小城镇河流水质数学模型参数估计

本文以(由页)塘河为例,研究了中小河流水质模型参数,提出了一整套更为简便易行的估计中、小河流模型参数的方法,分析了准确估计模型参数的影响因素,得到了适用于(由页)塘河水质模型参数K_1、K_2、K_3及D_L的估计结果.该结果可直接应用于(由页)塘河的水质数值模拟,对于其它中、小河流也有一定借鉴意义.     

离散规划在近海地区排海废水污染物总量控制中的应用

本文采用离散规划方法对某海新区排海废水污染物总量控制方案进行了优化分析得到了不同消减率下的最优方案,并对近岸海域海水水质进行了模拟预测。     

长江下游水源地突发性水污染事故预警应急系统研究

针对长江下游主要水源地特点及水污染事故特征,以长江江苏段为例,建立整体一维、分段二维的水量水质预测模型,构建适用于该地区的水源地风险预警模型,实现对于固定源、移动源等水污染事故风险过程的动态模拟及风险预警;将水质预测模型、风险预警模型、地理信息系统、远程控制及自动化等技术有机耦合,研发水源地突发性水污染事故预警应急系统,可准确直观地表现水污染事故的影响范围及发展趋势,为水源地水污染事故风险预警应急工作决策提供科学依据。     

灰色聚类法综合评价滴水湖水系环境质量

将滴水湖水系看作一个灰色系统进行研究,利用灰色聚类理论,以地表水环境标准和富营养化分级标准为基础建立灰类评价体系,确定聚类指标的隶属度和标准灰类的权重,得到各聚类指标对标准灰类的聚类系数,最大聚类系数关联的等级即水体质量等级。2010年,每2周1次对滴水湖水系11个监测点的溶解氧（DO）、化学需氧量（CODMn）、五日生化需氧量（BOD5）、总磷（TP）、总氮（TN）、氨氮（NH3-N）、透明度（SD）及叶绿素a（Chl.a）进行监测,取各指标的年均值,建立灰类评价系统。结果表明：滴水湖水源大治河及引水干道随塘河处水质属于Ⅳ类～Ⅴ类,呈富营养化和极度富营养化状态;闸外引水河芦潮引河段水质类型为Ⅲ类,为富营养化水平;闸内引水河道水质总体呈现富营养化状态,水质类型为Ⅲ类;滴水湖湖区水体质量良好,水质类型为Ⅲ类,呈中营养水平。     

黄河宁夏段水质评价及其污染源分析

以黄河宁夏段干流水质监测资料为基础,利用多元统计法对水质样本点进行主因子分析、方差检验和聚类分组,对分组后的水质指标样本点应用综合水质标识指数进行综合评定.结果表明:(1)黄河宁夏段干流水体水质量为Ⅱ—Ⅴ类水质,入境断面水质量为Ⅱ类水质,出境断面水质量为Ⅲ类水质.(2)从上游到下游断面水质呈现恶化趋势,平水期最好,丰水期次之,枯水期最差.(3)水质评价结果分辨率高,客观反映了黄河宁夏段综合水质特征信息.因此,基于多元统计与综合水质标识指数的水质评价方法对黄河宁夏段水环境的水质评价工作具有较好的适用性.     

小城镇河网水质规划及水环境经济系统协调发展对策研究

本文用系统工程方法进行河网地区小城镇水质最优规划.研究选择震泽镇作为典型,对现有水质规划方法加以推广,得到了河网水质模型建模的改进方法,提出求解以此模型为基础的河网水质线性问题的分解—协调算法.通过对典型镇环境现状的系统分析结果,提出了小城镇控制水污染的规划技术要点,以及水环境—经济系统协调发展对策     

神经网络模型在黄河水质预报中的应用分析

为提高黄河水质预测的精度和实用性,引入神经网络技术建立黄河水质预测模型,采用LM(Levenberg-Marquardt)算法提高预测精度,并将模型应用于黄河小花段(小浪底-花园口段)的水质预测.预测结果表明,在建立黄河入河污染物和功能区水质的输入响应关系模型的实际应用中,神经网络模型和LM算法可以取得较好的预测效果.     

QUAL2E模型在大沽河干流青岛段水质模拟中的应用

采用QUAL2E模型对大沽河干流青岛段的水质进行了模拟和预测。针对大沽河的具体情况,选用BOD5、COD和氮作为模拟预测指标,用实验模拟方法、模型率定法并参考相关文献确定了BOD耗氧系数k1、BOD复氧系数k2、BOD沉降系数k3、COD耗氧系数和弥散系数等水质参数,并对模拟结果进行了验证,表明预测值和实测值的相关性较好;对BOD5、k1、k2和Q(流量)进行了灵敏度分析,结果表明对大沽河DO浓度影响敏感的参数依次是:Q、k2、BOD、k1,即流量Q是模型最敏感的参数,说明河流的水力学参数对DO影响较大。     

浏河引排水对城厢镇水质影响的数值计算

城厢镇地表水水源的污染特点在很大程度上取决于浏河闸的运行启闭情况,对通过引排水方案来改善滞流期恶劣的水质状况.文中首先用一维Saint—Venant方程组及CBOD—NBOD—DO联偶反应方程组分别建立水量及水质模式,通过有限差分法数值求解.利用1990年11月5日开闸排水期间水量与水质同步监测资料进行验证,结果表明,模式能够满足水质规划的要求.本文对水质的动态变化规律进行评价,认为只要合理组合引排方案,就能达到改善城厢镇地表水源水质的目的.     

OUAL2E模型在大沽河干流青岛段水质模拟中的应用

采用QUAL2E模型对大沽河干流青岛段的水质进行了模拟和预测。针对大沽河的具体情况，选用BODs、COD和氮作为模拟预测指标，用实验模拟方法、模型率定法并参考相关文献确定了BOD耗氧系数k3、BOD复氧系数k2、BOD沉降系数k3、COD耗氧系数和弥散系数等水质参数，并对模拟结果进行了验证，表明预测值和实测值的相关性较好；对BOD5,k1、k2和Q(流量)进行了灵敏度分析，结果 表明对大沽河DO浓度影响敏感的参数依次是：Q、k2、BOD、k1，即流量Q是模型最敏感的参数，说明河流的水力学参数对DO影响较大。     

特定小流域水质模型的选择

根据特定小流域的水文特点和实际要求，建立一个流域综合水质模型，它由三个子模型支持，即：河流水质模型、水库富养营化模型和非点源污染模型。本工作对河流水质模型（ＱＵＡＬ２ＥＵ）加以改进，使之适应Ｗ河的具体情况，并在ＱＵＡＬ２ＥＵ增加了模拟ＣＯＤ、网箱养鱼和底泥泛起的功能。完成了作了水源地的水库地的水库富营养化模型，解决了流域内农业耕作、污水灌溉等造成的非点源污染随降雨径流进入水体过程的模拟和计算，使之     

An integrated river basin-coast-sea modelling scenario for nitrogen management in coastal waters

Linked river basin and coastal water models were applied to analyse the effects of an optimal nitrogen management scenario in the Oder/Odra river basin on water quality in the Oder (Szczecin) Lagoon and the Pomeranian Bay (Baltic Sea). This scenario would reduce nitrogen loads into the coastal waters by about 35%, a level which is similar to the load of the late 1960’s. During summer the primary production and algae biomass in the Oder estuary is limited by nitrogen, which makes a nitrogen management reasonable. The comparison of the late 1960’s and the mid 1990’s shows that an optimal nitrogen management has positive effects on coastal water quality and algae biomass. However, this realistic nitrogen reduction scenario would not ensure a good coastal water quality according to the European Water Framework Directive. A good water quality in the river will not be sufficient to ensure a good water quality in the lagoon. Nitrogen load reductions bear the risk of increased potentially toxic, blue-green algae blooms, especially in the Baltic coastal sea. However, to reach water quality improvements in lagoons and inner coastal waters, nitrogen cuts are necessary. A mere focus on phosphorus is not sufficient.     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.     

长江下游重点江段水质污染及对鱼类的毒性影响

通过对长江下游安庆、南京、镇江、南通４江段水质污染及其对鱼类毒性影响的调查监测可发现,长江下游江段主航道水质较好,符合国家地面水Ⅱ类水质标准．但近岸带水质由于受污染带的影响,水质污染较严重,其主要污染物为石油类和挥发酚等．上述这些污染物质对鱼类具有一定的毒性影响,其不但可引起鱼类的急性中毒,而且可诱发鱼类产生微核,并对鼠伤寒沙门氏菌ＴＡ９８菌株表现出一定的致突变性．同时在上述几个江段鱼体内可检出较高的蓄积性污染物的残留量．研究结果显示,长江下游江段正在遭受沿江诸多工业废水的污染,尤其是污染带的作用,并且这种污染对区域性渔业有一定的影响     

论流域水质与水量集成规划

“水量”与“水质”是流域大系统的两个特征,水环境研究偏重于水质,而水资源研究则更偏重水量,但是“质”与“量”是不能截然分开的,它们之间既相互关联,又互影响,相互制约,构成矛盾的统一体,基于这一出发点,本文从规划方法学的历史发展与流域水环境大系统的结构分析入手,系统阐述了提出流域水质与水量集成规划基本概念,目标及其策略与技术路线。     

用大型底栖动物和ODP系统评价珠江的有机污染

采用大型底栖动物需氧有机体百分率ODP(oxygen demander percentage)法对广州珠江前航道、西航道和流溪河的下游段进行河流有机污染评价.结果显示:底栖动物需氧类群密度在三河段间分布确有显著性差异,并根据其ODP可以判断流溪河水质相对较好,水质级别为中国地表水环境质量标准(EQSSW)Ⅳ级,西航道和前航道水质级别都为Ⅴ级.通过测试,这一方法能成功地应用在珠江及流溪河,且该法可以较好地匹配于EQSSW五级评价系统,初步认为ODP系统可以成为一个较好的河流水质生物监测方法.图3表4参13     

Eco-hydrological modelling in a highly regulated lowland catchment to find measures for improving water quality

Water quality modelling in the meso-scale Rhin catchment in the German federal state Brandenburg was done (1) to answer some specific questions concerning identification of point and diffuse sources of nutrient pollution in the catchment, (2) to assess the influences of possible climate and land use changes on water quantity and quality and (3) to evaluate potential measures to be done in order to achieve a “good ecological status” of the river and its lakes as required by the Water Framework Directive (WFD).The Rhin catchment is a typical highly regulated lowland river basin in Northern Germany. The regulations complicate water quantity and quality modelling in the catchment. The research was done by using the eco-hydrological model SWIM (Soil and Water Integrated Model), which simulates water and nutrient fluxes in soil and vegetation, as well as transport of water and nutrients to and within the river network. The modelling period was from 1981 until 2005. After calibrating the hydrological processes at different gauges within the basin with satisfactory results, water quality (nitrogen and phosphorus) modelling was done taking into account the emissions of different point sources (sewage treatment plants, etc.) and identifying the amount of diffuse pollution caused mainly by agriculture.For suggesting some feasible measures to improve water quality and to reduce diffuse pollution considering possible climate and land use changes, different reasonable scenarios were applied in consultation with the Environmental Agency of Brandenburg (LUA). The study revealed that the amount of water discharge has significant influence on the concentration of nutrients in the river network, and that nitrogen pollution, caused mainly by diffuse sources, could be notably reduced by application of agricultural measures, whereas the pollution by phosphorus could be diminished most effectively by the reduction of point source emissions.     

Design of a water quality monitoring network in a large river system using the genetic algorithm

Despite several decades of operations and the increasing importance of water quality monitoring networks, the authorities still rely on experiential insights and subjective judgments in siting water quality monitoring stations. This study proposes an integrated technique which uses a genetic algorithm (GA) and a geographic information system (GIS) for the design of an effective water quality monitoring network in a large river system. In order to develop a design scheme, planning objectives were identified for water quality monitoring networks and corresponding fitness functions were defined using linear combinations of five selection criteria that are critical for developing a monitoring system. The criteria include the representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness levels were obtained through a series of calculations of the fitness functions using GIS data. A sensitivity analysis was performed for major parameters such as the numbers of generations, population sizes and probability of crossover and mutation, in order to determine a good fitness level and convergence for optimum solutions. The proposed methodology was applied to the design of water quality monitoring networks in the Nakdong River system, in Korea. The results showed that only 35 out of 110 stations currently in operation coincide with those in the new network design, therefore indicating that the effectiveness of the current monitoring network should be carefully re-examined. From this study, it was concluded that the proposed methodology could be a useful decision support tool for the optimized design of water quality monitoring networks.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.