A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea
A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea
复制标题
长江口及邻近海域多元海洋环境监测网络分层优化方法
DOI：
10.1080/13658816.2015.1024254
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发表时间：
2015-08-03
影响因子：
5.7
通讯作者：
Chen, Zi-Yue
中科院分区：
文献类型：
--
作者：
Gao, Bing-Bo;Wang, Jin-Feng;Chen, Zi-Yue
有效的监测网络对于评价海洋环境质量、保护和管理海洋环境具有重要意义。在河口中，有锋分隔明显不同的水团，并影响物质输送，营养物分布，污染物聚集和扩散。这种分层非均匀表面既不满足克里格法的固定要求，也不能通过去除空间连续趋势来充分处理。本文提出了一种多元监测网的分层优化方法。该方法采用主成分分析（PCA）对相关目标进行降维，并采用非均匀表面均值（MSN）方法对空间分层非均匀表面进行最佳线性无偏估计，以解决不满足Kriging估计要求的问题.以长江口及邻近海域为例，对一年前采用有目的抽样设计的监测网进行了优化。优化包括两个步骤：减少冗余的监测站点，然后以最佳方式向剩余站点添加新站点。优化后的监测网络中包括51个站点被发现产生一个较小的总估计误差比目前的网络，其中有70个站点;此外，使用55个站点可以产生更高的精度估计的所有三个主成分（PC）比目前的70个站点。结果表明，该方法适用于具有明显分层非均匀性和涉及多个因素的环境监测点的优选。
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.