Meteorological Normalisation with {deweather}

library(deweather)

Introduction

Meteorology plays a central role in affecting the concentrations of pollutants in the atmosphere. When considering trends in air pollutants it can be very difficult to know whether a change in concentration is due to emissions or meteorology.

The deweather package uses a powerful statistical technique based on boosted regression trees using the gbm package ¹. Statistical models are developed to explain concentrations using meteorological and other variables. These models can be tested on randomly withheld data with the aim of developing the most appropriate model.

Example data set

The deweather package comes with a comprehensive data set of air quality and meteorological data. The air quality data is from Marylebone Road in central London (obtained from the openair package) and the meteorological data from Heathrow Airport (obtained from the worldmet package).

The road_data data frame contains various pollutants such a NO_x, NO₂, ethane and isoprene as well as meteorological data including wind speed, wind direction, relative humidity, ambient temperature and cloud cover. Code to obtain this data directly can be found here.

head(road_data)
#> # A tibble: 6 × 11
#>   date                  nox   no2 ethane isoprene benzene    ws    wd air_temp
#>   <dttm>              <dbl> <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>    <dbl>
#> 1 1998-01-01 00:00:00   546    74     NA       NA      NA   1     280     3.6 
#> 2 1998-01-01 01:00:00    NA    NA     NA       NA      NA   1     230     3.5 
#> 3 1998-01-01 02:00:00    NA    NA     NA       NA      NA   1.5   180     4.25
#> 4 1998-01-01 03:00:00   944    99     NA       NA      NA  NA      NA    NA   
#> 5 1998-01-01 04:00:00   894   149     NA       NA      NA   1.5   180     3.8 
#> 6 1998-01-01 05:00:00   506    80     NA       NA      NA   1     190     3.5 
#> # ℹ 2 more variables: RH <dbl>, cl <dbl>

To speed up the examples in this article we’ll randomly sample some of road_data.

Construct and test model(s)

The testMod() function is used to build and test various models to help derive the most appropriate.

In this example, we will restrict the data to model to 4 years. Note that variables such as "hour" and "weekday" are used as variables that can be used to explain some of the variation. "hour" for example very usefully acts as a proxy for the diurnal variation in emissions. These temporal emission proxies are also important to include to help the model differentiate between emission versus weather-related changes. For example, emissions tend to change throughout a day and so do variables such as wind speed and ambient temperature.

library(openair)
# select only part of the data set
dat_part <- selectByDate(road_data, year = 2001:2004)

# to speed up the example, sample rows in `dat_part`
# not needed in reality
dat_part <- dplyr::slice_sample(dat_part, prop = 1/10)

# test a model with commonly used covariates
mod_test <-
  testMod(
    dat_part,
    vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp", "week"),
    pollutant = "no2"
  )
#> ℹ Optimum number of trees is 398
#> ℹ RMSE from cross-validation is 23.59
#> ℹ Percent increase in RMSE using test data is 28.0%

A statistical summary of a deweather model test.

The output shows by default the performance of the model when applied to a withheld random 20% (by default) of the data, i.e., the model is evaluated against data not used to build the model. Common model evaluation metrics are also given.

Build a model

Assuming that a good model can be developed, it can now be explored in more detail using the optimum number of trees from testMod().

mod_no2 <- buildMod(
  dat_part,
  vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp", "week"),
  pollutant = "no2",
  n.trees = mod_test$optimum_trees, 
  n.core = 6
)

This function returns a deweather object that can be interrogated as shown below.

Examine the partial dependencies

Plot all partial dependencies

One of the benefits of the boosted regression tree approach is that the partial dependencies can be explored. In simple terms, the partial dependencies show the relationship between the pollutant of interest and the covariates used in the model while holding the value of other covariates at their mean level.

plotPD(mod_no2, nrow = 4)

The 7 partial dependencies of the deweather model.

Plot two-way interactions

It can be very useful to plot important two-way interactions. In this example the interaction between "ws" and "air_temp" is considered. The plot shows that NO₂ tends to be high when the wind speed is low and the temperature is low, i.e., stable atmospheric conditions. Also NO₂ tends to be high when the temperature is high, which is most likely due to more O₃ available to convert NO to NO₂. In fact, background O₃ would probably be a useful covariate to add to the model.

plot2Way(mod_no2, variable = c("ws", "air_temp"))

A two-way interaction plot showing the interaction between wind speed and air temperature

Apply meteorological averaging

An indication of the meteorologically-averaged trend is given by the plotPD() function above, and can even be isolated using the variable argument.

plotPD(mod_no2, variable = "trend")

The partial dependence plot of the ‘trend’ component.

A much better indication is given by using the model to predict many times with random sampling of meteorological conditions. This sampling is carried out by the metSim() function. Note that in this case there is no need to supply the "trend" component because it is calculated using metSim().

demet <- metSim(mod_no2,
  newdata = dat_part,
  metVars = c("ws", "wd", "air_temp")
)

Now it is possible to plot the resulting trend.

openair::timePlot(demet, "no2", ylab = "Deweathered no2 (ug/m3)")

A deweathered nitrogen dioxide trend.

The plot shows the trend in NO₂ controlling for the main weather variables. The plot now reveals the strong diurnal and weekly cycle in NO₂ that is driven by variations in the sources of NO₂ (NO_x) rather than meteorology i.e. road traffic which has strong hourly and daily variations throughout the year. It can be useful to simply average the results to provide a better indication of the overall trend. For example:

openair::timePlot(demet, "no2", avg.time = "week", ylab = "Deweathered no2 (ug/m3)")

A time-averaged deweathered nitrogen dioxide trend.