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. Therefore, it is also very useful to gain the advantages of using default function in
R. In addition, we mostly practice visualization functions and do not pay much attention to statistical approaches to analyze air quality data; we also analyze the effect of meteorological parameters on them. Therefore, from the previous exercise, we started to practice how to make the most beneficial default
R functions to produce professional plots, along with going through different statistical techniques to analyze air quality data.
In this exercise, we will do step by step to analyze particulate matter (PM) pollution, which is another important and complicated air pollutant. PM can be emitted directly into the air as a primary pollutant, or it can be formed as secondary air pollutants. It is one of the main pollutants, particularly in urban areas.
Answers to the 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)
Similar to most air pollutants, the very first fundamental step to analyze PM concentration is looking at temporal variations. First, calculate the monthly average of the PM10 and PM2.5 concentration. Then plot the calculated monthly concentrations. Try to interpret the results. For example, try to identify the months in which the PM concentration is elevated.
Another important temporal analysis of PM concentration is looking at the hourly concentration or diurnal cycle. Plot the hourly mean concentrations of PM10 and PM2.5 and try to interpret the results. For example, identify the hours in which PM concentration is elevated.
Next, we have to see whether the concentration of the pollutant exceeds the standard. If it does, how often?
Estimate and plot how many days in each month the 24-average PM10 and PM2.5 concentration was above the standard. Assume the standard concentration for PM10 and PM2.5 are 80 and 40 ppm, respectively.
As mentioned before, PM is considered as both a primary and secondary pollutant. So, its transport, as well as formations, could be highly dependent on meteorological factors. In this exercise, calculate the correlation of daily average PM10 and PM2.5 concentrations with wind speed and try to discuss the results.
Finally, we can look at the correlation between PM10 and PM2.5. Doing so will give us a general perspective to see whether the potential source of PM is different. For example, typically, PM10 represents local sources of PM and PM2.5 can represent both local and regional sources of the PM. Also, plot the linear regression between PM10 and PM2.5.