The ACTRIS-COVID-19 NRT (near-real-time) lidar measurement campaign was performed between 1 to 31 May 2020, involving 21 stations of the European Aerosol Research Lidar Network (EARLINET, https://www.earlinet.org, last access: 1 October 2021). A map with the participating EARLINET stations can be found on the EARLINET website (https://www.earlinet.org/index.php?id=covid-19, last access: 1 October 2021). This intensive observation campaign was focused on the lidar observations of aerosols during the relaxation period after the lockdown periods.

Based on the availability of the vertical profiles of backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios at 532 nm for dust-free pollen periods during the campaign, four lidar stations (Hohenpeißenberg, Germany; Kuopio, Finland; Leipzig, Germany; and Warsaw, Poland; Table 1) were selected for the pollen investigation. These stations belong to the Raman and polarization lidar network PollyNET (Baars et al., 2016; http://polly.tropos.de, last access: 1 October 2021).

Hohenpeißenberg station (HPB) is situated on top of an isolated mountain in the foothills of the Alps at Hohenpeißenberg in Germany. The observatory is a major Global Atmospheric Watch global station. This rural site is surrounded by spruce forests (Picea abies) mixed with some common beeches (Fagus sylvatica), maple (Acer platanoides), and ash (Fraxinus) trees. About a third of the area is pasture land. Kuopio station (KUO) is located ∼18 km from the city centre of Kuopio, in Eastern Finland. This is a rural site mainly surrounded by forest. Dominant tree species include silver birch (Betula pendula), Norway spruce (Picea abies), and Scots pine (Pinus sylvestris). Leipzig station (LEI) is located in the lowlands of eastern Germany. The surrounding is dominated by agricultural areas and some forest together with wetlands. Typical trees are birch, lime, beech, oak, maple, and pine among others. Main agricultural plants are all kinds of corn, maize, rape, and grass. The city of Leipzig itself has a lot of parks and a high biodiversity. Many kinds of trees and other plants can be found. The pollution level is medium to low, as Leipzig is usually well circulated by the dominant wind systems as no hills or mountains are around. Besides the times of intensive agricultural activity (early spring or late autumn) or periods of Saharan dust arrival, no depolarizing aerosol is observed in Leipzig, leading to a background particle depolarization ratio of ∼0.01. Warsaw station (WAW) is located in the city centre of the capital of Poland; however, in the nearby vicinity there are several green parks. In May, typically observed pollen species are pine (Pinus), birch (Betula), and blue grass (Poa). The fungi spores represent very high contribution in vegetation season.

Birch pollen is recognized as one of the most important allergenic sources (D'Amato et al., 2007), which has a diameter around 20–30 µm and near-spherical shape with three pores on the edge. Beech, maple, and ash pollen is quite similar to birch pollen in terms of shape and size. Pine and spruce pollen grains, belonging to the Pinaceae family, are significantly larger, with the diameter on the longest axis of ∼65–80 µm or ∼90–110 µm, respectively (Nilsson et al., 1977). They possess two air bladders which assist those pollen grains to be dispersed by wind despite their large size. The Poaceae family, known as grasses, comprises over 12 000 species classified into 771 grass genera (Soreng et al., 2015). Grass pollen grains are spheroidal to sub-oblate in shape with a single circular pore, whereas the size range is highly variable (García-Mozo, 2017; Joly et al., 2007; Salgado-Labouriau and Rinaldi, 2009). Microphotographs of pollen grains can be found at PalDat – a palynological database (https://www.paldat.org, last access: 1 October 2021; Halbritter and Heigl, 2020).

These four PollyNET stations are all equipped with ground-based multi-wavelength Raman polarization lidars PollyXT (Baars et al., 2016; Engelmann et al., 2016). Full details on the setup and principle of PollyXT can be found in Engelmann et al. (2016). Measurement capabilities of the lidars are somewhat different, but they all have emission wavelengths at both 355 and 532 nm and depolarization channels at 532 nm. The lidar near-real-time quick-looks are publicly accessible at the PollyNET website (http://polly.tropos.de, last access: 1 October 2021).

Lidar data were processed in a centralized way using the Single Calculus Chain (SCC) tool, with specific configurations and settings, and they were made publicly available. The SCC is a tool for the automatic analysis of aerosol lidar measurements developed within EARLINET (D'Amico et al., 2015, 2016; Mattis et al., 2016). The aerosol optical products after the reanalysis were used (two types of data were issued for the campaign: near-real-time data and reanalysis data; https://www.earlinet.org/index.php?id=covid-19-guidelines, last access: 9 March 2022), which are available on the THREDDS server (https://login.earlinet.org:8443/thredds/catalog/covid19re/catalog.html, last access: 1 October 2021). Out of all available data products, this study focused on particle backscatter coefficients (BSCs) at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. The processing vertical resolution is ∼60 m, and the integration time is of 2 h or less (depending on the cloud-free time available).

