Urban ozone concentration forecasting with artificial neural network in Corsica

TL;DR

This paper develops an artificial neural network-based model to forecast ozone and PM10 pollution levels in Corsica, aiming to improve short-term air quality predictions considering local climatic and geographical factors.

Contribution

The study introduces a tailored ANN approach for Corsica's unique conditions, enhancing short-term pollution forecasting accuracy over existing deterministic models.

Findings

ANN models effectively predict ozone and PM10 levels in Corsica.

Clustering meteorological conditions improves forecast reliability.

The approach aids in pollution peak anticipation for better public health measures.

Abstract

Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France) region, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for meso-scale or local forecasting, but need powerful large variable sets, a good knowledge of atmospheric processes, and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in a Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANN) that have shown good results in the prediction of ozone concentration at horizon h+1 with data measured locally. The purpose of this study is to build a predictor to realize predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to anticipate pollution peak formation and to take appropriated prevention measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN models will be used, for meteorological conditions clustering and for operational forecasting.