Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

FAN Haimei; GAO Bingbo; XU Ren; WANG Jinfeng

doi:10.1007/s13131-017-0969-3

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2017 > 36(2): 111-121

FAN Haimei, GAO Bingbo, XU Ren, WANG Jinfeng. Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity[J]. Acta Oceanologica Sinica, 2017, 36(2): 111-121. doi: 10.1007/s13131-017-0969-3

Citation:

FAN Haimei, GAO Bingbo, XU Ren, WANG Jinfeng. Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity[J]. Acta Oceanologica Sinica, 2017, 36(2): 111-121. doi: 10.1007/s13131-017-0969-3

Citation:

FAN Haimei, GAO Bingbo, XU Ren, WANG Jinfeng. Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity[J]. Acta Oceanologica Sinica, 2017, 36(2): 111-121. doi: 10.1007/s13131-017-0969-3

PDF( 5255 KB)

Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

doi: 10.1007/s13131-017-0969-3

1.
East China Sea Environmental Monitoring Center, State Oceanic Administration, Shanghai 201206, China
2.
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100037, China
3.
State Key Laboratory of Resources & amp;Environmental Information System, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received Date: 2015-10-16
Rev Recd Date: 2016-03-28

Abstract

Abstract

The water quality grades of phosphate (PO₄-P) and dissolved inorganic nitrogen (DIN) are integrated by spatial partitioning to fit the global and local semi-variograms of these nutrients. Leave-one-out cross validation is used to determine the statistical inference method. To minimize absolute average errors and error mean squares, stratified Kriging (SK) interpolation is applied to DIN and ordinary Kriging (OK) interpolation is applied to PO₄-P. Ten percent of the sites is adjusted by considering their impact on the change in deviations in DIN and PO₄-P interpolation and the resultant effect on areas with different water quality grades. Thus, seven redundant historical sites are removed. Seven historical sites are distributed in areas with water quality poorer than Grade IV at the north and south branches of the Changjiang (Yangtze River) Estuary and at the coastal region north of the Hangzhou Bay. Numerous sites are installed in these regions. The contents of various elements in the waters are not remarkably changed, and the waters are mixed well. Seven sites that have been optimized and removed are set to water with quality Grades III and IV. Optimization and adjustment of unrestricted areas show that the optimized and adjusted sites are mainly distributed in regions where the water quality grade undergoes transition. Therefore, key sites for adjustment and optimization are located at the boundaries of areas with different water quality grades and seawater.

FullText(HTML)

References(33)

References

Borysova O, Kondakov A, Palcari S, et al. 2005. Eutrophication in the Black Sea Region; Impact Assessment and Causal Chain Analysis. Kalmar:University of Kalmar

Chen Jiyu, Chen Shenliang. 2003. The changes of ecologic environment in Yangtze river estuary and some suggestions for estuary regulation. Water Resources and Hydropower Engineering (in Chinese), 34(1):19-25

Chen Shenliang, Yan Shuzhuang, Li Yuzhong. 2009. Characteristics of surface sediment distribution in the Yangtze estuary and its adjacent waters. Resources and environment in the Yangtze Basin (in Chinese), 18(2):152-156

Fan Haimei, Gao Bingbo, Yu Jiang, et al. 2015. The trend of nutrient contents in seawater around shanghai and an analysis on their correlation with fluxes via the Yangtze River. Shanghai Environmental Sciences (in Chinese), 34(1):1-5, 25

Fang Qian. 2008. The study on sources of main chemical pollutants and fluxes flowing into the East China Sea in recent 30 years (in Chinese)[dissertation]. Qingdao:Ocean University of China

Gao Bingbo, Wang Jinfeng, Fan Haimei, et al. 2015. A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea. International Journal of Geographical Information Science, 29(8):1332-1349

Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. New York:Oxford University Press

Han Xiurong, Wang Xiulin, Sun Xia, et al. 2003. Nutrient distribution and its relationship with occurrence of red tide in coastal area of East China Sea. Chinese Journal of Applied Ecology (in Chinese), 14(7):1097-1101

HELCOM. 2010. Ecosystem Health of the Baltic Sea 2003-2007:HELCOM initial holistic assessment. In:Andersen J H, Korpinen S, Laamanen M, et al., eds. Baltic Sea Environmental Proceedings No. 122. Helsinki:Baltic Environmental Protection Commission, Helsinki Commission

