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 openair package along with some geospatial packages to spatially analyze and visualize air quality data. However, sometimes it is difficult to costomize or modify those functions according our interest. So, it is also very useful to get advantages of using default function in R. In, addition so far we mostly practice visualization functions and did not pay much attention to statistical approaches to analyze air quality data and also analyze the effect of meteorological parameters on them. Therefore, from this exercise we are going to first practice how to make most benefit of default R functions to produce professional plots and also we will go through different statistical techniques to analyze air quality data.
In this exercise we will go step by step to analyze ozone pollution which is one of the most important and complicated air pollutants. ozone is a primary pollutant and is not emitted directly into the air. It is produced by its precursors mainly NOX and VOCs. The production of ozone is highly dependent to sunlight and so it is only produced during daytime. This process is non-linear and so very complicated. In this exercise we are not going through very complicated analysis. Instead we will practice some useful techniques and tools to analyse ozone pollution.
Answers to the exercises are available here.
For other parts of this exercise set, follow the tag openair.
For this exercise we need to install the following library to download are quality and meteorological data:
install.packages("devtools")
library(devtools)
install_github("NateByers/region5air")
data("chicago_air")
library(openair)
my1data <- importAURN(site = 'MY1', year = 2016, met = TRUE)
Exercise 1
One of the first step to analyze air pollution including ozone, is to see whether the concentration of pollutant exceed the standard and how often.
Estimate and plot how many days in each month o3 concentration was above 100 (ppb) for my1data.
Exercise 2
Next step is to look at temporal variations at different scales. Since ozone production is highly dependent to its precursors. So produce diurnal and monthly variation of ozone nox of my1data, and try to interpret the results.
Exercise 3
When is comes to ozone, another important analysis is identify the ozone production regime. As already mentioned ozone production is strongly non-linear and highly dependent to its precursors. Sometimes ozone production is sensitive to nox concentration which is called nox-sensitive regime, and sometimes it is sensitive to vocs, which is called voc-sensitive. This is important when we want to develop control strategies. Because typically in voc-sensitive regimes the ozone concentration will increase by decreasing nox, and voc-sensitive regime is a typical regime for urban areas with high nox emission from vehicles. In such areas sometimes ozone concentration is higher during weekends than working days which is due to voc-sensitive ozone production regime.
According to the above introduction, in this exercise we will take a look at weekend,weekday difference.
First plot the average ozone concentration for each day of week, and visually identify if there is any tangible difference between weekend and working day consideration. However, we should also try to see if there statistically significant difference between them. For this you use Wilcoxon rank-sum test.
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- Work with Spatial data and maps
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Exercise 4
Ozone production and its transport is highly dependent to meteorological factor. As, mentioned sunlight is a key factor for ozone production process. Accordingly, temperature can highly affect ozone concentration. In this exercise first, calculate the correlation of daily ozone concentration in chicago_air with temperature, and then plot their time series.
Exercise 5
Also, we can do a regression between ozone and temperature to see how it varies with temperature. So, in this exercise plot the linear regression line for ozone and temperature and print slope and intercept.
Related exercise sets:
Spatial Data Analysis: Introduction to Raster Processing (Part 1)
Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification
Spatial Data Analysis: Introduction to Raster Processing: Part-3
Working With Air Quality and Meteorological Data Exercises (Part 5)
Density-Based Clustering Exercises
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