TITLE:
Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon
AUTHORS:
Alya Atoui, Kamal Slim, Samir Abbad Andaloussi, Régis Moilleron, Zaher Khraibani
KEYWORDS:
Air Pollution, Air Quality Index, Times Series, Prediction
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.12 No.4,
October
31,
2022
ABSTRACT: During the last decades, air pollution has become a
serious environmental hazard. Its impact on public health and safety, as well
as on the ecosystem, has been dramatic. Forecasting the levels of air pollution
to maintain the climatic conditions and environmental protection becomes
crucial for government authorities to develop strategies for the prevention of
pollution. This study aims to evaluate the atmospheric air pollution of the
city of Zahleh located in the geographic zone of Bekaa. The study aims to
determine a relationship between variations in ambient particulate
concentrations during a short time. The data was collected from June 2017 to
June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential
Smoothing, TBATS (a forecasting method to model time series data), and Seasonal
Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models
for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative
Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE =
0.16). The results reveal that SARIMA can be applied to cities like Zahleh to
assess the level of air pollution and to prevent
harmful impacts on health. Furthermore, the authorities responsible for
controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a
mechanism to identify the high peaks of air pollution.