This function plots gridded back trajectories. This function requires that
data are imported using the `importTraj()`

function.

## Usage

```
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,
.combine = NA,
sigma = 1.5,
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,
plot = TRUE,
...
)
```

## Arguments

- mydata
Data frame, the result of importing a trajectory file using

`importTraj`

.- 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
`type`

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.- smooth
Should the trajectory surface be smoothed?

- statistic
Statistic to use for

`trajLevel()`

. By default, the function will plot the trajectory frequencies (`statistic = "frequency"`

). As an alternative way of viewing trajectory frequencies, 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.There are also various ways of plotting concentrations.

It is 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 frequencies of the origin of air masses. The comparison is made by comparing the percentage change in gridded frequencies. For example, such a plot could show that the top 10\ tend to originate from air-mass origins to the east.If

`statistic = "pscf"`

then a Potential Source Contribution Function map is produced. This statistic method interacts with`percentile`

.If

`statistic = "cwt"`

then concentration weighted trajectories are plotted.If

`statistic = "sqtba"`

then Simplified Quantitative Transport Bias Analysis is undertaken. This statistic method interacts with`.combine`

and`sigma`

.- percentile
The percentile concentration of

`pollutant`

against which the all trajectories are compared.- map
Should a base map be drawn? If

`TRUE`

the world base map from the`maps`

package is used.- lon.inc, lat.inc
The longitude and latitude intervals to be used for binning data.

- min.bin
The minimum number of unique points in a grid cell. Counts below

`min.bin`

are set as missing.- .combine
When statistic is "SQTBA" it is possible to combine lots of receptor locations to derive a single map.

`.combine`

identifies the column that differentiates different sites (commonly a column named`"site"`

). Note that individual site maps are normalised first by dividing by their mean value.- sigma
For the SQTBA approach

`sigma`

determines the amount of back trajectory spread based on the Gaussian plume equation. Values in the literature suggest 5.4 km after one hour. However, testing suggests lower values reveal source regions more effectively while not introducing too much noise.- 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

`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.cols
If

`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.- map.alpha
The transparency 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

`mapproj`

package. See`?mapproject`

for extensive details and information on setting other parameters and orientation (see below).- parameters
From the

`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`

.- orientation
From the

`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
The colour of the map grid to be used. To remove the grid set

`grid.col = "transparent"`

.- origin
If true a filled circle dot is shown to mark the receptor point.

- plot
Should a plot be produced?

`FALSE`

can be useful when analysing data to extract plot components and plotting them in other ways.- ...
other arguments are passed to

`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,`trajLevel()`

passes the argument`cex`

on to`scatterPlot()`

which in turn passes it on to`lattice::xyplot()`

where it is applied to set the plot symbol size.

## Value

an openair object

## Details

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 frequencies of the origin of
air masses of the highest concentration trajectories compared with the
trajectories on average. The comparison is made by comparing the percentage
change in gridded frequencies. For example, such a plot could show that the
top 10\
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`

.

## References

Pekney, N. J., Davidson, C. I., Zhou, L., & Hopke, P. K. (2006). Application of PSCF and CPF to PMF-Modeled Sources of PM 2.5 in Pittsburgh. Aerosol Science and Technology, 40(10), 952-961.

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.

## See also

Other trajectory analysis functions:
`importTraj()`

,
`trajCluster()`

,
`trajPlot()`

## Examples

```
# 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
if (FALSE) { # \dontrun{
lond <- importTraj("london", 2010)
} # }
# more examples to follow linking with concentration measurements...
# import some measurements from KC1 - London
if (FALSE) { # \dontrun{
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"
)
} # }
```