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This paper presents the first results of a research project aimed at building a pollution peaks predictor using Artificial Neural Networks (ANNs) with data measured locally. We focus more particularly on the ozone concentration prediction in the Corsica Island at horizon “h+1”. We mainly look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures both in the Environment...
Air pollution can cause the human health, plants growth and daily mortality in numerous studies over the past decade. Therefore, forecasting and analysis of air quality are important topics of research today. The causes of poor air quality are global warming, greenhouse effects, acid rain, etc. Air pollution problems are related to the emissions of sulfur dioxides (SO2), nitrogen dioxide (NO2), suspended...
The present paper focuses on the prediction of hourly levels up to 8 hours ahead for five pollutants (SO2, CO, NO2, NO and O3) and six locations in the area of Bilbao, Spain. To that end, 216 models based on neural networks (NN) have been built. The database used to fit the NN's has been historical records of the traffic, meteorological and air pollution networks existing in the area corresponding...
This paper presents a methodology for the retrieval of the vertical profile of atmospheric gas pollutants in the boundary layer from ground-based remote sensing measurements. Nitrogen dioxide (NO2) and ozone (O3) slant column amounts were obtained with the differential optical absorption spectroscopy (DOAS) technique used in the multiple-axis configuration (referred to as multiaxis DOAS). The measurements...
In this work we present a tomographic-like methodology for the retrieval of the vertical profile of nitrogen dioxide (NO2) and ozone (O3) in the boundary layer from ground based remote sensing measurements. A set of NO2 and O3 slant column densities at different elevation angles can be obtained with the Differential Optical Absorption Spectroscopy (DOAS) technique used in the multiple axis configuration...
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