In order to make sure that there is no dust contamination in the pollen properties retrieval, only dust-free periods were considered in this study, which were identified using prediction by NMMB/BSC-Dust (Non-hydrostatic Multiscale Model/Barcelona Supercomputing Center; Pérez et al., 2011; https://ess.bsc.es/bsc-dust-daily-forecast, last access: 1 October 2021). NMMB/BSC-Dust is an online multi-scale atmospheric dust model designed to accurately describe the dust cycle in the atmosphere, and is intended to provide short- to medium-range dust forecasts for both regional and global domains. It provides vertical profiles of dust concentration every 6 h, with a horizontal resolution of 0.3∘ × 0.3∘. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory, https://ready.arl.noaa.gov/HYSPLIT.php, last access: 1 October 2021) backward trajectories were analysed to study the air mass origins.

Pollen types and concentrations were determined by the model forecasting and/or in situ measurements at the ground level when available. The SILAM (System for Integrated modeLling of Atmospheric coMposition) dispersion model (Sofiev et al., 2015a; https://silam.fmi.fi, last access: 1 October 2021) provides the forecasts of pollen distribution over Europe, with 10 km and 1 h as spatial and time resolutions, respectively. Vertical profiles of pollen concentrations are available for 10 height levels (with layer midpoint height from 12.5 to 7725 m from the surface), including six pollen types (alder, birch, grass, mugwort, olive, and ragweed pollen; Siljamo et al., 2013; Sofiev, 2017; Sofiev et al., 2013, 2015b). A Hirst-type Burkard pollen sampler was placed ∼4 m above ground level (a.g.l.) at Kuopio station during the campaign to enable identification of pollen types and concentration microscopically with a 2 h time resolution (more detailed descriptions can be found in Bohlmann et al., 2019 and reference therein). In Germany, the pollen monitoring is available online at six locations (including the Leipzig station), using the fully automatic pollen monitor BAA500 (Hund GmbH; https://www.hund.de/en/service/pollen-monitor, last access: 1 October 2021) that combines advanced computer-aided microscopy, camera, and image-recognition technology to determine and pollen grain count with a 3 h time resolution.

Previous lidar-derived pollen studies show tendencies towards smaller Ångström exponents with increasing depolarization ratios (Bohlmann et al., 2019, 2021; Shang et al., 2020), indicating the increasing impact of larger and non-spherical pollen particles. Here, we investigate, mathematically, the relationship of the backscatter-related Ångström exponent (BAE, denoted as Å) and the particle linear depolarization ratio (PDR, denoted as δ). Note that the BAE depends on the particle size, shape, and complex refractive index (e.g. Miffre et al., 2020; Mishchenko et al., 2002) and thus demonstrates higher sensitivity to the changes in aerosol mixture composition. Two aerosol populations, depolarizing (d) and non-depolarizing (nd) aerosols, are considered. The total particle backscatter coefficient (βtotal) is the sum of the backscatter coefficients of depolarizing (βd) and non-depolarizing (βnd) aerosols.

The BAE describes the wavelength dependence on the backscatter coefficients between two wavelengths λ1 and λ2 (Ångström, 1964): 1Åxλ1,λ2=-ln⁡βxλ1βxλ2ln⁡λ1λ2, with the index x for aerosol type, which can be d (for depolarizing particle, e.g. pollen), nd (for non-depolarizing particle, e.g. background), or total (for total particles). The wavelength pair λ1,λ2 was selected as (355, 532) in this study. For simplicity of the later calculation, we introduce the parameter η: 2ηxλ1,λ2=λ1λ2-Åxλ1,λ2. From now on, the wavelength pair (λ1,λ2) for η and Å expressions is omitted in the following derivations.

