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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
AQMesh. (2015). Aqmesh website.
Boas, M. L. (1966). Mathematical methods in the physical sciences (vol. 2). New York: Wiley.
Cullis, C., Hirschler, M. (1980). Atmospheric sulphur: Natural and man-made sources. Atmospheric Environment (1967), 14(11), 1263–1278.
Duyzer, J., van den Hout, D., Zandveld, P., & van Ratingen, S. (2015). Representativeness of air quality monitoring networks. Atmospheric Environment, 104, 88–101.
Fishbain, B. & Moreno-Centeno, E. (2016). Self calibrated wireless distributed environmental sensory networks. Scientific reports, 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.
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.
Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362–367.
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.
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.
Leighton, P. (2012). Photochemistry of air pollution. Elsevier.
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.
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.
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.
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.
Nazaroff, W., & Alvarez-Cohen, L. (2001). Environmental Engineering Science. John Wiley.
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.
Rao, S. T., & Zurbenko, I. G. (1994). Detecting and tracking changes in ozone air quality. Air & waste, 44(9), 1089–1092.
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.
Schwartz, J., & Marcus, A. (1990). Mortality and air pollution j london: A time series analysis. American Journal of Epidemiology, 131(1), 185–194.
Tchepel, O., & Borrego, C. (2010). Frequency analysis of air quality time series for traffic related pollutants. Journal of Environmental Monitoring, 12(2), 544–550.
US Environmental Protection Agency. (2012). Epa national ambient air quality standards. Technical report.
Varotsos, C., Ondov, J., & Efstathiou, M. (2005). Scaling properties of air pollution in Athens, Greece and Baltimore, Maryland. Atmospheric Environment, 39(22), 4041–4047.
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.
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.
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.
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.
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.
Zurbenko, I. (1986). The spectral analysis of time series.North-Holland, Inc.: Elsevier.
Acknowledgments
This work was partially supported by the 7th European Framework Program (FP7) ENV.2012.6.5-1, grant agreement no. 308524 (CITI-SENSE), the Technion Center of Excellence in Exposure Science and Environmental Health (TCEEH) and the Environmental Health Fund (EHF).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Fishbain, B., Moshenberg, S., Lerner, U. (2017). Optimal Noise Filtering of Sensory Array Gaseous Air Pollution Measurements. In: Wohlgemuth, V., Fuchs-Kittowski, F., Wittmann, J. (eds) Advances and New Trends in Environmental Informatics. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-44711-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-44711-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44710-0
Online ISBN: 978-3-319-44711-7
eBook Packages: Business and ManagementBusiness and Management (R0)