Atmospheric air pollution is one of the most important environmental concerns in many countries around the world; it is strongly affected by meteorological conditions. In this set of exercises, we will use the
openair package to work and analyze air quality and meteorological data. This package provides tools to directly import data from air quality measurement networks across the UK, as well as tools to analyze and produce reports.
In the previous exercise sets, we used some functions in the
openair package, along with some geospatial packages to spatially analyze and visualize air quality data. However, sometimes it is difficult to customize or modify those functions according our interest. So, it is also very useful to gain the advantages of using default function in
R. In addition, so far, we mostly practice visualization functions and do not pay much attention to statistical approaches to analyze air quality data or analyze the effect of meteorological parameters on them. Therefore, from the last two exercise sets, we started to analyze individual air pollutants using functions we had practiced before, along with using default functions available in
In this exercise, we will continue to analyze PM data used in the previous exercise.
Answers to these exercises are available here.
For other parts of this exercise set, follow the tag openair.
For this exercise, we will need to import the following data from the
my1data <- importAURN(site = 'MY1', year = 2016, met = TRUE)
In this exercise, use the
pollutionRose function to investigate the effect of wind direction on PM10 and PM2.5. Try to determine the wind direction in which PM pollution is higher.
Since the data used here is from a station located in an urban area and near a road, the emission from mobile sources is expected to have significant contribution to the pollution concentration for this site. Accordingly, it would be of much interest to look at the difference between PM concentration during weekdays and weekends.
First, plot the average PM10 concentration for each day of week and visually identify if there is any considerable difference between weekend and weekday concentration. However, we should also try to see if there is statistically significant differences between them. For this, you use the Wilcoxon rank-sum test.
Another method to investigate the potential sources for PM, as well as other pollutants, is using polar plots. Produce polar plots for PM10 and PM2.5 data and try to identify the potential sources of PM at the measurement site.
polar.annulus is another useful function in
openair, which as an extension of the
polar.plot function, can be used to plot data by different temporal resolutions (hour of day, month of year, day of week and trend.) By using this function, there will be less compression at the center, which can help provide a better visual impression of how concentrations vary. In this exercise, use this function to investigate the PM10 concentration in terms of temporal and spatial variation.
Also, it would be useful to see the relation between PM and other gaseous pollutants. In this exercise, plot the scatter plots of PM10 with no, no2, co, so2, and o3. Try to find the relationship between PM concentration and other gaseous pollutants.