Impact of clustered meteorological parameters on air pollutants concentrations in the region of Annaba, Algeria

详细信息	查看全文 | 推荐本文 |

• 作者：Soufiane Khedairia^a ; ^b ; ^{khedairia@labged.net} ; Mohamed Tarek Khadir^a
• 关键词：Meteorological day type identification ; Clustering ; Impact of meteorological factors to urban air pollution ; Artificial Neural Networks (ANN)
• 刊名：Atmospheric Research
• 出版年：2012
• 期刊代码：10_01698095
• 类别：ear
• 出版时间：September, 2012
• 卷：113
• 期：Complete
• 页码：89-101
• 文件大小：1063 K

摘要

The main objective of this study is the characterization of meteorological conditions in the region of Annaba (Algeria) using clustering tools. The proposed two stages clustering approach is based on using the Self-Organizing Maps (SOMs) and the well known K-means clustering algorithm. Quantitative (using two categories of validity indices) and qualitative criteria were introduced to compare and verify the correctness of the results. The different experiments developed, extracted five classes, which were related to typical meteorological conditions in the area. The obtained meteorological clusters are then used to better elucidate the dependency of meteorology on air quality in the presence of seven measured pollutants. In the current paper, Artificial Neural Networks (ANNs), and more precisely, Multi-Layered Perceptron (MLP) is used for modeling air pollutants, as well as, simulating their behaviour in relation to the meteorological parameters of interest. This behaviour is also investigated with the aid of correlation coefficient, where only results are shown for comparison, several relations and conclusions have been drawn.