Function for importing hourly mean UK Automatic Urban and Rural Network (AURN) air quality archive data files for use with the openair 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, verbose = FALSE)



Site code of the AURN site to import e.g. "my1" is Marylebone Road. Several sites can be imported with site = c("my1", "nott") --- to import Marylebone Road and Nottingham for example.


Year or years to import. To import a sequence of years from 1990 to 2000 use year = 1990:2000. To import several specfic years use year = c(1990, 1995, 2000) for example.


Pollutants to import. If omitted will import all pollutants ffrom a site. To import only NOx and NO2 for example use pollutant = c("nox", "no2").


A few sites have hydrocarbon measurements available and setting hc = TRUE will ensure hydrocarbon data are imported. The default is however not to as most users will not be interested in using hydrocarbon data and the resulting data frames are considerably larger.


Should meta data be returned? If TRUE the site type, latitude and longitude are returned.


Should the function give messages when downloading files? Default is FALSE.


Returns a data frame of hourly mean values with date in POSIXct class and time zone GMT.


The 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 importMeta.

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 openair functions.

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 field names site, code (the site code) and pollutant. Sometimes it is useful to have columns of site data. This can be done using the 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.

See also


## import all pollutants from Marylebone Rd from 1990:2009
# NOT RUN { mary <- importAURN(site = "my1", year = 2000:2009) # }
## import nox, no2, o3 from Marylebone Road and Nottingham Centre for 2000
# NOT RUN { thedata <- importAURN(site = c("my1", "nott"), year = 2000, pollutant = c("nox", "no2", "o3")) # }
## import over 20 years of Mace Head O3 data!
# NOT RUN { o3 <- importAURN(site = "mh", year = 1987:2009) # }
## import hydrocarbon (and other) data from Marylebone Road
# NOT RUN { mary <- importAURN(site = "my1", year =1998, hc = TRUE) # }
## reshape the data so that each column represents a pollutant/site
# NOT RUN { require(reshape2) thedata <- importAURN(site = c("nott", "kc1"), year = 2008, pollutant = "o3") thedata <- melt(thedata, measure.vars = "o3") thedata <- dcast(thedata, ... ~ variable + site + code) ## thedata now has columns o3_Nottingham Centre_NOTT o3_London N. Kensington_KC1 # }