importKCL(site = "my1", year = 2009, pollutant = "all", met = FALSE,
units = "mass", extra = FALSE, meta = FALSE)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 reliable and free meteorological data is problematic.
units =
"volume" to use ppb etc. PM10_raw TEOM data are multiplied by 1.3 and
PM2.5 have no correction applied. See details below concerning PM10
concentrations.TRUE the site type, latitude and longitude are returned.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. Where FDMS data are used the volatile and non-volatile components
are separately reported i.e. v10 = volatile PM10, v2.5 = volatile
PM2.5, nv10 = non-volatile PM10 and nv2.5 = non-volatile
PM2.5. Therefore, PM10 = v10 + nv10 and PM2.5 = v2.5 + nv2.5. 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 http://www.volatile-correction-model.info/. All PM10 statistics on the LondonAir web site, including the bulletins and
statistical tools (and in the RData objects downloaded using
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
## Not run: mary <- importKCL(site = "my1", year = 2000:2009)
## import nox, no2, o3 from Marylebone Road and North Kensignton for 2000
## Not run: ------------------------------------
# thedata <- importKCL(site = c("my1", "kc1"), year = 2000,
# pollutant = c("nox", "no2", "o3"))
## ---------------------------------------------
## import met data too...
## Not run: my1 <- importKCL(site = "my1", year = 2008, met = TRUE)
## reshape the data so that each column represents a pollutant/site
## Not run: ------------------------------------
# require(reshape2)
# 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
#
## ---------------------------------------------
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