R: Import data from individual UK Air Pollution Networks

importAURN {openair}

R Documentation

Import data from individual UK Air Pollution Networks

Description

These functions act as wrappers for importUKAQ() to import air pollution data from a range of UK networks including the Automatic Urban and Rural Network (AURN), the individual England (AQE), Scotland (SAQN), Wales (WAQN) and Northern Ireland (NI) Networks, and many "locally managed" monitoring networks across England. While importUKAQ() allows for data to be imported more flexibly, including across multiple monitoring networks, these functions are provided for convenience and back-compatibility.

Usage

importAURN(
  site = "my1",
  year = 2009,
  data_type = "hourly",
  pollutant = "all",
  hc = FALSE,
  meta = FALSE,
  meteo = TRUE,
  ratified = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

importAQE(
  site = "yk13",
  year = 2018,
  data_type = "hourly",
  pollutant = "all",
  meta = FALSE,
  meteo = TRUE,
  ratified = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

importSAQN(
  site = "gla4",
  year = 2009,
  data_type = "hourly",
  pollutant = "all",
  meta = FALSE,
  meteo = TRUE,
  ratified = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

importWAQN(
  site = "card",
  year = 2018,
  data_type = "hourly",
  pollutant = "all",
  meta = FALSE,
  meteo = TRUE,
  ratified = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

importNI(
  site = "bel0",
  year = 2018,
  data_type = "hourly",
  pollutant = "all",
  meta = FALSE,
  meteo = TRUE,
  ratified = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

importLocal(
  site = "ad1",
  year = 2018,
  data_type = "hourly",
  pollutant = "all",
  meta = FALSE,
  to_narrow = FALSE,
  verbose = FALSE,
  progress = TRUE
)

Arguments

`site`	Site code of the site to import, e.g., `"my1"` is Marylebone Road. Site codes can be discovered through the use of `importMeta()`. Several sites can be imported at once. For example, `site = c("my1", "nott")` imports both Marylebone Road and Nottingham.
`year`	Year(s) to import. To import a series of years use, e.g., `2000:2020`. To import several specific years use `year = c(2000, 2010, 2020)`.
`data_type`	The type of data to be returned, defaulting to `"hourly"` data. Alternative data types include: `"daily"`: Daily average data. `"monthly"`: Monthly average data with data capture information for the whole network. `"annual"`: Annual average data with data capture information for the whole network. `"15_min"`: 15-minute average SO2 concentrations. `"8_hour"`: 8-hour rolling mean concentrations for O3 and CO. `"24_hour"`: 24-hour rolling mean concentrations for particulates. `"daily_max_8"`: Maximum daily rolling 8-hour maximum for O3 and CO. `"daqi"`: Daily Air Quality Index (DAQI). See here for more details of how the index is defined. Note that this `data_type` is not available for locally managed monitoring networks.
`pollutant`	Pollutants to import. If omitted will import all pollutants from a site. To import only NOx and NO2 for example use `pollutant = c("nox", "no2")`. Pollutant names can be upper or lower case.
`hc`	Include hydrocarbon measurements in the imported data? Defaults to `FALSE` as most users will not be interested in using hydrocarbon data.
`meta`	Append the site type, latitude and longitude of each selected `site`? Defaults to `FALSE`.
`meteo`	Append modelled meteorological data, if available? Defaults to `TRUE`, which will return wind speed (`ws`), wind direction (`wd`) and ambient temperature (`air_temp`). The variables are calculated from using the WRF model run by Ricardo Energy & Environment and are available for most but not all networks. Setting `meteo = FALSE` is useful if you have other meteorological data to use in preference, for example from the `worldmet` package.
`ratified`	Append `qc` column(s) to hourly data indicating whether each species was ratified (i.e., quality-checked)? Defaults to `FALSE`.
`to_narrow`	Return the data in a "narrow"/"long"/"tidy" format? By default the returned data is "wide" and has a column for each pollutant/variable. When `to_narrow = TRUE` the data are returned with a column identifying the pollutant name and a column containing the corresponding concentration/statistic. Defaults to `FALSE`.
`verbose`	Print messages to the console if hourly data cannot be imported? Default is `FALSE`. `TRUE` is useful for debugging as the specific `year`(s), `site`(s) and `source`(s) which cannot be imported will be returned.
`progress`	Show a progress bar when many sites/years are being imported? Defaults to `TRUE`.

Importing UK Air Pollution Data

This family of functions has been written to make it easy to import data from across several UK air quality networks. Ricardo have provided .RData files (R workspaces) of all individual sites and years, as well as up to date meta data. 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. The function also has the advantage that the proper site name is imported and used in openair functions.

Users should take care if using data from both openair and web portals (for example, UK AIR). One key difference is that the data provided by openair is date beginning, whereas the web portal provides date ending. Hourly concentrations may therefore appear offset by an hour, for example.

The data are imported by stacking sites on top of one another and will have field names site, code (the site code) and pollutant.

By default, the function returns hourly average data. However, annual, monthly, daily and 15 minute data (for SO2) can be returned using the option data_type. Annual and monthly data provide whole network information including data capture statistics.

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.

Some sites report hourly and daily PM10 and / or PM2.5. When data_type = "daily" and there are both hourly and 'proper' daily measurements available, these will be returned as e.g. "pm2.5" and "gr_pm2.5"; the former corresponding to data based on original hourly measurements and the latter corresponding to daily gravimetric measurements.

The function returns modelled hourly values of wind speed (ws), wind direction (wd) and ambient temperature (air_temp) if available (generally from around 2010). These values are modelled using the WRF model operated by Ricardo.

The 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 data from individual UK Air Pollution Networks

Description

Usage

Arguments

Importing UK Air Pollution Data

See Also