Assessment of pollutant mean concentrations in the Yangtze estuary based on MSN theory.
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.
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.
Jing
J
Ren
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.
Bing-Bo
BB
Gao
East China Sea Marine Environmental Monitoring Center, Shanghai, China. Electronic address: fhm@eastsea.gov.cn.
Hai-Mei
HM
Fan
Center for Environmental Risk and Damage Assessment, Chinese Academy for Environmental Planning, Beijing, China.
Zhi-Hong
ZH
Zhang
National Marine Hazard Mitigation Service, State Oceanic Administration, Beijing, China.
Yao
Y
Zhang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China. Electronic address: wangjf@lreis.ac.cn.
Jin-Feng
JF
Wang
D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D063366:Estuaries; D008962:Models, Theoretical; D045483:Rivers / Q000737:chemistry*; D062206:Spatial Analysis; D014874:Water Pollutants, Chemical / Q000032:analysis*
D014874:Water Pollutants, Chemical
MSN; Mean concentration; Pollutant; Variance of estimation error; Yangtze estuary