智能实时生物气溶胶传感

具有最高效的激光分析和嵌入式智能的实时空气颗粒识别器

Rapid-E+ 是一种智能生物气溶胶传感器,利用获得专利的专有激光技术实时分析单个气溶胶颗粒。广受欢迎的 Rapid-E 仪器的更新版本改进了光学测量,可更有效地采样。其新开发的空气采样头提供了更大的空气流量,损失更小,性能优于所有现有的替代产品。

Rapid-E+ 也是唯一通过 GPU(图形处理单元)加速实现集成智能的仪器。它能更快地采集和处理数据,为复杂环境中的气溶胶跟踪和识别带来革命性的性能。

所有的人工智能都可以通过网络或者来日内瓦进行培训。客户可以使用数据分析工具进行实时在线分析。数据分析工具由 Plair 提供,包含在软件包中。

Rapid-E+ 可以连续测量和表征 0.3 到 100 微米范围内的任何空气传播颗粒,包括细菌、真菌孢子、病毒、花粉和其他气溶胶。Plair 的技术基于散射光模式分析和荧光光谱学的独特组合,经过多年连续测量验证,可使研究人员可靠地实时监测环境空气。Rapid-E+ 可以自主和远程操作,并随时随地访问数据。

应用

花粉实时计数

颗粒物监测

细菌和真菌孢子检测

病毒气溶胶研究

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2. M. Smith, P. Matavulj, G. Mimić, et al.: Why should we care about high temporal resolution monitoring of bioaerosols in ambient air?, Science of the Total Environment (2021).

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

4. 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.

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

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

7. 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.

8. Kabir, E. et al.: Recent advances in monitoring, sampling, and sensing techniques for bioaerosols in the atmosphere ACS sensors, 5(5), 1254-1267, 2020.

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

10. 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.

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

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

13. 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.

14. 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.

15. Huffman, J, et al.: Real-time sensing of bioaerosols: Review and current perspectives Aerosol Science and Technology (Vol. 54, Issue 5, pp. 465–495), 2019.

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

17. 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).

18. 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.

19. 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.

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

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

22. 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).

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

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

25. 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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