The PS2 draws a sample from the ambient air with the help of a suction fan that produces an air flow of 0.7 Lmin-1 through the sensor. The sample is illuminated with 790 nm light from a laser diode, and the light scattered from the particles in the sample is detected at 135-degree scattering angle with two light detectors (Fig. ). The two detectors are used to discriminate pollen from other particles based on two factors. First, the scattered light intensity is measured by one detector (PD2 in Fig. ), which outputs the parameter Ppol that is a relative measure related to particle size. Secondly, the other scattered light intensity, denoted as Spol, is measured by the other detector (PD1 in Fig. ) through a polarization filter whose orientation is perpendicular to the incident polarization. The combined output of these is then denoted as the degree of polarization (DoP), a relative index related to particle shape: 1DoP=Ppol-SpolPpol+Spol. Since the light scattered from particles with complex shapes or rough surfaces like pollen changes the incident light polarization plane, part of the scattered light is able to pass the polarizing filter (PF) (Fig. ).

Irregular particles such as dust strongly depolarize the incident light and therefore produce a relatively higher signal in the polarized channel, whereas more spherical or weakly depolarizing particles, such as many pollen grains, retain a larger fraction of the original polarization and are more strongly detected in the non-polarized channel (Fig. ). This difference in scattering intensity and polarization response enables discrimination between pollen and other particles. In this work we installed PS2 sensors at three locations for measuring the vertical profile of pollen concentrations: at the roof level of the measurement station (at the height of 4 m) and on the mast at the heights of 115 and 272 m. Prior to the vertical measurements, an intercomparison campaign was conducted by operating all PS2 units side-by-side at the 4 m rooftop location for a two-week period to ensure data consistency.

The PS2 was intercompared using pollen grains from most abundant taxa, in order to identify different pollen types. Two PS2 sensors were intercompared in the laboratory as follows. The sensors were mounted on the inner surface of a local exhaust ventilation hood. Pollen of Alnus incana, Betula pendula, Urtica dioica, Populus tremula, Salix, Pinus sylvestris, and Picea abies were placed in cuvettes, and each cuvette was tapped at the bottom to gradually release the pollen, while the open end was held close to the sensor inlets. The target was to reach more than one thousand counts per sensor to improve statistical reliability. To prevent cross-contamination between different pollen taxa, the sensors and their surroundings were cleaned with laboratory pressurized air between tests. The cleanliness was verified by performing one-minute blank tapping tests, which produced fewer than 20 counts. Background levels were also assessed after cleaning by measuring for 5–90 min, yielding less than one count per minute.

Figure  shows the SEM images of the most typical pollen grains observed in Vehmasmäki. The images show that the size range and shape of conifer pollen grains (Pinus sylvestris and Picea abies) differ from the rest pollen grains.

Figure  summarizes the PS2 count patterns in the Ppol–DoP plane for selected pollen grains, measured in the laboratory, and fog droplets, measured in the field (Vehmasmäki). As we can see in Fig. , fog droplets show low Ppol values (1.6–2.0) but high DoP (0.6–0.9), while different pollen taxa form distinct clusters with moderate DoP (-0.05–0.4). Based on these specific polarization signatures for every aerosol type, we found them suitable for particle classification and thus used the limits of Ppol>1.3 and -0.1<DoP<0.5 in field measurements for pollen grains to distinguish them from fog droplets.

In the field measurements, the presence of fog droplets was also identified based on dew point depression calculations. Fog forms when the temperature and dew point of the air approach the same value. So, the difference between air temperature and dew point (dT<1°C) was used as an indicator of fog formation and ambient PS2 dataset from Vehmasmäki was used for this purpose.

Airborne pollen was monitored using a Burkard 7 d recording (Burkard Manufacturing Co Ltd) volumetric air sampler operated at 4 ma.g.l. (above ground level). The sampler draws air through a 14 mm × 2 mm orifice at a flow rate of 10 Lmin-1, impacting particles with sufficient inertia (>4 µm) onto a greased tape mounted on a drum rotating on a seven-day cycle . The tape was divided into daily segments and analyzed in the Department of Biodiversity Sciences (University of Turku, Finland) by light microscopy, providing pollen information with a temporal resolution of 2 h. Different microscopic counting strategies have been proposed for Hirst-type samplers, and previous studies have shown that commonly used counting methods provide comparable estimates of airborne pollen concentrations . Using this method, pollen grains were identified to taxonomic type based on their morphological characteristics, and number concentrations were calculated from the known sampling flow. In total, up to 24 pollen types have been detected, of which the most abundant taxa in Vehmasmäki were considered in this study. Main sources of uncertainty include flow variability, counting methodology, and mounting media, with reported discrepancies of ∼16 % for concentrations >10 Pollengrainsm-3 and larger deviations at lower concentrations .

The Cloud Droplet Analyzer (CDA) is a high-resolution optical aerosol spectrometer system to measure the size distribution and number concentration of aerosols. The measurement accuracy of the device has been developed using a white light source and 90° scattered light detection, which enables a well defined calibration curve throughout the entire particle size range of 0.6–100 µm . An accurate determination of the particles has been reached using the T-aperture technique, which enables the particle size and particle number measurements in high concentrations without border zone error. In this study, CDA was adapted to monitor pollen grains, applying the particle measurement size range of 20–100 µm and using a pollen particle density of 600 kgm-3, plus a refractive index of 1.54+0i in the data analysis .

