trajLevel.Rd
This function plots gridded back trajectories. This function
requires that data are imported using the 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, ...)
mydata  Data frame, the result of importing a trajectory
file using 

lon  Column containing the longitude, as a decimal. 
lat  Column containing the latitude, as a decimal. 
pollutant  Pollutant to be plotted. By default the trajectory height is used. 
type 
It is also possible to choose

smooth  Should the trajectory surface be smoothed? 
statistic  For For There are also various ways of plotting concentrations. It is also possible to set If If 
percentile  For 
map  Should a base map be drawn? If 
lon.inc  The longitudeinterval to be used for binning data
for 
lat.inc  The latitudeinterval to be used for binning data
when 
min.bin  For 
map.fill  Should the base map be a filled polygon? Default is to fill countries. 
map.res  The resolution of the base map. By default the
function uses the ‘world’ map from the 
map.cols  If 
map.alpha  The transpency level of the filled map which takes values from 0 (full transparency) to 1 (full opacity). Setting it below 1 can help view trajectories, trajectory surfaces etc. and a filled base map. 
projection  The map projection to be used. Different map
projections are possible through the 
parameters  From the 
orientation  From the 
grid.col  The colour of the map grid to be used. To remove
the grid set 
origin  should the receptor origin be shown by a black dot? 
...  other arguments are passed to 
An alternative way of showing the trajectories compared with
plotting trajectory lines is to bin the points into
latitude/longitude intervals. For these purposes 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
airmass 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 determining
\(m_{ij}\) 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
.
This function is under active development and is likely to change
Pekney, N. J., Davidson, C. I., Zhou, L., & Hopke, P. K. (2006). Application of PSCF and CPF to PMFModeled Sources of PM 2.5 in Pittsburgh. Aerosol Science and Technology, 40(10), 952961.
Seibert, P., KrompKolb, H., Baltensperger, U., Jost, D., 1994. Trajectory analysis of highalpine air pollution data. NATO Challenges of Modern Society 18, 595595.
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), 58315847.
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 # 1521 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") # }