Swisens Poleno
SwisensPoleno, the highly innovative technology for accurate and reliable aerosol identification. It is specifically optimized for the continuous, real-time, automatic identification of pollen types and other aerosols.
SwisensPoleno marks the beginning of a new era in the measurement of aerosols in general and pollen in particular.
Downloads
Technical and commercial documentation
- Brochure and general technical specifications (SwisensPoleno Mars)
- Brochure and general technical specifications (SwisensPoleno Jupiter)
Relevant scientific articles
Operating principle
This is the flow cytometer that provides the highest amount of data per particle on the market. This rich mosaic of information, including high-resolution images, processed with state-of-the-art algorithms based on neural networks and machine learning, allows for excellent particle classification.
SwisensPoleno is based on phased observation using morphological (size, shape, etc.) and biochemical (fluorescence: intensity and latency) analysis. The phases are as follows:
- Light scattering.
- Digital holography.
- UV-induced fluorescence (intensity and latency).
- Polarization.
The system is supplied by a constant air sample of 40 l/min, which enters through a Sigma-2 inlet in accordance with VDI 2119:2013. The sample is directed toward a particle concentrator (whose function is to provide very good temporal resolution, eliminate coarser particles, and generate a representative sample of all the particles contained in the sample).
After the concentrator, the two 90° lasers (450 and 685 nm) detect the particle flow (by beam scattering or light scattering), activating the sampling and making an initial estimate, based on the combination of both, of velocity, alignment, and size. After this stage, and using digital holography, two 90° images of each particle are obtained with the following characteristics:
- 55 image pairs/second.
- Resolution of 0.56 μm/pixel.
- Image size of 200 x 200 px (115 x 115 μm).
SwisensPoleno Mars
SwisensPoleno Jupiter
Using digital holography, the SwisensPoleno is capable of counting pollen grains, determining their typology, and distinguishing them from other particles. This recognition and classification process is carried out using:
- Particle identification algorithms based on shape and size.
- Deep learning algorithms (neural networks) for assimilating the shapes and patterns of pollen grains.
Understanding that each particle (including pollen grains) has a particular signature obtained using the methods described above, additional characteristics can be determined by the SwisensPoleno Jupiter. These complementary characteristics are obtained by measuring fluorescence (intensity and latency) and polarization.
Thus, after holography, the particle flow is observed in the fluorescence measurement stage. By exciting the particles using three sources (two 280 and 365 nm LEDs; one 405 nm laser diode), their response is recorded in five windows (between 320 and 750 nm). Measuring the intensity and latency of this response in these five windows generates rich (and complementary) information about each particle to refine particle classification.
In a final step, the particle flow is analyzed (again from a morphological perspective) according to its polarization (vertical and horizontal backscattering of light with a resolution of 4 μs). This method provides additional data on particle size, surface structure, and polarization factor.
Optionally, prior to the sampled air outlet, a sample collector can be installed for manual laboratory contrast of the particles measured with the SwisensPoleno (available for both models).
Differentiating features
Aerobiology is the branch of biology that studies pollens, spores, and other bioaerosols that are transported by air or diffuse through the air. Aerobiological studies are carried out, in a standardized manner in Europe and much of the world, by sampling air using Hirst-type collector networks.
This type of collector was invented in 1952 by Jim M. Hirst. It is a device that draws in a volume of air (10 liters/minute) using a vacuum pump. The sampled air volume impacts an adhesive tape mounted on a rotating drum equipped with a clockwork mechanism. The orientation of the air intake is controlled by a vane that positions it in the direction of the wind.
The technicians or scientists in charge of operating a Hirst collector must remove the adhesive tape daily/weekly and replace it with a fresh one. The removed tape must be processed manually in the laboratory (at sampling stations where there is no laboratory, it is usually mailed weekly). Essentially, tape processing consists of the following steps:
- Sectioning the tape into daily fragments.
- Identification, arrangement, and mounting of the tape fragments on slides.
- Staining the sample for optimal identification of pollen grains using a fuchsin-stained glycerogelatin solution.
After sample processing and microscopy, pollen grain and spore counting is performed manually (fuchsin favors the highlighting of bioaerosols of plant origin). Since counting all the pollen grains on the surface of each strip fragment would be extremely time-consuming, a lateral scan of the strip is usually performed using a microscope to obtain a representative sample of pollen grains for each 24-hour period, counting a minimum of 10% of the sample (the limit set by the EAN standard, the European Aeroallergen Network).
Once the count is complete, the grains are digitized, converted to standard units (pollen grains/cubic meter), and sent for evaluation/publication by various health services, meteorology departments, research centers, etc.
Sample collection
The usual sample collection process is summarized below, describing the main drawbacks of each step.
Sampling
- 1952 design
- Mechanical elements subject to wear and tear
- Limited resolution
Tape changes
- Manual labor
- Consumables
- Systematic and spurious errors
Sample shipping
- Transit time
- Shipping costs
Manual preparation and counting
- Trained personnel
- Very costly in terms of hours
- Human factors
- Sampling of 10-15% of the total
Digitization
- Costly in terms of hours
- Spurious errors
Data visualization and publication
- Sample delay (7 to 10 days)
- Unfeasible for forecasts and alerts
Measuring pollen and aerosols using the SwisensPoleno Meter offers significant advantages over the conventional method:
Automatic observation of particles
in real time and with high resolution (particle by particle).
