First evaluation of a novel screening tool for outlier detection in large scale ambient air quality datasets
by Oliver Kracht; Michel Gerboles; Hannes I. Reuter
International Journal of Environment and Pollution (IJEP), Vol. 55, No. 1/2/3/4, 2014

Abstract: Systematic collection of long term meso- to large-scale datasets of ambient air quality provides an indispensible means for air pollution monitoring. However, the quality of these monitoring data depends on the chosen method of measurements and the QA/QC procedures applied. We present the first version of a prototyped screening tool for the automatic detection of outliers in large data volume air quality monitoring records. The method is based on an adaption of the existing Smooth Spatial Attribute Method, which considers both attribute values and spatio-temporal relationships. An application example of the method is demonstrated by computing warnings on abnormal records in the 2006A/2007 time series of PM10 daily values of background stations reported in the European air quality database AirBase.

Online publication date: Wed, 17-Dec-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environment and Pollution (IJEP):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email