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Higher Pollution Episode Detection Using Image Classification Techniques

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Abstract

Image classification techniques have been applied to detect higher pollution episodes in modelled air pollution data. These techniques are widely used in video processing to find patterns in videos. An attempt for the first time has been made to apply these techniques by considering air pollution as continuous video frames as the spatio-temporal changes in the pollution are linked to its previous state of the atmosphere. The applicability of these techniques has been tested over Northern Italy to detect ozone pollution episodes in year 2004 using model simulated concentrations. The methods tested in this paper are pixel, block-based, histogram, pertinent pixel and twin-comparison method. While these techniques have some kind of merits and demerits, a modified pertinent pixel comparison algorithm has been proposed to detect pollution episodes. The proposed method has been validated to detect PM10 episodes over Milan metropolitan area during 2 months in 2008 and is able to detect PM10 episodic events as well as non-events. This method provides a single binary index that can be applied by the air quality modellers and decision makers to determine the pollution episode over a given domain.

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Acknowledgments

This research was done as a project during doctoral degree at the University of Brescia, Italy. Author acknowledges Claudio Carnevale, Giovanna Finzi, Nicola Adami and Sergio Benini for their support and discussions.

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Authors and Affiliations

  1. Centre for Atmospheric and Instrumentation Research (CAIR), University of Hertfordshire, College Lane, Hatfield, AL10 9AB, UK

    Vikas Singh

  2. National Atmospheric Research Laboratory, Gadanki, Andhra Pradesh, 517 112, India

    Vikas Singh

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  1. Vikas Singh
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Correspondence to Vikas Singh.

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Singh, V. Higher Pollution Episode Detection Using Image Classification Techniques. Environ Model Assess 21, 591–601 (2016). https://doi.org/10.1007/s10666-015-9497-8

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  • DOI: https://doi.org/10.1007/s10666-015-9497-8

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