The most frequent questions and answers

Swisens FAQs

Swisens FAQs at a glance

Real-time measurement of aerosol particles means direct measurement and identification of particles in the ambient air within seconds. The measurement systems provide real-time information on local concentrations of airborne primarily biological aerosol particles (pollen, spores etc.) and other coarse particles. The data is output as concentration values and as individual “events”. An “event” contains a combination of measurement data describing the properties of a single particle. If this combination is known to the integrated AI, the measuring system informs the user of the applicable particle class (e.g. pollen, birch).

Digital holography:
Digital holography generates high-resolution holographic images and allows the measurement of the morphological properties of the individual particles. From this, the absolute size as well as shape, area, perimeter, sphericity and many other parameters can be determined. These parameters allow precise particle identification through artificial intelligence. Implemented in SwisenPoleno Mars and SwisensPoleno Jupiter.


Fluorescence spectroscopy:
This measurement method can measure the chemical composition of individual particles based on fluorescence emission. Complementary to digital holography or even stand-alone, more extensive “fingerprints” of the particles can be generated, which enable precise identification, especially for smaller particles under 10 micrometres. In doing so, the measurement systems collect data on fluorescence intensity and lifetime. Implemented in SwisensPoleno Jupiter.


Scattered light and polarisation measurement:
Illumination of a surface can cause the scattered light to show polarisation effects depending on the structure of the surface. The built-in vertical laser source can provide such illumination, while two photodetectors are available to measure the scattered light. Both detectors are equipped with polarisation filters, one horizontal and one vertical. The ratio of these two signals can provide additional information about the surface of the particle. Implemented in SwisensPoleno Jupiter.

The measurement systems generate a digital “fingerprint” of the particles, which are processed by an algorithm. The artificial intelligence recognises the key characteristics of the particles and assigns the particles to the corresponding particle classes. To enable this automatic process of identification, the systems and their algorithms are trained with a data set we know. This approach is also known as “supervised machine learning” and already has many applications in industry and science.

The measuring system continuously sucks in air at 40 litres per minute. It measures and counts the particles in flight at a speed of 0.5 metres per second. The measurement data is available within a few microseconds and the output values are available on demand within a few seconds.

We offer measurement systems for airborne particles between 0.5µm and 300µm. The lower detection threshold of SwisensPoleno Mars is 2µm while SwisensPoleno Jupiter allows measurements down to 0.5µm.

This depends on the application, the measurement system, the required reliability and precision. The most up-to-date and tested machine learning algorithms identify the most important pollens for allergic persons in Central Europe. These include hazel, birch, alder, beech, ash, oak and the grass family. The extended and still untested portfolio includes more than 30 other pollen types as well as spores and other bioaerosols and coarse aerosol particles.


We are happy to respond to customer-specific requirements and are continuously expanding our database

Of course – this is where the full potential of SwisensEcosystems comes into play. The combination of hardware, software and services allows our customers to independently expand and improve the machine learning algorithms. How this works, you can find out here.

For the transmission of the initial values (particle class & concentration), the data volume corresponds to a few megabytes per day. If the complete raw data is required, the data volume to be transmitted increases to several gigabytes per day. We will be happy to provide you with detailed information during a personal consultation.

Access to the output values and measurement data is possible via the central database, REST API and the software components within the SwisensEcosystem.

The SwisensDataExplorer enables the viewing of current and historical events and concentration values. The browser-based software does not require any complex installation and allows remote access from the user’s own workstation. The concentration values can be viewed in almost infinite time resolution. The configured time histories can be exported as CSV files. The individual events show the images and identification results generated by the system and can be viewed in a configurable time period and exported as a JSON file. In the future, a simple export to a CSV file will also be possible here.

For advanced analysts, the SwisensDataAnalyzer enables a detailed analysis and evaluation of the measurement data based on Docker Container, Python Script and Jupyter Notebook.

The robust and filterless design enables uninterrupted and continuous operation throughout the year. Equipped with an automated monitoring system and a remote-controlled cleaning programme, possible failures, e.g. due to clogging, can be prevented. Nevertheless, we recommend an annual cleaning and maintenance of the systems for a carefree long-term operation. Details of the Swisens service offer you can be find here.

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