openair (version 1.0)

timeVariation: Diurnal, day of the week and monthly variation

Description

Plots the diurnal, day of the week and monthly variation for different variables, typically pollutant concentrations. Four separate plots are produced.

Usage

timeVariation(mydata, pollutant = "nox", local.tz = NULL,
  normalise = FALSE, xlab = c("hour", "hour", "month", "weekday"),
  name.pol = pollutant, type = "default", group = NULL,
  difference = FALSE, statistic = "mean", conf.int = 0.95, B = 100,
  ci = TRUE, cols = "hue", key = NULL, key.columns = 1, start.day = 1,
  auto.text = TRUE, alpha = 0.4, ...)

Arguments

mydata

A data frame of hourly (or higher temporal resolution data). Must include a date field and at least one variable to plot.

pollutant

Name of variable to plot. Two or more pollutants can be plotted, in which case a form like pollutant = c("nox", "co") should be used.

local.tz

Should the results be calculated in local time that includes a treatment of daylight savings time (DST)? The default is not to consider DST issues, provided the data were imported without a DST offset. Emissions activity tends to occur at local time e.g.

normalise

Should variables be normalised? The default is FALSE. If TRUE then the variable(s) are divided by their mean values. This helps to compare the shape of the diurnal trends for variables on very different scales.

xlab

x-axis label; one for each sub-plot.

name.pol

Names to be given to the pollutant(s). This is useful if you want to give a fuller description of the variables, maybe also including subscripts etc.

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

group

This sets the grouping variable to be used. For example, if a data frame had a column site setting group = "site" will plot all sites together in each panel. See examples below.

difference

If two pollutants are chosen then setting difference = TRUE will also plot the difference in means between the two variables as pollutant[2] - pollutant[1]. Bootstrap 95% confidence intervals of the difference in means are also c

statistic

Can be mean (default) or median. If the statistic is mean then the mean line and the 95% confidence interval in the mean are plotted by default. If the statistic is median then the median

conf.int

The confidence intervals to be plotted. If statistic = "mean" then the confidence intervals in the mean are plotted. If statistic = "median" then the conf.int and 1 - conf.int quantiles are plot

B

Number of bootstrap replicates to use. Can be useful to reduce this value when there are a large number of observations available to increase the speed of the calculations without affecting the 95% confidence interval calculations by much.

ci

Should confidence intervals be shown? The default is TRUE. Setting this to FALSE can be useful if multiple pollutants are chosen where over-lapping confidence intervals can over complicate plots.

cols

Colours to be used for plotting. Options include default, increment, heat, jet and RColorBrewer colours --- see the openair openColours function for

key

By default timeVariation produces four plots on one page. While it is useful to see these plots together, it is sometimes necessary just to use one for a report. If key is TRUE, a key is added to all plots allowing t

key.columns

Number of columns to be used in the key. With many pollutants a single column can make to key too wide. The user can thus choose to use several columns by setting columns to be less than the number of pollutants.

start.day

What day of the week should the plots start on? The user can change the start day by supplying an integer between 0 and 6. Sunday = 0, Monday = 1, ...For example to start the weekday plots on a Saturday, choose start.day = 6.

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.

alpha

The alpha transparency used for plotting confidence intervals. 0 is fully transparent and 1 is opaque. The default is 0.4

...

Other graphical parameters passed onto lattice:xyplot and cutData. For example, in the case of cutData the option hemisphere = "southern".

Value

• As well as generating the plot itself, timeVariation also returns an object of class ``openair''. The object includes three main components: call, the command used to generate the plot; data, the data used to make the four components of the plot (or subplots); and plot, the associated subplots. If retained, e.g. using output <- timeVariation(mydata, "nox"), this output can be used to recover the data, reproduce or rework the original plot or undertake further analysis.

  An openair output can be manipulated using a number of generic operations, including print, plot and summary.

  The four components of timeVariation are: day.hour, hour, day and month. Associated data.frames can be extracted directly using the subset option, e.g. as in plot(object, subset = "day.hour"), summary(output, subset = "hour"), etc, for output <- timeVariation(mydata, "nox")

Details

The variation of pollutant concentrations by hour of the day and day of the week etc. can reveal many interesting features that relate to source types and meteorology. For traffic sources, there are often important differences in the way vehicles vary by vehicles type e.g. less heavy vehicles at weekends.

