Cite this paper:
KONG Xianyu, CHE Xiaowei, SU Rongguo, ZHANG Chuansong, YAO Qingzhen, SHI Xiaoyong. A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine[J]. HaiyangYuHuZhao, 2018, 36(2): 249-262

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

KONG Xianyu, CHE Xiaowei, SU Rongguo, ZHANG Chuansong, YAO Qingzhen, SHI Xiaoyong
Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China
Abstract:
There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C=64 and the kernel parameter γ=1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.
Key words:    eutrophication assessment|chlorophyll a|dissolved oxygen|turbidity|support vector machine   
Received: 2016-09-19   Revised:
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Articles by KONG Xianyu
Articles by CHE Xiaowei
Articles by SU Rongguo
Articles by ZHANG Chuansong
Articles by YAO Qingzhen
Articles by SHI Xiaoyong
References:
Baban S M J. 1996. Trophic classification and ecosystem checking of lakes using remotely sensed information. Hydrological Sciences Journal, 41(6):939-957.
Badran M I. 2001. Dissolved oxygen, chlorophyll a and nutrients:seasonal cycles in waters of the Gulf of Aquaba, Red Sea. Aquat. Ecosyst. Health Manage., 4(2):139-150.
Bashir M B, Latiff M S B A, Coulibaly Y, Yousif A. 2016. A survey of grid-based searching techniques for large scale distributed data. Journal of Network and Computer Applications, 60:170-179.
Behzad M, Asghari K, Eazi M, Palhang M. 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36(4):7 624-7 629.
Bilotta G S, Brazier R E. 2008. Understanding the influence of suspended solids on water quality and aquatic biota. Water Res., 42(12):2 849-2 861.
Bricker S B, Longstaff B, Dennison W, Jones A, Boicourt K, Wicks C, Woerner J. 2008. Effects of nutrient enrichment in the nation's estuaries:a decade of change. Harmful Algae, 8(1):21-32.
Busse L B, Simpson J C, Cooper S D. 2006. Relationships among nutrients, algae, and land use in urbanized southern California streams. Can. J. Fish. Aquat. Sci., 63(12):2 621-2 638.
Cabrita M T, Silva A, Oliveira P B, Angélico M M, Nogueira M. 2015. Assessing eutrophication in the Portuguese continental exclusive economic zone within the European marine strategy framework directive. Ecological Indicators, 58:286-299.
Carraro E, Guyennon N, Hamilton D, Valsecchi L, Manfredi E C, Viviano G, Salerno F, Tartari G, Copetti D. 2012.Coupling high-resolution measurements to a threedimensional lake model to assess the spatial and temporal dynamics of the cyanobacterium Planktothrix rubescens in a medium-sized lake. Hydrobiologia, 698(1):77-95.
Chen Y, Yang G P, Liu L, Zhang P Y, Leng W S. 2016. Sources, behaviors and degradation of dissolved organic matter in the East China Sea. J. Mar. Syst., 155:84-97.
Chesterton R N, Pfeiffer D U, Morris R S, Tanner C M. 1989.Environmental and behavioural factors affecting the prevalence of foot lameness in New Zealand dairy herds-a case-control study. New Zeal. Vet. J., 37(4):135-142.
Cristianini N, Shawe-Taylor J. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge.
Crossland C J, Kremer H H, Lindeboom H, Marshall Crossland J I, Le Tissier M D A. 2005. Coastal Fluxes in the Anthropocene. Springer, Berlin.
Fernández J R A, Nieto P J G, Muñiz C D, Antón J C Á. 2014. Modeling eutrophication and risk prevention in a reservoir in the Northwest of Spain by using multivariate adaptive regression splines analysis. Ecological Engineering, 68:80-89.
Ferreira J G, Andersen J H, Borja A, Bricker S B, Camp J, Cardoso da Silva M, Garcés E, Heiskanen A S, Humborg C, Ignatiades L, Lancelot C, Menesguen A, Tett P, Hoepffner N, Claussen U. 2010. Marine Strategy Framework Directive-Task Group 5 Report Eutrophication. JRC Scientific and Technical Reports. Office for Official Publications of the European Communities, Luxembourg. 49p.
France R L, Peters R H. 1995. Predictive model of the effects on lake metabolism of decreased airborne litterfall through riparian deforestation. Conserv. Biol., 9(6):1 578-1 586.
