pollutionRose: Pollution rose variation of the traditional wind rose plot

Description

The traditional wind rose plot that plots wind speed and wind direction by different intervals. The pollution rose applies the same plot structure but substitutes other measurements, most commonly a pollutant time series, for wind speed.

Usage

pollutionRose(
  mydata,
  pollutant = "nox",
  key.title = pollutant,
  key.position = "right",
  breaks = 6,
  paddle = FALSE,
  seg = 0.9,
  normalise = FALSE,
  plot = TRUE,
  key = NULL,
  ...
)

Value

an openair object. Summarised proportions can be extracted directly using the $data operator, e.g. object$data for output <- windRose(mydata). This returns a data frame with three set columns: cond, conditioning based on type; wd, the wind direction; and calm, the statistic for the proportion of data unattributed to any specific wind direction because it was collected under calm conditions; and then several (one for each range binned for the plot) columns giving proportions of measurements associated with each ws or pollutant range plotted as a discrete panel.

Arguments

mydata

A data frame containing fields ws and wd

pollutant

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

key.title

Used to set the title of the legend. The legend title is passed to quickText() if auto.text = TRUE.

key.position

Location where the legend is to be placed. Allowed arguments include "top", "right", "bottom", "left" and "none", the last of which removes the legend entirely.

breaks

Most commonly, the number of break points for pollutant concentrations. The default, 6, attempts to breaks the supplied data at approximately 6 sensible break points. However, breaks can also be used to set specific break points. For example, the argument breaks = c(0, 1, 10, 100) breaks the data into segments <1, 1-10, 10-100, >100.

paddle

Either TRUE or FALSE. If TRUE plots rose using 'paddle' style spokes. If FALSE plots rose using 'wedge' style spokes.

seg

seg determines with width of the segments. For example, seg = 0.5 will produce segments 0.5 * angle.

normalise

If TRUE each wind direction segment is normalised to equal one. This is useful for showing how the concentrations (or other parameters) contribute to each wind sector when the proportion of time the wind is from that direction is low. A line showing the probability that the wind directions is from a particular wind sector is also shown.

plot

When openair plots are created they are automatically printed to the active graphics device. plot = FALSE deactivates this behaviour. This may be useful when the plot data is of more interest, or the plot is required to appear later (e.g., later in a Quarto document, or to be saved to a file).

key

Deprecated; please use key.position. If FALSE, sets key.position to "none".

...

Arguments passed on to windRose

ws: Name of the column representing wind speed.

wd

Name of the column representing wind direction.

ws2,wd2

The user can supply a second set of wind speed and wind direction values with which the first can be compared. See pollutionRose() for more details.

ws.int

The Wind speed interval. Default is 2 m/s but for low met masts with low mean wind speeds a value of 1 or 0.5 m/s may be better.

angle

Default angle of “spokes” is 30. Other potentially useful angles are 45 and 10. Note that the width of the wind speed interval may need adjusting using width.

calm.thresh

By default, conditions are considered to be calm when the wind speed is zero. The user can set a different threshold for calms be setting calm.thresh to a higher value. For example, calm.thresh = 0.5 will identify wind speeds below 0.5 as calm.

bias.corr

When angle does not divide exactly into 360 a bias is introduced in the frequencies when the wind direction is already supplied rounded to the nearest 10 degrees, as is often the case. For example, if angle = 22.5, N, E, S, W will include 3 wind sectors and all other angles will be two. A bias correction can made to correct for this problem. A simple method according to Applequist (2012) is used to adjust the frequencies.

grid.line

Grid line interval to use. If NULL, as in default, this is assigned based on the available data range. However, it can also be forced to a specific value, e.g. grid.line = 10. grid.line can also be a list to control the interval, line type and colour. For example grid.line = list(value = 10, lty = 5, col = "purple").

width

For paddle = TRUE, the adjustment factor for width of wind speed intervals. For example, width = 1.5 will make the paddle width 1.5 times wider.

max.freq

Controls the scaling used by setting the maximum value for the radial limits. This is useful to ensure several plots use the same radial limits.

dig.lab

The number of significant figures at which scientific number formatting is used in break point and key labelling. Default 5.

include.lowest

Logical. If FALSE (the default), the first interval will be left exclusive and right inclusive. If TRUE, the first interval will be left and right inclusive. Passed to the include.lowest argument of cut().

