A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I_2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I_2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I_2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.     

经济效益与水质提升双视角下南水北调水资源供应链契约协调

南水北调工程是国家水网的重要组成部分,为进一步发挥南水北调工程水资源优化配置能力,确保优质南水供给,推动后续运营管理工作有序开展,助力国家水网建设,结合博弈论与供应链理论,从考虑经济效益和水质提升双目标视角,探讨南水北调水资源供应链协调问题.基于对南水北调水资源供应系统的概化及相关假设,建立水资源供应链的市场需求函数、水质效用函数和水质提升成本函数,分别构建包括集中决策、批发价格契约和收益共享-水质提升成本共担契约下的水资源供应链多目标模型并探讨相应的协调策略.最后通过数值分析对结论进行验证.研究表明,水质效用与经济效益之间存在冲突,批发价格契约无法实现供应链的协调,而收益共享-水质提升成本共担契约可以实现水质效用目标的协调,以及经济目标的帕累托改进.     

Hydrologic and water quality impacts and biomass production potential on marginal land

Marginal land is proposed as viable land resources for biofuel production. However, environmental impacts of perennial biomass production on marginal lands is not clear. This study defined three marginal land types and assessed their availability and potential for biofuel production in the St. Joseph River watershed. The potential impacts were evaluated using the Agricultural Policy/Environmental eXtender (APEX) model. The total area of marginal land was estimated to be 611 km² covering 21.7% of the watershed. 161 and 207 million liters of bioethanol could be produced from the marginal land utilizing switchgrass and Miscanthus, respectively. Converting marginal land currently under corn/soybean production to switchgrass and Miscanthus reduced water yield by 13.4–36.3% and improved water quality by reducing soil erosion by 27%–98%. Similarly, total nitrogen losses were reduced by 30–91% and total phosphorus losses were reduced by 65–76%, respectively, at the field scales under various energy crop production scenarios.     

A multisensor in thick-film technology for water quality control

A set of sensors for water quality measurement has been developed. It consists of a multisensor designed to measure the following parameters: pH, temperature, dissolved oxygen, conductivity, redox potential and turbidity. The system has been built-up in thick-film technology, using when possible commercially available serigraphic pastes. The multisensor can be implemented in in situ portable or stationary instrumentation for water quality monitoring. The technology used in the multisensor implementation provides fundamental properties of miniaturization, reasonable precision and low cost. The complete system also includes electronic acquisition, signal conditioning and computing capabilities.     

基于物联网技术的成都沙河水质实时监测系统设计

针对流经成都市的沙河流域传统水质监测取样难、成本高等问题,设计了一种包括无线传感、ZigBee等物联网技术的水质实时监测系统,实现了对水质中重金属、PH值、温度、水位、浊度等多参数的远程监测。该系统可以实时感知到当前的水质数据,并可以在发现污染源后及时提供水质预警。     

Toward optimal waste management policies for a river basin

A mathematical analysis of marginal cost functions, where the total basin-wide treatment cost for a series of users of water in a river basin is to be minimized subject to constraints, is presented. The situation considered is where a series of water-using firms are located along a river; the river being the sole source of water supply and the only place to discharge wastewater. In such a situation, upstream users, if they discharge waste, impose damage costs on those downstream. On the other hand, if the upstream user is restricted in discharging wastes into the river, the costs for withholding or treating of these wastes are imposed on him. This trade-off is the basis for the minimization of the total basin-wide treatment cost.The results of this study show that under most conditions an optimal solution can be found which balances the upstream marginal waste withholding cost with the downstream damage costs. The analysis is concluded with a presentation of conditions which make these marginal cost relationships sufficient as well as necessary for optimally. Although stream-flow, inflow and outflow are treated as deterministic, this model should be valid except during extreme, transient conditions.     

Hybrid water treatment cost prediction model for raw water intake optimization

In order to reduce the total cost of a dual source drinking water treatment plant operation, a comprehensive hybrid prediction model was built to estimate the necessary chemicals dosage and pumping energy costs for alternative source selection scenarios. Correlations between the water quality parameters and the required treatment chemicals were estimated using historical data and the expected pH variations associated with each chemical addition, which was based on the Caldwell–Lawrence diagram. The pumping energy costs were also estimated using a data-driven approach that was based on historical plant data. The research has practical implications for water treatment operators seeking to minimize plant operational costs through selecting raw water intake volumes for their treatment plant based on multiple source options and offtake tower gate levels. Future research seeks to better link current and future water treatment dosage cost predictions directly to water quality measurements taken from vertical profiling systems.     

