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

Nowosad J, Stach A, Kasprzyk I, Grewling Ł, Latałowa M, Puc M, Myszkowska D, Weryszko-Chmielewska E, Piotrowska-Weryszko K, Chłopek K, Majkowska-Wojciechowska B, Uruska A - Aerobiologia (Bologna) (2014)

Bottom Line: The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions.The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back.These results can help to improve the quality of forecasting models.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Dzięgielowa 27, 61-680 Poznań, Poland.

ABSTRACT

The aim of the study was to determine the characteristics of temporal and space-time autocorrelation of pollen counts of Alnus, Betula, and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001-2005 and 2009-2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus, Betula, and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30-40 % of pollen count variation); (2) long-lasting factors (50-60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.

No MeSH data available.


Related in: MedlinePlus

Correlation of concentrations of individual pollen taxa with a 1-day lag between the locations as a function of distance. The diagrams present linear regression curves and their 95 % confidence intervals (shaded), formulae for the models employed, and the significance level (p value) of functions. Only outlying pairs of stations are labelled
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Fig8: Correlation of concentrations of individual pollen taxa with a 1-day lag between the locations as a function of distance. The diagrams present linear regression curves and their 95 % confidence intervals (shaded), formulae for the models employed, and the significance level (p value) of functions. Only outlying pairs of stations are labelled

Mentions: The correlation of individual pollen counts between the monitoring sites as a function of distance was shown in Figs. 7 (the same day) and 8 (with a 1-day offset). Few sites with regimes of pollen counts different to the remaining were identified. Pollen concentrations in Gdańsk were notably distinctive, especially to those noted in Szczecin, Poznań, and Lublin. Also, the correlation between some pairs of the cities was higher than others, especially in Kraków–Łódź (Alnus, Betula, and Corylus pollen), Lublin–Sosnowiec, Szczecin–Sosnowiec (Betula pollen), Gdańsk–Łódź, and Szczecin–Kraków (Corylus pollen).Fig. 7


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

Nowosad J, Stach A, Kasprzyk I, Grewling Ł, Latałowa M, Puc M, Myszkowska D, Weryszko-Chmielewska E, Piotrowska-Weryszko K, Chłopek K, Majkowska-Wojciechowska B, Uruska A - Aerobiologia (Bologna) (2014)

Correlation of concentrations of individual pollen taxa with a 1-day lag between the locations as a function of distance. The diagrams present linear regression curves and their 95 % confidence intervals (shaded), formulae for the models employed, and the significance level (p value) of functions. Only outlying pairs of stations are labelled
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4555345&req=5

Fig8: Correlation of concentrations of individual pollen taxa with a 1-day lag between the locations as a function of distance. The diagrams present linear regression curves and their 95 % confidence intervals (shaded), formulae for the models employed, and the significance level (p value) of functions. Only outlying pairs of stations are labelled
Mentions: The correlation of individual pollen counts between the monitoring sites as a function of distance was shown in Figs. 7 (the same day) and 8 (with a 1-day offset). Few sites with regimes of pollen counts different to the remaining were identified. Pollen concentrations in Gdańsk were notably distinctive, especially to those noted in Szczecin, Poznań, and Lublin. Also, the correlation between some pairs of the cities was higher than others, especially in Kraków–Łódź (Alnus, Betula, and Corylus pollen), Lublin–Sosnowiec, Szczecin–Sosnowiec (Betula pollen), Gdańsk–Łódź, and Szczecin–Kraków (Corylus pollen).Fig. 7

Bottom Line: The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions.The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back.These results can help to improve the quality of forecasting models.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Dzięgielowa 27, 61-680 Poznań, Poland.

ABSTRACT

The aim of the study was to determine the characteristics of temporal and space-time autocorrelation of pollen counts of Alnus, Betula, and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001-2005 and 2009-2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus, Betula, and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30-40 % of pollen count variation); (2) long-lasting factors (50-60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.

No MeSH data available.


Related in: MedlinePlus