statistic

The statistic to be applied to each data bin in the plot. Options currently include “prop.count”, “prop.mean” and “abs.count”. The default “prop.count” sizes bins according to the proportion of the frequency of measurements. Similarly, “prop.mean” sizes bins according to their relative contribution to the mean. “abs.count” provides the absolute count of measurements in each bin.

annotate

If TRUE then the percentage calm and mean values are printed in each panel together with a description of the statistic below the plot. If FALSE then only the statistic will be printed.

border

Border colour for shaded areas. Default is no border.

type

Character string(s) defining how data should be split/conditioned before plotting. "default" produces a single panel using the entire dataset. Any other options will split the plot into different panels - a roughly square grid of panels if one type is given, or a 2D matrix of panels if two types are given. type is always passed to cutData(), and can therefore be any of:

A built-in type defined in cutData() (e.g., "season", "year", "weekday", etc.). For example, type = "season" will split the plot into four panels, one for each season.
The name of a numeric column in mydata, which will be split into n.levels quantiles (defaulting to 4).
The name of a character or factor column in mydata, which will be used as-is. Commonly this could be a variable like "site" to ensure data from different monitoring sites are handled and presented separately. It could equally be any arbitrary column created by the user (e.g., whether a nearby possible pollutant source is active or not).

Most openair plotting functions can take two type arguments. If two are given, the first is used for the columns and the second for the rows.

cols

Colours to use for plotting. Can be a pre-set palette (e.g., "turbo", "viridis", "tol", "Dark2", etc.) or a user-defined vector of R colours (e.g., c("yellow", "green", "blue", "black") - see colours() for a full list) or hex-codes (e.g., c("#30123B", "#9CF649", "#7A0403")). See openColours() for more details.

angle.scale

In radial plots (e.g., polarPlot()), the radial scale is drawn directly on the plot itself. While suitable defaults have been chosen, sometimes the placement of the scale may interfere with an interesting feature. angle.scale can take any value between 0 and 360 to place the scale at a different angle, or FALSE to move it to the side of the plots.

offset

offset controls the size of the 'hole' in the middle and is expressed on a scale of 0 to 100, where 0 is no hole and 100 is a hole that takes up the entire plotting area.

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". Passed to quickText().

Details

pollutionRose() is a windRose() wrapper which brings pollutant forward in the argument list, and attempts to sensibly rescale break points based on the pollutant data range by by-passing ws.int.

By default, pollutionRose() will plot a pollution rose of nox using "wedge" style segments and placing the scale key to the right of the plot.

It is possible to compare two wind speed-direction data sets using pollutionRose(). There are many reasons for doing so e.g. to see how one site compares with another or for meteorological model evaluation. In this case, ws and wd are considered to the the reference data sets with which a second set of wind speed and wind directions are to be compared (ws2 and wd2). The first set of values is subtracted from the second and the differences compared. If for example, wd2 was biased positive compared with wd then pollutionRose will show the bias in polar coordinates. In its default use, wind direction bias is colour-coded to show negative bias in one colour and positive bias in another.

Examples

Run this code

# pollutionRose of nox
pollutionRose(mydata, pollutant = "nox")

# source apportionment plot - contribution to mean
if (FALSE) {
pollutionRose(mydata, pollutant = "pm10", type = "year", statistic = "prop.mean")

# example of comparing 2 met sites
# first we will make some new ws/wd data with a postive bias
mydata$ws2 <- mydata$ws + 2 * rnorm(nrow(mydata)) + 1
mydata$wd2 <- mydata$wd + 30 * rnorm(nrow(mydata)) + 30

# need to correct negative wd
id <- which(mydata$wd2 < 0)
mydata$wd2[id] <- mydata$wd2[id] + 360

# results show postive bias in wd and ws
pollutionRose(mydata, ws = "ws", wd = "wd", ws2 = "ws2", wd2 = "wd2")

## add some wd bias to some nighttime hours
id <- which(as.numeric(format(mydata$date, "%H")) %in% c(23, 1, 2, 3, 4, 5))
mydata$wd2[id] <- mydata$wd[id] + 30 * rnorm(length(id)) + 120
id <- which(mydata$wd2 < 0)
mydata$wd2[id] <- mydata$wd2[id] + 360

pollutionRose(
  mydata,
  ws = "ws",
  wd = "wd",
  ws2 = "ws2",
  wd2 = "wd2",
  breaks = c(-11, -2, -1, -0.5, 0.5, 1, 2, 11),
  cols = c("dodgerblue4", "white", "firebrick"),
  type = "daylight"
)
}