CE-QUAL-W2模型在上海市大型饮用水水库中的应用

通过二维纵向横向平均水动力学和水质模型（CE-QUAL-W2模型）来模拟上海大型饮用水水库的水质状况。模型初步模拟了该水库中不同深度水层的水质状况,使用2011年1月到2011年11月单月（包含一年四季代表性）的数据对模型主要参数进行校正。结果显示,水质状况的模型计算值与实测值比较接近,与实际情况较吻合,AME和RMS值较好,预测该水库的水质是Ⅰ～Ⅱ类。通过本文对该（CE-QUAL-W2模型）的应用得出的结果显示,该模型也适合模拟一些具有湖泊特性的河流,尤其适用于对水体的水质评估。     

基于多核LS-SVM的河涌水质预测模型

研究了多变量非线性河涌水质预测问题,提出了多核最小二乘支持向量机河涌水质预测模型。模型采用协同结构的非线性函数将水质时序样本映射到高维特征空间,进行多元线性回归。然后将该回归问题转化成半无限线性规划问题,运用交换集法求解。文章利用东江流域河涌水质数据进行了拟合预测实验,结果表明,与单核最小二乘支持向量机河涌水质预测模型相比,多核模型的预测误差减小了23%以上,它较单核模型具有更高的预测精度和更好的泛化推广性能。     

非机理性水质模型研究综述

水对社会和经济的发展都起着不可估计的作用。保护和改善水环境问题已成为当前世界关注的一个焦点。虽然机理性水质模型能较精确地描述水污染过程中的物理、化学、生物化学的内在规律和相互联系,但是需要大量的模型参数和环境参数,而得到这些参数需要大量物力、财力,从而制约了这些模型在水环境中的应用。随着现代随机数学争统计学的发展,非机理性水质模型得到了广泛的应用;非机理性水质模型虽然是一种黑箱式方法,但因其针对某一特定的物质系统,通过数学统计或其他数学方法建立模型,模拟预测也得到了较好的效果。它们为水环境的综合整治和科学管理提供科学依据。最后综观水质模型的研究历史和应用前景,系统分析了水质模型的发展趋势。     

基于EFDC模型的滇池水质模拟

从应用层面介绍EFDC模型的主要编程原理和数据结构,以及主控文件、初始化数据、气象和负荷数据以及模型输出的内容和结构。利用1988、1989年的负荷数据和观测资料,对滇池水质进行了模拟。结果表明,模型的水动力模块模拟结果与实际情况较接近,水质模块的模拟结果尚可接受,模型实用程度的提高有赖于基础数据的积累。     

基于三角模糊数的贝叶斯水质评价模型

将贝叶斯理论和模糊集理论引入水质评价领域,分别描述了水质评价过程中模型结构和参数的不确定性,建立了基于三角模糊数的贝叶斯模糊综合水质评价模型.对Ⅰ、Ⅱ、Ⅲ、Ⅳ、Ⅴ类水分别赋值,并根据监测点水质对各类别的后验概率计算水质的综合得分进而确定水质类别.同时,选取了TP、NH4+-N、COD、DO、As及粪大肠菌群为水质评价因子,将建立的模型应用于2010年洞庭湖水质评价中.结果表明,小河嘴、横岭湖、万子湖、目平湖、洞庭湖出口水质达到Ⅱ类水标准;南嘴、岳阳楼水质为Ⅲ类水;东洞庭湖和扁山水质介于Ⅱ类水和Ⅲ类水之间,对各等级的隶属度分别为0.4983/Ⅲ+0.5017/Ⅱ、0.7962/Ⅲ+0.2038/Ⅱ.各监测点位中,仅南嘴水质未达到相应的水质标准.较均值模型、三角模糊数模型和传统贝叶斯模型而言,基于三角模糊数的贝叶斯水质评价模型对不确定性的表达更为全面、更符合实际.     

