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)site = c("my1", "nott") --- to import
Marylebone Road and Nottingham for example.year = 1990:2000.
To import several specfic years use year = c(1990,
1995, 2000) for example.pollutant = c("nox",
"no2").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 daimportAURN 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.
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', 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 teh PM2.5 data. The
volatile component of FDMS PM2.5 (where available) is
shown in the 'v2.5' column.
While the function is being developed, the following site
codes should help with selection. importKCL, importADMS,
importSAQN## import all pollutants from Marylebone Rd from 1990:2009
mary <- importAURN(site = "my1", year = 2000:2009)
## import nox, no2, o3 from Marylebone Road and Nottingham Centre for 2000
thedata <- importAURN(site = c("my1", "nott"), year = 2000,
pollutant = c("nox", "no2", "o3"))
## import over 20 years of Mace Head O3 data!
o3 <- importAURN(site = "mh", year = 1987:2009)
## import hydrocarbon (and other) data from Marylebone Road
mary <- importAURN(site = "my1", year =1998, hc = TRUE)
## reshape the data so that each column represents a pollutant/site
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
## now can export as a csv file:
write.csv(thedata, file = "~/temp/thedata.csv")Run the code above in your browser using DataLab