Working With Air Quality and Meteorological Data Exercises (Part 5)

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 set, we practiced some advanced and useful functions in the openair package to analyze and visualize air back-trajectories. In this exercise, first, we will practice another useful function in openair to analyze air pollution data. Then we will use openair, along with some other geospatial packages, to spatially analyze air pollution and meteorological data.

Answers to the exercises are available here.

For other parts of this exercise set, follow the tag openair.

For this exercise set beside openair you will need to install and load the following packages

install.packages('ggplot2')
library(ggplot2)
install.packages('plyr')
library(plyr)
install.packages('ggmap')
library(ggmap)

Exercise 1
In this exercise, we will use calendarPlot to visualize air quality and meteorological data. This function overlays data with a calendar.

Use calendarPlot to visualize O3 and PM10 concentration from my1 data, which was used in the previous exercise sets and shows the concentration by colors.

Exercise 2
It is also possible with calendarPlot to highlight days in which concentration is higher than a specified limit.

Use calendarPlot to visualize O3 concentration and highlight those days with O3 >= 80 ppb

Exercise 3
Another useful ability of calendarPlot is to annotate a plot with daily mean wind angle.

Use calendarPlot to visualize PM10 data and annotate daily mean wind angle data.

Exercise 4
In this exercise, we are going to practice more advanced and interesting geospatial air quality analysis. It would be very useful to compare the concentration of pollutants at different stations at the same time; it would also give a lot of information. For this practice, first, we should download hourly o3 concentration sample data for 2006 from 16 stations across the UK. Also, we need to download data for locations of each station. This data is placed into the openair server. To download data, run the following function using the openair package:

load(url("http://www.erg.kcl.ac.uk/downloads/Policy_Reports/AQdata/o3Measurements.RData"))
load(url("http://www.erg.kcl.ac.uk/downloads/Policy_Reports/AQdata/siteDetails.RData"))

After importing data, first, calculate the annual mean o3 by ddaply function in the plyr package. Then merge the o3 data with site details and put the result in a data frame called “annual.” Finally, overlay the annual o3 concentration data with the world map using ggplot2 and ggmap packages.

Exercise 5
This exercise will be the same as the previous exercises, but instead of using annual mean, use maximum o3 for winter.

Related exercise sets:

Working With Air Quality and Meteorological Data Exercises (Part 7) Working with air quality and meteorological data Exercises (Part-4) Working With Air Quality and Meteorological Data Exercises (Part 6) Working with air quality and meteorological data Exercises (Part-3) Working with air quality and meteorological data Exercises (Part-2)