Calculate summary statistics for air pollution data by year

This function calculates a range of common and air pollution-specific statistics from a data frame. The statistics are calculated on an annual basis and the input is assumed to be hourly data. The function can cope with several sites and years, e.g., using type = "site". The user can control the output by setting transpose appropriately. Note that the input data is assumed to be in mass units, e.g., ug/m3 for all species except CO (mg/m3).

Usage

aqStats(
  mydata,
  pollutant = "no2",
  type = "default",
  data.thresh = 0,
  percentile = c(95, 99),
  transpose = FALSE,
  progress = TRUE,
  ...
)

Arguments

mydata: A data frame containing a date field of hourly data.
pollutant: The name of a pollutant e.g. pollutant = c("o3", "pm10"). Additional statistics will be calculated if pollutant %in% c("no2", "pm10", "o3").
type: type allows timeAverage() to be applied to cases where there are groups of data that need to be split and the function applied to each group. The most common example is data with multiple sites identified with a column representing site name e.g. type = "site". More generally, type should be used where the date repeats for a particular grouping variable. However, if type is not supplied the data will still be averaged but the grouping variables (character or factor) will be dropped.
data.thresh: The data capture threshold to use (%). A value of zero means that all available data will be used in a particular period regardless if of the number of values available. Conversely, a value of 100 will mean that all data will need to be present for the average to be calculated, else it is recorded as NA. See also interval, start.date and end.date to see whether it is advisable to set these other options.
percentile: Percentile values to calculate for each pollutant.
transpose: The default is to return a data frame with columns representing the statistics. If transpose = TRUE then the results have columns for each pollutant-type combination.
progress: Show a progress bar when many groups make up type? Defaults to TRUE.
...: Other arguments, currently unused.

Details

The following statistics are calculated:

For all pollutants:

data.capture — percentage data capture over a full year.
mean — annual mean.
minimum — minimum hourly value.
maximum — maximum hourly value.
median — median value.
max.daily — maximum daily mean.
max.rolling.8 — maximum 8-hour rolling mean.
max.rolling.24 — maximum 24-hour rolling mean.
percentile.95 — 95th percentile. Note that several percentiles can be calculated.

When pollutant == "o3":

roll.8.O3.gt.100 — number of days when the daily maximum rolling 8-hour mean ozone concentration is >100 ug/m3. This is the target value.
roll.8.O3.gt.120 — number of days when the daily maximum rolling 8-hour mean ozone concentration is >120 ug/m3. This is the Limit Value not to be exceeded > 10 days a year.
AOT40 — is the accumulated amount of ozone over the threshold value of 40 ppb for daylight hours in the growing season (April to September). Note that latitude and longitude can also be passed to this calculation.

When pollutant == "no2":

hours.gt.200 — number of hours NO2 is more than 200 ug/m3.

When pollutant == "pm10":

days.gt.50 — number of days PM10 is more than 50 ug/m3.

For the rolling means, the user can supply the option align, which can be "centre" (default), "left" or "right". See rollingMean() for more details.

There can be small discrepancies with the AURN due to the treatment of rounding data. The aqStats() function does not round, whereas AURN data can be rounded at several stages during the calculations.

Author

David Carslaw

Examples


# Statistics for 2004. NOTE! these data are in ppb/ppm so the
# example is for illustrative purposes only
aqStats(selectByDate(mydata, year = 2004), pollutant = "no2")
#> # A tibble: 1 × 15
#>   default   pollutant  year date                dat.cap  mean   min   max median
#>   <fct>     <chr>     <dbl> <dttm>                <dbl> <dbl> <int> <int>  <dbl>
#> 1 01 Janua… no2        2004 2004-01-01 00:00:00    99.8  55.0     0   185     51
#> # ℹ 6 more variables: max_daily <dbl>, roll_8_max <dbl>, roll_24_max <dbl>,
#> #   percentile.95 <dbl>, percentile.99 <dbl>, hours <int>