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  • 1.
    Ekebom, Agneta
    et al.
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Dahl, Åslög
    Göteborgs universitet.
    Pollensäsongen 2017: Sammanställning av pollenförekomsten i Sverige2018Report (Other academic)
  • 2.
    Ekebom, Agneta
    et al.
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Dahl, Åslög
    Göteborgs universitet.
    Pollensäsongen 2018: Sammanställning av pollenförekomsten i Sverige2019Report (Other academic)
  • 3. Lind, Tomas
    et al.
    Ekebom, Agneta
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Alm Kübler, Kerstin
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Östensson, Pia
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Bellander, Tom
    Lõhmus, Mare
    Pollen Season Trends (1973-2013) in Stockholm Area, Sweden2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 11, p. 1-12Article in journal (Refereed)
  • 4.
    Olstrup, Henrik
    et al.
    Atmospheric Science Unit, Department of Environmental Science and Analytical Chemistry, Stockholm University, 11418 Stockholm, Sweden..
    Johansson, Christer
    Atmospheric Science Unit, Department of Environmental Science and Analytical Chemistry, Stockholm University, 11418 Stockholm, Sweden. Environment and Health Administration, SLB, Box 8136, 104 20 Stockholm, Sweden..
    Forsberg, Bertil
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Yrkes- och miljömedicin..
    Tornevi, Andreas
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Yrkes- och miljömedicin..
    Ekebom, Agneta
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Meister, Kadri
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik..
    A Multi-Pollutant Air Quality Health Index (AQHI) Based on Short-Term Respiratory Effects in Stockholm, Sweden2019In: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, ISSN 1661-7827, Vol. 16, no 1, article id 105Article in journal (Refereed)
    Abstract [en]

    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%.

  • 5.
    Ritenberga, Olga
    et al.
    University of Latvia Faculty of Geography and Earth Sciences.
    Sofiev, Mikhail
    Finnish Meteorological Institute.
    Siljamo, Pilvi
    Finnish Meteorological Institute.
    Saarto, Annika
    Unit of Aerobiology, University of Turku.
    Dahl, Aslog
    Department of Biological and Environmental Sciences, University of Gothenburg.
    Ekebom, Agneta
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Sauliene, Ingrida
    Research Institute, Siauliai University.
    Shalaboda, Valentina
    Institute for Experimental Botany of the NAS of Belarus.
    Severova, Elena
    Moscow State University.
    Hoebeke, Lucie
    Belgian Aerobiological Network, Mycology and Aerobiology service, Scientific Institute of Public Health.
    Ramfjord, Hallvard
    Department of Biology, Norwegian University of Science and Technology.
    A statistical model for predicting the inter-annual variability of birchpollen abundance in Northern and North-Eastern Europe2018In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 615, p. 228-239Article in journal (Refereed)
    Abstract [en]

    The paper suggests amethodology for predicting next-year seasonal pollen index (SPI, a sumof daily-mean pollen concentrations)over large regions and demonstrates its performance for birch in Northern andNorth-Eastern Europe. Astatistical 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 inEurope, where the observed SPI exhibits similar patterns of the multi-annual variability.We built the model for thenorthern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia andNorway,where the lack of data did not allow for conclusive analysis. The constructed modelwas capable of predictingthe SPI with correlation coefficient reaching up to 0.9 for somestations, odds ratio is infinitely high for 50% of sites insidethe region and the fraction of prediction fallingwithin 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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