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Complementary CO-Kriging: Spatial Prediction Using Data Combined from, Several Pollution Monitoring Networks

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Abstract:We consider the problem of optimal spatial prediction of an environmental variable using data from more than one sampling network. A model incorporating spatial dependence and measurement errors with network-specific biases and variances serves as the basis for the analysis of the combined data from all networks. We develop the associated optimal pre- diction methodology, which we call complementary co-kriging because (a) data from each network complements the other, and (b) the solutions to several prediction problems of interest are co-kriging predictors. A hypothetical example illustrates how much better the complementary co-kriging predictor can be, when compared to the ordinary kriging predictors from each network alone and to a "naive" combined predictor. We use the methodology to obtain optimal predictions of wet nitrate concentration data over the eastern U .S. using data combined from the National Atmospheric Deposition Program/National Trends Net- work (NADP/NTN) and the Clean Air Status and Trends Network (CASTNet).
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Citation:Zimmerman, D., and D. M. Holland. Complementary CO-Kriging: Spatial Prediction Using Data Combined from, Several Pollution Monitoring Networks. ENVIRONMETRICS 16(3):219-234, (2005).
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Contact: Chris Siebert - (702) 798-2234 or siebert.christopher@epa.gov
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Division: Environmental Sciences Division
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Branch: Landscape Characterization Branch (RTP)
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Product Type: Journal
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Published: 08/02/2005
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Bullet Item Complementary CO-Kriging: Spatial Prediction Using Data Combined from, Several Pollution Monitoring Networks
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Bullet Item Atmospheric Ecosystem Stressor Pattern and Trend Analysis
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Bullet Item Environmental Statistics Initiative
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