Time series air quality forecasting with R Language and R Studio

Abstract

The purpose of this study is to demonstrate how to make air quality forecasting to predict the Nitrogen Dioxide quality index in the future. In this paper, we demonstrate exploratory data analysis and compare the performance of the Autoregressive Integrated Moving Average and Exponential Smoothing Model. We used R Language and R Studio to integrate all the datasets, exploratory data analysis, data preparation, performing Autoregressive Integrated Moving Average and Exponential Smoothing methods, model evaluation, and visualization. This study used data from the automatic remote air quality-monitoring station located in an urban area in Madrid, Spain. The dataset in the period from 1 January 2001 to 31 December 2017. The dataset recorded six pollutants such as Nitrogen Dioxide, Particulate Matter 10 micrometres, Sulphur Dioxide, Carbon Monoxide, Ozone and Particulate Matter 2.5 micrometres. In this study, we focus only on Nitrogen Dioxide pollutants. From our model, we saw that exponential smoothing has better accuracy compared to the Autoregressive Integrated Moving Average. We also exposed that Nitrogen Dioxide pollutant shows unhealthy for sensitive group's level in November to March and has the lowest level in June and July.