• DocumentCode
    1763459
  • Title

    High-Resolution Monitoring of Atmospheric Pollutants Using a System of Low-Cost Sensors

  • Author

    Rajasegarar, Sutharshan ; Havens, Timothy C. ; Karunasekera, Shanika ; Leckie, Christopher ; Bezdek, James C. ; Jamriska, Milan ; Gunatilaka, Ajith ; Skvortsov, Alex ; Palaniswami, Marimuthu

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Parkville, VIC, Australia
  • Volume
    52
  • Issue
    7
  • fYear
    2014
  • fDate
    41821
  • Firstpage
    3823
  • Lastpage
    3832
  • Abstract
    Increased levels of particulate matter (PM) in the atmosphere have contributed to an increase in mortality and morbidity in communities and are the main contributing factor for respiratory health problems in the population. Currently, PM concentrations are sparsely monitored; for instance, a region of over 2200 square kilometers surrounding Melbourne in Victoria, Australia, is monitored using ten sensor stations. This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes. Our evaluation reveals that local PM estimation accuracies improve with higher densities of low-precision sensor nodes. Our analysis examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy.
  • Keywords
    air pollution; atmospheric measuring apparatus; health hazards; sensors; Australia; Melbourne; Victoria; high-precision expensive particulate matter devices; high-resolution atmospheric pollutant monitoring; local particulate matter estimation accuracies; lost-cost sensors; low-cost lower precision particulate matter sensor nodes; low-precision sensor nodes; morbidity; mortality; overall estimation accuracy; particulate matter concentrations; respiratory health problems; sensor stations; Atmospheric modeling; Computational modeling; Data models; Monitoring; Pollution measurement; Sensor phenomena and characterization; Air pollution; Bayesian maximum entropy; geospatial analysis; kriging; particulate matter; spatiotemporal estimation; wireless sensor networks;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2013.2276431
  • Filename
    6587138