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The Derivation of Nutrient Criteria for the Adjacent Waters of Yellow River Estuary in China

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Abstract

Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent years. The Yellow River Estuary has been seriously affected by human activities. Especially, it has been severely polluted by the nitrogen and phosphorus from land sources, which have caused serious eutrophication and harmful algal blooms. Nutrient criteria, however, was not developed for the Yellow River Estuary, which hindered nutrient management measures and eutrophication risk assessment in this key ecological function zone of China. Based on field data during 2004–2019, we adopted the frequency distribution method, correlation analysis, Linear Regression Model (LRM), Classification and Regression Tree (CART) and Nonparametric Changepoint Analysis (nCPA) methods to establish the nutrient criteria for the adjacent waters of Yellow River Estuary. The water quality criteria of dissolved inorganic nitrogen (DIN) and soluble reactive phosphorus (SRP) are recommended as 244.0 µgL−1 and 22.4 µgL−1, respectively. It is hoped that the results will provide scientific basis for the formulation of nutrient standards in this important estuary of China.

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References

  • Bishara, A. J., and Hittner, J. B., 2012. Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological Methods, 17(3): 399–417, https://doi.org/10.1037/a0028087.

    Article  Google Scholar 

  • Bonett, D. G., and Wright, T. A., 2000. Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika, 65(1): 23–28, https://doi.org/10.1007/bf02294183.

    Article  Google Scholar 

  • Bouwman, A. F., Van Drecht, G., Knoop, J. M., Beusen, A. H. W., and Meinardi, C. R., 2005. Exploring changes in river nitrogen export to the world’s oceans. Global Biogeochemical Cycles, 19(1): 1–14, https://doi.org/10.1029/2004gb002314.

    Article  Google Scholar 

  • Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F., and Klve, B., 2018. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Science of the Total Environment, 615: 272–281, https://doi.org/10.1016/j.scitotenv.2017.09.293.

    Article  Google Scholar 

  • Cloern, J. E., 2001. Our evolving conceptual model of the coastal eutrophication problem. Marine Ecology Progress Series, 210: 223–253, https://doi.org/10.3354/meps210223.

    Article  Google Scholar 

  • De’ath, G., and Fabricius, K. E., 2000. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81(11): 3178, https://doi.org/10.2307/177409.

    Article  Google Scholar 

  • Dortch, Q., and Whitledge, T. E., 1992. Does nitrogen or silicon limit phytoplankton production in the Mississippi River plume and nearby regions? Continental Shelf Research, 12(11): 1293–1309, https://doi.org/10.1016/0278-4343(92)90065-r.

    Article  Google Scholar 

  • Eberly, L. E., 2007. Correlation and Simple Linear Regression. Methods in Molecular Biology™, 404: 143–164, https://doi.org/10.1007/978-1-59745-530-5_8.

    Article  Google Scholar 

  • Efron, B., and Tibshirani, R. J., 1993. An introduction to the bootstrap. Journal of the American Statistical Association, 89(428): 436.

    Google Scholar 

  • Evans-White, M. A., Haggard, B. E., and Scott, J. T., 2013. A Review of stream nutrient criteria development in the United States. Journal of Environmental Quality, 42(4): 1002, https://doi.org/10.2134/jeq2012.0491.

    Article  Google Scholar 

  • Fakiola, M., Mishra, A., Rai, M., Singh, S. P., O’Leary, R. A., Ball, S., et al., 2010. Classification and regression tree and spatial analyses reveal geographic heterogeneity in genome wide linkage study of Indian Visceral Leishmaniasis. PLoS One, 5(12): e15807, https://doi.org/10.1371/journal.pone.0015807.

    Article  Google Scholar 

  • Fan, H., and Huang, H., 2008. Response of coastal marine eco-environment to river fluxes into the sea: A case study of the Huanghe (Yellow) River mouth and adjacent waters. Marine Environmental Research, 65(5): 378–387, https://doi.org/10.1016/j.marenvres.2008.01.003.

    Article  Google Scholar 

  • Galimberti, G., Soffritti, G., and Maso, M. D., 2012. Classification trees for ordinal responses in R: The rpartScore Package. Journal of Statistical Software, 47(10): 1–25.

