Optimal Noise Filtering of Sensory Array Gaseous Air Pollution Measurements

Conference paper
Part of the Progress in IS book series (PROIS)

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.

Keywords

Air pollution measurements Noise filtering Micro sensing units 

References

  1. AQMesh. (2015). Aqmesh website.Google Scholar
  2. Boas, M. L. (1966). Mathematical methods in the physical sciences (vol. 2). New York: Wiley.Google Scholar
  3. Cullis, C., Hirschler, M. (1980). Atmospheric sulphur: Natural and man-made sources. Atmospheric Environment (1967), 14(11), 1263–1278.Google Scholar
  4. Duyzer, J., van den Hout, D., Zandveld, P., & van Ratingen, S. (2015). Representativeness of air quality monitoring networks. Atmospheric Environment, 104, 88–101.CrossRefGoogle Scholar
  5. Fishbain, B. & Moreno-Centeno, E. (2016). Self calibrated wireless distributed environmental sensory networks. Scientific reports, 6.Google Scholar
  6. Fishbain, B., Yaroslavsky, L. P., & Ideses, I. (2008). Spatial, temporal, and interchannel image data fusion for long-distance terrestrial observation systems. Advances in Optical Technologies.Google Scholar
  7. Gupta, M., Shum, L. V., Bodanese, E., & Hailes, S. (2011). Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network. In 2011 IEEE 36th Conference on Local Computer Networks (LCN) (pp. 1003–1010). IEEE.Google Scholar
  8. Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362–367.CrossRefGoogle Scholar
  9. Künzli, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., et al. (2000). Public-health impact of outdoor and traffic-related air pollution: a european assessment. The Lancet, 356(9232), 795–801.CrossRefGoogle Scholar
  10. Laumbach, R. J., & Kipen, H. M. (2012). Respiratory health effects of air pollution: Update on biomass smoke and traffic pollution. Journal of Allergy and Clinical Immunology, 129(1), 3–11.CrossRefGoogle Scholar
  11. Leighton, P. (2012). Photochemistry of air pollution. Elsevier.Google Scholar
  12. Lerner, U., Yacobi, T., Levy, I., Moltchanov, S. A., Cole-Hunter, T., & Fishbain, B. (2015). The effect of ego-motion on environmental monitoring. Science of the Total Environment, 533, 8–16.CrossRefGoogle Scholar
  13. Levy, I., Mihele, C., Lu, G., Narayan, J., & Brook, J. R. (2014). Evaluating multipollutant exposure and urban air quality: Pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant. Environmental Health Perspectives, 122(1), 65.Google Scholar
  14. Mead, M., Popoola, O., Stewart, G., Landshoff, P., Calleja, M., Hayes, M., et al. (2013). The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment, 70, 186–203.CrossRefGoogle Scholar
  15. Moltchanov, S., Levy, I., Etzion, Y., Lerner, U., Broday, D. M., & Fishbain, B. (2015). On the feasibility of measuring urban air pollution by wireless distributed sensor networks. Science of The Total Environment, 502, 537–547.CrossRefGoogle Scholar
  16. Nazaroff, W., & Alvarez-Cohen, L. (2001). Environmental Engineering Science. John Wiley.Google Scholar
  17. Peng, R. D., Dominici, F., & Louis, T. A. (2006). Model choice in time series studies of air pollution and mortality. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(2), 179–203.CrossRefGoogle Scholar
  18. Rao, S. T., & Zurbenko, I. G. (1994). Detecting and tracking changes in ozone air quality. Air & waste, 44(9), 1089–1092.CrossRefGoogle Scholar
  19. Robinson, E., & Robbins, R. C. (1970). Gaseous nitrogen compound pollutants from urban and natural sources. Journal of the Air Pollution Control Association, 20(5), 303–306.CrossRefGoogle Scholar
  20. Schwartz, J., & Marcus, A. (1990). Mortality and air pollution j london: A time series analysis. American Journal of Epidemiology, 131(1), 185–194.Google Scholar
  21. Tchepel, O., & Borrego, C. (2010). Frequency analysis of air quality time series for traffic related pollutants. Journal of Environmental Monitoring, 12(2), 544–550.CrossRefGoogle Scholar
  22. US Environmental Protection Agency. (2012). Epa national ambient air quality standards. Technical report.Google Scholar
  23. Varotsos, C., Ondov, J., & Efstathiou, M. (2005). Scaling properties of air pollution in Athens, Greece and Baltimore, Maryland. Atmospheric Environment, 39(22), 4041–4047.CrossRefGoogle Scholar
  24. Wang, T., Poon, C., Kwok, Y., & Li, Y. (2003). Characterizing the temporal variability and emission patterns of pollution plumes in the pearl river delta of China. Atmospheric Environment, 37(25), 3539–3550.CrossRefGoogle Scholar
  25. Weinstein, B., Steyn, D., & Jackson, P. (2016). Modelling photochemical air pollutants from industrial emissions in a constrained coastal valley with complex terrain. In Air pollution modeling and its application XXIV (pp. 289–294). Springer.Google Scholar
  26. Williams, D. E., Henshaw, G. S., Bart, M., Laing, G., Wagner, J., Naisbitt, S., et al. (2013). Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network. Measurement Science and Technology, 24(6), 065803.CrossRefGoogle Scholar
  27. Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1–41.CrossRefGoogle Scholar
  28. Yaroslavsky, L. (2014). Signal Resotoration by means of linear filtering. In Digital signal processing in experimental research—Fast transform methods in digital signal processing (pp. 67–80). Sharjah, U.A.E: Bentham Science Publishers Ltd.Google Scholar
  29. Zurbenko, I. (1986). The spectral analysis of time series.North-Holland, Inc.: Elsevier.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Faculty of Civil & Environmental EngineeringTechnion - Israel Institute of TechnologyHaifaIsrael

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