SILAM (System for Integrated modeLling of Atmospheric Composition) is a global mesoscale chemical transport and dispersion model developed by the Finnish Meteorological Institute for air quality and atmospheric composition applications . The model employs an Eulerian and a Lagrangian particle dispersion mode to simulate transport of pollutants in 3-D. SILAM features eight chemicophysical transformation modules for gas chemistry, secondary aerosol formation and aerosol dynamics, wet and dry deposition processes, radioactive decay, and pollen transformations. The model can use as inputs a wide range of emission sources, including anthropogenic pollutants, biogenic volatile organic compounds from vegetation, natural aerosols such as sea salt and dust, smoke from biomass burning, and even ship emissions or nuclear accident plumes. SILAM also provides pollen forecasts throughout Europe with a spatial resolution of 10 km and a temporal resolution of 1 h. Vertical profiles of pollen concentrations are available for 10 atmospheric layers, ranging from the surface up to 9 km. For the SILAM data used in this study (version 5.9), the forecasted pollen taxa include alder, birch, grass, hazel, mugwort, olive and ragweed pollen (http://silam.fmi.fi/, last access: 21 November 2025).

For all the campaigns that were conducted during May and July at Vehmasmäki station for the years 2021–2024 the dominant pollen types measured by the Hirst–type air sampler were consistently birch pollen (Betula) and pine pollen (Pinus). Every year, both pollen types show variability, not only in the timing of occurrence, but also in the magnitude of the concentration, reflecting variability in reproduction and phenology and the influence of meteorological conditions during each season. Figure  shows the time series of the two dominant pollen types at the Vehmasmäki station for each year during the period 1 May–1 July, using 2 h concentrations on a logarithmic scale. In 2021, birch pollen exhibited an exceptionally strong season, with a maximum 2–hourly concentration of 64 384 Pollengrainsm-3 on 12 May. Pine pollen peaked later, on 5 June 2021, reaching 10 571 Pollengrainsm-3. In 2022, the concentrations were weaker with birch pollen peak at 1991 Pollengrainsm-3 on 22 May and pine pollen at 8649 Pollengrainsm-3 on 12 June. In 2023, birch pollen again appeared in early May, peaking at 2701 Pollengrainsm-3 on 15 May. Pine pollen reached its maximum of 7619 Pollengrainsm-3 on 11 June. The 2024 season showed a similar variability, with birch pollen peaking at 14 003 Pollengrainsm-3 on 15 May and pine pollen at 9816 Pollengrainsm-3 on 29 May. The 2024 pollen season is visually shorter due to an early stop in measurements. For all years, birch pollen systematically showed an earlier peak than pine pollen. Peak birch pollen days consistently occur within a narrow 5–10 d window each year, while pine pollen exhibits a broader and later maximum, around early to mid June. Compared with birch pollen, the occurrence of the pine pollen peak shows greater annual variability, with shifts of up to one week between years. Figure  presents the data availability for each campaign year, including the operational periods of the PS2-Mast, the PS2-Roof and the identified IPP's for birch and pine pollen.

The average pollen concentration was 1340 Pollengrainsm-3 (PS21) throughout intercomparison campaign 10 May–2 June 2023 at Vehmasmäki station (Fig. a). Observed pollen concentration was close to the average particle concentration (NCDA) of 1300 Pollengrainsm-3 measured in size range of 20–100 µm, so pollen grains clearly dominated that size range. Simple linear regression analyses of PS2 raw data, where PS21 data were used as a predictor, gave original slope values of 0.94 (R2=0.91) for PS22 and 0.77 (R2=0.90) for PS23. After applying slope corrections to minimize device bias by dividing PS22 and PS23 data with their respective slope factors as described in the Sect. , the slopes improved to 0.99 and 0.97 respectively. Based on the intercomparison data, PS2s have a strong agreement, since the correlation coefficients are equal to 0.91 and 0.90, respectively (Fig. b and c).

