Wang Dong, Zeng Debiao, Singh Vijay P, Xu Pengcheng, Liu Dengfeng, Wang Yuankun, Zeng Xiankui, Wu Jichun, Wang Lachun
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Environ Res. 2016 Aug;149:113-121. doi: 10.1016/j.envres.2016.05.012. Epub 2016 May 17.
Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.
湖泊至关重要，因为它们具有多种功能，如供水、娱乐、捕鱼和提供栖息地等。然而，富营养化限制了湖泊发挥这些功能的能力。为了减少富营养化，第一步是对其进行评估。评估过程存在随机性和模糊性，因此必须将其纳入考量。本研究提出了一种富营养化评估方法，称为多维正态云模型（MNCM）。该模型将每个评估因子视为MNCM的一维属性，选择合理的参数，并通过熵确定评估因子的权重。由此生成属于每个富营养化水平的MNCM的所有因子，并通过确定度确定最终的富营养化水平。然后使用MNCM对中国12个典型湖泊和水库的富营养化情况进行评估，并将其结果与参考方法（一维正态云模型、相关加权营养状态指数法、评分法和模糊综合评价法）的结果进行比较。发现MNCM的结果与实际水质状况一致；因此，MNCM可以成为一种有效的评估工具。相对于之前的一维正态云模型，MNCM在不增加其复杂性的情况下对参数进行了改进。MNCM可以根据确定度直接确定富营养化水平，并能确定最终的富营养化程度；因此，它更符合水体富营养化评估的复杂性。