The timeVariation function makes it easy to see how concentrations (and many other variable types) vary by hour of the day and day of the week.

The plots also show the 95% confidence intervals in the mean. The 95% confidence intervals in the mean are calculated through bootstrap simulations, which will provide more robust estimates of the confidence intervals (particularly when there are relatively few data).

The function can handle multiple pollutants and uses the flexible type option to provide separate panels for each 'type' --- see cutData for more details. timeVariation can also accept a group option which is useful if data are stacked. This will work in a similar way to having multiple pollutants in separate columns.

The option difference will calculate the difference in means of two pollutants together with bootstrap estimates of the 95% confidence intervals in the difference in the mean. This works in two ways: either two pollutants are supplied in separate columns e.g. pollutant = c("no2", "o3"), or there are two unique values of group. The difference is calculated as the second pollutant minus the first and is labelled as such. Considering differences in this way can provide many useful insights and is particularly useful for model evaluation when information is needed about where a model differs from observations by many different time scales. The manual contains various examples of using difference = TRUE.

Note also that the timeVariation function works well on a subset of data and in conjunction with other plots. For example, a polarPlot may highlight an interesting feature for a particular wind speed/direction range. By filtering for those conditions timeVariation can help determine whether the temporal variation of that feature differs from other features --- and help with source identification.

In addition, timeVariation will work well with other variables if available. Examples include meteorological and traffic flow data.

Depending on the choice of statistic, a subheading is added. Users can control the text in the subheading through the use of sub e.g. sub = "" will remove any subheading.

See Also

polarPlot, linearRelation

Examples

# basic use
timeVariation(mydata, pollutant = "nox")

# for a subset of conditions
timeVariation(subset(mydata, ws > 3 & wd > 100 & wd < 270),
pollutant = "pm10", ylab = "pm10 (ug/m3)")

# multiple pollutants with concentrations normalised
timeVariation(mydata, pollutant = c("nox", "co"), normalise = TRUE)

# show BST/GMT variation (see ?cutData for more details)
# the NOx plot shows the profiles are very similar when expressed in
# local time, showing that the profile is dominated by a local source
# that varies by local time and not by GMT i.e. road vehicle emissions

timeVariation(mydata, pollutant = "nox", type = "dst", local.tz = "Europe/London")

## In this case it is better to group the results for clarity:
timeVariation(mydata, pollutant = "nox", group = "dst", local.tz = "Europe/London")

# By contrast, a variable such as wind speed shows a clear shift when
#  expressed in local time. These two plots can help show whether the
#  variation is dominated by man-made influences or natural processes

timeVariation(mydata, pollutant = "ws", group = "dst", local.tz = "Europe/London")

## It is also possible to plot several variables and set type. For
## example, consider the NOx and NO2 split by levels of O3:

timeVariation(mydata, pollutant = c("nox", "no2"), type = "o3", normalise = TRUE)

## difference in concentrations
timeVariation(mydata, poll= c("pm25", "pm10"), difference = TRUE)

# It is also useful to consider how concentrations vary by
# considering two different periods e.g. in intervention
# analysis. In the following plot NO2 has clearly increased but much
# less so at weekends - perhaps suggesting vehicles other than cars
# are important because flows of cars are approximately invariant by
# day of the week

mydata <- splitByDate(mydata, dates= "1/1/2003", labels = c("before Jan. 2003", "After Jan. 2003"))
timeVariation(mydata, pollutant = "no2", group = "split.by", difference = TRUE)

## sub plots can be extracted from the openair object
myplot <- timeVariation(mydata, pollutant = "no2")
plot(myplot, subset = "day.hour") # top weekday and plot

## individual plots
## plot(myplot, subset="day.hour") for the weekday and hours subplot (top)
## plot(myplot, subset="hour") for the diurnal plot
## plot(myplot, subset="day") for the weekday plot
## plot(myplot, subset="month") for the monthly plot

## numerical results (mean, lower/upper uncertainties)
## results(myplot, subset = "day.hour") # the weekday and hour data set
## summary(myplot, subset = "hour") #summary of hour data set
## head(myplot, subset = "day") #head/top of day data set
## tail(myplot, subset = "month") #tail/top of month data set

## plot quantiles and median
timeVariation(mydata, stati="median", poll="pm10", col = "firebrick")

## with different intervals
timeVariation(mydata, stati="median", poll="pm10", conf.int = c(0.75, 0.99),
col = "firebrick")