In the present study, the phenological and quantitative changes in the pollen seasons between 1973 and 2013 in the Stockholm region of Sweden were studied for nine types of pollen (hazel, alder, elm, birch, oak, grass, mugwort, willow and pine). Linear regression models were used to estimate the long term trends in duration, start- and end-dates, peak-values and the yearly accumulated pollen sums of the pollen seasons. The pollen seasons of several arboreal plant species (e.g. birch, oak and pine) were found to start significantly earlier today compared to 41 years earlier, and have an earlier peak-date, while the season of other species seemed largely unaffected. However, the long term trends in the end-dates of pollen seasons differed between arboreal and herbaceous species. For herbaceous species (grass and mugwort), a significant change towards later end-dates was observed and the duration of season was found to have increased. A significant trend towards an earlier end-date was found in the majority of the arboreal plant species (i.e. elm, oak, pine and birch), but the length of the season seemed unaffected. A trend towards an increase in yearly concentrations of pollen was observed for several species; however the reasons for this phenomenon cannot be explained unambiguously by the present study design. The trend of increasing yearly mean air temperatures in the Stockholm area may be the reason to changed phenological patterns of pollen seasons.

The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical model is constructed using meteorological, geophysical and biological characteristics of the previous year). A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and Norway, where the lack of data did not allow for conclusive analysis. The constructed model was capable of predicting the SPI with correlation coefficient reaching up to 0.9 for some stations, odds ratio is infinitely high for 50% of sites inside the region and the fraction of prediction falling within factor of 2 from observations, stays within 40-70%. In particular, model successfully reproduced both the bi-annual cycle of the SPI and years when this cycle breaks down.

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In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NOx, O3 and PM10, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM10 with 3.1%, followed by O3 with 2.0%.

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