importKCL(site = "my1", year = 2009, pollutant = "all",
met = FALSE, units = "mass")site = c("my1", "kc1") --- to import
Marylebone Road and North Kensignton for example.year = 1990:2000.
To import several specfic years use year = c(1990,
1995, 2000) for example.pollutant = c("nox", "no2").FALSE. If TRUE
wind speed (m/s), wind direction (degrees), solar
radiation and rain amount are available. See details
below.
Access to reliabunits = "volume" to use ppb
etc. PM10_raw TEOM data are multiplied by 1.3 and PM2.5
have no correction applied. importKCL function has been written to make it
easy to import data from the King's College London air
pollution networks. KCL have provided .RData files (R
workspaces) of all individual sites and years for the KCL
networks. These files are updated on a weekly 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 date,
site, code (the site code) and
pollutant(s). Sometimes it is useful to have columns of
site data. This can be done using the reshape
function --- see examples below.
The situation for particle measurements is not
straightforward given the variety of methods used to
measure particle mass and changes in their use over time.
The importKCL function imports two measures of
PM10 where available. PM10_raw are TEOM
measurements with a 1.3 factor applied to take account of
volatile losses. The PM10 data is a current best
estimate of a gravimetric equivalent measure as described
below. NOTE! many sites have several instruments that
measure PM10 or PM2.5. In the case of FDMS measurements,
these are given as separate site codes (see below). For
example "MY1" will be TEOM with VCM applied and "MY7" is
the FDMS data.
For the assessment of the EU Limit Values, PM10 needs to
be measured using the reference method or one shown to be
equivalent to the reference method. Defra carried out
extensive trials between 2004 and 2006 to establish which
types of particulate analysers in use in the UK were
equivalent. These trials found that measurements made
using Partisol, FDMS, BAM and SM200 instruments were
shown to be equivalent to the PM10 reference method.
However, correction factors need to be applied to
measurements from the SM200 and BAM instruments.
Importantly, the TEOM was demonstrated as not being
equivalent to the reference method due to the loss of
volatile PM, even when the 1.3 correction factor was
applied. The Volatile Correction Model (VCM) was
developed for Defra at King's to allow measurements of
PM10 from TEOM instruments to be converted to reference
equivalent; it uses the measurements of volatile PM made
using nearby FDMS instruments to correct the measurements
made by the TEOM. It passed the equivalence testing using
the same methodology used in the Defra trials and is now
the recommended method for correcting TEOM measurements
(Defra, 2009). VCM correction of TEOM measurements can
only be applied after 1st January 2004, when sufficiently
widespread measurements of volatile PM became available.
The 1.3 correction factor is now considered redundant for
measurements of PM10 made after 1st January 2004.
Further information on the VCM can be found at
importKCL), now report
PM10 results as reference equivalent. For PM10
measurements made by BAM and SM200 analysers the
applicable correction factors have been applied. For
measurements from TEOM analysers the 1.3 factor has been
applied up to 1st January 2004, then the VCM method has
been used to convert to reference equivalent.
The meteorological data are meant to represent 'typical'
conditions in London, but users may prefer to use their
own data. The data provide a an estimate of general
meteorological conditions across Greater London. For
meteorological species (wd, ws, rain, solar) each data
point is formed by averaging measurements from a subset
of LAQN monitoring sites that have been identified as
having minimal disruption from local obstacles and a long
term reliable dataset. The exact sites used varies
between species, but include between two and five sites
per species. Therefore, the data should represent 'London
scale' meteorology, rather than local conditions.
While the function is being developed, the following site
codes should help with selection. We will also make
available other meta data such as site type and location
to make it easier to select sites based on other
information. Note that these codes need to be refined
because only the common species are available for export
currently i.e. NOx, NO2, O3, CO, SO2, PM10, PM2.5.
importAURN, importADMS,
importSAQN## import all pollutants from Marylebone Rd from 1990:2009
mary <- importKCL(site = "my1", year = 2000:2009)
## import nox, no2, o3 from Marylebone Road and North Kensignton for 2000
thedata <- importKCL(site = c("my1", "kc1"), year = 2000,
pollutant = c("nox", "no2", "o3"))
## import met data too...
my1 <- importKCL(site = "my1", year = 2008, met = TRUE)
## reshape the data so that each column represents a pollutant/site
thedata <- importKCL(site = c("my1", "kc1"), year = 2008,
pollutant = "o3")
thedata <- melt(thedata, measure.vars="o3")
thedata <- dcast(thedata, ... ~ site + code + variable)
## thedata now has columns for O3 at MY1 and KC1
## now can export as a csv file:
write.csv(thedata, file = "~/temp/thedata.csv")Run the code above in your browser using DataLab