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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

Matrix of sample (experimental) correlogram plots for the entire data set (a) and after the elimination of extreme figures (b-first threshold, c-second threshold) for the individual locations (rows) and taxa (columns)
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Fig5: Matrix of sample (experimental) correlogram plots for the entire data set (a) and after the elimination of extreme figures (b-first threshold, c-second threshold) for the individual locations (rows) and taxa (columns)

Mentions: Since all the measurement series exhibited highly skewed distributions with a predominance of low and average values and single, far outlying extreme values, the autocorrelation analysis was conducted twice: for the entire set and with the extremes eliminated. For the determination of extreme values, two threshold values were used. The first was put at the place of disruption of the frequency histogram (selection 1), the other cut off the data outliers (selection 2) (Fig. 5).


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)

Matrix of sample (experimental) correlogram plots for the entire data set (a) and after the elimination of extreme figures (b-first threshold, c-second threshold) for the individual locations (rows) and taxa (columns)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Matrix of sample (experimental) correlogram plots for the entire data set (a) and after the elimination of extreme figures (b-first threshold, c-second threshold) for the individual locations (rows) and taxa (columns)
Mentions: Since all the measurement series exhibited highly skewed distributions with a predominance of low and average values and single, far outlying extreme values, the autocorrelation analysis was conducted twice: for the entire set and with the extremes eliminated. For the determination of extreme values, two threshold values were used. The first was put at the place of disruption of the frequency histogram (selection 1), the other cut off the data outliers (selection 2) (Fig. 5).

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