基于GPRS的水质自动监测系统的设计

针对我国主要的水质监测系统存在水质采样能力弱、数据处理不及时、缺乏水质变化预警机制等问题,提出了一种基于通用分组无线业务(GPRS)技术的水质自动监测系统设计方案。阐述了系统组成,重点介绍了GPRS模块的工作过程与监控中心子系统的通信部分。同时,对水质采集子系统的软件设计进行了阐述,提出多状态、多任务、多线程的软件设计思想,详细分析了GPRS模块流程,实现了系统功能。测试表明:系统性能稳定,时效性好,运行成本低,适合水质监测需要。     

A sensor network for high frequency estimation of water quality constituent fluxes using surrogates

Characterizing spatial and temporal variability in the fluxes and stores of water and water borne constituents is important in understanding the mechanisms and flow paths that carry constituents to a stream and through a watershed. High frequency data collected at multiple sites can be used to more effectively quantify spatial and temporal variability in water quality constituent fluxes than through the use of low frequency water quality grab sampling. However, for many constituents (e.g., sediment and phosphorus) in-situ sensor technology does not currently exist for making high frequency measurements of constituent concentrations. In this paper we describe how water quality measures such as turbidity or specific conductance, which can be measured in-situ with high frequency, can be used as surrogates for other water quality constituents that cannot economically be measured with high frequency to provide continuous time series of water quality constituent concentrations and fluxes. We describe the observing infrastructure required to make high frequency estimates of water quality constituent fluxes based on surrogate data at multiple sites within a sensor network supporting an environmental observatory. This includes the supporting sensor, communication, data management, and data storage and processing infrastructure. We then provide a case study implementation in the Little Bear River watershed of northern Utah, USA, where a wireless sensor network has been developed for estimating total phosphorus and total suspended solids fluxes using turbidity as a surrogate.     

基于物联网的水产养殖水质监控系统设计

为方便监测和调节罗非鱼养殖水域的水质参数,设计了基于物联网（IoT）的水产养殖水质监控系统。前端传感器检测水体相关物理参数,通过采集单元的通信网络发送到智能水质监控单元,智能监控单元可以对增氧机、循环水泵及物料投放机等进行自动控制。同时,智能监控单元通过以太网或无线公用网络将数据传送到数据服务中心。用户可以通过移动终端或计算机实时了解水质信息,也可以通过监控软件下发命令,控制现场设备,实现对水质参数的手动调节。测试结果表明：该系统具有运行稳定、操作方便、成本经济等优点,可以应用于水产品的养殖生产活动。     

The cleanroom-free rapid fabrication of a liquid conductivity sensor for surface water quality monitoring

Conductivity is an important issue of water quality detection. Although there are a number of microfluidic liquid conductivity sensors, most of them were difficult to fabricate and required cleanroom facilities, which significantly increased the cost of sensors. Here, we presented a novel liquid conductivity sensor that was built on interdigitated copper electrodes. Microscale sensor electrodes can be fabricated through the modified microfabrication process without any help of cleanroom facilities. In comparison with FEA simulation and commercial conductivity device, measured results showed that the fabricated liquid conductivity sensor was able to measure a wide range of liquids. Considering the ease of fabrication and its low cost, the cleanroom-free fabricated sensors have much potential for water quality monitoring.     

基于物联网的水质在线监测系统设计

针对传统水质监测方案布线困难、成本高等不足,设计了一种基于物联网的水质在线监测系统,实现了对溶解氧、PH值、温度等多参数的采集、传输、处理。该方案便于远程监测,适用于饮用水及养殖业水质监测等领域。     

基于无线传感器网络的水质在线监测系统研究

为了解决传统水质监测系统实时性差、网络布局困难、维护成本高等问题,设计一种基于无线传感器网络的水质在线监测系统.该监测系统可以在线大范围监测水质状况,并将采集数据通过GPRS网络传输给远程数据管理中心.给出了系统总体设计方案,详述了系统软、硬件和数据包结构设计.实验结果表明:该系统可以在恶劣环境下稳定工作,实现水质参数...     

Developing a knowledge-based early warning system for fish disease/health via water quality management

Fish disease is becoming the major factor damaging the sustainable development of aquaculture and the quality security of aquatic product in China. Many researchers provide assistances and advice in fish disease diagnosis and appropriate treatment measures. However, the risk of misdiagnosis, incorrectly treatment or over-treatment also exists due to carrying out after disease outbreaks. So it is necessary to develop an early warning system. Taking into account that fish disease outbreak is mainly caused by suboptimal water quality in north of China, the best way is to develop decision aids tool that can assess and early warning the risk of fish disease based on water quality management. This paper introduces a knowledge-based early warning system for fish disease via water quality management. The architecture, the process for knowledge engineering, main components and their functions, especially a series of water quality management mathematic models are described. At present, the system is in pilot in the city of Tianjin in China. The stage achievements in developing the intellectual system of fish disease early warning are summarized.     

Quantifying forecast quality of IT business value

This article discusses how to quantify the forecasting quality of IT business value. We address a common economic indicator often used to determine the business value of project proposals, the Net Present Value (NPV). To quantify the forecasting quality of IT business value, we develop a generalized method that is able to account for asymptotic cases and negative valued entities. We assess the generalization with real-world data of four organizations together consisting of 1435 IT assets with a total investment cost of 1232+ million Euro for which 6328 forecasts were made. Using the generalized method, we determine the forecasting quality of the NPV, along with the benefits and cost using real-world data of another 102 IT assets with a total business value of 1812 million Euro. For the real-world case study, we will find that the quality of the forecasted NPVs is lower than the forecasted benefits, which is again lower than the forecasting quality of the cost. Also, we perform a sensitivity analysis to investigate the impact on the quality of an asset’s forecasted NPV when the forecasting quality of benefits or cost improves. Counterintuitively, it turned out in this case study that if the quality of cost forecasts would improve, the overall quality of its NPV predictions would degrade. This underlines the importance of both accurate cost and benefit predictions. Finally, we show how to use the quantified forecast information to enhance decision information using two simulation examples.     

Regulated quality diffusion under quadratic costs

We study how to regulate quality processes so as to attain a desirable target. The effect of the regulation varies in a random fashion due to disturbances. The interaction between regulatory actions and outcomes is termed quality diffusion . In this paper, we address a specific version of such diffusion known as a mean-reverting process (also termed Ornstein Uhlenbeck process). The total production cost includes both a quadratic cost of quality deviations, and a linear cost of production control. Assuming that both regulation and quality processes are continuously reviewed, an optimal quality regulation policy is sought to minimize the total production cost over a finite horizon. The optimal regulation is found to be a surge-discharge control that can be characterized by a triplet including a discharge time, a stopping time, and a discharge function. The surge-discharge control is easy to be implemented for a variety of applications. Computational algorithms for the optimal control are developed.     

A software platform for process monitoring: Applications to water treatment

Effective online monitoring of the performance of water treatment is critical. Industrial wastewater treatment, for example, has to confront important challenges concerning both cost management of treatment plants and fulfillment of regulations. Moreover, when drinking water is produced, water safety is an important consideration, and possible changes in water quality should be detected as soon as possible. On the other hand, the large amount of measurement data available creates its own challenges for water treatment processes, which means that advanced tools for monitoring, analysis, and control are often required. Therefore, it would be useful to have a monitoring system which is able to handle all available measurements and present the available information in a simple, user-friendly and flexible manner. In this paper, we introduce a piece of software which can be utilized in the monitoring of water treatment processes. The structure of the software is designed so that it can be easily modified according to the user’s needs. The system is demonstrated using process measurements from an activated sludge treatment plant, which is part of a pulp and paper plant, and from a pilot treatment plant for producing drinking water.     

Software quality control and prediction model for maintenance

We develop a quality control and prediction model for improving the quality of software delivered by development to maintenance. This model identifies modules that require priority attention during development and maintenance by using Boolean discriminant functions. The model also predicts during development the quality that will be delivered to maintenance by using both point and confidence interval estimates of quality. We show that it is important to perform a marginal analysis when making a decision about how many metrics to include in a discriminant function. If many metrics are added at once, the contribution of individual metrics is obscured. Also, the marginal analysis provides an effective rule for deciding when to stop adding metrics. We also show that certain metrics are dominant in their effects on classifying quality and that additional metrics are not needed to increase the accuracy of classification. Related to this property of dominance is the property of concordance, which is the degree to which a set of metrics produces the same result in classifying software quality. A high value of concordance implies that additional metrics will not make a significant contribution to accurately classifying quality; hence, these metrics are redundant. Data from the Space Shuttle flight software are used to illustrate the model process.