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

Andrea Marinoni, Paolo Gamba, Daniele De Vecchi, Devis Tuia

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

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.
Original languageEnglish
Title of host publication2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings
Subtitle of host publicationObserving, Understanding And Forecasting The Dynamics Of Our Planet
PublisherIEEE Xplore
Pages2062-2065
ISBN (Electronic)9781538671504, 9781538671498
ISBN (Print)9781538671511
DOIs
Publication statusPublished - 5 Nov 2018
EventIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

ConferenceIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
CountrySpain
CityValencia
Period22/07/1827/07/18

Fingerprint

Air pollution
Air quality
Feature extraction
Public administration
Sustainable development

Cite this

Marinoni, A., Gamba, P., De Vecchi, D., & Tuia, D. (2018). Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction. In 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet (pp. 2062-2065). IEEE Xplore. https://doi.org/10.1109/IGARSS.2018.8519052
Marinoni, Andrea ; Gamba, Paolo ; De Vecchi, Daniele ; Tuia, Devis. / Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction. 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, 2018. pp. 2062-2065
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title = "Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction",
abstract = "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.",
author = "Andrea Marinoni and Paolo Gamba and {De Vecchi}, Daniele and Devis Tuia",
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Marinoni, A, Gamba, P, De Vecchi, D & Tuia, D 2018, Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction. in 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, pp. 2062-2065, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8519052

Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction. / Marinoni, Andrea; Gamba, Paolo; De Vecchi, Daniele; Tuia, Devis.

2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, 2018. p. 2062-2065.

Research output: Chapter in Book/Report/Conference proceedingConference paper

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N2 - 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.

AB - 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.

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Marinoni A, Gamba P, De Vecchi D, Tuia D. Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction. In 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore. 2018. p. 2062-2065 https://doi.org/10.1109/IGARSS.2018.8519052