Fu M Z, Wang Z L, Pu X M, Qu P, Li Y, Wei Q S, Jiang M J. 2016. Response of phytoplankton community to nutrient enrichment in the subsurface chlorophyll maximum in Yellow Sea Cold Water Mass. Acta Ecologica Sinica, 36(1):39-44.
Gao L, Fan D D, Li D J, Cai J G. 2010. Fluorescence characteristics of chromophoric dissolved organic matter in shallow water along the Zhejiang coasts, southeast China. Mar. Environ. Res., 69(3):187-197.
Gao X, Hou J. 2016. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process.Neurocomputing, 174:906-911.
García Nieto P J, Alonso Fernández J R, de Cos Juez F J, Sánchez Lasheras F S, Díaz Muñiz C. 2013. Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). Environ. Res., 122:1-10.
García Nieto P J, García-Gonzalo E, Alonso Fernández J R, Díaz Muñiz C. 2014. Hybrid PSO-SVM-based method for long-term forecasting of turbidity in the Nalón river basin:a case study in Northern Spain. Ecological Engineering, 73:192-200.
García Nieto P J, García-Gonzalo E, Alonso Fernández J R, Díaz Muñiz C. 2016. A hybrid PSO optimized SVMbased model for predicting a successful growth cycle of the Spirulina platensis from raceway experiments data. J. Comput. Appl. Math., 291:293-303.
García Nieto P J, García-Gonzalo E, Sánchez Lasheras F, de Cos Juez F J. 2015. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliab. Eng. Syst. Saf., 138:219-231.
Garson G D. 2008. Path analysis. from Statnotes:topics in multivariate analysis. Retrieved, 9(5):2009. Gibson G, Carlson R, Simpson J, Smeltzer E. 2000. Nutrient criteria technical guidance manual:lakes and reservoirs(EPA-822-B-00-001). United States Environment Protection Agency, Washington DC.
Giovanardi F, Vollenweider R A. 2004. Trophic conditions of marine coastal waters:experience in applying the Trophic Index TRIX to two areas of the Adriatic and Tyrrhenian seas. J. Limnol., 63(2):199-218.
Gokcen I, Peng J. 2002. Comparing linear discriminant analysis and support vector machines. In:Yakhno T ed. Advances in Information Systems:Lecture Notes in Computer Science, 2457. Springer, Berlin Heidelberg. p.104-113.
Gong G C, Wen Y H, Wang B W, Liu G J. 2003. Seasonal variation of chlorophyll a concentration, primary production and environmental conditions in the subtropical East China Sea. Deep Sea Research Part Ⅱ:Topical Studies in Oceanography, 50(6-7):1 219-1 236.
Hartnett M, Nash S. 2004. Modelling nutrient and chlorophyll_a dynamics in an Irish brackish waterbody. Environ. Modell. Softw., 19(1):47-56.
HosseinAbadi H Z, Amirfattahi R, Nazari B, Mirdamadi H R, Atashipour S A. 2014. GUW-based structural damage detection using WPT statistical features and multiclass SVM. Appl. Acoust., 86:59-70.
Howarth R, Chan F, Conley D J, Garnier J, Doney S C, Marino R, Billen G. 2011. Coupled biogeochemical cycles:eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems. Front. Ecol. Environ., 9(1):18-26.
Hoyer M V, Frazer T K, Notestein S K, Canfield Jr D E. 2002. Nutrient, chlorophyll, and water clarity relationships in Florida's nearshore coastal waters with comparisons to freshwater lakes. Can. J. Fish. Aquat. Sci., 59(6):1 024-1 031.
Hur J, Cho J. 2012. Prediction of BOD, COD, and total nitrogen concentrations in a typical urban river using a fluorescence excitation-emission matrix with PARAFAC and UV absorption indices. Sensors, 12(1):972-986.
Ignatiades L, Vassiliou A, Karydis M. 1985. A comparison of phytoplankton biomass parameters and their interrelation with nutrients in Saronicos Gulf (Greece). Hydrobiologia, 128(3):201-206.
Jeffrey S W, Humphrey G F. 1975. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz., 167:191-194.
Jiang Y P, Xu Z X, Yin H L. 2006. Study on improved BP artificial neural networks in eutrophication assessment of China eastern lakes. J. Hydrodyn. Ser. B., 18(S3):528-532.
