Critical Sampling Points Methodology: Case Studies of Geographically Diverse Watersheds

Only with a properly designed water quality monitoring network can data be collected that can lead to accurate information extraction. One of the main components of water quality monitoring network design is the allocation of sampling locations. For this purpose, a design methodology, called critical sampling points (CSP), has been developed for the determination of the critical sampling locations in small, rural watersheds with regard to total phosphorus (TP) load pollution. It considers hydrologic, topographic, soil, vegetative, and land use factors. The objective of the monitoring network design in this methodology is to identify the stream locations which receive the greatest TP loads from the upstream portions of a watershed. The CSP methodology has been translated into a model, called water quality monitoring station analysis (WQMSA), which integrates a geographic information system (GIS) for the handling of the spatial aspect of the data, a hydrologic/water quality simulation model for TP load estimation, and fuzzy logic for improved input data representation. In addition, the methodology was purposely designed to be useful in diverse rural watersheds, independent of geographic location. Three watershed case studies in Pennsylvania, Amazonian Ecuador, and central Chile were examined. Each case study offered a different degree of data availability. It was demonstrated that the developed methodology could be successfully used in all three case studies. The case studies suggest that the CSP methodology, in form of the WQMSA model, has potential in applications world-wide.     

Feasibility of On-line Measurement of Sewage Components Using the UV Absorbance and the Neural Network

The objective of this study was to test the feasibility of a new method to improve the accuracy in the estimation of sewage components. Adding to the regression of sewage components with UV (ultraviolet) absorbance values, a proposed method considered an unclear but existing relationship among characteristic of sewage production. Sewage production showed very defined profiles due to the daily human activities. So the main idea was the combination of measuring the UV absorbance values and analyzing the characteristics of the sewage production. For this purpose, 446 sewage samples taken at every 2-h interval for 51 days at a wastewater treatment plant were statistically analyzed using neural network (NN). NN was trained with 350 data sets (about 29 days) of UV absorbance values, flow rate and time. And as a result, it could predict 96 data (12 days) as a validation, indicating that estimation accuracies were improved to higher level than those of the linear regressions. The proposed method could estimate concentrations of total nitrogen (TN) and total phosphate (TP) within practical accuracies as well as total suspended solid.     

Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN’s structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (μ) that represents the level of aspiration achieved in one objective and the conjugate of μ, (1 − μ), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN⁻, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying μ (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN⁻ and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.     

基于L-M神经网络的道路交通噪声预测研究

神经网络具有很强的预测功能.根据石家庄公路交通噪声的实测数据,利用L-M优化算法的多层神经网络预测模型进行道路交通噪声的预测,经检验,计算值与实测值接近,预测精度令人满意.     

无线传感器网络在环境监测中的应用

介绍了一种基于无线传感器网络的环境监测系统组成及架构,讨论了系统网络节点、网关的硬件设计,并对基于TinyOS的应用程序组件结构和节点工作流程做了说明。     

基于故障树的生物质气化中毒事故BN-LOPA分析

为降低生物质气化中毒事故的概率,本文提出基于故障树的贝叶斯—保护层复合分析法(BN-LOPA),对生物质气化中毒事故进行风险分析。以辽宁省某气化站为例,通过贝叶斯网络计算出该气化站中毒事故发生概率为1.11×10^-4,基于贝叶斯网络推导出中毒事故基本事件的后验概率,得到导致事故发生的故障节点,并对其设置独立防护层,经推导计算,该气化站的气化中毒事故发生概率可降低至5.35×10^-6,风险等级从7级降至5级。结果表明:该气化站增加独立防护层后,符合安全生产的要求。     

强化环境质量监测 推进监测事业科学发展

从强化环境质量监测、评价与研究的重要性角度，简述了构建完善的由纵向网、立体网、横向网等3大网络体系构成的环境质量监测预警体系的必要性，提出该体系的构建需尽快完善法律、人才、经费、技术等4大长效保障机制。