文献类型： 外文期刊
作者： Ren, Jing 1 ; Gao, Bing-Bo 1 ; Fan, Hai-Mei 3 ; Zhang, Zhi-Hong 5 ; Zhang, Yao 4 ; Wang, Jin-Feng 2 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.East China Sea Marine Environm Monitoring Ctr, Shanghai, Peoples R China
4.State Ocean Adm, Natl Marine Hazard Mitigat Serv, Beijing, Peoples R China
5.Chinese Acad Environm Planning, Ctr Environm Risk & Damage Assessment, Beijing, Peoples R China
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
关键词： MSN;Pollutant;Mean concentration;Variance of estimation error;Yangtze estuary
期刊名称：MARINE POLLUTION BULLETIN （ 影响因子：5.553； 五年影响因子：5.907 ）
ISSN：
年卷期：
页码：
收录情况： SCI
摘要： Reliable assessment of water quality is a critical issue for estuaries. Nutrient concentrations show significant spatial distinctions between areas under the influence of fresh-sea water interaction and anthropogenic effects. For this situation, given the limitations of general mean estimation approaches, a new method for surfaces with non homogeneity (MSN) was applied to obtain optimized linear unbiased estimations of the mean nutrient concentrations in the study area in the Yangtze estuary from 2011 to 2013. Other mean estimation methods, including block Kriging (BK), simple random sampling (SS) and stratified sampling (ST) inference, were applied simultaneously for comparison. Their performance was evaluated by estimation error. The results show that MSN had the highest accuracy, while SS had the highest estimation error. ST and BK were intermediate in terms of their performance. Thus, MSN is an appropriate method that can be adopted to reduce the uncertainty of mean pollutant estimation in estuaries. (C) 2016 Elsevier Ltd. All rights reserved.
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