Jones S, Carrasco N K, Perissinotto R. 2015. Turbidity effects on the feeding, respiration and mortality of the copepod Pseudodiaptomus stuhlmanni in the St Lucia Estuary, South Africa. Journal of Experimental Marine Biology and Ecology, 469:63-68.
Kim L H, Choi E, Gil K I, Stenstrom M K. 2004. Phosphorus release rates from sediments and pollutant characteristics in Han River, Seoul, Korea. Sci. Total Environ., 321(1-3):115-125.
Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petković D, Hashim R. 2015. A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl. Math. Comput., 270:731-743.
Koroleff F. 1983a. Determination of phosphorus. In:Grasshoff K, Ehrhardt M, Kremling K eds. Methods of Seawater Analysis. Verlag Chemie, Weinheim, Germany. p.125-139.
Koroleff F. 1983b. Total and organic nitrogen. In:Grasshoff K, Ehrhardt M, Kremling K eds. Methods of Seawater Analysis. Verlag Chemie, Weinheim, Germany. p.162-173.
Kovačević M, Bajat B, Gajić B. 2010. Soil type classification and estimation of soil properties using support vector machines. Geoderma, 154(3-4):340-347.
Kuo J T, Hsieh M H, Lung W S, She N. 2007. Using artificial neural network for reservoir eutrophication prediction. Ecol. Model., 200(1-2):171-177.
Lattin J M, Carroll J D, Green P E. 2003. Analyzing Multivariate Data. Thomson Brooks/Cole, Pacific Grove, CA.
Li B H, Feng C H, Li X, Chen Y X, Niu J F, Shen Z Y. 2012. Spatial distribution and source apportionment of PAHs in surficial sediments of the Yangtze Estuary, China. Mar. Pollut. Bull., 64(3):636-643.
Li C C. 1975. Introduction, multiple regression and correlation, standardized variables; path coefficients. In:Li C C ed.Path Analysis-A Primer. Pacific Grove, California. p.75-100.
Li H M, Zhang C S, Han X R, Shi X Y. 2015. Changes in concentrations of oxygen, dissolved nitrogen, phosphate, and silicate in the southern Yellow Sea, 1980-2012:sources and seaward gradients. Estuar. Coast. Shelf Sci., 163:44-55.
Lillesand T M, Johnson W L, Deuell R L, Lindstrom O M, Meisner D E. 1983. Use of Landsat data to predict the trophic state of Minnesota lakes. Photogram. Eng. Remote Sensing, 49(2):219-229.
Liu F, Zhou Z G. 2015. A new data classification method based on chaotic particle swarm optimization and least squaresupport vector machine. Chemom. Intell. Lab. Syst., 147:147-156.
Liu S M, Qi X H, Li X N, Ye H R, Wu Y, Ren J L, Zhang J, Xu W Y. 2016a. Nutrient dynamics from the Changjiang(Yangtze River) estuary to the East China Sea. J. Mar.Syst., 154:15-27.
Liu S M. 2015. Response of nutrient transports to water-sediment regulation events in the Huanghe basin and its impact on the biogeochemistry of the Bohai. J. Mar. Syst., 141:59-70.
Liu X, Lu W, Jin S, Li Y, Chen N. 2006. Support vector regression applied to materials optimization of sialon ceramics. Chemom. Intell. Lab. Syst., 82(1-2):8-14.
Liu Y, Guo H C, Yang P J. 2010. Exploring the influence of lake water chemistry on chlorophyll a:a multivariate statistical model analysis. Ecol. Model., 221(4):681-688.
Liu Y, Wang H F, Zhang H, Liber K. 2016b. A comprehensive support vector machine-based classification model for soil quality assessment. Soil and Tillage Research, 155:19-26.
Lundberg C, Jakobsson B M, Bonsdorff E. 2009. The spreading of eutrophication in the eastern coast of the Gulf of Bothnia, northern Baltic Sea-an analysis in time and space. Estuar. Coast. Shelf Sci., 82(1):152-160.
Lušić D V, Peršić V, Horvatić J, Viličić D, Traven L, Đakovac T, Mićović V. 2008. Assessment of nutrient limitation in Rijeka Bay, NE Adriatic Sea, using miniaturized bioassay.Journal of Experimental Marine Biology and Ecology, 358(1):46-56.
