Real-time pollen monitoring
SwisensPoleno Mars
Future standard in pollen monitoring
Stable long-term measurement
Reliably measure and identify local pollen concentrations in real time. High availability thanks to robust design and high-quality technology.
Unsurpassed quality
Digital holography revolutionizes automated pollen monitoring. Proven key technology continues on its path.
Core element of the network solution
Designed for network use with low maintenance operation. Meets the requirements for autonomous monitoring of pollen concentrations.
Compact lightweight
Suitable for roof installation with low payload. Can be integrated into existing measuring stations due to compact design.
SwisensPoleno Mars is the latest generation of real-time pollen monitors on the market. Thanks to its technology and network compatibility, SwisensPoleno Mars enables fully autonomous and stable long-term measurement of local pollen concentrations. SwisensPoleno Mars combines the measurement methods of digital holography with artificial intelligence and transparent data analysis to a reliable measurement system for automatic measurement and identification of pollen.
SwisensPoleno Mars briefly explained
SwisensPoleno Mars and its measurement method:
- Holographic images
- Pollen identification with machine learning
The most important specifications at a glance:
- Particle classes within 2 – 300µm
- Air sampling volume 40l/min
- Sigma-2 geometry sample inlet
- Integrated particle concentrator
Further benefits of SwisensPoleno Mars
- High temporal resolution of local pollen concentrations.
- Non-invasive measurement method.
- Immediate verification of identification results.
- Fully remote operation, configuration and updates.
- Fast and easy installation.
Developed as future standard in real-time pollen monitoring.
Breaking new ground with Swisens
Read more about real-time pollen monitoring
Pollen and other aerosol particles
SwisensPoleno’s artificial intelligence uses a pre-filter to separate different particle categories. In this short blog article, we explain what the prefilter for automatic pollen identification is all about. In doing so, we answer the following question: How can SwisensPoleno distinguish pollen from other aerosol particles?
From particle to Screen
This article describes how we convert individual bioaerosol particles into data available on our screen. In general, we try to answer one main question: How does real-time bioaerosol data get to users?
How to analyze aerosol data from real-time measurement
The latest technologies allow the measurement and monitoring of aerosol particles in real time. A measuring system continuously draws in air and measures and identifies the particles it contains. This is also the case with the measurement systems from Swisens. In this article, we will show you one way to analyze aerosol data from real-time measurements using our measurement systems and software solutions.
Can SwisensPoleno identify pollen in my region?
Can SwisensPoleno identify pollen in my region? To address this question, in this article we look at our systems for real-time monitoring of pollen. We show which pollen types SwisensPoleno can currently identify. In addition, you will learn what ways exist should the pollen taxa you want not be on the list.
Cost comparison: automatic versus traditional pollen monitoring
In this blog post we venture a comparison of the costs of automated versus traditional pollen monitoring. We look at the initial costs as well as the long-term operating costs of traditional pollen counting with the “Hirst method” versus an automated solution for real-time pollen monitoring.
Particle morphology from holographic images
This article explains the basics of automatic aerosol particle identification. We explain how our measurement systems extract information on particle morphology from holographic images and how you can distinguish between different aerosol particles.
Further Components of SwisensEcosystems
Hardware Components
Hardware
Hardware
SwisensPoleno Mars is the new generation of real-time pollen monitoring using our sophisticated technology and offering network compatibility.
Add-on Components
Tools
SwisensAtomizer is the compact particle disperser for solid and dry particle samples such as pollen, mineral dust and other materials.
Software Components
Software
Swisens DataExplorer is a browser-based software with helpful tools for checking measurement results and monitoring hardware components in the SwisensEcosystem.
Software
Swisens DataAnalyzer is a tool set that enables efficient analysis of large amounts of data in the deep dive of BigData.
Service Components
Service
With Swisens Accelerators, we offer practical training to operate our measurement systems.
Service
Swisens Care is our service for software updates and maintenance on all Swisens systems.
Service
SwisensData is a service with server-integrated data management in the SwisensEcosystem.
Service
The SwisensAI Factory is a competence centre that offers and imparts practical training and knowledge related to automatic particle identification.
This is what our
satisfied customers say
Benoît Crouzy –
Surface Data, Project Manager Swiss Automated Pollen Network
Federal Office of Meteorology and Climatology MeteoSwiss
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Prof Martin Gallagher –
Centre for Atmospheric Science,
University of Manchester
“I speak on behalf of my colleagues who are Urban Observatory Principal Investigators and Research Collaborators from the University of Manchester that we have rarely seen, or been provided with such exemplary support as that provided by Swisens. Swisens customer support has been remarkable given the current situation, from remote training and commissioning to rapid support for hardware and software tools specific to our needs to extremely well organised seminars that actually focus on customer feedback and needs. In particular we have been impressed with the Swisens open source data approach and their attention to different users specific needs. Swisens technical innovations has already generated enormous interest in a bioaerosol-aerobiology community that until recently has been limited with respect to real-world and near real-time detection and importantly quantification of airborne bioaerosol concentrations. Their approach has developed significant rapport with multi-disciplinary researchers and Swisens should be lauded for this.”
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Mikhail Sofiev –
Research Professor
Atmospheric Composition Research Department
Finnish Meteorological Institute
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Debora Käser –
Team Leader
Department for Work Safety / Health Protection
Suva
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