    Article  Google Scholar 

  • Gichuhi, A. W., Franke, J., and Kihoro, J. M., 2012. Parametric change point estimation, testing and confidence interval applied in business. Journal of Agriculture Science & Technology, 14(2): 136–148.

    Google Scholar 

  • Glibert, P. M., and Burkholder, J. M., 2006. The complex relationships between increases in fertilization of the earth, coastal eutrophication and proliferation of harmful algal blooms. Springer Berlin Heidelberg, 341–354, https://doi.org/10.1007/978-3-540-32210-8_26.

  • Hallegraeff, G. M., 2010. Ocean climate change, phytoplankton community responses, and harmful algal blooms: A formidable predictive challenge. Journal of Phycology, 46(2): 220–235, https://doi.org/10.1111/j.1529-8817.2010.00815.x.

    Article  Google Scholar 

  • Hu, Y. Y., Wang, J. Y., Zhang, Z. F., Zong, H. M., and Chen, S. M., 2011. Establishment of recommended benchmark values for nutrients in the coastal waters of Liaohe Estuary. China Environmental Science, 31(6): 996–1000 (in Chinese with English abstract).

    Google Scholar 

  • Huang, H. Y., Kang, L. C., Yang, Y., Yang, L., Liu, S. M., Wang, Q. L., et al., 2016. Research on the species and distribution of red tide triggering species in my country’s coastal waters in 2013. Marine Science, 11: 17–27 (in Chinese with English abstract).

    Google Scholar 

  • Huang, S. G., Yang, J. D., Ji, W. D., Yang, X. L., and Chen, C. X., 1983. Spatial and temporal variation of reactive Si, N, P and their relationship in the Changjiang Estuary water. Taiwan Strait, 5(2): 114–123 (in Chinese with English abstract).

    Google Scholar 

  • Huang, S. L., Zang, C. J., Du, S. L., Wu, M., Gao, F., Lin, C., et al., 2011. Research on the correlation between pH, dissolved oxygen, and chlorophyll a II: Non-aquaculture waters. Chinese Journal of Environmental Engineering, 5(8): 1681–1688 (in Chinese with English abstract).

    Google Scholar 

  • Huo, S. L., and Xi, B. D., 2014. The Pressure-Response Model and Case Study of Lake Nutrient Standards, Science Press, Beijing, chap5 (5.2.1): 109–110.

  • Huo, S. L., Ma, C. Z., Xi, B. D., Zhang, Y. L., Wu, F. C., and Liu, H. L., 2018. Development of methods for establishing nutrient criteria in lakes and reservoirs: A review. Journal of Environmental Sciences, 67: 54–66, https://doi.org/10.1016/j.jes.2017.07.013.

    Article  Google Scholar 

  • Jamshidi, S., and Mohebbalipour, N., 2014. Biodiversity phenoltypic and genotypic polymorphism data correlation analysis using SPSS 16.0 software. International Conference on Biological, Environment and Food Engineering, August 4–5.

  • Jiang, X. H., Lu, W. X., Gao, Y., and Chang, Y. Y., 2019. The historical changes of the Yellow River and the evolution of the area and length characteristic data. People’s Yellow River, 41(1): 10–13 (in Chinese with English abstract).

    Google Scholar 

  • Justić, D., Rabalais, N. N., Turner, R. E., and Dortch, Q., 1995. Changes in nutrient structure of river-dominated coastal waters: Stoichiometric nutrient balance and its consequences. Estuarine, Coastal and Shelf Science, 40(3): 339–356, https://doi.org/10.1016/s0272-7714(05)80014-9.

    Article  Google Scholar 

  • King, R. S., and Richardson, C. J., 2003. Integrating bioassessment and ecological risk assessment: An approach to developing numerical water-quality criteria. Environmental Management, 31(6): 795–809, https://doi.org/10.1007/s00267-002-0036-4.

    Article  Google Scholar 

  • Koh, H. S., 2019. Using algal biomass-phosphorus (P) relationships and nutrient limitation theory to evaluate the adequacy of P water quality criteria for regulated monsoon rivers and reservoirs. Chemistry and Ecology, 35(5): 408–430, https://doi.org/10.1080/02757540.2019.1567719.

    Article  Google Scholar 

  • Li, G., Gao, K., Yuan, D., Zheng, Y., and Yang, G., 2011. Relationship of photosynthetic carbon fixation with environmental changes in the Jiulong River Estuary of the South China Sea, with special reference to the effects of solar UV radiation. Marine Pollution Bulletin, 62(8): 1852–1858, https://doi.org/10.1016/j.marpolbul.2011.02.050.

