This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a record-low pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting.

Pollen monitoring has traditionally been carried out using manual methods first developed in the early 1950s. Although this technique has been recently standardised, it suffers from several drawbacks, notably data usually only being available with a delay of 3–9 days and usually delivered at a daily resolution. Several automatic instruments have come on to the market over the past few years, with more new devices also under development. This paper provides a comprehensive overview of all available and developing automatic instruments, how they measure, how they identify airborne pollen, what impacts measurement quality, as well as what potential there is for further advancement in the field of bioaerosol monitoring.

The European Commission Cooperation in Science and Technology (COST) Action FA1203 “SMARTER” aims to make recommendations for the sustainable management of Ambrosia across Europe and for monitoring its efficiency and cost-effectiveness. The goal of the present study is to provide a baseline for spatial and temporal variations in airborne Ambrosia pollen in Europe that can be used for the management and evaluation of this noxious plant. The study covers the full range of Ambrosia artemisiifolia L. distribution over Europe (39°N–60°N; 2°W–45°E). Airborne Ambrosia pollen data for the principal flowering period of Ambrosia (August–September) recorded during a 10-year period (2004–2013) were obtained from 242 monitoring sites. The mean sum of daily average airborne Ambrosia pollen and the number of days that Ambrosia pollen was recorded in the air were analysed. The mean and standard deviation (SD) were calculated regardless of the number of years included in the study period, while trends are based on those time series with 8 or more years of data. Trends were considered significant at p < 0.05. There were few significant trends in the magnitude and frequency of atmospheric Ambrosia pollen (only 8% for the mean sum of daily average Ambrosia pollen concentrations and 14% for the mean number of days Ambrosia pollen were recorded in the air). The direction of any trends varied locally and reflected changes in sources of the pollen, either in size or in distance from the monitoring station. Pollen monitoring is important for providing an early warning of the expansion of this invasive and noxious plant.

Spores of the genus Alternaria belong to one of the most prevailing constituents of the air in all regions of the world. They form infectious inoculum of numerous plant species as well as severe inhaled allergies. The aim of this study was to compare the biological pollution with Alternaria spores of the air of 12 cities located in central and eastern Europe. The experiment was done in 2010 and it covered the territory of Latvia (LV), Lithuania (LT), Poland (PL) and Ukraine (UA). The spores were counted using an identical method and standard equipment (7-day Lanzoni volumetric sampler) followed by extensive statistical calculations. The timing of the day of maximum concentration changed mainly along the N-S direction and had a positive correlation with latitude. The most important factor determining the increase in Alternaria spore concentration was the temperature, whereas other weather parameters were not related or of low significance. Regardless of geographical location, the first phase of the season (0–0.9 % of Alternaria spores in the air) was the longest (up to 60 days) and the last (97.5 to 99 %) was the shortest (22 days or less). The means of daily concentrations of Alternaria spores ranged from 11 spores m⁻³ in Klaipeda (LT, Baltic Sea coast) to 187 in Poznan (west PL, agricultural plain). The threshold value of 80 spores m⁻³ that triggers the first allergy symptoms was exceeded in 8 to 86 days (Vinnitsa, UA, temperate continental, forest-steppes region). There were considerable differences between the highest number of spores per cubic metre of air, varying from 139 in the north (Klaipeda, LT) to 2,295 in central west (Poznan, PL). The biological pollution by Alternaria spores in several places of central and eastern Europe was high; the number of days exceeding the threshold value of 300 spores m⁻³ connected with serious health problems of atopic people ranged from 0 to 1 on the north (LV, LT) to 29 in central west (Poznan, PL).

The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN). Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and shown to remain nearly optimal throughout the whole period.