importTraj
function.trajLevel(mydata, lon = "lon", lat = "lat", pollutant = "height",
type = "default", smooth = FALSE, statistic = "frequency",
percentile = 90, map = TRUE, lon.inc = 1, lat.inc = 1, min.bin = 1,
map.fill = TRUE, map.res = "default", map.cols = "grey40",
map.alpha = 0.3, projection = "lambert", parameters = c(51, 51),
orientation = c(90, 0, 0), grid.col = "deepskyblue", origin = TRUE, ...)importTrajtype determines how the data are split
i.e. conditioned, and then plotted. The default is will produce a
single plot using the entire data. Type can be one of the built-in
types as detailed in cutData e.g. "season", "year",
"weekday" and so on. For example, type = "season" will
produce four plots --- one for each season.It is also possible to choose type as another variable in
the data frame. If that variable is numeric, then the data will be
split into four quantiles (if possible) and labelled
accordingly. If type is an existing character or factor variable,
then those categories/levels will be used directly. This offers
great flexibility for understanding the variation of different
variables and how they depend on one another.
type can be up length two e.g. type = c("season",
"weekday") will produce a 2x2 plot split by season and day of the
week. Note, when two types are provided the first forms the
columns and the second the rows.
trajLevel. By default the function
will plot the trajectory frequencies.For trajLevel, the argument method = "hexbin" can be
used. In this case hexagonal binning of the trajectory
points (i.e. a point every three hours along each back
trajectory). The plot then shows the trajectory frequencies uses
hexagonal binning. This is an alternative way of viewing
trajectory frequencies compared with statistic =
"frequency".
There are also various ways of plotting concentrations.
It is also possible to set statistic = "difference". In
this case trajectories where the associated concentration is
greater than percentile are compared with the the full set
of trajectories to understand the differences in freqeuncies of
the origin of air masses. The comparsion is made by comparing the
percentage change in gridded frequencies. For example, such a plot
could show that the top 10% of concentrations of PM10 tend to
orginate from air-mass origins to the east.
If statistic = "pscf" then a Potential Source Contribution
Function map is produced. If statistic = "cwt" then
concentration weighted trajectories are plotted.
If statistic = "cwt" then the Concentration Weighted
Trajectory approach is used. See details.
trajLevel. The percentile
concentration of pollutant against which the all
trajectories are compared.TRUE the world
base map from the maps package is used.trajLevel.trajLevel.trajLevel the minimum number of unique
points in a grid cell. Counts below min.bin are set as
missing. For trajLevel gridded outputs.maps
package. If map.res = "hires" then the (much) more detailed
base map ‘worldHires’ from the mapdata package is
used. Use library(mapdata). Also available is a map showing
the US states. In this case map.res = "state" should be
used.map.fill = TRUE map.cols controls
the fill colour. Examples include map.fill = "grey40" and
map.fill = openColours("default", 10). The latter colours
the countries and can help differentiate them.mapproj
package. See ?mapproject for extensive details and information
on setting other parameters and orientation (see below).mapproj package. Optional
numeric vector of parameters for use with the projection
argument. This argument is optional only in the sense that certain
projections do not require additional parameters. If a projection
does not require additional parameters then set to null
i.e. parameters = NULL.mapproj package. An optional
vector c(latitude, longitude, rotation) which describes where the
"North Pole" should be when computing the projection. Normally
this is c(90, 0), which is appropriate for cylindrical and conic
projections. For a planar projection, you should set it to the
desired point of tangency. The third value is a clockwise rotation
(in degrees), which defaults to the midrange of the longitude
coordinates in the map.grid.col = "transparent".cutData and
scatterPlot. This provides access to arguments used in both
these functions and functions that they in turn pass arguments on
to. For example, plotTraj passes the argument cex on
to scatterPlot which in turn passes it on to the
lattice function xyplot where it is applied to set
the plot symbol size.trajLevel
should be used. There are several trajectory statistics that can
be plotted as gridded surfaces. First, statistic can be set
to “frequency” to show the number of back trajectory points
in a grid square. Grid squares are by default at 1 degree
intervals, controlled by lat.inc and lon.inc. Such
plots are useful for showing the frequency of air mass
locations. Note that it is also possible to set method =
"hexbin" for plotting frequencies (not concentrations), which
will produce a plot by hexagonal binning. If statistic = "difference" the trajectories associated
with a concentration greater than percentile are compared
with the the full set of trajectories to understand the
differences in freqeuncies of the origin of air masses of the
highest concentration trajectories compared with the trajectories
on average. The comparsion is made by comparing the percentage
change in gridded frequencies. For example, such a plot could show
that the top 10% of concentrations of PM10 tend to orginate from
air-mass origins to the east. If statistic = "pscf" then the Potential Source
Contribution Function is plotted. The PSCF calculates the
probability that a source is located at latitude \(i\) and
longitude \(j\) (Pekney et al., 2006).The basis of PSCF is that
if a source is located at (i,j), an air parcel back trajectory
passing through that location indicates that material from the
source can be collected and transported along the trajectory to
the receptor site. PSCF solves $$PSCF = m_{ij}/n_{ij}$$ where
\(n_{ij}\) is the number of times that the trajectories passed
through the cell (i,j) and \(m_{ij}\) is the number of times
that a source concentration was high when the trajectories passed
through the cell (i,j). The criterion for de-termining
\(m_{ij}\) is controlled by percentile, which by default
is 90. Note also that cells with few data have a weighting factor
applied to reduce their effect. A limitation of the PSCF method is that grid cells can have the
same PSCF value when sample concentrations are either only
slightly higher or much higher than the criterion. As a result, it
can be difficult to distinguish moderate sources from strong
ones. Seibert et al. (1994) computed concentration fields to
identify source areas of pollutants. The Concentration Weighted
Trajectory (CWT) approach considers the concentration of a species
together with its residence time in a grid cell. The CWT approach
has been shown to yield similar results to the PSCF approach. The
openair manual has more details and examples of these approaches. A further useful refinement is to smooth the resulting surface,
which is possible by setting smooth = TRUE.importTraj to import trajectory data from the King's
College server and trajPlot for plotting back trajectory lines.
# show a simple case with no pollutant i.e. just the trajectories
# let's check to see where the trajectories were coming from when
# Heathrow Airport was closed due to the Icelandic volcanic eruption
# 15--21 April 2010.
# import trajectories for London and plot
## Not run: ------------------------------------
# lond <- importTraj("london", 2010)
## ---------------------------------------------
# more examples to follow linking with concentration measurements...
# import some measurements from KC1 - London
## Not run: ------------------------------------
# kc1 <- importAURN("kc1", year = 2010)
# # now merge with trajectory data by 'date'
# lond <- merge(lond, kc1, by = "date")
#
# # trajectory plot, no smoothing - and limit lat/lon area of interest
# # use PSCF
# trajLevel(subset(lond, lat > 40 & lat < 70 & lon >-20 & lon <20),
# pollutant = "pm10", statistic = "pscf")
#
# # can smooth surface, suing CWT approach:
# trajLevel(subset(lond, lat > 40 & lat < 70 & lon >-20 & lon <20),
# pollutant = "pm2.5", statistic = "cwt", smooth = TRUE)
#
# # plot by season:
# trajLevel(subset(lond, lat > 40 & lat < 70 & lon >-20 & lon <20), pollutant = "pm2.5",
# statistic = "pscf", type = "season")
## ---------------------------------------------
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