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COMPLEMENTARY CO-KRIGING: SPATIAL PREDICTION USING DATA COMBINED FROM, SEVERAL POLLUTION MONITORING NETWORKS
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).
Impact/Purpose:
Our main objective is to assess the exposure of selected ecosystems to specific atmospheric stressors. More precisely, we will analyze and interpret environmental quality (primarily atmospheric) data to document observable changes in environmental stressors that may be associated with legislatively-mandated emissions reductions.
Description:
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).