Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

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), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, 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 the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks

“…nRMSE is also lower than in Ajaccio, average concentrations in Bastia being higher for the same reason. We note that precision for O 3 forecasting is equivalent to that which was obtained in previous work (Tamas et al, 2014) with models and data comparable to fMLP. However, we had used a heavier feature selection process, using mutual information, replaced here by the easier use of PCA on a large dataset.…”

“…As observed in a previous study (Tamas et al, 2014), the main difficulty encountered with MLP was to obtain good performances for high concentration episodes. Some authors use boosting, that is to say increase the frequency of such episodes in the training set (Kukkonen et al, 2003;Paschalidou et al, 2010), but it can lead to overfitting.…”

“…Thus, our preliminary work focused on ozone (O 3 ) concentration forecasting one hour ahead (Paoli et al, 2011). Then a work on h + 24 hourly O 3 concentration forecasting with MLP was initiated (Tamas et al, 2014) and showed a good reliability (IA reaching 0.88), in the same order of magnitude than previous studies found in the literature as Coman et al (2008). As clustering of training data appeared to offer an improvement of ANN predictive models abilities (Davis and Bouldin, 1979;Lu et al, 2006;Poggi and Portier, 2011), we decided to apply such a method to improve our capicity to detect pollutant peacks.…”

Flare performance modeling and set point determination using artificial neural networks

“…The developed NN models have been used at the ISP and SLF to determine the operation set points in compliance with the current EPA regulations. The specific set point of a manipulated variable (steam-/air-assisted or makeup fuel or NHV CZ ) was solved by specifying opacity and NHV CZ ≥ 270 BTU/scf or NHV dil ≥ 22 BTU/ft 2 for steam-and air-assisted flare data, respectively [55,57]. For cases that do not satisfy the minimum heating value requirement, make-up fuel needed to be added to increase it to the minimum required value (NHV CZ = 270 BTU/scf or NHV dil = 22 BTU/ft 2 .…”

“…Another useful application to estimate the efficiency for digester gas and landfill gas flares has been proposed by Water Environment Research Foundation [56]. In another study by Tamas [57], statistical models, particularly artificial neural networks, were found to show good results in the prediction of ozone concentration. ANN modeling was used with pollutant and meteorological data for operational forecasting of atmospheric pollutants in their study.…”