A Study on Statistical Data Mining Algorithms for the Prediction of Ground-Level Ozone Concentration in the El Paso–Juarez Area

Abstract

This paper deals with ground-level ozone classification in the El Paso-Juarez area using statistical data mining algorithms. The persistence of highly concentrated ozone levels in the troposphere does harm to humans, animals, and plants. So, early detection of high ozone levels is essential to ensure a healthy environment, especially for the elderly, the children, and the asthma patients. For this study, we have used the data sets of air pollutants and meteorological variables from 2015 to 2019 from the El Paso area, which frequently exceeds the National Ambient Air Quality Standard. Five supervised machine learning techniques are studied to classify the likelihood of high/low ozone days in the atmosphere with great accuracy. These techniques help to obtain primary variables that cause high ozone concentration. We predicted the data based on the key variables and computed the prediction accuracy using several evaluation metrics. The results suggest that supervised machine learning techniques are useful in classifying high/low ozone days in this area.