Articles | Volume 12, issue 6
https://doi.org/10.5194/amt-12-3435-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-3435-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps
Ingrida Šaulienė
CORRESPONDING AUTHOR
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Laura Šukienė
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Gintautas Daunys
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Gediminas Valiulis
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Lukas Vaitkevičius
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Predrag Matavulj
BioSense Institute – Research Institute for Information Technologies
in Biosystems, University of Novi Sad, Novi Sad, 21000, Serbia
Sanja Brdar
BioSense Institute – Research Institute for Information Technologies
in Biosystems, University of Novi Sad, Novi Sad, 21000, Serbia
Marko Panic
BioSense Institute – Research Institute for Information Technologies
in Biosystems, University of Novi Sad, Novi Sad, 21000, Serbia
Branko Sikoparija
BioSense Institute – Research Institute for Information Technologies
in Biosystems, University of Novi Sad, Novi Sad, 21000, Serbia
Bernard Clot
Federal Office of Meteorology and Climatology MeteoSwiss, Payerne,
1530, Switzerland
Benoît Crouzy
Federal Office of Meteorology and Climatology MeteoSwiss, Payerne,
1530, Switzerland
Mikhail Sofiev
Institute of Regional Development, Šiauliai University, Šiauliai, 76352 Lithuania
Finnish Meteorological Institute, Helsinki, 00560, Finland
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- The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
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34 citations as recorded by crossref.
- Real-time pollen monitoring using digital holography E. Sauvageat et al. 10.5194/amt-13-1539-2020
- Desert dust has a notable impact on aerobiological measurements in Europe B. Šikoparija 10.1016/j.aeolia.2020.100636
- Automatic 3D Pollen Recognition Based on Convolutional Neural Network Z. Wang et al. 10.1155/2021/5577307
- The need for Pan‐European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper F. Tummon et al. 10.1002/clt2.12015
- 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
- RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
- Multi-Input Convolutional Neural Networks for Automatic Pollen Classification M. Boldeanu et al. 10.3390/app112411707
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- 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
- A first evaluation of multiple automatic pollen monitors run in parallel F. Tummon et al. 10.1007/s10453-021-09729-0
- Automatisches Pollenmonitoring in Deutschland J. Buters et al. 10.1007/s15007-020-2527-0
- The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe B. Clot et al. 10.1007/s10453-020-09666-4
- Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
- 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
- Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector G. Daunys et al. 10.1371/journal.pone.0247284
- Why should we care about high temporal resolution monitoring of bioaerosols in ambient air? M. Smith et al. 10.1016/j.scitotenv.2022.154231
- Automatic pollen monitoring: first insights from hourly data C. Chappuis et al. 10.1007/s10453-019-09619-6
- 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
- Automatic particle detectors lead to a new generation in plant diversity investigation I. ŠAULIENĖ et al. 10.15835/nbha49312444
- 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
- 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
- Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen . Mondol et al. 10.3390/s19204428
- Assessment of real-time bioaerosol particle counters using reference chamber experiments G. Lieberherr et al. 10.5194/amt-14-7693-2021
- 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
- Detection and Microscopy of Alnus glutinosa Pollen Fluorescence Peculiarities . Šaulienė et al. 10.3390/f10110959
- 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
- Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning M. DaOrazio et al. 10.1109/TBME.2021.3109384
- Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
- 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
- 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
- Global Climate Change and Pollen Aeroallergens J. Davies et al. 10.1016/j.iac.2020.09.002
- In-flight sensing of pollen grains via laser scattering and deep learning J. Grant-Jacob et al. 10.1088/2631-8695/abfdf8
- On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers S. Adamov et al. 10.1007/s10453-021-09724-5
- 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
4 citations as recorded by crossref.
- Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
- On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
- 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
- Extension of WRF-Chem for birch pollen modelling—a case study for Poland M. Werner et al. 10.1007/s00484-020-02045-1
Latest update: 27 Mar 2023
Short summary
The goal is to evaluate the capabilities of the new Rapid-E monitor and to construct a first-level pollen recognition algorithm. The output data were treated with ANN aiming at classification of the injected pollen. Algorithms based on scattering and fluorescence data alone fall short of acceptable quality. The combinations of these exceeded 80 % accuracy for 5 out of 11 pollen species. Constructing multistep algorithms with sequential discrimination of pollen can be a possible way forward.
The goal is to evaluate the capabilities of the new Rapid-E monitor and to construct a...