For each year, we identified the IPPs for birch and pine pollen based on the Burkard time series at Vehmasmäki and the constraints we described in Sect. . The IPPs are summarized in Table . Results of PS2 measurements for IPPs are shown in Fig. . During these selected periods, the data points represent 2 h averaged PS2 measurements. The contribution of background PM10 to the total aerosol mixture was used as a color scale for the polarization scatter plots. Circles denote birch pollen and squares denote pine pollen. The size of the markers is proportional to pollen concentration. Finally, we depicted the distribution for each pollen type using covariance ellipses. The number of samples for each pollen type is indicated in each panel. The analysis of the IPPs across the three years of Vehmasmäki campaigns reveals two clearly separated clusters in the polarization scatter plot. Birch pollen consistently shows higher DoP of 0.24±0.03, 0.17±0.04, and 0.29±0.04 for 2021, 2022, and 2023 respectively, while pine's DoP remains very low at 0.01±0.02, 0.04±0.04, and 0.06±0.04, respectively. Lower Ppol values of 2.41±0.13, 2.06±0.21, and 2.14±0.29 for 2021, 2022, and 2023 characterize birch pollen, whereas pine pollen is distinguished by higher mean Ppol values of 4.31±0.21, 4.27±0.30, and 3.51±0.41. This separation is consistent across all three years. The ellipse extents show that pine pollen covers a broader range in both Ppol and DoP exhibiting a negative correlation. The largest markers (highest pollen concentrations) remain close to the cluster centers. The color reveals that most of the IPP samples correspond to relatively low PM10 shares less than 40 % of the aerosol mass, confirming the dominance of pollen in the selected periods. As the share of PM10 increases, both pollen types are biased to lower Ppol values, due to dispersion of the signal by fine particles and higher DoP, because the background is dominated by more spherical, fine–mode aerosols.

These findings are in very good agreement with the laboratory polarization scatter plot produced by PS2 intercomparison experiments in the laboratory. Birch pollen occupies the region of higher DoP associated with more spherical particles, while pine pollen shows a lower DoP with high Ppol, characteristic of larger and more irregular particles. The strong agreement between the in situ polarization scatter plot and the laboratory reference scatter plot provides a sound basis for classifying pollen taxa using optical sensors.

The small offsets likely reflect the more complex and variable atmospheric mixture in the in situ measurements. Overall, the comparison shows that the in situ PS2 measurements during the IPPs are fully compatible with the PS2 intercomparison polarization scatter plot for birch and pine pollen.

To examine the vertical distribution of birch pollen, we combined simultaneous observations from three PS2s at 4, 115 m, 272 ma.g.l. and Burkard at 4 m, with birch pollen concentrations simulated by the SILAM model. The analysis was focused from 14 to 20 May 2024, during birch pollen season. The PS2 measurements at 115 and 272 m were not individually classified using their polarization properties. Instead, the same IPPs identified from the Burkard Hirst-type sampler at 4 m were used for all three PS2 instruments, and the simultaneous measurements were assumed to be representative of the dominant pollen type during each period. We ensured that the IPP was following the criteria referred in Sect.  and excluded the timestamps with precipitation and relative humidity above 80 %. After filtering, we calculated the mean number concentrations at each height. Predicted birch pollen number concentrations were obtained from SILAM at multiple layers with mid-heights of 12.5, 50, 125, 275, and 575 m. For each height, we computed the mean and standard deviation of predicted birch pollen concentration during the selected IPP.

In Fig. , the time series of birch pollen concentration shows that the SILAM model overestimated the concentrations throughout the period. Peak concentrations were roughly twice as high as those measured at the corresponding heights. Moreover, while the observations displayed more pronounced daily fluctuations, the temporal variability in SILAM was smoother because the model replicated broad, persistent peaks during several days, probably affected by episodic release and weather.

In Fig. , both the measurements and the model show a clear decrease in concentration with height, with the highest pollen concentration near the ground. This implies that the gradient of vertical distribution of pollen was accurately replicated by the model, indicating that the transport and dispersion processes are functioning effectively in the model. At 4 m, however, the Burkard sampler measured consistently higher birch pollen concentrations than the PS2, a behavior also reported in previous intercomparison studies showing that Hirst-type samplers tend to yield higher concentrations than optical sensors due to differences in sampling efficiency and counting methodology . This suggests that part of the ground concentration difference arises from instrumental sampling characteristics rather than true atmospheric variability. Despite that, as shown by the time series, SILAM profile is biased toward higher pollen concentrations at all heights of the curve. Although previous evaluations have demonstrated that SILAM reproduces the overall temporal evolution and magnitude of airborne pollen concentrations reasonably well, model performance is sensitive to the pollen emission parameterization and meteorological input, and biases may occur depending on the study region and season .

To investigate the possibility of classifying in real–time the dominant airborne pollen types using PS2 measurements, we applied a machine–learning approach. The RF classifier achieved an overall classification accuracy of 76.9 %, as shown in Fig. . Performance was highest for Poaceae (90.2 %), Betula (88.2 %) and Pinus (79.7 %), while Alnus (50 %) and Picea (56.7 %) showed lower accuracies and frequent confusion with the NONE and MIX classes. Urtica were detected with low success (16.2 %), with many samples misclassified as MIX or NONE. The NONE class was generally robust (84.5 %), although some Alnus and Urtica were incorrectly labeled as NONE. The largest residual confusion was between MIX and NONE, reflecting cases where pollen concentrations were low or multiple types contributed without a single dominant type. The results support the broader applicability of the PS2 sensors for continuous pollen monitoring. Using 3 d temporally grouped validation reduced the overall classification accuracy only moderately, from 76.9 % to 74.5 %, indicating that temporal dependence between neighboring observations does not substantially affect the overall classification performance. Overall, these results demonstrate that combining PS2 data with meteorology, dominant pollen types can be identified effectively. The method is adequate to capture the primary seasonal dynamics of in situ measurements of airborne pollen, even though classification accuracy varies depending on the type of pollen.