Journal of Ocean University of China

, Volume 17, Issue 4, pp 846–854 | Cite as

Optimization of Shanghai Marine Environmental Monitoring Sites in the Identification of Boundaries of Different Water Quality Grades

  • Haimei Fan
  • Bingbo GaoEmail author
  • Jinfeng WangEmail author
  • Xiaoguang Qin
  • Pengxia Liu
  • Maogui Hu
  • Peng Xu


Water quality is critical to ensure that marine resources and the environment are utilized in a sustainable manner. The objective of this study is therefore to investigate the optimum placement of marine environmental monitoring sites to monitor water quality in Shanghai, China. To improve the mapping or estimation accuracy of the areas with different water quality grades, the monitoring sites were fixed in transition bands between areas of different grades rather than in other positions. Following bidirectional optimization method, first, 18 candidate sites were selected by filtering out specific site categories. Second, three of these were, in turn, eliminated because of the rule defined by the changes in the areas of water quality grades and by the standard deviation of the interpolation errors of dissolved inorganic nitrogen (DIN) and phosphate (PO4-P). Furthermore, indicator kriging was employed to depict the transition bands between different water quality grades whenever new sampling sites were added. The four optimization projects of the newly added sites reveal that, all optimized sites were distributed in the transition bands of different water grades, and at the same time in the areas where the historical sites were sparsely distributed. New sites were also found in the overlap region of different transition bands. Additional sites were especially required in these regions to discriminate the boundaries of different water quality grades. Using the bidirectional optimization method of the monitoring sites, the boundaries of different water quality grades could be determined with a higher precision. As a result, the interpolation errors of DIN and PO4-P could theoretically decrease.

Key words

bidirectional optimization method boundaries of water quality grades Changjiang River Estuary and its adjacent areas transition bands indicator kriging 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This study is supported by the National Natural Science Foundation of China (Nos. 41376190, 41531179, 41421001 and 41601425), the Scientific Research Project of Shanghai Marine Bureau (No. HuHaiKe2016-05), and the Ocean Public Welfare Scientific Research Project, State Oceanic Administration of the People’s Republic of China (Nos. 201505008 and 201305027). The authors would like to specifically thank the East China Sea Environmental Monitoring Center of China for their marine environment monitoring data.