Manasrah R, Raheed M, Badran M I. 2006. Relationships between water temperature, nutrients and dissolved oxygen in the northern Gulf of Aqaba, Red Sea.Oceanologia, 48(2):237-253.
Meeuwig J J, Kauppila P, Pitkänen H. 2000. Predicting coastal eutrophication in the Baltic:a limnological approach.Can. J. Fish. Aquat. Sci., 57(4):844-855.
Moncheva S, Dontcheva V, Shtereva G, Kamburska L, Malej A, Gorinstein S. 2002. Application of eutrophication indices for assessment of the Bulgarian Black Sea coastal ecosystem ecological quality. Water Sci. Technol., 46(8):19-28.
Mozetič P, Malačič V, Turk V. 2008. A case study of sewage discharge in the shallow coastal area of the Northern Adriatic Sea (Gulf of Trieste). Mar. Ecol., 29(4):483-494.
Nasrollahzadeh H S, Din Z B, Foong S Y, Makhlough A. 2008.Trophic status of the Iranian Caspian Sea based on water quality parameters and phytoplankton diversity. Cont.Shelf Res., 28(9):1 153-1 165.
Nicholls K H, Steedman R J, Carney E C. 2003. Changes in phytoplankton communities following logging in the drainage basins of three boreal forest lakes in northwestern Ontario (Canada), 19912000. Can. J. Fish. Aquat. Sci., 60(1):43-54.
Ning X, Lin C, Su J, Liu C, Hao Q, Le F. 2011. Long-term changes of dissolved oxygen, hypoxia, and the responses of the ecosystems in the East China Sea from 1975 to 1995. J. Oceanogr., 67(1):59-75.
Pang C G, Li K, Hu D X. 2016. Net accumulation of suspended sediment and its seasonal variability dominated by shelf circulation in the Yellow and East China Seas. Mar. Geol., 371:33-43.
Papatheodorou G, Demopoulou G, Lambrakis N. 2006. A long-term study of temporal hydrochemical data in a shallow lake using multivariate statistical techniques.Ecol. Model., 193(3-4):759-776.
Park Y, Cho K H, Park J, Cha S M, Kim J H. 2015. Development of early-warning protocol for predicting chlorophyll -a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Sci. Total Environ., 502:31-41.
Parkhomenko A V, Kuftarkova E A, Subbotin A A, Gubanov V I. 2003. Results of hydrochemical monitoring of Sevastopol Black Sea's offshore waters. J. Coastal Res., 19(4):907-911.
Penna N, Capellacci S, Ricci F. 2004. The influence of the Po River discharge on phytoplankton bloom dynamics along the coastline of Pesaro (Italy) in the Adriatic Sea. Mar.Pollut. Bull., 48(3-4):321-326.
Pettine M, Casentini B, Fazi S, Giovanardi F, Pagnotta R. 2007. A revisitation of TRIX for trophic status assessment in the light of the European Water Framework Directive:application to Italian coastal waters. Mar. Pollut. Bull., 54(9):1 413-1 426.
Picard R R, Cook R D. 1984. Cross-validation of regression models. J. Amer. Statist. Assoc., 79(387):575-583.
Pinto U, Maheshwari B, Shrestha S, Morris C. 2012. Modelling eutrophication and microbial risks in peri-urban river systems using discriminant function analysis. Water Res., 46(19):6 476-6 488.
Primpas I, Karydis M, Tsirtsis G. 2008. Assessment of clustering algorithms in discriminating eutrophic levels in coastal waters. Glob. NEST J., 10(3):359-365.
Primpas I, Karydis M. 2010. Improving statistical distinctness in assessing trophic levels:the development of simulated normal distributions. Environ. Monit. Assess., 169(1-4):353-365.
Primpas I, Karydis M. 2011. Scaling the trophic index (TRIX) in oligotrophic marine environments. Environ. Monit. Assess., 178(1-4):257-269.
Rabalais N N, Cai W J, Carstensen J, Conley D, Fry B, Hu X, Quiñones-Rivera Z, Rosenberg R, Slomp C P, Turner R E, Voss M, Wissel B, Zhang J. 2014. Eutrophication-driven deoxygenation in the coastal ocean. Oceanography, 27(1):172-183.
Ribeiro R, Torgo L. 2008. A comparative study on predicting algae blooms in Douro River, Portugal. Ecol. Model., 212(1-2):86-91.
