data_london

<p>These example data are daily means of NO2 and NOx observations at London 
Marylebone Road. The accompanying surface meteorological data are from
London Heathrow, a major airport located 23 km west of Central London.</p>

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 function

<p>Tibble with 15676 observations and 11 variables. The variables are:
<code>date</code>, <code>date_end</code>, <code>site</code>, <code>site_name</code>, <code>value</code>, 
<code>air_temp</code>, <code>atmospheric_pressure</code>, <code>rh</code>, <code>wd</code>, and 
<code>ws</code>. The dates are in <code>POSIXct</code> format, the site variables are 
characters and all other variables are numeric.</p>

data_london: Example observational data for the rmweather package.

Description

Usage

Arguments

Format

Author

Details

Examples