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Optimal Noise Filtering of Sensory Array Gaseous Air Pollution Measurements

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Barak Fishbain

    (Technion - Israel Institute of Technology)

  • Shai Moshenberg

    (Technion - Israel Institute of Technology)

  • Uri Lerner

    (Technion - Israel Institute of Technology)

Abstract

One of the fundamental components in assessing air quality is continuous monitoring. However, all measuring devices are bound to sensing noise. Commonly the noise is assumed to have zero mean and, thus, is removed by averaging data over temporal windows. Generally speaking, the larger the window, the better the noise removal. This operation, however, which corresponds to low pass filtering, might result in loss of real abrupt changes in the signal. Therefore, the need arises to set the window size so it optimally removes noise with minimum corruption of real data. This article presents a mathematical model for finding the optimal averaging window size. The suggested method is based on the assumption that while real measured physical phenomenon affects the measurements of all collocated sensors, sensing noise manifests itself independently in each of the sensors. Hence, the smallest window size which presents the highest correlation between the collocated sensors, is deemed as optimal. The results presented here show the great potential of the method in air quality measurements.

Suggested Citation

  • Barak Fishbain & Shai Moshenberg & Uri Lerner, 2017. "Optimal Noise Filtering of Sensory Array Gaseous Air Pollution Measurements," Progress in IS, in: Volker Wohlgemuth & Frank Fuchs-Kittowski & Jochen Wittmann (ed.), Advances and New Trends in Environmental Informatics, pages 275-285, Springer.
    • Handle: RePEc:spr:prochp:978-3-319-44711-7_22
    DOI: 10.1007/978-3-319-44711-7_22
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