data_london_normalised

These example data are derived from the observational data included in 
rmweather and represent meteorologically normalised NO2 
concentrations at London Marylebone Road, aggregated to monthly resolution.

datasets

An integrated set of tools to allow data users to conduct
meteorological normalisation and counterfactual modelling for air quality
data. The meteorological normalisation technique uses predictive random
forest models to remove variation of pollutant concentrations so trends and
interventions can be explored in a robust way. For examples, see
Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and
Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random
forest models can also be used for counterfactual or business as usual (BAU)
modelling by using the models to predict, from the model's perspective, the
future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.

Stuart K. Grange

rmweather

Tools to Conduct Meteorological Normalisation and Counterfactual
Modelling for Air Quality Data

data_london_normalised function

Tibble with 258 observations and 5 variables. The variables are:
<code>date</code>, <code>date_end</code>, <code>site</code>, <code>site_name</code>, and 
<code>value_predict</code>. The dates are in <code>POSIXct</code> format, the site 
variables are characters and <code>value_predict</code> is numeric.

data_london_normalised: Example of meteorologically normalised data for the rmweather package.

Description

Usage

Arguments

Format

Author

See Also

Examples