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",
"weektrajLevel. 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
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 mapdatamap.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 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
projectiongrid.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 trajLevel
should be used. There are several trajectory statistics that can
be plotted as gridded surfaces. First, statistic can be set
to 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.
Seibert, P., Kromp-Kolb, H., Baltensperger, U., Jost, D., 1994. Trajectory analysis of high-alpine air pollution data. NATO Challenges of Modern Society 18, 595-595.
Xie, Y., & Berkowitz, C. M. (2007). The use of conditional probability functions and potential source contribution functions to identify source regions and advection pathways of hydrocarbon emissions in Houston, Texas. Atmospheric Environment, 41(28), 5831-5847.
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
lond <- importTraj("london", 2010)
# more examples to follow linking with concentration measurements...
# import some measurements from KC1 - London
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")Run the code above in your browser using DataLab