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

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

Changes to Airborne Pollen Counts across Europe

A progressive global increase in the burden of allergic diseases has affected the industrialized world over the last half century and has been reported in the literature. The clinical evidence reveals a general increase in both incidence and prevalence of respiratory diseases, such as allergic rhinitis (common hay fever) and asthma. Such phenomena may be related not only to air pollution and changes in lifestyle, but also to an actual increase in airborne quantities of allergenic pollen. Experimental enhancements of carbon dioxide (CO2) have demonstrated changes in pollen amount and allergenicity, but this has rarely been shown in the wider environment. The present analysis of a continental-scale pollen data set reveals an increasing trend in the yearly amount of airborne pollen for many taxa in Europe, which is more pronounced in urban than semi-rural/rural areas. Climate change may contribute to these changes, however increased temperatures do not appear to be a major influencing factor. Instead, we suggest the anthropogenic rise of atmospheric CO2 levels may be influentia

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

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

API trends against temperature trends by species.

<p>Proportional annual change of yearly pollen sums was plotted against local temperature trends for 23 pollen taxa. Temperature trends were calculated for each location for the mean temperature of two seasons, January to April (associated with the flowering of <i>Alnus</i>, <i>Betula</i>, <i>Carpinus</i>, <i>Corylus</i>, Cupressaceae, <i>Fagus</i>, <i>Fraxinus</i>, <i>Olea</i>, Pinaceae, <i>Platanus</i>, <i>Populus</i>, <i>Quercus</i>, <i>Salix</i>, and <i>Ulmus</i>) or April to August (related to <i>Ambrosia</i>, <i>Artemisia</i>, <i>Castanea</i>, Chenopodiaceae, <i>Plantago</i>, Poaceae, <i>Rumex</i>, <i>Tilia</i>, and <i>Urtica</i>), over the years 1977–2009. A regression line has been superimposed for <i>Betula</i> and <i>Carpinus</i>, the only statistically significant relationships.</p

Maximum duration of pollen series by location.

<p>The local longest monitored period is shown as a red bar for each of the 97 locations considered. Missing years, occurring in few cases, have been omitted for clarity.</p

API trends by environment type.

<p>Boxplots show the proportional annual change of yearly pollen sums for different environments. Mann-Whitney tests show a significant increase (median different from zero, ) of airborne pollen in urban environments. The notches are calculated as and the height of each boxplot is related to sample size. On the right, the percentages of significant trends are indicated for each type of environment (of which the percentages of positive trends are given in parentheses).</p

Mean API against mean local temperature.

<p>Log-scaled mean annual sum of airborne pollen was plotted against local mean temperature for 23 pollen taxa. Mean temperatures were calculated for two periods, January to April (associated with the flowering of <i>Alnus</i>, <i>Betula</i>, <i>Carpinus</i>, <i>Corylus</i>, Cupressaceae, <i>Fagus</i>, <i>Fraxinus</i>, <i>Olea</i>, Pinaceae, <i>Platanus</i>, <i>Populus</i>, <i>Quercus</i>, <i>Salix</i>, and <i>Ulmus</i>) or April to August (related to <i>Ambrosia</i>, <i>Artemisia</i>, <i>Castanea</i>, Chenopodiaceae, <i>Plantago</i>, Poaceae, <i>Rumex</i>, <i>Tilia</i>, and <i>Urtica</i>), over the period 1977–2009. Only significant regression lines are shown.</p