Forecasting O3 levels in industrial area surroundings up to 24Â h in advance, combining classification trees and MLP models

The obtained generalization model performances are very good to classify in advance the expected class of O3 concentration level. Performance results vary from 70% of success to forecast O3 class above 70 μg/m3 24 h in advance up to 99% to predict the next hour in advance. These successful results are favorable to be implemented in a real-time tool for health and environmental advisories, allowing the forecast of air pollutants concentrations up to 24 h ahead, improving the local air quality management systems.