Package 'openair'

132 timeVariationAuthor(s)David CarslawSee AlsopolarPlot, linearRelationExamples# basic usetimeVariation(mydata, pollutant = "nox")# for a subset of conditionstimeVariation(subset(mydata, ws > 3 & wd > 100 & wd < 270),pollutant = "pm10", ylab = "pm10 (ug/m3)")# multiple pollutants with concentrations normalised## Not run: 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## Not run: timeVariation(mydata, pollutant = "nox", type = "gmtbst")## In this case it is better to group the results for clarity:## Not run: timeVariation(mydata, pollutant = "nox", group = "gmtbst")# 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## Not run: timeVariation(mydata, pollutant = "ws", group = "gmtbst")## It is also possible to plot several variables and set type. For## example, consider the NOx and NO2 split by levels of O3:## Not run: timeVariation(mydata, pollutant = c("nox", "no2"), type = "o3", normalise = TRUE)## difference in concentrationstimeVariation(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## Not run:mydata