globalchange  > 过去全球变化的重建
DOI: 10.1016/j.marpolbul.2016.09.021
Scopus记录号: 2-s2.0-84994478300
论文题名:
Assessment of pollutant mean concentrations in the Yangtze estuary based on MSN theory
作者: Ren J.; Gao B.-B.; Fan H.-M.; Zhang Z.-H.; Zhang Y.; Wang J.-F.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2016
卷: 113, 期:2018-01-02
起始页码: 216
结束页码: 223
语种: 英语
英文关键词: Mean concentration ; MSN ; Pollutant ; Variance of estimation error ; Yangtze estuary
Scopus关键词: Errors ; Estimation ; Nutrients ; Pollution ; Seawater ; Water quality ; Anthropogenic effects ; Estimation approaches ; Estimation errors ; Mean concentrations ; Nutrient concentrations ; Pollutant ; Simple random sampling ; Yangtze Estuary ; Estuaries ; accuracy assessment ; concentration (composition) ; environmental assessment ; error analysis ; estimation method ; estuarine pollution ; performance assessment ; pollution monitoring ; estuary ; human ; human tissue ; kriging ; pollutant ; stratified sample ; theoretical model ; uncertainty ; analysis ; chemistry ; China ; environmental monitoring ; procedures ; river ; spatial analysis ; water pollutant ; China ; Yangtze Estuary ; water pollutant ; China ; Environmental Monitoring ; Estuaries ; Models, Theoretical ; Rivers ; Spatial Analysis ; Water Pollutants, Chemical
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science ; Earth and Planetary Sciences: Oceanography ; Environmental Science: Pollution
英文摘要: 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. © 2016 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/86577
Appears in Collections:过去全球变化的重建
全球变化的国际研究计划

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作者单位: Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; East China Sea Marine Environmental Monitoring Center, Shanghai, China; National Marine Hazard Mitigation Service, State Oceanic Administration, Beijing, China; Center for Environmental Risk and Damage Assessment, Chinese Academy for Environmental Planning, Beijing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

Recommended Citation:
Ren J.,Gao B.-B.,Fan H.-M.,et al. Assessment of pollutant mean concentrations in the Yangtze estuary based on MSN theory[J]. Marine Pollution Bulletin,2016-01-01,113(2018-01-02)
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