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

A two-step methodology was developed to forecast tropospheric ozone (O-3) concentration levels, k hours ahead (k = 1, 8, 12, 24), combining meteorological, air quality and industrial emissions data, across three air quality monitoring stations in Sines Portuguese region. Firstly, the best O-3 concentration predictors have been identified through Classification and Regression Trees techniques; then Multilayer Perceptron models were adopted to forecast O-3 levels for each monitoring site. The obtained generalization model performances are very good to classify in advance the expected class of O-3 concentration level. Performance results vary from 70% of success to forecast O-3 class above 70 mg/m(3) 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. Copyright (C) 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.