Trajectory level plots with conditioning

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,
  lon.inc = 1,
  lat.inc = lon.inc,
  min.bin = 1,
  .combine = NULL,
  sigma = 1.5,
  cols = "default",
  crs = 4326,
  map = TRUE,
  map.fill = TRUE,
  map.cols = "grey40",
  map.border = "black",
  map.alpha = 0.3,
  map.lwd = 1,
  map.lty = 1,
  grid.col = "deepskyblue",
  grid.nx = 9,
  grid.ny = grid.nx,
  origin = TRUE,
  key = TRUE,
  key.position = "right",
  key.columns = NULL,
  strip.position = "top",
  auto.text = TRUE,
  plot = TRUE,
  ...
)

Arguments

mydata

Data frame, the result of importing a trajectory file using importTraj().

lon, lat

Columns containing the decimal longitude and latitude.

pollutant

Pollutant (or any numeric column) to be plotted, if any. Alternatively, use group.

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

One of:

"frequency" (the default) shows trajectory frequencies.
"hexbin", which is similar to "frequency" but shows a hexagonal grid of counts.
"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\ to the east.
"pscf" for a Potential Source Contribution Function map. This statistic method interacts with percentile.
"cwt" for concentration weighted trajectories.
"sqtba" to undertake Simplified Quantitative Transport Bias Analysis. This statistic method interacts with .combine and sigma.

percentile

The percentile concentration of pollutant against which the all trajectories are compared.

lon.inc, lat.inc

The longitude and latitude intervals to be used for binning data. If statistic = "hexbin", the minimum value out of of lon.inc and lat.inc is passed to the binwidth argument of ggplot2::geom_hex().

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.

cols

Colours for plotting. Passed to openColours().

crs

The coordinate reference system to use for plotting. Defaults to 4326, which is the WGS84 geographic coordinate system, the standard, unprojected latitude/longitude system used in GPS, Google Earth, and GIS mapping. Other crs values are available - for example, 27700 will use the the OSGB36/British National Grid.

map

Should a base map be drawn? If TRUE the world base map provided by ggplot2::map_data() will be used.

map.fill

Should the base map be a filled polygon? Default is to fill countries.

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.border

The colour to use for the map outlines/borders. Defaults to "black".

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.

map.lwd

The map line width, a positive number, defaulting to 1.

map.lty

The map line type. Line types can either be specified as an integer (0 = blank, 1 = solid (default), 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash) or as one of the character strings "blank", "solid", "dashed", "dotted", "dotdash", "longdash", or "twodash", where "blank" uses 'invisible lines' (i.e., does not draw them).

grid.col

The colour of the map grid to be used. To remove the grid set grid.col = "transparent".

grid.nx, grid.ny

The approximate number of ticks to draw on the map grid. grid.nx defaults to 9, and grid.ny defaults to whatever value is passed to grid.nx. Setting both values to 0 will remove the grid entirely. The number of ticks is approximate as this value is passed to scales::breaks_pretty() to determine nice-looking, round breakpoints.

origin

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

key

Should a key be drawn? Defaults to TRUE.

key.position

Location where the scale key should be plotted. Allowed arguments currently include "top", "right", "bottom", and "left".

key.columns

Number of columns to be used in the key.

strip.position

Location where the facet 'strips' are located when using type. When one type is provided, can be one of "left", "right", "bottom" or "top". When two types are provided, this argument defines whether the strips are "switched" and can take either "x", "y", or "both". For example, "x" will switch the 'top' strip locations to the bottom of the plot.

auto.text

Either TRUE (default) or FALSE. If TRUE titles and axis labels will automatically try and format pollutant names and units properly e.g. by subscripting the ‘2’ in NO2.

plot

Should a plot be produced? FALSE can be useful when analysing data to extract plot components and plotting them in other ways.

...

Addition options are passed on to cutData() for type handling. Some additional arguments are also available:

xlab, ylab and main override the x-axis label, y-axis label, and plot title.
layout sets the layout of facets - e.g., layout(2, 5) will have 2 columns and 5 rows.
fontsize overrides the overall font size of the plot.
border sets the border colour of each tile.

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 statistic = "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.

Note

This function is under active development and is likely to change

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.

Author

David Carslaw

Jack Davison

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"
)
} # }