Shrinking a wet deposition network

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Abstract

Suppose that we must delete stations from a monitoring network. Which stations should be deleted if we wish the remaining network to have the smallest possible trend estimate variances? We use the spatial-temporal model described in Oehlert, to model concentration of sulfate in wet deposition. Based on this model and three criteria, we choose good sets of candidate stations for deletion from the NADP/NTN network. We use the criteria: that the sum of 11 regional trend estimate variances be as small as possible, that the sum of local trend estimation variance be as small as possible, and that the sum of local mean estimation variance be as small as possible. Good choices of stations for deletion result in a modest increase in criteria (about 7 to 34%) for 100 stations deleted from the network, while random sets of 100 stations can increase criteria by a factor of two or more.

Original languageEnglish (US)
Pages (from-to)1347-1357
Number of pages11
JournalAtmospheric Environment
Volume30
Issue number8
DOIs
StatePublished - Apr 1996

Bibliographical note

Funding Information:
Consider the plight of the federal scientist or agency charged with documenting the effect of the Clean Air Act Amendment's (CAAA) mandated sulfur emissions reductions on the changes in national, regional, and local sulfate concentration in wet deposition and simultaneously charged with reducing the monitoring budget (a not unthinkable combination). At the end of 1987, there were 249 monitoring stations operating in the NADP/NTN (U.S.A.) and CANSAP and APIOS-C (Canada) networks. The locations of these stations are shown in Fig. 1. Suppose that we are required to delete 100 of the 195 NADP/NTN stations from the network to balance the budget. Which 100 stations *This research was supported in part by the U.S. Environmental Protection Agency through contract CR-819638gll-0 to the National Institute of Statistical Sciences and in part by a grant of computer time from the Minnesota Supercomputer Institute.

Keywords

  • Monitoring network
  • Network design
  • Spatial smoothing
  • Trend analysis

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