Huang Shanggao, Yang Jiadong, Ji Weidong, et al. 1986. Spatial and temporal variation of reactive Si, N, P and their relationship in the Changjiang Estuary water. Taiwan Strait (in Chinese), 5(2):114-123

Isaaks E H, Srivastava R M. 1989. Applied Geostatistics. New York:Oxford University Press

Karydis M, Kitsiou D. 2012. Eutrophication and environmental policy in the Mediterranean Sea:a review. Environmental Monitoring and Assessment, 184(8):4931-4984

Karydis M, Kitsiou D. 2013. Marine water quality monitoring:a review. Marine Pollution Bulletin, 77(1-2):23-36

Li Dan. 2009. The study on the hydro-chemical characteristics and the flux to the sea about the rivers in the East of China (in Chinese)[dissertation]. Shanghai:East China Normal University

Li Zheng, Shen Zhiliang, Zhou Shuqing, et al. 2007. Distributions and variations of phosphorus in the Changjiang estuary and its adjacent sea areas. Marine Science (in Chinese), 31(1):28-36, 42

Lindkvist M, Gren I M, Elofsson K. 2013. A study of climate change and cost effective mitigation of the Baltic sea eutrophication. In:Singh B R, ed. Climate Change-Realities, Impacts Over Ice Cap, Sea Level and Risks. Rijeka, Croatia:InTech

Matheron G. 1963. Principles of geostatistics. Economic Geology, 58(8):1246-1266

Matheron G. 1967. Kriging, or polynomial interpolation procedures. Canadian Mining and Metallurgical Bulletin, 60:1041-1045

Pebesma E J. 2004. Multivariable geostatistics in S:the gstat package. Computers & Geosciences, 30(7):683-691

Quan Weimin, Shen Xinqiang, Han Jindi, et al. 2005. Analysis and assessment on eutrophication status and developing trend in Changjiang Estuary and adjacent sea. Marine Environmental Science (in Chinese), 24(3):13-16

Sheikhy Narany T, Ramli M F, Aris A Z, et al. 2014. Spatial assessment of groundwater quality monitoring wells using indicator kriging and risk mapping, amol-babol plain, Iran. Water, 6(1):68-85

Shen Yaqi, Wu Yanqing. 2013. Optimization of marine environmental monitoring sites in the Yangtze River estuary and its adjacent sea, China. Ocean & Coastal Management, 73:92-100

Shi Xiaoyong, Wang Xiulin, Han Xiurong, et al. 2003. Nutrient distribution and its controlling mechanism in the adjacent area of Changjiang River Estuary. Chinese Journal of Applied Ecology (in Chinese), 14(7):1086-1092

US EPA. 2015. National coastal condition assessment:site evaluation guidelines. https://www.epa.gov/sites/production/files/2016-09/documents/final_ncca15_seg_09-14-2016.pdf.[2015-04-20/2016-09-14]

van Groenigen J W, Pieters G, Stein A. 2000. Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics, 11(2):227-244

van Groenigen J W, Siderius W, Stein A. 1999. Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma, 87(3-4):239-259

van Groenigen J W, Stein A, Zuurbier R. 1997. Optimization of environmental sampling using interactive GIS. Soil Technology, 10(2):83-97

Wang Baodong, Zhan Run, Zang Jiaye. 2002. Distributions and transportation of nutrients in Changjiang River Estuary and its adjacent sea areas. Haiyang Xuebao (in Chinese), 24(1):53-58

Wang Jinfeng, Haining R, Cao Zhidong. 2010. Sample surveying to estimate the mean of a heterogeneous surface:reducing the error variance through zoning. International Journal of Geographical Information Science, 24(4):523-543

Wang Juncheng, Wang Zhongqiu, Wang Yiming, et al. 2016. Current situation and trend of marine data buoy and monitoring network technology of China. Acta Oceanologica Sinica, 35(2):1-10

Yang Xiaolan, Lin Yian, Zhang Jian. 1989. The characteristics of environmental hydrochemistry in the Changjiang Estuary and its adjacent area. Danghai Marine Science (in Chinese), 7(2):60-65

Zhou Junli, Liu Zhengtao, Meng Wei, et al. 2006. The characteristics of nutrients distribution in the Yangtze River Estuary. Research of Environmental Sciences (in Chinese), 19(6):139-144

Zimmerman D L. 2006. Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction. Environmetrics, 17(6):635-652

Relative Articles

Supplements(0)

Cited By

Proportional views