Forecasting Air Pollution Using Time Series

Abstract

        Concern for the environment is an important priority in different countries; Environmental pollution is the most important source of environmental threat. Pollution levels in the water, air and land environment have reached serious limits, requiring researchers in various sciences to take care of researches that reduce, monitor and reduce their causes within the limits allowed. Air pollution is one of the main threats to environmental pollution, which has a direct impact on human life. It is due to the increase in the temperature of the earth and the depletion of the ozone layer due to the dangerous emissions of gases directly into the atmosphere, mainly NO₂ and SO₂ .

Because of the great evolution in recording data through modern digital devices, as these data take the form of time series, and using these data suggested several mathematical models to model the behavior of many of the pollutants for use for control and prediction .

In this research, the use of the Box & Jenkins  method, Auto Regressive Integrated Moving Average (ARIMA). The polluted letter included NO₂  in Baghdad for the period 2015-2017 and a weekly average of 157 views.

This research showed that the time series of the variables is stable, Regressive Integrated Moving Average models. The appropriate model for the NO₂ data is ARIMA (1.0.0).