Shang et al. (2020) demonstrated the power-law relationship between the BAE of total particles (Åtotal) and the pollen backscatter contribution (the ratio of the pollen backscatter coefficient and the total particle backscatter coefficient) (see Eqs. 4–5 in Shang et al., 2020). Similarly, the backscatter contribution of depolarizing or non-depolarizing aerosols can be expressed as: 3βdλ2βdλ2+βndλ2=ηtotal-ηndηd-ηndβndλ2βdλ2+βndλ2=ηtotal-ηdηnd-ηd. The particle linear depolarization ratio of the total particles (δtotal), containing depolarizing and non-depolarizing aerosols, can be calculated using the backscatter coefficients and the depolarization ratios of each type as 4δtotal=βd⋅δdδd+1+βnd⋅δndδnd+1βdδd+1+βndδnd+1. We divide both numerator and denominator by the total particle backscatter coefficient, i.e. (βd+βnd), and replace the expressions in Eq. (3). Simple conversion yields 5δtotal=ηtotalδd-δnd-ηndδdδnd+ηndδnd-ηdδndδd-ηdδndηtotalδnd-δd-ηndδnd+ηnd-ηdδd-ηd, and after further rearrangements, we obtain 6ηtotal=ηndδdδnd+1-ηdδndδd+1+ηndδnd+1-ηdδd+1δd-δndδtotal+1-ηndδnd+1-ηdδd+1δd-δnd. This equation can be expressed in a simplified way as 7λ1λ2-Åtotal=a1+a2δtotalλ2+1-a2, with two coefficients (a1, a2) defined from four characteristic parameters (ηnd, ηd, δnd, δd) as 8a1=ηndδdδnd+1-ηdδndδd+1δd-δnda2=ηndδnd+1-ηdδd+1δd-δnd. The relationship between lidar-derived BAE and PDR of total particles is fixed for the mixture of two aerosol types. It can be mathematically derived if the characteristic values of these two aerosol types (Åd, δd and Ånd, δnd) are known. Synthetic examples are given in Fig. 1 where the backscatter coefficient profiles of depolarizing (βd), non-depolarizing (βnd), and total particles (βtotal) were simulated. Under different initial characteristic values (case 1 or case 2) of depolarizing and non-depolarizing particles, the PDR and BAE profile of total particles are different (e.g. Fig. 1b–c in blue or green). The relationships between simulated Åtotal and δtotal under each assumption are shown in Fig. 1d: the bottom-right (top-left) boundary point of the fitting line is determined by Å and δ of the depolarizing (non-depolarizing) particles, shown as dark brown squares (light brown triangles), whereas the curve shape of fitting lines is determined by Eq. (7), i.e. different values of a1 and a2 defined by Eq. (8). Note that the two boundary points of each fitting line are independent, as they are determined separately by the characteristic values (Åx and δx) of each aerosol type. Such a relationship is valid under two constraints: (i) only two aerosol populations present in the mixture, (ii) both Å and δ of the two aerosol types should be different. These two aerosol types can be dust and non-dust aerosols or pollen and non-depolarizing background aerosols. The method application for synthetic examples of three aerosol types in the mixture is present and discussed in the Supplement. For two (or more) types of depolarizing aerosols and one non-depolarizing aerosol mixture, the estimated δd values represent a combination of two (or more) depolarizing aerosols, with a value between the characteristic (pure) depolarization ratios of each type. However, the authors recommend using the method under the constraints mentioned above.

The pollen periods were selected for each station in May 2020 (Table 2), with the following criteria: (1) dust-free as indicated by the NMMB/BSC-Dust model (see Supplement) and (2) relatively high pollen concentrations (from the SILAM model forecasting and/or in situ measurements when available). Since the closest layer to the ground is assumed to contain the highest pollen concentration and share, the lowest layers were considered the pollen layers in this study. In addition, the retrieved BSC at 532 and 355 nm should be larger than 0.05 and 0.1 Mm-1 sr-1, respectively. These threshold values were adapted from the ones used in Baars et al. (2017), in which the quasi-BSC at 1064 nm below 0.01 or 0.2 Mm-1 sr-1 was classified as “clean atmosphere” or “non-typed particles/low concentration”, respectively.

In Kuopio station, there was frequent rain in the first two-thirds of May, and almost no pollen was measured by the Burkard sampler. Birch pollen was observed from 23 to 31 May, with the highest concentration of ∼4000 m-3 on 26 May. 23–26 May were selected as the pollen period, when there was clear sky. During the period, quite nice diurnal cycles (see Sect. 3.3.1) were found from lidar observations with enhanced backscatter signals and volume depolarization ratios in the planetary boundary layer.

In Warsaw station, two periods were selected in this study (see Sect. 3.3.2): period no. 1, birch pollen period from 26 to 29 May, and period no. 2, birch pollen mixture period on 31 May. High birch concentrations (with a median hourly value of 4800 m-3 at the lowest level) were indicated from the SILAM model for both periods, with almost 0 concentration of the other five pollen types. In Sect. 3.2, only period no. 1 is considered, whereas period no. 2 will be discussed in Sect. 3.3.2.

In Hohenpeißenberg station, high birch concentrations were found on 9 and 10 May with the highest value at the lowest level of ∼180 m-3; however, there was dust present (from NMMB/BSC-Dust) on these days. In order to avoid the dust mixture impact on the pollen property retrieval, 2 dust-free days (7 and 8 May, see Sect. 3.3.3) were selected as the pollen period, where nice diurnal cycles of enhanced backscatter signals and volume depolarization ratios in the planetary boundary layer can be found. SILAM model forecasts suggest the presence of birch and a small amount of grass pollen, with the highest concentration of ∼60 m-3 at the lowest level.