    Article  Google Scholar 

  • Li, J. L., Zheng, B. H., Zhang, L. S., Jin, X. W., and Shao, J. B., 2016. Eutrophication characteristics and difference analysis of major estuaries and bays in China. China Environmental Science, 36(2): 506–516 (in Chinese with English abstract).

    Google Scholar 

  • Liu, B., Cao, W., Huang, Z., Chen, W., Chen, H., and Liu, L., 2018. Developing nutrient criteria for the Jiulong River Estuary, Southeast China. Acta Oceanologica Sinica, 37(2): 1–13, https://doi.org/10.1007/s13131-017-1121-0.

    Article  Google Scholar 

  • Muradian, R., 2001. Ecological thresholds: A survey. Ecological Economics, 38(1): 7–24, https://doi.org/10.1016/s0921-8009(01)00146-x.

    Article  Google Scholar 

  • Olson, J. R., and Hawkins, C. P., 2013. Developing site-specific nutrient criteria from empirical models. Freshwater Science, 32(3): 719–740, https://doi.org/10.1899/12-113.1.

    Article  Google Scholar 

  • Qian, S. S., King, R. S., and Richardson, C. J., 2003. Two statistical methods for the detection of environmental thresholds. Ecological Modelling, 166(1–2): 87–97, https://doi.org/10.1016/s0304-3800(03)00097-8.

    Article  Google Scholar 

  • Redfield, A. C., 1958. The biological control of chemical factors in the environment. American Scientist, 46(3): 230A, 205–221, https://doi.org/10.2307/27827150.

    Google Scholar 

  • Russo, R. C., 2002. Development of marine water quality criteria for the USA. Marine Pollution Bulletin, 45(1–12): 84–91, https://doi.org/10.1016/s0025-326x(02)00136-4.

    Article  Google Scholar 

  • Shi, H., Li, Q., Sun, J., Gao, G., Sui, Y., Qiao, S., et al., 2020. Variation of Yellow River runoff and its influence on salinity in Laizhou Bay. Journal of Ocean University of China, 19(6): 1235–1244, https://doi.org/10.1007/s11802-020-4413-5.

    Article  Google Scholar 

  • Su, J., Yang, F. X., Su, R. G., and Yao, Q. Z., 2016. Establishment method of nutrient reference value in Liaohe Estuary. Periodcal of Ocean University of China, 46(9): 78–84 (in Chinese with English abstract).

    Google Scholar 

  • US EPA, 1998. National Strategy for the Development of Regional Nutrient Criteria. EPA-822-R-98–002. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • US EPA, 2000a. Nutrient Criteria Technical Guidance Manual Rivers and Streams. Water Quality Criteria for the Protection of Human Health, EPA-822-B-00–002. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • US EPA, 2000b. Nutrient Criteria Technical Guidance Manual Lakes and Reservoirs. Water Quality Criteria for the Protection of Human Health, EPA-822-B-00–001. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • US EPA, 2000c. Nutrient Criteria Technical Guidance Manual Estuarine and Coastal Marine Waters. EPA-822-B-01–003. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • US EPA, 2008. Nutrient Criteria Technical Guidance Manual Wetlands. EPA-822-B-08–001. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • US EPA, 2010. Using Stressor-Response Relationships to Derive Numeric Nutrient Criteria. EPA-820-S-10–001. U.S. Environmental Protection Agency, Office of Water, Washington, D. C.

    Google Scholar 

  • Whitfield, A., and Elliott, M., 2011. Ecosystem and biotic classifications of estuaries and coasts. Treatise on Estuarine and Coastal Science, 1: 99–124, https://doi.org/10.1016/b978-0-12-374711-2.00108-x.

    Article  Google Scholar 

  • Wu, X., Duan, H., Bi, N., Yuan, P., Wang, A., and Wang, H., 2016. Interannual and seasonal variation of chlorophyll-a off the Yellow River Mouth (1997–2012): Dominance of river inputs and coastal dynamics. Estuarine, Coastal and Shelf Science, 183: 402–412, https://doi.org/10.1016/j.ecss.2016.08.038.

