38 citations as recorded by crossref.

1. Real-time pollen monitoring using digital holography E. Sauvageat et al. 10.5194/amt-13-1539-2020
2. Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija 10.1016/j.aeolia.2020.100636
3. Automatic 3D Pollen Recognition Based on Convolutional Neural Network Z. Wang et al. 10.1155/2021/5577307
4. The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
5. 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
6. RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
7. Multi-Input Convolutional Neural Networks for Automatic Pollen Classification M. Boldeanu et al. 10.3390/app112411707
8. Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
9. 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. 10.1016/j.jqsrt.2021.107761
10. A first evaluation of multiple automatic pollen monitors run in parallel F. Tummon et al. 10.1007/s10453-021-09729-0
11. Automatisches Pollenmonitoring in Deutschland J. Buters et al. 10.1007/s15007-020-2527-0
12. The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe B. Clot et al. 10.1007/s10453-020-09666-4
13. Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
14. 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
15. Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector G. Daunys et al. 10.1371/journal.pone.0247284
16. Why should we care about high temporal resolution monitoring of bioaerosols in ambient air? M. Smith et al. 10.1016/j.scitotenv.2022.154231
17. Automatic pollen monitoring: first insights from hourly data C. Chappuis et al. 10.1007/s10453-019-09619-6
18. A demonstration project of Global Alliance against Chronic Respiratory Diseases: Prediction of interactions between air pollution and allergen exposure—the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach M. Sofiev et al. 10.1097/CM9.0000000000000916
19. Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al. 10.15835/nbha49312444
20. 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
21. 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
22. Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen . Mondol et al. 10.3390/s19204428
23. Assessment of real-time bioaerosol particle counters using reference chamber experiments G. Lieberherr et al. 10.5194/amt-14-7693-2021
24. 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
25. Detection and Microscopy of Alnus glutinosa Pollen Fluorescence Peculiarities . Šaulienė et al. 10.3390/f10110959
26. 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
27. Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning M. DaOrazio et al. 10.1109/TBME.2021.3109384
28. Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
29. 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
30. 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
31. Global Climate Change and Pollen Aeroallergens J. Davies et al. 10.1016/j.iac.2020.09.002
32. In-flight sensing of pollen grains via laser scattering and deep learning J. Grant-Jacob et al. 10.1088/2631-8695/abfdf8
33. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. 10.1007/s10453-021-09724-5
34. 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
35. Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
36. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
37. 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
38. Extension of WRF-Chem for birch pollen modelling—a case study for Poland M. Werner et al. 10.1007/s00484-020-02045-1