Description Usage Arguments Details Value References See Also Examples
Function to calculate the main seasonal indexes of the pollen season (Start Date, Peak Date, End Date and Pollen Integral). Trends analysis of the parameters over the seasons. Summary dot plot showing the distribution of the main seasonal indexes over the years.
1 2 3 4 |
data |
A |
interpolation |
A |
int.method |
A |
export.plot |
A |
export.format |
A |
export.result |
A |
method |
A |
quantil |
A |
significant |
A |
split |
A |
result |
A |
... |
Additional arguments for the function |
This function allows to study time series trends of the pollen season. Even though the package was originally designed to treat aeropalynological data, it can be used to study many other atmospheric components (e.g., bacteria in the air, fungi, insects ...) (Buters et al., 2018; Oteros et al., 2019). The study of trends in pollen time series is a common approach to study the impact of climate change or other environmental factors on vegetation (Galan et al., 2016; Garcia_Mozo et al., 2016; Recio et al., 2018). This tool can also be useful for studying trends in other fields (Oteros et al., 2015).
If result == "plot"
, the function returns a list of objects of class ggplot2; if result == "table"
, the function returns a data.frame with the hourly patterns.
The plot is of the class ggplot2 or a list of plots of the class ggplot2 (depending on the argument split
). This is a combined dot plot showing the trends (slope and p value) of the main seasonal features.
The object of the class data.frame
has the information about the slope
(beta coefficient of a lineal model using as predictor the year and as dependent variable one of the main pollen season indexes). The information is referred to the main pollen season indexes: Start Date, Peak Date, End Date and Pollen Integral.
Buters, J. T. M., Antunes, C., Galveias, A., Bergmann, K. C., Thibaudon, M., Galan, C., ... & Oteros, J. (2018). Pollen and spore monitoring in the world. Clinical and translational allergy, 8(1), 9.
Galan, C., Alcazar, P., Oteros, J., Garcia_Mozo, H., Aira, M. J., Belmonte, J., ... & Perez_Badia, R. (2016). Airborne pollen trends in the Iberian Peninsula. Science of the Total Environment, 550, 53_59.
Garcia_Mozo, H., Oteros, J. A., & Galan, C. (2016). Impact of land cover changes and climate on the main airborne pollen types in Southern Spain. Science of the Total Environment, 548, 221_228.
Oteros, J., Garcia_Mozo, H., Botey, R., Mestre, A., & Galan, C. (2015). Variations in cereal crop phenology in Spain over the last twenty_six years (1986_2012). Climatic Change, 130(4), 545_558.
Oteros, J., Bartusel, E., Alessandrini, F., Nunez, A., Moreno, D. A., Behrendt, H., ... & Buters, J. (2019). Artemisia pollen is the main vector for airborne endotoxin. Journal of Allergy and Clinical Immunology.
Recio, M., Picornell, A., Trigo, M. M., Gharbi, D., Garcia_Sanchez, J., & Cabezudo, B. (2018). Intensity and temporality of airborne Quercus pollen in the southwest Mediterranean area: Correlation with meteorological and phenoclimatic variables, trends and possible adaptation to climate change. Agricultural and Forest Meteorology, 250, 308_318.
1 2 | data("munich_pollen")
analyse_trend(munich_pollen, interpolation = FALSE, export.result = FALSE, export.plot = FALSE)
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