Temporal and spatiotemporal autocorrelation of daily concentrations of Alnus, Betula, and Corylus pollen in Poland

Nowosad, Jakub; Stach, Alicja; Kasprzyk, Idalia; Grewling, Łukasz; Latałowa, Małgorzata; Puc, Małgorzata; Myszkowska, Dorota; Weryszko-Chmielewska, Elżbieta; Piotrowska-Weryszko, Krystyna; Chłopek, Kazimiera; Majkowska-Wojciechowska, Barbara; Uruska, A.

doi:10.1007/s10453-014-9354-2

Cited by 15 publications

(24 citation statements)

References 28 publications

(29 reference statements)

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“…Nonetheless, Alnus (and Corylus ) pollen seasons are highly changeable from year to year. In addition, the location of aerobiological monitoring sites influences the variability of the pollen count of these taxa (Nowosad et al 2015). The Betula model had the best values of model evaluation statistics.…”

Section: Discussionmentioning

confidence: 99%

“…Afterwards, preprocessed meteorological variables from the grid cells corresponding to the location of aerobiological sites were selected as predictor variables (Table 2):

The average monthly temperatures for each month over the previous year for each site

Four- and 16-day averages, calculated for each of the meteorological parameters. The temporal span of these factors was based on a recent study which showed that the temporal variations in Corylus , Alnus , and Betula pollen counts are related to factors that change (i) diurnally, (ii) approximately every 3.5 days, and (iii) in more than 15 days (Nowosad et al 2015). These values were then lagged by 1 day

Cumulated growing degree days (GDD), lagged by 1 day

Longitude, latitude, and altitude of grid cell

…”

Section: Methodsmentioning

confidence: 99%

“…The temporal span of these factors was based on a recent study which showed that the temporal variations in Corylus , Alnus , and Betula pollen counts are related to factors that change (i) diurnally, (ii) approximately every 3.5 days, and (iii) in more than 15 days (Nowosad et al 2015). These values were then lagged by 1 day…”

Section: Methodsmentioning

confidence: 99%

“…In addition, recent studies have shown that the temporal variations in Corylus , Alnus , and Betula pollen counts are related to three groups of factors. The temporal span of these factors are (i) daily, (ii) approximately 3.5 days, and (iii) more than 15 days (Nowosad et al 2015). …”

Section: Introductionmentioning

confidence: 99%

“…Spatial analyses in aerobiology mainly involve the following: the comparison between two or more different localizations (Stach et al 2008; Puc and Kasprzyk 2013; Sauliene et al 2014); the description of spatial variation of pollen season properties or pollen concentrations (Emberlin et al 2002; Rieux et al 2008; Myszkowska et al 2010; Nowosad et al 2015); or the investigation of pollen transportation using back trajectories (Skjoth et al2008, 2009; Veriankaite et al2009; Rojo and Pérez-Badia 2015). There have been only a few studies in which spatial models of Betula pollen count were built.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

Nowosad

2015

Int J Biometeorol

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Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

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Section: Discussionmentioning

confidence: 99%

“…Afterwards, preprocessed meteorological variables from the grid cells corresponding to the location of aerobiological sites were selected as predictor variables (Table 2):

The average monthly temperatures for each month over the previous year for each site

Cumulated growing degree days (GDD), lagged by 1 day

Longitude, latitude, and altitude of grid cell

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

Nowosad

2015

Int J Biometeorol

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Alder pollen concentrations in the air during snowfall

Kasprzyk

Borycka

2019

Int J Biometeorol

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The focus of our study was airborne alder pollen because it is one of the main causes of inhalant allergies in many countries in the Northern Hemisphere. The main research setback was pollen concentrations during snowfall. Analyses from a 21-year database showed that the hourly patterns of occurrence of airborne Alnus pollen during snowfall differ. Snowfall can cause a decrease in pollen concentrations in the air that may persist for several hours. However, during the snowfall period of 2018, an increase in pollen concentrations was observed. High temperatures during the days preceding snowfall stimulate thermal currents, and pollen could become airborne. During snowfall, airborne pollen grains are supposedly washed out of the atmosphere and numerous pollen grains are deposited on the snow surface. Hypotheses about the long-distance transport of pollen have also been verified. Back-trajectory analysis has revealed that air masses from Belarus and Ukraine were transported to Rzeszow. We found that the influence of snowfall on pollen concentrations is ambiguous and individuals prone to allergies also notice symptoms on days with snowfall and temperatures below zero.

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Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count

et al. 2015

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The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

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Temporal and spatiotemporal autocorrelation of daily concentrations of Alnus, Betula, and Corylus pollen in Poland

Cited by 15 publications

References 28 publications

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

Alder pollen concentrations in the air during snowfall

Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count

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