openair (version 0.3-8)

polarPlot: Bivariate polarPlot with smoothing

Description

Function for plotting pollutant concentration in polar coordinates showing concentration by wind speed and direction. Mean concentrations are calculated for wind speed-direction bins (0-1, 1-2 m/s,... and 0-10, 10-20 degrees etc.). To aid interpretation, gam smoothing is carried out using mgcv.

Usage

polarPlot(polar, 
    pollutant = "nox", type = "default",
    resolution = "normal", limits = c(0, 100), 
    exclude.missing = TRUE, uncertainty = FALSE, 
    cols = "default", min.bin = 1, upper = 10, 
    ws.int = 5, angle.scale = 45, units = "(m/s)", 
    force.positive = TRUE, k = 100, main = "", 
    key.header = "", key.footer = pollutant, 
    key.position = "right", key = NULL, 
    auto.text = TRUE, ...)

Arguments

polar

A data frame minimally containing ws, wd and a pollutant. Can also contain date if plots by time period are required.

pollutant

Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox"

type

The type of analysis to be done. The default is will produce a single plot using the entire data. Other types include "hour" (for hour of the day), "weekday" (for day of the week) and "month" (for month of the year), "year" for a polarPlot

resolution

Two plot resolutions can be set: "normal" (the default) and "fine", for a smoother plot. It should be noted that plots with a "fine" resolution can take longer to render and the default option should be sufficient or most circumstances.

limits

The function does its best to choose sensible limits automatically. However, there are circumstances when the user will wish to set different ones. An example would be a series of plots showing each year of data separately. The limits are s

exclude.missing

Setting this option to TRUE (the default) removes points from the plot that are too far from the original data. The smoothing routines will produce predictions at points where no data exist i.e. they predict. By removing the po

uncertainty

Should the uncertainty in the calculated surface be shown? If TRUE three plots are produced on the same scale showing the predicted surface together with the estimated lower and upper uncertainties at the 95% confidence interva

cols

Colours to be used for plotting. Options include "default", "increment", "heat", "jet" and user defined. For user defined the user can supply a list of colour names recognised by R (type colours() to see the full list). An exam

min.bin

The minimum number of points allowed in a wind speed/wind direction bin. The default is 1. A value of two requires at least 2 valid records in each bin an so on; bins with less than 2 valid records are set to NA. Care should be taken when

upper

This sets the upper limit wind speed to be used. Often there are only a relatively few data points at very high wind speeds and plotting all of them can reduce the useful information in the plot.

ws.int

This sets the (dashed) circular grid line spacing. The default is 5. It can be useful, for example, to set ws.int to a lower value when the wind speeds are low to get a sensible grid spacing.

angle.scale

The wind speed scale is by default shown at a 45 degree angle. Sometimes the placement of the scale may interfere with an interesting feature. The user can therefore set angle.scale to another value (between 0 and 360 degrees)

units

The units shown on the wind speed scale. These are only shown on the third highest ws.int interval to reduce chart clutter.

force.positive

The default is TRUE. Sometimes if smoothing data with streep gradients it is possible for predicted values to be negative. force.positive = TRUE ensures that predictions remain postive. This is useful for several r

k

This is the smoothing parameter that is set if auto.smooth is set to FALSE. Typically, value of around 100 (the default) seems to be suitable and will resolve more features in the plot.

main

The plot title; default is no title.

key.header, key.footer

Adds additional text/labels to the scale key. For example, passing the options key.header = "header", key.footer = "footer1" adds addition text above and below the scale key. These arguments are passed to drawOpenKey

key.position

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

key

Fine control of the scale key via drawOpenKey. See drawOpenKey for further details.

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.

...

Other graphical parameters.

Warning

Some plots can take a long time to produce e.g. if there are many years of data and type = "year" is chosen. The function may fail if there are insufficient data to smooth the surface.

Details

The bivariate polarPlot is a useful diagnostic tool for quickly gaining an idea of potential sources. Wind speed is one of the most useful variables to use to separate source types (see references). For example, ground-level concentrations resulting from buoyant plumes from chimney stacks tend to peak under higher wind speed conditions. Conversely, ground-level, non-buoyant plumes such as from road traffic, tend to have highest concentrations under low wind speed conditions. Other sources such as from aircraft engines also show differing characteristics by wind speed. The plots can vary considerably depending on how much smoothing is done. The approach adopted here is based on the very flexible and capable mgcv package that uses Generalized Additive Models. While methods do exist to find an optimum level of smoothness, they are not necessarily useful. The principal aim of polarPlot is as a graphical analysis rather than for quantitative purposes. In this respect the smoothing aims to strike a balance between interesting (real) features and overly noisy data. The defaults used in polarPlot are based on the analysis of data from many different sources. More advanced users may wish to modify the code and adopt other smoothing approaches. These plots often show interesting features at higher wind speeds (see references below). For these conditions there can be very few measurements and therefore greater uncertainty in the calculation of the surface. There are several ways in which this issue can be tackled. First, it is possible to avoid smoothing altogether and use polarFreq. Second, the effect of setting a minimum number of measurements in each wind speed-direction bin can be examined through min.bin. It is possible that a single point at high wind speed conditions can strongly affect the surface prediction. Therefore, setting min.bin = 3, for example, will remove all wind speed-direction bins with fewer than 3 measurements before fitting the surface. Third, consider setting uncertainty = TRUE. This option will show the predicted surface together with upper and lower 95% confidence intervals, which take account of the frequency of measurements. Variants on polarPlot include polarAnnulus and polarFreq.

References

Carslaw, D.C., Beevers, S.D, Ropkins, K and M.C. Bell (2006). Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment. 40/28 pp 5424-5434. Henry, R.C., Chang, Y.S., Spiegelman, C.H., 2002. Locating nearby sources of air pollution by nonparametric regression of atmospheric concentrations on wind direction. Atmospheric Environment 36 (13), 2237-2244. Westmoreland, E.J., N. Carslaw, D.C. Carslaw, A. Gillah and E. Bates (2007). Analysis of air quality within a street canyon using statistical and dispersion modelling techniques. Atmospheric Environment. Vol. 41(39), pp. 9195-9205. Yu, K.N., Cheung, Y.P., Cheung, T., Henry, R.C., 2004. Identifying the impact of large urban airports on local air quality by nonparametric regression. Atmospheric Environment 38 (27), 4501-4507.

See Also

polarAnnulus, polarFreq

Examples

# load example data from package
data(mydata)

# basic plot
polarPlot(mydata, pollutant = "nox")

# polarPlots by year on same scale
polarPlot(mydata, pollutant = "so2", type = "year", main = "polarPlot of so2")

# set minimum number of bins to be used to see if pattern remains similar
polarPlot(mydata, pollutant = "nox", min.bin = 3)

# plot by day of the week

polarPlot(mydata, pollutant = "pm10", type = "weekday")

# show the 95\% confidence intervals in the surface fitting
polarPlot(mydata, pollutant = "so2", uncertainty = TRUE)