|
Abstract
|
|
|---|---|
| Over the last ten years, Salamanca has been considered among the most polluted cities in Mexico, with the most important air pollutants being SO2 and PM10. Currently, in Salamanca, an Environmental Monitoring Network (EMN) is installed in which time series of criteria pollutants and meteorological variables are obtained. Unfortunately air pollution level is computed in each monitoring station without taking into account those meteorological variables. In this paper, we propose a novel methodology to compute air pollution levels taking the meteorological variables as a decision factor by means of data fusion and neural networks. First, in preprocessing stage two Feature Vectors (FVSO2 and FVPM10) are built for each monitoring station. Next, in data fusion stage, a Representative Feature Vector by pollutant (RFVSO2 and RFVPM10) is built with the maximum value of the three FVs. Finally, an Artificial Neural Network (ANN) is trained with the RFV in order to classify future environmental situations. Self-Organizing Map (SOM) is the ANN applied. In this paper, time series of pollutant concentrations and meteorological variables are obtained from the EMN. EMN is composed for the three monitoring stations in Salamanca. Data used in this study have approved according to Proaire environmental authority standards. | |
|
International
|
Si |
|
Congress
|
Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on |
|
|
960 |
|
Place
|
Cardiff, UK |
|
Reviewers
|
Si |
|
ISBN/ISSN
|
1935-4576 |
|
|
10.1109/INDIN.2009.5195858 |
|
Start Date
|
23/06/2009 |
|
End Date
|
26/06/2009 |
|
From page
|
522 |
|
To page
|
527 |
|
|
Proc. of Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on |