In Leipzig station, the number of available optical profiles was limited due to the frequent rain. From the SILAM model, there were few occasions with the presence of pollen in May. Pollen period was selected as 4 d (26, 27, 30, 31), when there was mainly birch and grass pollen; only four lidar-derived optical profiles of a full set were available in the period. The highest value of SILAM hourly pollen concentrations is about 100 m-3. The pollen monitor BAA500 shows mean values of the daily pollen concentration of 13 and 26 m-3 for birch and grass pollen during the period.

Due to the small number of profiles, values of all bins inside predefined pollen layers were used (see Table 2). The bottoms of the pollen layers are limited due to the overlap of the lidar instrument (the lowest reliable heights after the quality control tests are about 900, 500, 700, or 600 m a.g.l. for KUO, WAW, HPB, or LEI, respectively), whereas the tops are defined as the lowest observed layers based on the gradient method applied on both BSCs and PDRs. The mean values of PDR and BAE in Table 2 are the averages of the layer-mean values (in the selected layers) of all selected profiles per each station. Averaged layer-mean values of PDRs in pollen layers of four stations are slightly enhanced (from about 0.04 to 0.09) than the background conditions, suggesting the presence of non-spherical particles in the atmosphere.

We assumed that inside the pollen layers there are only two aerosol types: pollen and non-depolarizing background aerosol (bg). Based on the method presented in Sect. 2.4, we applied a simplified equation (similar to Eq. 7) here: 9y=a1+a2x+1-a2, where x is the bin value of measured PDR at 532 nm inside the pollen layer, i.e. δtotal (532), and y is the bin value defined from BAE calculated by the measured BSCs at 355 and 532 nm inside the pollen layer, i.e. y=355532-Åtotal(355,532).

The non-linear least square regression fitting, based on the Jacobian matrix, was applied using Eq. (9) to the dataset for each station to evaluate the coefficients (a1, a2), with values given in Fig. 2 with their standard deviations. The values of the coefficients (a1, a2) are different for stations, as they are defined (Eq. 8) from characteristic values of two aerosol types, i.e. pollen (Åpollen, δpollen) and non-depolarizing background aerosol (Åbg, δbg). Under ideal conditions (i.e. two aerosol populations present in the mixture, with different mixing ratio at different height or time), the unique solution can be found for the coefficients (a1, a2) with a high accuracy. But many solutions on the four characteristic values can result in the same coefficient couple (a1, a2), by reason of two equations with four unknowns. Regarding the fitting Eq. (9), the value couple of Åx and δx of one pure particle type (pollen or bg) should be located on the fitting curve theoretically (or under ideal conditions). Thus, with the knowledge of one parameter, the other can be evaluated. In reality, the depolarization ratio of the background particles (δbg) can be reasonably estimated or assumed, whereas the BAE of pure pollen (Åpollen) can be assumed to be 0, as pollen grains are quite large particles (e.g. birch pollen has a diameter around 20–30 µm). Hence, the other two characteristic parameters (Åbg, δpollen) can be calculated and vice versa. Final estimations of characteristic parameters for all stations are given in Table 3. There are no values of the Ångström exponent for pure pollen in the literature; for large particles such as dust, Mamouri and Ansmann (2014) reported extinction-related Ångström exponents between 440 and 675 nm, with values of -0.2 for coarse dust and 0.25 for total dust. If the true value of Åpollen is assumed between -0.5 and 0.5, the possible ranges of δpollen for each station can be given; refer to Table 3.

For Kuopio and Warsaw stations, the depolarization ratios at 532 nm of pure pollen (birch dominant) were found to be 0.24, which is in agreement with the birch depolarization ratio of 0.24 reported in Shang et al. (2020) for lidar observations in Kuopio in 2016. The pollen depolarization ratios at Hohenpeißenberg and Leipzig stations have relatively smaller values, probably due to the mixture of birch and grass pollen, as indicated by SILAM model. Grass pollen, depending on the genera, can be more spherical in shape compared to birch pollen; thus, smaller depolarization ratio is expected. These measurements were not affected by extreme meteorological events and represent values for pollen under ambient atmospheric condition in the spring season (similar conclusions in Bohlmann et al., 2019). Note that different characteristic values of pollen could be observed under extreme humid or extreme dry conditions: (i) pollen grains can fold up and change shape while dehydrating, e.g. commercially available pollen for laboratory measurements, and (ii) pollen grains can swell by taking up water especially after reaching a relative humidity over 89 % (see Fig. 2 in Griffiths et al., 2012).

The present method (Sect. 2.4) was used to evaluate the characteristic values of the pure particle type, e.g. to estimate the pure pollen depolarization ratios, and a case example for Kuopio station is presented here (Sect. 3.3.1). It can also be applied for the aerosol classification. Two case examples from the campaign periods are presented (Sects. 3.3.2 and 3.3.3).