基于三角随机模拟和延拓盲数模型的水环境污染评价

发布时间：2019-02-24 12:16

【摘要】：水环境资源是人类和一切生物赖以生存与发展不可或缺的物质基础。随着我国经济社会发展水平的不断增加,水环境污染现象不断加剧,尤其以河流重金属污染和湖泊富营养化问题较为突出。水环境评价作为一项基础性工作,已经成为预防和治理水污染的前提和依据,对于保护水资源和改善水环境具有重要的作用和意义。目前,国内外关于河流沉积物中的重金属污染和湖泊富营养化现象的评价方法已较多,但是大部分针对于确定性或系统中某一方面的不确定性因素进行评价。由于各种客观和主观认识上的不确定性,导致水环境是一个随机性、模糊性、灰性和未确知性等多种不确定性共存或交叉存在的复杂系统,而且不确定信息的来源、类型以及性质等较为复杂多变。如果在评价过程中,忽视这些信息,极有可能导致结果产生错误,造成决策失误。本论文基于水环境系统中存在的随机性、模糊性、灰性以及未确知性信息等不确定性信息,分别将三角模糊数和随机模拟模型结合,以及将三角模糊数和盲数相结合,建立三角随机模拟模型和延拓盲数模型,并且分别应用于湘江重金属污染风险评价和洞庭湖湖泊富营养化评价领域中,对其中存在的不确定因素进行分析研究,以降低评价过程中存在的不确定性信息,从而更加客观、真实地表征评价区域内水环境的污染状况,为区域的预防和治理提供更多全面、合理以及真实的信息,为水环境评价提供一种新的思路和方法。论文将基于三角随机模拟的沉积物重金属污染评价模型和基于延拓盲数的湖泊富营养化综合营养状态评价模型分别与常规的确定性方法进行比较,研究结果表明,该两种模型可以较为简单快速的得到重金属污染指数和综合营养指数相应的区间值,及其与之对应的隶属度,从而得到其综合污染水平,并且能够判断出有恶化趋势的相关区域,在一定程度上降低了评价过程中的不确定性,能够为决策者提供更多可靠的数据信息,具有较好的实用性和可操作性,更加全面、真实的反映水体的污染状况,从而将污染严重的相关区域作为重点监控治理对象,开展有针对性的预防和治理。
[Abstract]:The water environment resource is the indispensable material foundation for the survival and development of human beings and all living things. With the increasing level of economic and social development in China, the pollution of water environment is becoming more and more serious, especially the pollution of heavy metals in rivers and eutrophication of lakes. As a basic work, water environmental assessment has become the premise and basis for the prevention and control of water pollution, and plays an important role and significance in the protection of water resources and the improvement of water environment. At present, there are many methods for evaluating heavy metal pollution and lake eutrophication in river sediments at home and abroad, but most of them are focused on deterministic or uncertain factors in the system. Because of the uncertainty of various objective and subjective knowledge, the water environment is a complex system with many uncertainties, such as randomness, fuzziness, grey and unascertained, which coexist or cross exist, and the source of information is uncertain. Types and properties are more complex and changeable. If this information is ignored in the evaluation process, it is likely to result in errors and decision-making errors. Based on the uncertain information such as randomness, fuzziness, grey and unascertained information in water environment system, this paper combines triangular fuzzy number with random simulation model, and combines triangular fuzzy number with blind number, respectively. The triangular stochastic simulation model and continuation blind number model are established and applied to the risk assessment of heavy metal pollution in Xiangjiang River and eutrophication of Dongting Lake respectively. The uncertain factors are analyzed and studied. In order to reduce the uncertain information existing in the evaluation process, so as to represent the pollution situation of the water environment in the evaluation area more objectively and realistically, and to provide more comprehensive, reasonable and true information for the prevention and control of the region. It provides a new idea and method for water environment assessment. In this paper, the evaluation model of sediment heavy metal pollution based on triangular stochastic simulation and the lake eutrophication comprehensive nutrition evaluation model based on extension blind number are compared with the conventional deterministic methods. The two models can get the corresponding interval value of heavy metal pollution index and comprehensive nutrition index, and their corresponding membership degree, so as to get their comprehensive pollution level. And can judge the relevant areas with the trend of deterioration, to a certain extent reduce the uncertainty in the evaluation process, can provide more reliable data information for decision makers, have better practicability and operability, more comprehensive, It truly reflects the pollution situation of the water body, thus taking the seriously polluted areas as the focus of monitoring and control object, and carrying out targeted prevention and control.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：X824

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