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Eutrophication Assessment Based on the Cloud Matter Element Model

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Eutrophication Assessment Based on the Cloud Matter Element Model International Journal of Environmental Research and Public Health Article Eutrophication Assessment Based on the Cloud Matter Element Model Yumin Wang 1,* , Xian’e Zhang 2 and Yifeng Wu 1 1 School of Energy and Environment, Southeast University, Nanjing 210096, China; [email protected] 2 School of Environment and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; [email protected] * Correspondence: [email protected]; Tel.: +86-157-2292-5295 Received: 30 November 2019; Accepted: 2 January 2020; Published: 3 January 2020 Abstract: Eutrophication has become one of the most serious problems threatening the lakes/reservoirs in China over 50 years. Evaluation of eutrophication is a multi-criteria decision-making process with uncertainties. In this study, a cloud matter element (CME) model was developed in order to evaluate eutrophication level objectively and scientifically, which incorporated the randomness and fuzziness of eutrophication evaluation process. The elements belonging to each eutrophication level in the CME model were determined by means of certainty degrees through repeated simulations of cloud model with reasonable parameters of expectation Ex, entropy En, and hyper-entropy He. The weights of evaluation indicators were decided by a combination of entropy technology and analytic hierarchy process method. The neartudes of water samples to each eutrophication level of lakes/reservoirs in the CME model were generated and the eutrophication levels were determined by maximum neartude principal. The proposed CME model was applied to evaluate eutrophication levels of 24 typical lakes/reservoirs in China. The results of the CME model were compared with those of comprehensive index method, matter element model, fuzzy matter element model, and cloud model. Most of the results obtained by the CME model were consistent with the results obtained by other methods, which proved the CME model is an effective tool to evaluate eutrophication. Keywords: cloud matter element (CME); eutrophication evaluation; lakes; reservoirs 1. Introduction Rapid population growth and economic rise in past decades contribute to the pollution of water bodies in China, including lakes, reservoirs, rivers, and estuaries, which leads to the deterioration of water environment. Eutrophication is a natural process in which phosphorus and nitrogen stimulate primary production, which leads to enhanced algal growth and phytoplankton blooms. Lakes/reservoir eutrophication has become one of the most serious environmental issues worldwide, especially in developing countries [1]. Assessing eutrophication level scientifically and reasonably is essential for environmental management agencies. In the past literatures, many methods have been applied to evaluate eutrophication. However, subjectivity exists in indicators selection and weights determination in traditional eutrophication assessment. Therefore, it is reasonable and essential to interpolate multiple methods to assess eutrophication, such as principle component analysis method [2], fuzzy comprehensive assessment method [3], matter element (ME) method [4–6], projection pursuit method [7], artificial neutral network technology [8], support vector machine approach [9], random forest model [10], etc. These methods made eutrophication evaluation simple by using mathematic tools. Since the eutrophication assessment is a multi-criteria contradictory decision-making process, for example, some indicators belong to level A while the other indicators belong to level B, it is usually difficult to perform. The ME theory is Int. J. Environ. Res. Public Health 2020, 17, 334; doi:10.3390/ijerph17010334 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 2 of 19 Int.belong J. Environ. to level Res. PublicA while Health the2020 other, 17, 334 indicators belong to level B, it is usually difficult to perform. 2 ofThe 19 ME theory is suitable for solving such problems and can obtain better results [11]. Therefore, in this study, ME model is adopted for assessing eutrophication statuses of lakes in China [12]. In addition, suitableduring the for solvingprocess suchof eutrophication problems and evaluation, can obtain better fuzziness results and [11 randomness]. Therefore, exist in this in study, monitor ME of model data, isselection adopted of for statistical assessing methods eutrophication, and determinat statuses ofion lakes of weights in China [13] [12.]