Rixen T, Baum A, Sepryani H, Pohlmann T, Jose C, Samiaji J. 2010. Dissolved oxygen and its response to eutrophication in a tropical black water river. J. Environ. Manag., 91(8):1 730-1 737.
Sajan K S, Kumar V, Tyagi B. 2015. Genetic algorithm based support vector machine for on-line voltage stability monitoring. Int. J. Elec. Power Energy Syst., 73:200-208.
Shahrban M, Etemad-Shahidi A. 2010. Classification of the Caspian Sea coastal waters based on trophic index and numerical analysis. Environ. Monit. Assess., 164(1-4):349-356.
Shen X J, Mu L, Li Z, Wu H X, Gou J P, Chen X. 2016. Largescale support vector machine classification with redundant data reduction. Neurocomputing, 172:189-197.
Shi W, Wang M H. 2012. Satellite views of the Bohai Sea, Yellow Sea, and East China Sea. Prog. Oceanogr., 104:30-45.
Song K S, Li L, Li S, Tedesco L, Hall B, Li L H. 2012.Hyperspectral remote sensing of total phosphorus (TP) in three central Indiana water supply reservoirs. Water Air Soil Pollut., 223(4):1 481-1 502.
Song N Q, Wang N, Lu Y, Zhang J R. 2016. Temporal and spatial characteristics of harmful algal blooms in the Bohai Sea during 1952-2014. Cont. Shelf Res., 122:77-84.
Stedmon C A, Markager S, Tranvik L, Kronberg L, Slätis T, Martinsen W. 2007. Photochemical production of ammonium and transformation of dissolved organic matter in the Baltic Sea. Mar. Chem., 104(3-4):227-240.
Stefani F, Salerno F, Copetti D, Rabuffetti D, Guidetti L, Torri G, Naggi A, Iacomini M, Morabito G, Guzzella L. 2016. Endogenous origin of foams in lakes:a long-term analysis for Lake Maggiore (northern Italy). Hydrobiologia, 767(1):249-265.
Stefanou P, Tsirtsis G, Karydis M. 2000. Nutrient scaling for assessing eutrophication:the development of a simulated normal distribution. Ecol. Appl., 10(1):303-309.
Streiner D L. 2005. Finding our way:an introduction to path analysis. Can. J. Psychiatry, 50(2):115-122.
Sun S, Zhang F, Li C L, Wang S W, Wang M X, Tao Z C, Wang Y T, Zhang G T, Sun X X. 2015. Breeding places, population dynamics, and distribution of the giant jellyfish Nemopilema nomurai (Scyphozoa:Rhizostomeae) in the Yellow Sea and the East China Sea. Hydrobiologia, 754(1):59-74.
Taboada J, Matías J M, Ordóñez C, García P J. 2007. Creating a quality map of a slate deposit using support vector machines. J. Comput. Appl. Math., 204(1):84-94.
Takaara T, Sano D, Masago Y, Omura T. 2010. Surface-retained organic matter of Microcystis aeruginosa inhibiting coagulation with polyaluminum chloride in drinking water treatment. Water Res., 44(13):3 781-3 786.
Tekile A, Kim I, Kim J. 2015. Mini-review on river eutrophication and bottom improvement techniques, with special emphasis on the Nakdong River. J. Environ. Sci., 30:113-121.
Tsirtsis G, Karydis M. 1999. Application of discriminant analysis for water quality assessment in the Aegean. In:Proceedings of the 6th Conference on Environmental Science and Technology. Univ. of the Aegean, Samos, Greece.
Vapnik V N. 1995. The Nature of Statistical Learning Theory. Springer, New York.
Vilán Vilán J A, Alonso Fernández J R, García Nieto P J, Sánchez Lasheras F, de Cos Juez F J, Díaz Muñiz C. 2013. Support vector machines and multilayer perceptron networks used to evaluate the cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain). Water. Resour. Manage., 27:3 457-3 476.
Viviano G, Salerno F, Manfredi E C, Polesello S, Valsecchi S, Tartari G. 2014. Surrogate measures for providing high frequency estimates of total phosphorus concentrations in urban watersheds. Water Res., 64:265-277.
Vollenweider R A, Giovanardi F, Montanari G, Rinaldi A. 1998. Characterization of the trophic conditions of marine coastal waters with special reference to the NW Adriatic Sea:proposal for a trophic scale, turbidity and generalized water quality index. Environmetrics, 9(3):329-357.
