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RESEARCH PAPER
Analysis of Fraxinus pollen seasons and forecast models based on meteorological factors
1
Department of Applied Mathematics and Computer Science, University of Life Sciences, Lublin, Poland
2
Department of Botany, University of Life Sciences, Lublin, Poland
3
Department of Meteorology and Climatology, Maria Skłodowska-Curie University, Lublin, Poland
Corresponding author
Krystyna Piotrowska-Weryszko
Department of Botany, University of Life Sciences, Akademicka 15, 20-095 Lublin, Poland
Department of Botany, University of Life Sciences, Akademicka 15, 20-095 Lublin, Poland
Ann Agric Environ Med. 2018;25(2):285-291
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
The timings of Fraxinus and Betula flowering and pollen release overlap, which may cause increased allergic reactions in sensitive people. The aim of the present study was to characterize Fraxinus pollen seasons in Lublin (central-eastern Poland) and to identify meteorological factors that most determine the occurrence of airborne pollen of this taxon, as well as obtain forecast models for the basic characteristics of the pollen season.
Material and methods:
The study was conducted in Lublin during the period 2001–2016, employing the volumetric method. The seasons were compared by PCA (Principal Component Analysis). To determine relationships between meteorological conditions and the pattern of pollen seasons, regression analysis was used. Data for the period 2001–2015 were used to create forecast models by applying regression analysis, while the 2016 data served to verify these models.
Results:
Season end date and seasonal peak date were characterized by the lowest variation. The biggest differences were found for peak value and total annual pollen sum. The average dates of occurrence of ash pollen grains in the air of Lublin were between 13 April 13 – 3 May 3, whereas, on average, the pollen peak date occurred on 23 April. The factor loading values for the PC1 variable indicate that it is most strongly correlated with peak value and total pollen sum, while the PC2 variable correlated with the pollen season start date and season duration (a negative correlation). Regression models were developed for the following pollen season characteristics: season start, end and duration, seasonal peak date, and total annual pollen sum.
Conclusions:
The fit of the forecast models was at the level of 62–94%. Analysis of the data showed that weather conditions mainly in February were important factors controlling the Fraxinus pollen season.
The timings of Fraxinus and Betula flowering and pollen release overlap, which may cause increased allergic reactions in sensitive people. The aim of the present study was to characterize Fraxinus pollen seasons in Lublin (central-eastern Poland) and to identify meteorological factors that most determine the occurrence of airborne pollen of this taxon, as well as obtain forecast models for the basic characteristics of the pollen season.
Material and methods:
The study was conducted in Lublin during the period 2001–2016, employing the volumetric method. The seasons were compared by PCA (Principal Component Analysis). To determine relationships between meteorological conditions and the pattern of pollen seasons, regression analysis was used. Data for the period 2001–2015 were used to create forecast models by applying regression analysis, while the 2016 data served to verify these models.
Results:
Season end date and seasonal peak date were characterized by the lowest variation. The biggest differences were found for peak value and total annual pollen sum. The average dates of occurrence of ash pollen grains in the air of Lublin were between 13 April 13 – 3 May 3, whereas, on average, the pollen peak date occurred on 23 April. The factor loading values for the PC1 variable indicate that it is most strongly correlated with peak value and total pollen sum, while the PC2 variable correlated with the pollen season start date and season duration (a negative correlation). Regression models were developed for the following pollen season characteristics: season start, end and duration, seasonal peak date, and total annual pollen sum.
Conclusions:
The fit of the forecast models was at the level of 62–94%. Analysis of the data showed that weather conditions mainly in February were important factors controlling the Fraxinus pollen season.
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