INTELLIGENT REAL-TIME BIOAEROSOL SENSING

Real-Time Airborne Particle Identifier with the Most Efficient Laser Analysis and Embedded Intelligence

Rapid-E+ is an intelligent bioaerosol sensor that analyzes single aerosol particles in real time using patented, proprietary laser technology. The updated version of the popular Rapid-E instrument has improved optical measurements and more efficient sampling. Its newly developed air sampling head provides increased air flow with much less loss, outperforming all existing alternatives.

Rapid-E+ is also the only instrument with integrated intelligence through GPU (graphics processing unit) acceleration. It allows much faster data acquisition and processing, bringing game-changing performance to aerosol tracking and identification in complex environments.

Rapid-E+ continuously measures and characterizes any airborne particle ranging between 0.3 and 100 micrometers, including bacteria, fungal spores, viruses, pollen, and other aerosols. Proven by years of uninterrupted measurements, Plair’s technology, which is based on a unique combination of scattered light pattern analysis and fluorescence spectroscopy, enables researchers to reliably monitor ambient air in real time. Rapid-E+ operates autonomously and remotely, allowing access to data anywhere and anytime.

Applications

Real-time pollen counting

Particulate matter monitoring

Bacterial and fungal spore detection

Virus aerosol research

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2. Forde,E et al.: Intercomparison of Multiple UV-LIF Spectrometers Using the Aerosol Challenge Simulator Aerobiologia, 2021.

3. Mimić, G,. et al.: Analysis of airborne pollen time series originating from Hirst-type volumetric samplers—comparison between mobile sampling head oriented toward wind direction and fixed sampling head with two-layered inlet Aerobiologia (Vol. 37, Issue 2, pp. 321–331), 2021.

4. Tumonn, F, et al.: A first evaluation of multiple automatic pollen monitors run in parallel (Vol. 197, p. 111109). Elsevier BV., 2021.

5. Suanno, C, et al.: Monitoring techniques for pollen allergy risk assessment (Vol. 197, p. 111109). Elsevier BV., 2021.

6. Swanson, B. E. et al.: Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer Aerosol Science and Technology (Vol. 54, Issue 4, pp. 426–445),Informa UK Limited, 2020.

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8. Clot, B., et al.: The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe Aerobiologia (2020): 1263-1274.

9. Maya-Manzano, J. M., et al.: Recent developments in monitoring and modelling airborne pollen, a review Grana (Vol. 60, Issue 1, pp. 1–19), 2020.

10. Branko Šikoparija: Desert dust has a notable impact on aerobiological measurements in Europe Aeolian Research, Volume 47, December 2020.

11. Danijela Tešendić et al.: RealForAll: real-time system for automatic detection of airborne pollen Enterprise Information Systems, 2020.

12. Sofiev, M. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models Aerobiologia (Vol. 35, Issue 3, pp. 523–531), 2019.

13. Perspectives for nationwide pollen monitoring in Germany Perspektiven für ein bundesweites Pollenmonitoring in Deutschland. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (Vol. 62, Issue 5, pp. 652–661). Springer Science and Business Media LLC., 2019.

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15. Fernández, Francisco Moya: Sistemas automáticos para la detección y análisis de bioaerosoles Revista de Salud Ambiental 19 (2019): 130-132.

16. Kawashima, S. et al.: Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen Aerobiologia volume 33, pages 351–362(2017).

17. Ingrida Sauliene et al.: Automatic pollen recognition with the Rapid-E particle counter:the first-level procedure, experience and next steps Atmos. Meas. Tech., 12, 3435–3452, 2019.

18. Oteros, Jose, et al.: Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations Science of the total environment 688 (2019): 1263-1274.

19. Buters, J, et al.: Allergene pollen in Nederland

20. Buters, J, et al.: Pollen and spore monitoring in the world Clinical and Translational Allergy (Vol. 8, Issue 1), 2018.

21. Kawashima, S. et al.: Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen Aerobiologia volume 33, pages 351–362(2017).

22. Sindt, C. et al.: Alternative method for the measure of the biological particles in the air: RAPID-E example EAACI 2018.

23. Chappuis, C. et al.: Automatic pollen monitoring: first insights from hourly data Aerobiologia 36, 159–170 (2020)

24. Crouzy, B. et al.: All-optical automatic pollen identification: Towards an operational system Atmospheric Environment, Volume 140, September 2016, Pages 202–212.

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