Waters T F. 1995. Sediment in Streams:Sources, Biological Effects, and Control. American Fisheries Society, Bethesda, MD. 251p.
Wei Q S, Wei X H, Xie L P, Zang J Y, Zhan R. 2010. Features of dissolved oxygen distribution and its effective factors in the Southern Yellow Sea in spring, 2007. Adv. Mar.Sci., 28(2):179-185. (in Chinese)
Wheeler P A, Huyer A, Fleischbein J. 2003. Cold halocline, increased nutrients and higher chlorophyll off Oregon in 2002. Geophys. Res. Lett., 30(15):8 021.
Xie L Q, Xie P, Tang H J. 2003. Enhancement of dissolved phosphorus release from sediment to lake water by Microcystis blooms-an enclosure experiment in a hypereutrophic, subtropical Chinese lake. Environ. Pollut., 122(3):391-399.
Xu Y F, Ma C Z, Liu Q, Xi B D, Qian G R, Zhang D Y, Huo S L. 2015. Method to predict key factors affecting lake eutrophication-a new approach based on Support Vector Regression model. Int. Biodeter. Biodegr., 102:308-315.
Xue X B, Landis A E. 2010. Eutrophication potential of food consumption patterns. Environ. Sci. Technol., 44(16):6 450-6 456.
Yamaguchi H, Ishizaka J, Siswanto E, Son Y B, Yoo S, Kiyomoto Y. 2013. Seasonal and spring interannual variations in satellite-observed chlorophyll-a in the Yellow and East China Seas:new datasets with reduced interference from high concentration of resuspended sediment. Cont. Shelf Res., 59:1-9.
Yan H Y, Zhang X R, Dong J H, Shang M S, Shan K, Wu D, Yuan Y, Wang X, Meng H, Huang Y, Wang G Y. 2016. Spatial and temporal relation rule acquisition of eutrophication in Da'ning River based on rough set theory. Ecological Indicators, 66:180-189.
Yang B, Yang G P, Lu X L, Li L, He Z. 2015. Distributions and sources of volatile chlorocarbons and bromocarbons in the Yellow Sea and East China Sea. Mar. Pollut. Bull., 95(1):491-502.
Yuan D L, Zhu J R, Li C Y, Hu D X. 2008. Cross-shelf circulation in the Yellow and East China Seas indicated by MODIS satellite observations. J. Mar. Syst., 70(1-2):134-149.
Zhang F, Su R G, He J F, Cai M H, Luo W, Wang X L. 2010. Identifying phytoplankton in seawater based on discrete excitation-emission fluorescence spectra. J. Phycol., 46(2):403-411.
Zhang G L, Bai J H, Xi M, Zhao Q Q, Lu Q Q, Jia J. 2016. Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set.Ecological Indicators, 66:458-466.
Zhang L, Wang S R, Wu Z H. 2014. Coupling effect of pH and dissolved oxygen in water column on nitrogen release at water-sediment interface of Erhai Lake, China. Estuar.Coast. Shelf Sci., 149:178-186.
Zhang Y L, Yin Y, Feng L Q, Zhu G W, Shi Z Q, Liu X H, Zhang Y Z. 2011. Characterizing chromophoric dissolved organic matter in Lake Tianmuhu and its catchment basin using excitation-emission matrix fluorescence and parallel factor analysis. Water Res., 45(16):5 110-5 122.
Zheng L P, Chen B Z, Liu X, Huang B Q, Liu H B, Song S Q. 2015. Seasonal variations in the effect of microzooplankton grazing on phytoplankton in the East China Sea. Cont.Shelf Res., 111:304-315.
Zhu C, Wang Z H, Xue B, Yu P S, Pan J M, Wagner T, Pancost R D. 2011. Characterizing the depositional settings for sedimentary organic matter distributions in the Lower Yangtze River-East China Sea Shelf System. Estuar.Coast. Shelf Sci., 93(3):182-191.
Zhu Z Y, Ng W M, Liu S M, Zhang J, Chen J C, Wu Y. 2009.Estuarine phytoplankton dynamics and shift of limiting factors:a study in the Changjiang (Yangtze River) Estuary and adjacent area. Estuar. Coast. Shelf Sci., 84(3):393-401.