Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction

Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground

Discovering temporal patterns of air quality in different parts of Europe with data driven feature extraction

Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground