河流突发污染论文：河流突发环境污染事件源项反演及程序设计_环境污染与保护论文

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河流突发污染论文：河流突发环境污染事件源项反演及程序设计

【中文摘要】当前,水污染预警应急是我国的急需研究课题与热点问题。建立突发水污染预警应急系统能对污染响应与处置提供有效辅助决策支持。然而其前提是确知污染源位置及其排放历史。实际中在事故初期难以获得充分的污染源信息,难以对污染事件实时动态评估、预警响应,需要开展河流突发污染的源项反演问题研究。同时地表水的追踪溯源研究很不充分,鲜有文献报道。因此本文开展了河流污染源项反演算法研究和决策支持工具研发的工作。研究建立了地表水污染源项反演算法体系,并重点探讨了三类重要情景的源项反演算法。首先,基于相关系数优化法,结合地表水环境特征和污染物水质过程特征,推导出一维河道中单点源瞬时排放的源项反演算法。采用假想算例进行数值试验,综合分析了流速信息、污染物衰减、监测距离、监测数据误差及中间参数?T选取等因素对反演结果的影响,确定了该方法的适用条件和最优条件的寻找方式。最优条件下计算结果的平均相对误差在10%以内。并且方法具有监测布点简单高效,数据需求低,编程简单等优点。然后,基于遗传算法,结合水质模型和BP神经网络,建立了适用于一维河道的多点源瞬时排放和单点源连续排放的反演算法。通过假想算例检验算法,反演结果的平均相对误差均低于20%,取得了良好的结果。并考察参数对基于水质模型-遗传算法耦合的反演算法的影响,扩散系数为主要影响因素,但其计算结果的平均相对

误差不超过50%,在可接受范围内。基于上述研究结果,结合可视化技术和数据库技术,采用C#、C++编程语言开发了河流污染源项反演系统软件。该软件将河流基础信息、监测断面信息、污染物质信息等集合在一起,在计算机上实现了流域污染源识别。以实验室示踪剂实验和松花江硝基苯污染事件为背景,进行了具体应用分析,应用结果的平均相对误差均不超过20%,具有较好的效果。可为流域水环境管理提供强有力的技术支持,可应用于我国各大流域管理部门。

【英文摘要】At present, early warning and reponse to water pollutions has become a urgent iues and hot topics in academic in china.Establishing Early Warning/Emergency Response System can provide effectively aistant support for pollution response and disposal.However, the detailed contamination source information is the precondition of the system.In fact, it is difficult to know the information of pollution source when pollution occured.In order to meeting the need of real-time aement and early warning of pollutions, reseaches on pollution source inversion problem were carried out.Meanwhile, investigation on pollution source inverse problem of surface-water are insufficient so far.This thesis proposed solution framework of surface water pollution source inverse problems, and focuses on three inversion algorithms.First, a novel inversion algorithm based on an optimization approach for

river point pollution sources is developed.Ma transport and kinetics procees of the contaminants in surface waters are combined along with the discharge history.And other relative parameters are deduced under the scenario that singular source instantly discharges degradable and soluble chemicals into one-dimensional rivers.A series of numerical experiments on the hypothetic cases, analyzing inversion effects aociated with ambient river flow rates, contaminant decay rates, monitoring sites setting, sampling data errors and time intervals between two groups of sampling.The synthetical relative error of result is under 10%.Results show that parameters calculated fit well with the real values.In addition, the algorithms have the advantages such as efficient sampling proce, minimum data requirement as well as easy programming.It is worthwhile to utilize this method for emergency environmental management practices.Next, two inversion methods were established based on genetic algorithm coupled with water quality model and BP artifical network, for instantaneous discharge of multiple pollutions sources and continuous discharge of single pollution source, respectively.The numerical cases test the effectivene and accuracy of each algorithm, the synthetical relative errors are all below 20%.And the dispersion coefficient is the main facor of the algorithm, but the relative is under 50%, which responder can accepted.In order to control the detriment of the pollutions, river pollution source identification software was developed by C# and C++ programming language with water quality models and database technologies.The software integrated the control section information, pollution information and geographic information to realized pollution source identification on computer, which will play an auxiliary function for effective control of river point source pollutions.Moreover, a series of tracer experiments were conducted in labotory, providing data for concrete applied analysis, the results were satisfied.Therefore, this software can provide strong technical support for river basin water management.We recommned that this decision tool can be used by other relative administrative agency in China.【关键词】河流突发污染应急管理源项反演相关系数优化法遗传算法人工神经网络

【英文关键词】River Chemical Spill Emergengcy Management Pollution Source Inversion Correlation Coefficient Optimization Genetic Algorithm Artificial Neural Network

【目录】河流突发环境污染事件源项反演及程序设计4-5背景9-10Abstract5-6

第1章绪论9-16

摘要

1.1 课题1.2.1 环境水1.2 环境水力学反问题10-13力学反问题的定义10-11义11-1212-13

1.2.2 环境水力学反问题的研究意1.2.3 环境水力学反问题的研究现状1.3 污染源项反演问题13-14

1.3.1 污染源项反演问题的定义1313

1.3.2 污染源项反演问题的控制方程

1.4 主1.3.3 污染源项反演问题的研究现状13-14

1.4.1 主要研究内容要研究內容和技术路线14-1614-15方法16-2016-1818-191.4.2 研究技术路线15-16第2章实验材料与

2.1 污染源反演算法研究的基本方法2.2 示踪剂实验18-202.2.2 试验方法19-20

3.1 引言20

2.2.1 实验材料和设备第3章河流污染源项反3.2 反问题情景分类及演算法研究20-45描述20-2222-38算法22-33