山东省辖淮河流域河流水质趋势的灰色预测

目前,灰色系统模型越来越多地应用在水质预测中。山东省辖淮河流域包括南四湖流域和沂沭河流域,流域区域内的河流水质不仅与区域的社会经济发展相互联系,而且也影响着南水北调东线的水质。因此,依据2000-2009年流域内河流的国控断面高锰酸钾指数、NH3-N的监测数据资料,在对数据进行处理的基础上,文章利用灰色GM(1,1)模型结合新陈代谢原理预测河流水质的发展趋势。结果表明:大多数河流水质呈好转的趋势,但河流水环境变好趋势较缓慢,特别是沭河高峰头断面的水质呈变差趋势。河流水质的变化趋势应值得高度重视,为区域水环境保护和制定水环境规划提供参考。     

常州市环境管理信息系统中水质模型及其应用

本文在研究河流水质模型和河网水质模型的基础上,利用河流水质模型组成河流水质模拟,模型参数识别和水环境容量优化分配计算模块:利用河网水质模型组成河网水质模拟、河网水域水环境容量计算模块,并在此基础上形成水质模型软件系统。      

基于自由搜索的投影寻踪水质综合评价方法

为解决水质评价指标的不相容性以及现有评价方法对等级的分辨率较粗略等问题,提出了水质状况评价的投影寻踪方法(Projection Pursuit,PP).根据地表水环境等级划分标准,构建了能够反映水质状况变异信息的投影指标函数;引入自由搜索(Free Search,FS)算法,优化投影指标函数寻求最佳投影方向;利用最佳投影方向信息研究了各种水质指标对水质状况评价的影响程度.以山西省汾河流域为例,进行水质评价分析,并与基于加速遗传算法优化投影寻踪模型(RAGAPP)和利用粒子群优化投影寻踪模型(PSOPP)得到的投影值结果进行对比.实例应用表明,该模型评估结果误差精度优于RAGAPP和PSOPP,评价结果符合当地水质状况实际情况,且对水质等级划分清晰.     

苏州河水系非点源负荷模型研究及应用

开发了苏州河水系非点源负荷LOAD模型,对土地利用、降水量、人口密度和污染源进行分析,求得整个区域非点源的污染量.LOAD模型的计算结果为水质模型计算提供了定量化及较合理的非点源污染负荷数据,为正确估算苏州河水系污染负荷和水系水质奠定了基础,为有效控制和治理非点源污染提供了科学方法.     

填埋场渗滤液水质变化预测模型实验研究

利用垃圾污染负荷浸出实验装置对下坪垃圾填埋场渗滤液水质变化规律进行了模拟研究。并利用实验数据通过指数回归法确定了渗滤液水质预测模型的有关参数。实验结果和现场监测数据表明,单批填埋垃圾渗滤液的浓度在垃圾填埋75d后达到最大值,COD最大可达31581mg/L。利用渗滤液水质预测模型计算表明,垃圾填埋90．9d后垃圾渗滤液COD达到最大值,模拟计算结果和现场监测数据符合较好,说明该模型以及通过污染负荷浸出实验得出的模型参数可以较准确地对填埋场渗滤液水质变化进行预测。     

Comparison of measurements and integral model predictions of hot water plume behaviour in a crossflow

Measurements of the trajectory and dilution rate of a hot water jet entering vertically into a crossflow have been made in a water flume. Under various source conditions, thermistor probes at selected downstream locations were used to map out the cross-sectional extent of the plume, and thus to establish mean plume altitude and cross-sectional area as functions of distance from the source. These measurements were compared against the predictions of a simple integral model, similar to well-established models in the literature but modified for use in the flume study by the inclusion of an equation of state for water and an empirical correlation for the length of the potential core. An approximate analytical solution of the integral conservation equations, valid for both gaseous and water plumes, is presented. With entrainment and added mass constants very close to those previously estimated for gaseous plumes, good agreement between measured and predicted plume trajectory and dilution rate was found over the range of source conditions tested.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

A simulation analysis of the migration and transformation of pollutants contained in landfill leachate