  1. Chen, J. Y., and Chen, S. L., 2002. Estuary and coastal challenges in China. Chinese Journal of Oceanology and Limnology, 20 (2): 174–181 (in Chinese with English abstract).CrossRefGoogle Scholar
  2. Chen, J. Y., and Chen, S. L., 2003. The ecological environment changes in Changjiang River Estuary and some advices on estuary management. Water Resources and Hydropower Engineering, 34 (1): 19–25 (in Chinese with English abstract).Google Scholar
  3. Chen, Z., Xu, B., and Gao, B., 2015. Assessing visual green effects of individual urban trees using airborne Lidar data. Science of the Total Environment, 536: 232–244.CrossRefGoogle Scholar
  4. De Iaco, S., and Posa, D., 2012. Predicting spatio-temporal random fields: Some computational aspects. Computers & Geosciences, 41 (2): 12–24.CrossRefGoogle Scholar
  5. Fan, H. M., Gao, B. B., Xu, R., and Wang, J. F., 2017. Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity. Acta Oceanologica Sinica, 36 (2): 1–11.CrossRefGoogle Scholar
  6. Fan, H. M., Gao, B. B., Yu, J., and Liu, Z. G., 2015. The trend of nutrient contents in seawater around Shanghai and an analysis on their correlation with fluxes via the Changjiang River. Shanghai Environmental Science, 34 (1): 1–5 (in Chinese with English abstract).Google Scholar
  7. Fang, Q., 2008. The main source of chemical pollutants and the change in flux to the sea in recent 30 years in East China Sea. Master thesis. Ocean University of China (in Chinese with English abstract).Google Scholar
  8. Gao, B. B., Liu, Y., Pan, Y. C., Gao, Y. B., Chen, Z. Y., Li, X. L., and Zhou, Y. B., 2017. Error index for additional sampling to map soil contaminant grades. Ecological Indicators, 77: 129–138.CrossRefGoogle Scholar
  9. Gao, B. B., Wang, J. F., Fan, H. M., Xu, K., Hu, M. G., and Chen, Z. Y., 2015. A stratified optimization for a multivariate marine environmental monitoring network in the Changjiang River Estuary and its adjacent sea. International Journal of Geographical Information Science, 29 (8): 1332–1349.CrossRefGoogle Scholar
  10. Gething, P. W., Atkinson, P. M., Noor, A. M., Gikandi, P. W., Hay, S. I., and Nixon, M. S., 2007. A local space-time kriging approach applied to a national outpatient malaria data set. Computers & Geosciences, 33 (10): 1337–1350.CrossRefGoogle Scholar
  11. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, 284–328.Google Scholar
  12. Hengl, T., Minasny, B., and Gould, M., 2009. A geostatistical analysis of geostatistics. Scientometrics, 80 (2): 491–514.CrossRefGoogle Scholar
  13. Heuvelink, G. B. M., and Griffith, D. A., 2010. Space-time geostatistics for geography: A case study of radiation monitoring across parts of Germany. Geographical Analysis, 42 (2): 161–179.CrossRefGoogle Scholar
  14. Isaaks, E. H., and Srivastava, R. M., 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York, 421–435.Google Scholar
  15. Journel, A. G., 1983. Nonparametric estimation of spatial distributions. Mathematical Geology, 15: 45–468.Google Scholar
  16. Juang, K. W., Liao, W. J., Liu, T. L., Tsui, L., and Lee, D. Y., 2008. Additional sampling based on regulation threshold and kriging variance to reduce the probability of false delineation in a contaminated site. Science of the Total Environment, 389: 20–28.CrossRefGoogle Scholar
  17. Karydis, M., and Kitsiou, D., 2013. Marine water quality monitoring: A review. Marine Pollution Bulletin, 77 (1-2): 23–36.CrossRefGoogle Scholar
  18. Kyriakidis, P. C., and Journel, A. G., 1999. Geostatistical spacetime models: A review. Mathematical Geology, 31 (6): 651–684.CrossRefGoogle Scholar
  19. Kyriakidis, P. C., Miller, N. L., and Kim, J., 2004. A spatial time series framework for simulating daily precipitation at regional scales. Journal of Hydrology, 297 (1): 236–255.CrossRefGoogle Scholar
  20. Li, D., 2009. The study on the hydro-chemical characteristics and the flux to the sea about the rivers in the east of China. Master thesis. East China Normal University (in Chinese with English abstract).Google Scholar
  21. Li, Z., Shen, Z. L., Zhou, S. Q., and Yao, Y., 2007. Distributions and variations of phosphorus in the Changjiang Estuary and its adjacent sea areas. Marine Science, 31 (1): 28–36 (in Chinese with English abstract).Google Scholar
  22. Matheron, G., 1967. Kriging or polynomial interpolation procedures. Canadian Mining and Metallurgical Bulletin, 60: 1041–1045.Google Scholar
  23. Narany, T. S., Ramli, M. F., Aris, A. Z., Sulaiman, W. N. A., and Fakharian, K., 2013. Spatial assessment of groundwater quality monitoring wells using indicator Kriging and risk mapping, Amol-Babol Plain, Iran. Water, 6 (1): 68–85.CrossRefGoogle Scholar
  24. Shen, Y. Q., and Wu, Y. Q., 2013. Optimization of marine environmental monitoring sites in the Changjiang River Estuary and its adjacent sea, China. Ocean & Coastal Management, 73 (73): 92–100.CrossRefGoogle Scholar
  25. Shi, X. Y., Wang, X. L., Han, X. R., Zhu, C. J., Sun, X., and Zhang, C. S., 2003. Nutrient distribution and its controlling mechanism in the adjacent area of Changjiang River Estuary. Chinese Journal of Applied Ecology, 14 (7): 1086–1092 (in Chinese with English abstract).Google Scholar
  26. Snepvangers, J. J. J. C., Heuvelink, G. B. M., and Huisman, J. A., 2003. Soil water content interpolation using spatio-temporal kriging with external drift. Geoderma, 112 (3): 253–271.CrossRefGoogle Scholar
  27. US EPA, 2015. National coastal condition assessment: Site evaluation guidelines.
  28. Van Groenigen, J. W., Pieters, G., and Stein, A., 2000. Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics, 11 (2): 227–244.CrossRefGoogle Scholar
  29. Van Meirvenne, M., and Goovaerts, P., 2001. Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold. Geoderma, 102: 75–100.CrossRefGoogle Scholar
  30. Wang, B. D., Zhan, R., and Zhang, J. Y., 2002. Distribution and transportation of nutrients in Changjiang River Estuary and its adjacent sea areas. Acta Oceanologica Sinica, 24 (1): 53–58 (in Chinese with English abstract).Google Scholar
  31. Wang, J. F., Haining, R., and Cao, Z. D., 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.CrossRefGoogle Scholar
  32. Yang, L., Zhu, A. X., Qi, F., Qin, C. Z., Li, B., and Pei, T., 2013. An integrative hierarchical stepwise sampling strategy and its application in digital soil mapping. International Journal of Geographical Information Science, 27 (1): 1–23.CrossRefGoogle Scholar
  33. Zeng, Z. C., Lei, L. P., Guo, L. J., Zhang, L., and Zhang, B., 2013. Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China. Chinese Science Bulletin, 58 (16): 1948–1954.CrossRefGoogle Scholar
  34. Zhou, J. L., Liu, Z. T., Meng, W., Li, Z., and Li, J., 2006. The characteristics of nutrients distribution in the Changjiang River Estuary. Research of Environmental Sciences, 19 (6): 139–144 (in Chinese with English abstract).Google Scholar
  35. Zimmerman, D. L., 2006. Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction. Environmetrics, 17 (6): 635–652.CrossRefGoogle Scholar

Copyright information

© Science Press, Ocean University of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.East China Sea Environmental Monitoring CenterState Oceanic AdministrationShanghaiChina
  2. 2.Beijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
  3. 3.State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Nature Resources ResearchChinese Academy of SciencesBeijingChina

Personalised recommendations