Description Usage Arguments Details Value References See Also Examples
Function to calculate the pollen calendar from a historical database of several pollen types and using the most commonly used methods in the generation of the pollen calendars in the aerobiology field.
1 2 3 4 5 6 7 8 | pollen_calendar(data, method = "heatplot", n.types = 15,
start.month = 1, y.start = NULL, y.end = NULL, perc1 = 80,
perc2 = 99, th.pollen = 1, average.method = "avg_before",
period = "daily", method.classes = "exponential", n.classes = 5,
classes = c(25, 50, 100, 300), color = "green",
interpolation = TRUE, int.method = "lineal", na.remove = TRUE,
result = "plot", export.plot = FALSE, export.format = "pdf",
legendname = "Pollen grains / m3", ...)
|
data |
A |
method |
A |
n.types |
A |
start.month |
A |
y.start, y.end |
A |
perc1, perc2 |
A |
th.pollen |
A |
average.method |
A |
period |
A |
method.classes |
A |
n.classes |
A |
classes |
A |
color |
A |
interpolation |
A |
int.method |
A |
na.remove |
A |
result |
A |
export.plot |
A |
export.format |
A |
legendname |
A |
... |
Other additional arguments may be used to customize the exportation of the plots using |
This function allows to calculate and generate the pollen calendar using three different methods which are described below. The pollen calendar will be calculated and generated only for the period specified by the user using the y.start
and y.end
arguments, and for the specified number of the most abundant pollen types using the n.types
argument by the user. The most abundant pollen types will be selected according to the highest average annual amounts of pollen registered by the pollen types during the considered period.
"heatplot"
method. This pollen calendar is constructed based on the daily or weekly average of pollen concentrations, depending of the preferences of the user that may select "daily"
or "weekly"
as period
argument. Then, these averages may be classified in different categories following different methods selected by the user according to the method.classes
argument. If method.classes = "exponential"
the user will apply the classification based on exponential classes proposed by Stix and Ferreti (1974), which has been commonly used in aerobiology for the generation of pollen calendars. The classification based on exponential method considers 11 classes (1_2, 3_5, 6_11, 12_24, 25_49, 50_99, 100_199, 200_399, 400_799, 800_1600, >1600
). An example of this pollen calendar may be consulted in Rojo et al. (2016). This method to design pollen calendars is an adaptation from the pollen calendar proposed by Spieksma (1991) who considered 10_day periods instead of daily or weekly periods. Otherwise, if method.classes = "custom"
the user may customize the classification according to the number of classes selected (n.classes
argument) and the thresholds of the pollen concentrations used to define the classes (classes
argument). Average values below the level of the th.pollen
argument will be removed from the pollen calendar.
"phenological"
method. This pollen calendar is based on phenological definition of the pollen season and adapted from the methodology proposed by Werchan et al. (2018). After to obtain daily average pollen concentrations for the most abundant pollen types different pollination periods were calculated using the daily averages. The main pollination period was calculated based on the percentage defined by perc1
argument (selected by the user, 80% by default) of the annual total pollen. For example, if perc1 = 80
the beginning of the high season was marked when 10% of the annual value was reached and the end was selected when 90% was reached. In the case of the early/late pollination a total of the percentage defined by perc2
argument (selected by the user, 99% by default) of the annual total pollen will be registered during this period. For this kind of pollen calendar the th.pollen
argument will define the "possible occurrence" period as adapted by Werchan et al. (2018), considering the entire period between the first and the last day when this pollen level is reached. In an alternative way the average may be carried out after to define the pollen seasons using method_average = "avg_after"
(instead of "avg_before"
by default). "avg_after"
determines the pollen season for all years and all pollen types, and then an average for circular data is calculated from the start_dates and end_dates.
"violinplot"
method. This pollen calendar is based on the pollen intensity and adapted from the pollen calendar published by ORourke (1990). In first time the daily averages of the pollen concentrations are calculated and then these averages are represented using the violin plot graph. The shape of the violin plot display the pollen intensity of the pollen types in a relative way i.e. the values will be calculated as relative measurements regarding to the most abundant pollen type in annual amounts. Therefore, this pollen calendar shows a relative comparison between the pollen intensity of the pollen types but without scales and units. Average values below the level of the th.pollen
argument will be removed from the pollen calendar.
Pollen time series frequently have different gaps with no data and this fact could be a problem for the calculation of specific methods for defining the pollen season even providing incorrect results. In this sense by default a linear interpolation will be carried out to complete these gaps before to generate the pollen calendar. For more information to see the interpollen
function.
This function returns different results:
plot
in the active graphics window displaying the pollen calendar generated by the user when result = "plot"
. This plot may be included in an object by assignment operators.
data.frame
including the daily or weekly average pollen concentrations (according to the selection of the user) used to generate the pollen calendar. This data.frame
will be returned when result = "table"
.
If export.plot = TRUE
this plot displaying the pollen calendar will also be exported as file within the Plot_AeRobiology" directory created in the working directory.
If export.plot = TRUE
and export.format = pdf
a pdf file of the pollen calendar will be saved within the plot_AeRobiology directory created in the working directory. Additional characteristics may be incorporated to the exportation as pdf file (see grDevices package)
If export.plot = TRUE
and export.format = png
a png file of the pollen calendar will be saved within the plot_AeRobiology directory created in the working directory. Additional characteristics may be incorporated to the exportation as png file (see grDevices package).
ORourke, M.K., 1990. Comparative pollen calendars from Tucson, Arizona: Durhamvs. Burkard samplers. Aerobiologia, 6(2), p.136_140.
Rojo, J., Rapp, A., Lara, B., Sabariego, S., Fernandez_Gonzalez, F. and Perez_Badia, R., 2016. Characterisation of the airborne pollen spectrum in Guadalajara (central Spain) and estimation of the potential allergy risk. Environmental Monitoring and Assessment, 188(3), p.130.
Spieksma, F.T.M., 1991. Regional European pollen calendars. Allergenic pollen and pollinosis in Europe, pp.49_65.
Stix, E. and Ferretti, M.L., 1974. Pollen calendars of three locations in Western Germany. Atlas European des Pollens Allergisants, pp.85_94.
Werchan, M., Werchan, B. and Bergmann, K.C., 2018. German pollen calendar 4.0_update based on 2011_2016 pollen data. Allergo Journal International, 27, pp.69_71.
1 2 | data("munich_pollen")
pollen_calendar(munich_pollen, method = "heatplot", interplation = FALSE)
|
Warning messages:
1: In pollen_calendar(munich_pollen, method = "heatplot", interplation = FALSE) :
WARNING: the number of columns is smaller than 'n.types' argument. 'n.types' adjusted to 8
2: In interpollen(data, method = int.method, plot = F) :
WARNING: Gaps with more than 30 missing data have not been interpolated
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