Research Article

A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output

  1. Jan van de Kassteele1,2,*,
  2. Alfred Stein1

Article first published online: 7 NOV 2005

DOI: 10.1002/env.771

Issue

Environmetrics

Environmetrics

Volume 17, Issue 4, pages 309–322, June 2006

Additional Information

How to Cite

de Kassteele, J. v. and Stein, A. (2006), A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output. Environmetrics, 17: 309–322. doi: 10.1002/env.771

Author Information

  1. 1

    Mathematical and Statistical Methods Group—Biometris, Wageningen University, P.O. Box 100, 6700 AC Wageningen, The Netherlands

  2. 2

    Netherlands Environmental Assessment Agency—RIVM, Bilthoven, The Netherlands

*Mathematical and Statistical Methods Group—Biometris, Wageningen University, P.O. Box 100, 6700 AC Wageningen, The Netherlands.

Publication History

  1. Issue published online: 12 MAY 2006
  2. Article first published online: 7 NOV 2005
  3. Manuscript Accepted: 12 SEP 2005
  4. Manuscript Received: 9 FEB 2005

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Keywords:

  • geostatistics;
  • external drift kriging;
  • measurement error models;
  • Bayesian hierarchical models;
  • atmospheric dispersion models;
  • uncertainty assessment

Abstract

We present a method that combines uncertain air quality measurements with uncertain secondary information from an atmospheric dispersion model. The method combines external drift kriging and a measurement error (ME) model, and uses Bayesian techniques for inference. An illustration with simulated data shows what can theoretically be expected. The method is flexible for assigning different error variances to both the primary information and secondary information at each location. Next, we address actual NO2 data collected at an urban and a rural site in the Netherlands. Uncertainty assessments in terms of exceeding air quality standards are given. The study shows that biased uncertain secondary information can be used successfully in a spatial interpolation study at the national scale. Copyright © 2005 John Wiley & Sons, Ltd.

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