Function for importing hourly mean UK Automatic Urban and Rural Network
(AURN) air quality archive data files for use with the
package. Files are imported from a remote server operated by AEA that
provides air quality data files as R data objects.
importAURN( site = "my1", year = 2009, pollutant = "all", hc = FALSE, meta = FALSE, to_narrow = FALSE, verbose = FALSE )
Site code of the AURN site to import e.g. “my1” is Marylebone
Road. Several sites can be imported with
Year or years to import. To import a sequence of years from 1990
to 2000 use
Pollutants to import. If omitted will import all pollutants
ffrom a site. To import only NOx and NO2 for example use
A few sites have hydrocarbon measurements available and setting
Should meta data be returned? If
By default the returned data has a column for each
Should the function give messages when downloading files?
Returns a data frame of hourly mean values with date in POSIXct class and time zone GMT.
importAURN function has been written to make it easy to import
data from the UK AURN. AEA have provided .RData files (R workspaces) of all
individual sites and years for the AURN. These files are updated on a daily
basis. This approach requires a link to the Internet to work.
For an up to date list of available sites that can be imported, see
There are several advantages over the web portal approach where .csv files
are downloaded. First, it is quick to select a range of sites, pollutants
and periods (see examples below). Second, storing the data as .RData objects
is very efficient as they are about four times smaller than .csv files ---
which means the data downloads quickly and saves bandwidth. Third, the
function completely avoids any need for data manipulation or setting time
formats, time zones etc. Finally, it is easy to import many years of data
beyond the current limit of about 64,000 lines. The final point makes it
possible to download several long time series in one go. The function also
has the advantage that the proper site name is imported and used in
The site codes and pollutant names can be upper or lower case. The function will issue a warning when data less than six months old is downloaded, which may not be ratified.
The data are imported by stacking sites on top of one another and will have
code (the site code) and
Sometimes it is useful to have columns of site data. This can be done using
reshape function --- see examples below.
All units are expressed in mass terms for gaseous species (ug/m3 for NO, NO2, NOx (as NO2), SO2 and hydrocarbons; and mg/m3 for CO). PM10 concentrations are provided in gravimetric units of ug/m3 or scaled to be comparable with these units. Over the years a variety of instruments have been used to measure particulate matter and the technical issues of measuring PM10 are complex. In recent years the measurements rely on FDMS (Filter Dynamics Measurement System), which is able to measure the volatile component of PM. In cases where the FDMS system is in use there will be a separate volatile component recorded as 'v10' and non-volatile component 'nv10', which is already included in the absolute PM10 measurement. Prior to the use of FDMS the measurements used TEOM (Tapered Element Oscillating. Microbalance) and these concentrations have been multiplied by 1.3 to provide an estimate of the total mass including the volatile fraction.
The few BAM (Beta-Attenuation Monitor) instruments that have been incorporated into the network throughout its history have been scaled by 1.3 if they have a heated inlet (to account for loss of volatile particles) and 0.83 if they do not have a heated inlet. The few TEOM instruments in the network after 2008 have been scaled using VCM (Volatile Correction Model) values to account for the loss of volatile particles. The object of all these scaling processes is to provide a reasonable degree of comparison between data sets and with the reference method and to produce a consistent data record over the operational period of the network, however there may be some discontinuity in the time series associated with instrument changes.
No corrections have been made to the PM2.5 data. The volatile component of FDMS PM2.5 (where available) is shown in the 'v2.5' column.
## import all pollutants from Marylebone Rd from 1990:2009 if (FALSE) mary <- importAURN(site = "my1", year = 2000:2009) ## import nox, no2, o3 from Marylebone Road and Nottingham Centre for 2000 if (FALSE) thedata <- importAURN(site = c("my1", "nott"), year = 2000, pollutant = c("nox", "no2", "o3")) ## import over 20 years of Mace Head O3 data! if (FALSE) o3 <- importAURN(site = "mh", year = 1987:2009) ## import hydrocarbon (and other) data from Marylebone Road if (FALSE) mary <- importAURN(site = "my1", year =1998, hc = TRUE)