F UTURE WORK

There was very limited data available for the purpose of the research, however it gave us a glimpse of the trend in the reduction of air pollution. Future work should include comparisons of more cities around the world and comprehensive trend for every city should be modeled.

33

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39

Appendix: Regression and graph generation

Pollution_data

#Load libraries that will help us plot

library(ggplot2) library(dplyr)

##

## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':

##

## filter, lag

## The following objects are masked from 'package:base':

##

## intersect, setdiff, setequal, union library(tseries)

## Registered S3 method overwritten by 'quantmod':

## method from

## as.zoo.data.frame zoo library(stats)

#Load the No2 Data

data <- read.csv("no2.csv")

#Remove first two observations as they are NA data2 <- data[-c(1:2),]

#Extract date in date format

40 date <- as.Date(data$Date, "%d/%m/%y")

#Remove first two dates as they are NA date <- date[3:320]

#Run an OLS regression

reg<-lm(data2$max ~ date, data = data2)

#Plot the time series with the line of best fit running through it

plot(data2$max ~ date, type = "l", col = "blue", axes = T) abline(reg, col="red")

#Load SO2 data

dataso2 <- read.csv("so2.csv")

#Extract date

dateso2 <- as.Date(dataso2$Date, "%d/%m/%y")

41

#Run regression

reg<-lm(data2$max ~ date, data = dataso2)

#Plot the time series with the regression line

plot(dataso2$max ~ dateso2, type = "l", col = "blue", axes = T) abline(reg, col="red")

#Load PM10 data

datapm10 <- read.csv("pm10.csv")

#Extract date

datepm10 <- as.Date(datapm10$Date, "%d/%m/%y")

#Run the regression

reg<-lm(datapm10$max ~ datepm10, data = datapm10)

42

#Plot the time series with the regression line

plot(datapm10$max ~ datepm10, type = "l", col = "blue", axes = T) abline(reg, col="red")