. To In solve addition, the fuzziness during the problem, process offuzzy eutrophication mathematics evaluation, theory was fuzziness introduced and randomness into ME method exist in in monitor terms ofof data,membership selection functions of statistical to methods,construct andfuzzy determination matter element of weights (FME) [model13]. To with solve consideration the fuzziness problem,of fuzziness fuzzy and mathematics uncertainty theory of the waseutrophication introduced intoevaluation ME method [14–17] in. termsUnfortunately of membership, precise functions membership to construct functions fuzzy in matterFME model element is (FME)unable model to give with consideration consideration to ofboth fuzziness fuzziness and and uncertainty randomness of the of eutrophication objects and leads evaluation to inaccurate [14–17]. Unfortunately,evaluation results precise since membership the character functions of fuzzy in objects FME modelwas described is unable as to “ giveBoth considerationA and B” [18]. to Under both fuzzinesssuch circumstance, and randomness the cloud of objects model and was leads introduced to inaccurate into FME evaluation model results by extending since the the character accurate of fuzzymembership objects wasfunctions described to random as “Both membership A and B” [18 ].functions Under such in terms circumstance, of statistical the cloud distributions model was of introducedmembership into functions, FME model which by extendingis a new cognition the accurate mod membershipel of uncertainty functions based to on random probability membership theory functionsand fuzzy in set terms theory of statistical [19–21]. distributionsThe membership of membership functions in functions, FME model which were is a replaced new cognition by a certain model ofdegree uncertainty of cloud based drops. on probability Therefore, theory the cloud and fuzzymatter set element theory [19(CME)–21]. Themethod membership was generated functions with in FMEconsideration model were of replacedrandomn byess a certainand fuzziness degree of of cloud eutrophication drops. Therefore, evaluation the cloud systematically matter element by (CME)transforming method between was generated qualitative with notion consideration and quantitative of randomness instances. and fuzzinessThe CME of model eutrophication takes the evaluationadvantages systematically of both cloud by model transforming and matter between element qualitative model, notion and andcan quantitativebe applied instances.to assess Theeutrophicatio CME modeln status takes, which the advantages is contradictory, of both uncertain, cloud model fuzzy and, and matter random element [13]. model, and can be appliedIn tothis assess study, eutrophication a cloud matter status, element which is(CME) contradictory, model uncertain,is developed fuzzy, in andSection random 2 with [13]. the combinationIn this study, of ME a cloud model matter and cloud element model (CME). The model CME is- developedbased eutrophication in Section2 withassessment the combination process is ofdiscussed ME model in andSection cloud 2 including model. The framework, CME-based determination eutrophication of assessmentparameters,process weights is of discussed indicators, in Sectioncalculation2 including of neartude, framework, and determination determination of eutrophication of parameters, level. weights In Section of indicators, 3, the proposed calculation CME of neartude,model is applied and determination to eutrophication of eutrophication evaluation level.of 24 Intypical Section lake3,s/reservoirs the proposed of CME China, model and isthe applied results toof eutrophicationCME model were evaluation compared of 24 with typical the lakesother/ reservoirsmethods. ofIn China,Section and 4, conclusions the resultsof were CME stated. model were compared with the other methods. In Section4, conclusions were stated. 2. Methodology 2. Methodology 2.1.2.1. StudyStudy AreaArea InIn thisthis study,study, eutrophicationeutrophication evaluationsevaluations ofof 2424 typicaltypical lakeslakes/reservoirs/reservoirs inin China were performed. TheThe locationslocations ofof 2424 lakeslakes/reservoirs/reservoirs areare shownshown inin FigureFigure1 .1. According According to to the the eutrophication eutrophication features features of lakes/reservoirs, ChlorophyII-a (Chl-a), chemical oxygen demand (CODMn), total phosphorus (TP), of lakes/reservoirs, ChlorophyII-a (Chl-a), chemical oxygen demand (CODMn), total phosphorus (TP),total totalnitrogen nitrogen (TN), (TN), and andSecchi Secchi Disk Disk (SD) (SD) were were selected selected as as key key indicators for eutrophicationeutrophication evaluation. The data for Chl-a, CODMn, TP, TN, and SD, were monitored by branches of the Ministry evaluation. The data for Chl-a, CODMn, TP, TN, and SD, were monitored by branches of the Ministry of Environmentalof Environmental Protection Protection and and the Ministrythe Minist ofry Water, of Water and,annual and annual mean mean values values are shown are shown in Table in1 Table[ 22].
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