Spatial Prediction of Sulfur Dioxide in the Eastern United States View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1999

AUTHORS

David M. Holland , Nancy Saltzman , Lawrence H. Cox , Douglas Nychka

ABSTRACT

The U.S. Environmental Protection Agency initiated the development of the Clean Air Status and Trends Network (CASTNet) to determine the status and trends of selected air pollutants in rural areas of the U.S. Large-scale monitoring networks such as CASTNet can be evaluated based on statistical models for prediction of the distribution of pollution over space. Central to these models is accurate representation of the spatial covariance of pollution measurements. This paper describes the use of two spatial covariance functions based on isotropic correlations to predict spatial patterns of seasonally-adjusted sulfur dioxide concentrations. Additionally, a nonstationary covariance that has both parametric and non-parametric components is considered. Comparisons of these functions are made based on summary statistics of prediction standard errors over the design region. Finally, space-filling techniques are used to simulate thinning or augmenting the network with subsequent evaluation of their predictive performance. The nonstationary covariance model provided the smallest average prediction error relative to the other two models. More... »

PAGES

65-76

References to SciGraph publications

  • 1998. Design of Air-Quality Monitoring Networks in CASE STUDIES IN ENVIRONMENTAL STATISTICS
  • 1997-09. Spatial sampling and the environment: some issues and directions in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 1998. Case Studies in Environmental Statistics in NONE
  • Book

    TITLE

    geoENV II — Geostatistics for Environmental Applications

    ISBN

    978-90-481-5249-0
    978-94-015-9297-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-94-015-9297-0_6

    DOI

    http://dx.doi.org/10.1007/978-94-015-9297-0_6

    DIMENSIONS

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