High-resolution monitoring of atmospheric pollutants using a system of low-cost sensors

Rajasegarar, Sutharshan, Havens, Timothy C, Karunasekera, Shanika, Leckie, Christopher, Bezdek, James C, Jamriska, Milan, Gunatilaka, Ajith, Skvortsov, Alex and Palaniswami, Marimuthu 2014, High-resolution monitoring of atmospheric pollutants using a system of low-cost sensors, IEEE transactions on geoscience and remote sensing, vol. 52, no. 7, pp. 3823-3832, doi: 10.1109/TGRS.2013.2276431.

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Title High-resolution monitoring of atmospheric pollutants using a system of low-cost sensors
Author(s) Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Havens, Timothy C
Karunasekera, Shanika
Leckie, Christopher
Bezdek, James C
Jamriska, Milan
Gunatilaka, Ajith
Skvortsov, Alex
Palaniswami, Marimuthu
Journal name IEEE transactions on geoscience and remote sensing
Volume number 52
Issue number 7
Start page 3823
End page 3832
Total pages 10
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2014-07-01
ISSN 0196-2892
Keyword(s) Air pollution
Bayesian maximum entropy
geospatial analysis
particulate matter
spatiotemporal estimation
wireless sensor networks
Summary 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.
Language eng
DOI 10.1109/TGRS.2013.2276431
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Grant ID LP120100529
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089204

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