    Article  Google Scholar 

  • Wu, Y. N., Zhang, H. F., Wang, L., and Li, Z. Y., 2020. Water quality benchmark study of inorganic nitrogen and active phosphate in the Yangtze River Estuary. Oceans and Limnology, 51(5): 104–117 (in Chinese with English abstract).

    Google Scholar 

  • Xie, J., and Li, J. R., 1993. The relationship between Noctiluca spp. red tide and environmental factors. Ocean Bulletin, 12(2): 1–6 (in Chinese with English abstract).

    Google Scholar 

  • Xu, H. Y., 2020. Study on the transition of nutrient status and nutrient standards in the Bohai Sea. Ministry of Natural Resources, Maser thesis. First Institute of Oceanography

  • Xu, X., Chen, Z., and Feng, Z., 2019. From natural driving to artificial intervention: Changes of the Yellow River Estuary and delta development. Ocean & Coastal Management, 174(5): 63–70, https://doi.org/10.1016/j.ocecoaman.2019.03.009.

    Article  Google Scholar 

  • Yang, F., Mi, T., Chen, H., and Yao, Q., 2019. Developing numeric nutrient criteria for the Yangtze River Estuary and adjacent waters in China. Journal of Hydrology, 579: 124188, https://doi.org/10.1016/j.jhydrol.2019.124188.

    Article  Google Scholar 

  • Yang, F. X., 2016. Research on the establishment method and influencing factors of nutrient reference values in the Daliao River Estuary. Master Thesis. Ocean University of China.

  • Yang, Y. F., Wang, Q., Chen, J. F., and Pang, S. X., 2006. Research progress of zooplankton ecology in estuary. Acta Ecologica Sinica, 26(2): 576–585 (in Chinese with English abstract).

    Google Scholar 

  • Zhang, J. L., 2020. The Yellow River Basin ecological protection and high-quality development water strategy thinking. People’s Yellow River, 416(4): 5–10 (in Chinese with English abstract).

    Google Scholar 

  • Zhang, Y., Huo, S., Xi, B., Ma, C., and He, Z., 2016. Establish ing nutrient criteria in nine typical lakes in China: A conceptual model. CLEAN — Soil, Air, Water, 44(10): 1335–1344, https://doi.org/10.1002/clen.201500505.

    Article  Google Scholar 

  • Zhao, X., Liu, M. Q., Liao, Y., and Guo, Z. R., 2013. Threshold analysis of the Pearl River Estuary nutrient brine quality standard based on aquatic organism evaluation. 2013 Chinese Society for Environmental Sciences Academic Annual Conference Proceedings (Volume IV), 3151–3158 (in Chinese with English abstract).

  • Zhou, J., Wang, W., Wu, Z. H., Wang, Q. X., Wang, Y., and Gao, X., 2020. The basic characteristics of red tide disasters along the coast of Shandong and suggestions for prevention and control measures. Marine Environmental Science, 39(4): 537–543 (in Chinese with English abstract).

    Google Scholar 

  • Zhou, M., Shen, Z., and Yu, R., 2008. Responses of a coastal phytoplankton community to increased nutrient input from the Changjiang (Yangtze) River. Continental Shelf Research, 28(12): 1483–1489, https://doi.org/10.1016/j.csr.2007.02.009.

    Article  Google Scholar 

  • Zou, L., 2001. In situ nutrient enrichment experiment in the Bohai and Yellow Sea. Journal of Plankton Research, 23(10): 1111–1119, https://doi.org/10.1093/plankt/23.10.1111.

    Article  Google Scholar 

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Acknowledgement

The study is supported by the National Key Research and Development Program of China (No. 2018YFC1407 601).

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Authors and Affiliations

  1. College of Environmental Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Qi Lou, Xueqing Zhang & Zhengyan Li

  2. North China Sea Environmental Monitoring Center, State Oceanic Administration, Qingdao, 266033, China

    Bei Zhao & Jing Cao

  3. Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Qingdao, 266100, China

    Zhengyan Li

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  1. Qi Lou
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Correspondence to Zhengyan Li.

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Lou, Q., Zhang, X., Zhao, B. et al. The Derivation of Nutrient Criteria for the Adjacent Waters of Yellow River Estuary in China. J. Ocean Univ. China 21, 1227–1236 (2022). https://doi.org/10.1007/s11802-022-5141-9

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