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Outlier detection and gap filling methodologies for low-cost air quality measurements

Ottosen, Thor-Bjørn and Kumar, Prashant (2019) Outlier detection and gap filling methodologies for low-cost air quality measurements Environmental Science: Processes & Impacts, 21 (4). pp. 701-713.

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Abstract

Air pollution is a major environmental health problem around the world, which needs to be monitored. In recent years, a new generation of low-cost air pollution sensors has emerged. Poor or unknown data quality, resulting from the intrinsic properties of the sensor as well as the lack of a consensus on data processing methodologies for these sensors, has, among other factors, prevented widespread adoption of these sensors. To contribute to the creation of this consensus, we reviewed the available methodologies for quality control, outlier detection and gap filling and applied two outlier detection methodologies and five gap filling methodologies to a case study (consisting of an 11-month long air quality data set from a low-cost sensor). We showed that erroneous data can be detected in a fully automated way, that point and contextual outlier detection methodologies can be applied to low-cost air pollution data and yield meaningful results, and that linear interpolation has the best performance for gap filling for low-cost air pollution sensors. In conclusion, data cleaning procedures are important, and the presented methods can form part of a generalised data processing methodology for low-cost air pollution sensors.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Civil and Environmental Engineering
Authors :
NameEmailORCID
Ottosen, Thor-Bjørnt.ottosen@surrey.ac.uk
Kumar, PrashantP.Kumar@surrey.ac.uk
Date : 22 February 2019
Funders : European Union's Horizon 2020
DOI : 10.1039/C8EM00593A
Grant Title : H2020-SC5-04-2015
Copyright Disclaimer : © The Royal Society of Chemistry 2019
Uncontrolled Keywords : Air Pollution; Quality control; Missing data; Imputing; AQMesh
Depositing User : Clive Harris
Date Deposited : 05 Mar 2019 15:26
Last Modified : 23 Feb 2020 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/850672

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