A dynamic composite model for a soil-water system that can be used to simulate the movement of leachate from a landfill. The composite model includes nine sub-models that trace water movement and the migration and transformation of five pollutants(organic N, NE4^- ,NO3^- , NO2^-, and Cl^ - ) in saturated and unsaturated soil. The model to simulate the movement of leachate from a landfill in Laogang Town,Shanghai City was used. In this application, the values for the model parameters were oblained by performing a laboratory simulation experimentof water movement and pollutant migration and transformation in soil columns. Soil and leachate obtained from the landfill site and its vicinitywere used in the laboratory experiments. The model was then used to simulate leachate movement and pollutant activity during the ten-year period when the landfill was in operation and in the twenty-year period following its closure. The simulation results revealed that the leachate migrated into the groundwater at the rate of 90-100 meters per year. This model can be applied in the design of future landfills in China for the purpose of assessing and forecasting leachate plumes.     

佛山水道引水对环境容量影响研究

采用污染负荷优化分配的线性规划方法,通过感潮河网水环境数学模型模拟得出污染贡献率。求取不同引水方案时佛山水道的环境容量,并建立引水量与环境容量的回归方程。研究结果表明,环境容量与引水量呈线性递增关系。根据回归方程可反推得出引水方案或削污方案,为引水与截污的实施提供了数据支持,并且对佛山水道综合整治的优化决策具有指导意义。     

流域农村生活垃圾收运、处理模式探析研究——以巢湖流域为例

流域农村生活垃圾产量大,无害化处理水平低,已成为威胁流域水环境安全的重要因素.以巢湖流域为例,构建了农村生活垃圾收运、处理模式.从技术、经济、环境三方面分析,得出结论：农村“桶装车载直运模式”和“垃圾综合处理模式”可以充分利用现有垃圾处理设施,经村庄收集、乡镇转运,最终实现垃圾减量化、资源化、无害化.     

污水深井处置初探

介绍了深井处置技术概况，探讨了污水深井处置的优缺点、处理费用，确定了选择深井的原则和污水处理的原则，同时对深井处置的水质标准进行了讨论。     

基于地球化学因子影响的生活垃圾降解动态模型

在生物反应器填埋场厌氧填埋实验研究的基础上,进行了垃圾降解的地球化学过程分析,建立了受地球化学因子影响,反映生物反应器填埋场水质变化的COD、VFA、及NH4+-N浓度变化的动态模型。研究表明:该模型拟合值与实测值吻合较好,能较好地预测垃圾填埋降解过程中水质的变化,为填埋场的渗滤液处理和水质污染控制提供理论依据。     

我国填埋渗滤液产量影响因素分析及估算方法构建

在综述已有研究结果的基础上,构建了适合我国国情的渗滤液产量估算方法,根据来源将渗滤液概化为降水和垃圾自产水2部分.采用HELP模型获得31个典型城市气候特征下的渗出系数取值;按照产生原理,垃圾自产水分为压缩产水和降解产水,分别建立估算公式并给出田间持水率和组分降解率的取值建议.应用此方法,估算了10个城市的填埋渗滤液产生量.发现在填埋40a内,垃圾自产水占渗滤液总量的52%~82%,说明垃圾自产水的贡献不可忽视.我国不同地区的降水渗出系数和生活垃圾性质差异,使得填埋渗滤液产量具有显著的地域差异性,在选择渗滤液处理方式时应因地制宜.     

危险化学品运输槽罐车清洗污染物的综合处理

危险化学品运输槽罐车在清洗过程中会产生洗车废水、含油废水、有机废水以及甲醇、苯乙烯等废气。废气经过吸附处理和等离子氧化的方法处理后,其浓度和TVOC指标分别达到GB14554-93《恶臭污染物排放标准》和GB/T18883-2002《室内空气质量标准》。洗车废水采用混凝-斜板沉淀-过滤工艺处理,含油废水采用平流斜板隔油池-气浮池-砂滤柱组合工艺处理,上述两类废水经处理后的相关指标均达GB/T18920-2002《城市污水再生利用城市杂用水水质标准》的要求,可直接回用于车辆清洗;有机废水采用白土活性污泥法-厌氧滤池-曝气生物滤池组合工艺,出水COD去除率达90.7%,符合GB 8978-1996《污水综合排放标准》中的一级排放标准。