An efficient monitoring network is very important in accessing the marine environmental quality and its protection and management. In an estuary, there are fronts that separate distinctly different water masses and affect material transport, nutrient distribution, pollutant aggregation,
and diffusion. This stratified heterogeneous surface neither satisfies the stationary requirements of kriging, nor can be handled adequately by removing a spatially continuous trend. This article presents a stratified optimization method for a multivariate monitoring network. In this method,
principal component analysis (PCA) was used to reduce the dimensionality of the correlated targets, and the mean of surface with nonhomogeneity (MSN) method was adopted to produce the best linear unbiased estimator for a spatially stratified heterogeneous surface that failed to satisfy the
requirements for a kriging estimate. The existing monitoring network in the Yangtze River estuary and its adjacent sea, which was designed by purposive sampling year ago was optimized as an illustration. The optimization consisted of two steps: reduce the redundant monitoring sites and then
optimally add new sites to the remaining sites. After optimization, the inclusion of 51 sites in the monitoring network was found to produce a smaller total estimated error than that of the current network, which has 70 sites; moreover, the use of 55 sites can produce a higher precision of
estimation for all three principal components (PCs) than that of the current 70 sites. The results demonstrated that the proposed method is suitable for optimizing environmental monitoring sites that have dominant stratified nonhomogeneity and that involve multiple factors.
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mean of surface with nonhomogeneity;
monitoring site optimization;
spatial simulated annealing
Document Type: Research Article
LREIS, Institute of Geographic Sciences & Nature Resources Research, Chinese Academy of Sciences, Beijing, P. R. China
East China Sea Environmental Monitoring Center, SOA, Shanghai, P. R. China
Department of Mathematical Sciences, Tsinghua University, Beijing, China
College of Resources and Environmental, University of Chinese Academy of Sciences, Beijing, P. R. China
College of Global Change and Earth System Science, Beijing Normal University, Beijing, P. R. China
Publication date: August 3, 2015
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