No daily/weekly maintenance required
for changing adhesive strips (tape removal, drum preparation, string, etc.).
Automatic data transmission
over the Internet (using a 3/4G modem or local Internet).
Allows for forecasting and generating alerts to different users
distinguishing between allergen types (weather agencies, health services, research centers, allergy groups, etc.).
Automatic identification and classification
of taxa and other aerosols (including learning new types) with high reliability.
Automatic digitization
of particles and observed data (data stored in a database, remotely accessible from any location with Internet access).
Automatic publication
of data and integration into third-party systems (automatic data distribution and integration into third-party systems/servers such as meteorological or health services).
Low maintenance
No filters, no moving parts, remote monitoring of operating parameters, performance, etc.
Lightweight and easy to install
in the usual locations where Hirst-type collectors are installed (it can be installed by 2-3 people, without requiring expensive equipment).
Installation.
See how to install a SwisensPoleno on the roof of a conventional building
Technical specifications and general features
Aerosol sampling
- Particle Type: Pollen, spores, and other aerosols
- Measurement Range (Size): 1 μm to 300 μm
- Recommended Maximum Concentration: 30,000 particles/m3
- Sampling Time: Continuous sampling
- Air Flow: 40 L/min (normal operation), 300 L/min (maintenance/cleaning flow)
Measurement technology
- Particle Concentrator: Factor 1,000 (10 to 300 μm)
- Digital Holography:
- Two images at 90° per particle
- Resolution: 0.56 μm/pixel
- Maximum image size: 2,048 x 1,536 pixels
- Rate of up to 55 frames per second
- Polarization: 4 μs resolution
- Fluorescence:
- Two LEDs (280 and 365 nm) and 405 nm laser diode
- Intensity measured in 5 specific windows between 320 and 750 nm
- Latency measured in all 5 windows within a range of 0.5 to 20 ns
Power and communications
- Power:
- Voltage: 100 to 240 VAC
- Frequency: 50/60 Hz
- Consumption: 750 W (A/C included)
- Grounding (outdoor installations)
- Communications:
- Digital RS232/RS485
3G/4G Modem - Ethernet RJ45 Local
- Internet
- 4 Digital Inputs/Outputs
- 4 Analog Inputs/Outputs
- Digital RS232/RS485
Dimensions and weight
- Full size (strictly the Swisens Poleno size in parentheses):
- Width: 730 mm (320 mm)
- Height: 1500 mm (470 mm)
- Depth: 630 mm (280 mm)
- Total weight: 134 kg (26 kg for Swisens Poleno only)
Peripherals and auxiliary elements
- Outdoor cabinet equipped with a half-cylinder lock and internal hinges
- GSM/3G/4G modem (depending on the communication option chosen) and external antenna
- Service station consisting of:
- Industrial PC
22-inch display - Keyboard with stand
- Industrial PC
- Air conditioning (480W as standard; consult for specific air conditioning conditions)
- Metal support/base leg
Environmental conditions and degree of protection
- Optimal operating values:
- Air Temperature: 10°C to 40°C
- Relative Humidity: 10% to 90%
- Degree of Protection:
- Outdoor Cabinet: IP65
- Air Conditioning: IP24 (external circuit) IP54 (internal circuit)
Options.
There are several options to consider when purchasing one or more SwisensPoleno stations. There are essentially two models:
- SwisensPoleno Mars.
- SwisensPoleno Jupiter.
The Mars version includes light scattering and digital holography features, while the Jupiter version also includes fluorescence and polarization measurement features. Below are the essential features of each model so you can determine which of the two models (Mars or Jupiter) best suits your monitoring needs.
Swisens Poleno Basic
- Identification of the most common pollen types (more than 95% accuracy for pollen types Alnus, Ambrosia, Betula, Carpinus, Corylus, Fagus, Fraxinus, Pinus, Poaceae, Populus, Quercus, Urtica, etc., in addition to all taxa incorporated by Swisens up to the time of delivery).
- Lower price than the standard version (does not include fluorescence or polarization).
Swisens Poleno Standard
- Identification of most pollen types.
- Fluorescence and polarization measurement, allowing discrimination between pollen types with similar morphology.
- Additional identification of other types of bioaerosols such as spores. Monitoring and classification of particulate matter (PM1, PM2.5, and PM10).
Once the SwisensPoleno equipment level has been determined (Mars or Jupiter), the following application options must be considered:
- Complete outdoor sampling station (SwisensPoleno equipped with a climate-controlled outdoor cabinet, metal base, service station, Sigma-2 air intake, etc.).
- Station for laboratory operation (does not include auxiliary equipment).
If a complete station is required, the following issues must be considered:
Power requirements
• Availability of a nearby power supply.
• Stabilization and power security requirements (UPS).
• Grounding point (existence and implementation requirements).
Communications requirements
• Availability of a WLAN communications point for Ethernet connection.
Mobile network communication requirements
• 3/4G modem equipment, subject to mobile operator coverage availability.
Air conditioning requirements
• The system is supplied by default with 480W air conditioning (please inquire for specific air conditioning).
Meteorological data
• A compact weather station can be added to the sampling station.
Data mining
• Centralization of data on your own or a third-party server.
• Integration into third-party systems (meteorological agencies, health services, etc.).
For the assimilation of other types of pollen or other aerosols, the purchase of a SwisensAtomizer should be considered.
