31. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. 10.1007/s10453-021-09724-5
32. Pollen detection through integrated microfluidics and smartphone-driven deep learning systems K. Chen et al. 10.1016/j.rineng.2024.102867
33. Real-time pollen monitoring using digital holography E. Sauvageat et al. 10.5194/amt-13-1539-2020
34. 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
35. Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija 10.1016/j.aeolia.2020.100636
36. Airborne Prokaryotic, Fungal and Eukaryotic Communities of an Urban Environment in the UK H. Song et al. 10.3390/atmos13081212
37. Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al. 10.5194/acp-22-3931-2022
38. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
39. 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
40. Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain J. Renard et al. 10.3390/s22134984
41. Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment M. Triviño et al. 10.1007/s10453-023-09790-x
42. Automatic detection of airborne pollen: an overview J. Buters et al. 10.1007/s10453-022-09750-x
43. Multi-Input Convolutional Neural Networks for Automatic Pollen Classification M. Boldeanu et al. 10.3390/app112411707
44. Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields M. Sofiev et al. 10.1016/j.scitotenv.2023.165800
45. Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
46. 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
47. Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer B. Sikoparija et al. 10.5194/amt-17-5051-2024
48. Automatisches Pollenmonitoring in Deutschland J. Buters et al. 10.1007/s15007-020-2527-0
49. Manual and automatic quantification of airborne fungal spores during wheat harvest period I. Simović et al. 10.1007/s10453-023-09788-5
50. Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
51. 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
52. 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
53. Real-time automatic detection of starch particles in ambient air B. Šikoparija et al. 10.1016/j.agrformet.2022.109034
54. 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
55. 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
56. 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
57. 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
58. 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
59. A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques H. El Azari et al. 10.3390/s23062964
60. Air Sampling and Analysis of Aeroallergens: Current and Future Approaches E. Levetin et al. 10.1007/s11882-023-01073-2
61. Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
62. 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
63. 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
64. 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
65. 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
66. Global Climate Change and Pollen Aeroallergens J. Davies et al. 10.1016/j.iac.2020.09.002
67. Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation P. Matavulj et al. 10.1080/08839514.2022.2157593
68. In-flight sensing of pollen grains via laser scattering and deep learning J. Grant-Jacob et al. 10.1088/2631-8695/abfdf8
69. Comparison of computer vision models in application to pollen classification using light scattering G. Daunys et al. 10.1007/s10453-022-09769-0
70. Isolating the species element in grass pollen allergy: A review C. Frisk et al. 10.1016/j.scitotenv.2023.163661
71. 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
72. Imaging Flow Cytometry as a Quick and Effective Identification Technique of Pollen Grains from Betulaceae, Oleaceae, Urticaceae and Asteraceae I. Gierlicka et al. 10.3390/cells11040598
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74. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
75. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
76. 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
77. Extension of WRF-Chem for birch pollen modelling—a case study for Poland M. Werner et al. 10.1007/s00484-020-02045-1