deweather is an R package developed for the purpose of 'removing' the influence of meteorology from air quality time series data. It is part of the openair suite of packages designed to support the analysis of air quality data and related data.

The deweather package uses a boosted regression tree approach for modelling air quality data. These and similar techniques provide powerful tools for building statistical models of air quality data. They are able to take account of the many complex interactions between variables as well as non-linear relationships between the variables.

The modelling can be computationally intensive and therefore deweather makes use of the parallel processing, which should work on Windows, Linux and Mac OSX.

Installation of deweather from GitHub should be easy using the devtools package.

require(devtools)
install_github('davidcarslaw/deweather')

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 (Ridgeway, 2017). 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.

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.

library(deweather)
head(road_data)
##                  date nox no2 ethane isoprene benzene  ws  wd air_temp
## 1 1998-01-01 00:00:00 546  74     NA       NA      NA 1.0 280     3.60
## 2 1998-01-01 01:00:00  NA  NA     NA       NA      NA 1.0 230     3.50
## 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     3.80
## 6 1998-01-01 05:00:00 506  80     NA       NA      NA 1.0 190     3.50
##         RH cl
## 1 89.41776  2
## 2 90.67753  2
## 3 87.60679  2
## 4       NA NA
## 5 89.43347  1
## 6 89.40989 NA

For those interested in obtaining the data directly, the following code can be used.

library(openair)
library(worldmet)
library(dplyr)

# import AQ data
road_data <- importAURN(site = "my1", year = 1998:2016, hc = TRUE)

# import met data
met <- importNOAA(year = 1998:2016)

# join together but ignore met data in road_data because it is modelled
road_data <- left_join(select(road_data, -ws, -wd), met, by = "date")

road_data <- select(road_data, date, nox, no2, ethane, isoprene, 
                    benzene, ws, wd, air_temp, RH, cl)

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.

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

# test a model with commonly used covariates
testMod(dat = dat_part, 
        vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp", "week"),
        pollutant = "no2")

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 nt used to build the model. Common model evaluation metrics are also given.

Assuming that a good model can be developed, it can now be explored in more detail.

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

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

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.

plotAllPD(dw_model = mod_no2)

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

An indication of the meteorologically-averaged trend is given by the plotAllPD function above. A 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", "hour", "weekday", "air_temp", "week"))

Now it is possible to plot the resulting trend.

library(ggplot2)
ggplot(demet, aes(date, pred)) +
  geom_line()

The plot is rather noisy due to relatively few samples of meteorology being considered (200 by default, set with B = 200). The noise could be reduced by increasing the simulations, but this would add to run time. Alternatively, it can be useful to simply average the results. For example:

library(ggplot2)
ggplot(timeAverage(demet, "day"), aes(date, pred)) +
  geom_line(col = "dodgerblue", size = 1) +
  ylab(quickText("no2 (ug/m3)"))

Carslaw, D.C. and P.J. Taylor (2009). Analysis of air pollution data at a mixed source location using boosted regression trees. Atmospheric Environment. Vol. 43, pp. 3563–3570.

Carslaw, D.C., Williams, M.L. and B. Barratt A short-term intervention study — impact of airport closure on near-field air quality due to the eruption of Eyjafjallajökull. (2012) Atmospheric Environment, Vol. 54, 328–336.

Greg Ridgeway with contributions from others (2017). gbm: Generalized Boosted Regression Models. Rpackage version 2.1.3. (https://CRAN.R-project.org/package=gbm)