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.     

Development of a Model for Simulation of Solute Transport in a Stream–Aquifer System

A model for simulation of solute transport in a dynamic stream–aquifer system is developed by integrating four existing sub-models. Interface packages are created to link the sub-models. The developed model is successfully used to simulate chloride transport in the stream–aquifer system of the Arkansas River and the Equus Beds Aquifer in Kansas and demonstrates that chloride concentration in the aquifer decreases in the vicinity of the simulated channel over time.     

Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

Traditionally, environmental decision analysis in subsurface contamination scenarios is performed using cost–benefit analysis. In this paper, we discuss some of the limitations associated with cost–benefit analysis, especially its definition of risk, its definition of cost of risk, and its poor ability to communicate risk-related information. This paper presents an integrated approach for management of contaminated ground water resources using health risk assessment and economic analysis through a multi-criteria decision analysis framework. The methodology introduces several important concepts and definitions in decision analysis related to subsurface contamination. These are the trade-off between population risk and individual risk, the trade-off between the residual risk and the cost of risk reduction, and cost-effectiveness as a justification for remediation. The proposed decision analysis framework integrates probabilistic health risk assessment into a comprehensive, yet simple, cost-based multi-criteria decision analysis framework. The methodology focuses on developing decision criteria that provide insight into the common questions of the decision-maker that involve a number of remedial alternatives. The paper then explores three potential approaches for alternative ranking, a structured explicit decision analysis, a heuristic approach of importance of the order of criteria, and a fuzzy logic approach based on fuzzy dominance and similarity analysis. Using formal alternative ranking procedures, the methodology seeks to present a structured decision analysis framework that can be applied consistently across many different and complex remediation settings. A simple numerical example is presented to demonstrate the proposed methodology. The results showed the importance of using an integrated approach for decision-making considering both costs and risks. Future work should focus on the application of the methodology to a variety of complex field conditions to better evaluate the proposed methodology.     

A Comparison of the Precipitation Chemistry Measurements Obtained by the CAPMoN and NADP/NTN Networks

Precipitation chemistry measurementsobtained by the Canadian Air and PrecipitationMonitoring Network (CAPMoN) and the U.S. NationalAtmospheric Deposition Program/National Trends Network(NADP/NTN) have been examined using more than 7 yrof collocated data from two sites, namely, Sutton,Quebec, Canada and State College, Pennsylvania, U.S.A.In the case of the CAPMoN data, weeklyprecipitation-weighted mean concentrations, totalsample depths and total standard gauge depths werecomputed from daily data and compared to thecorresponding weekly sampling data of the NADP/NTNnetwork. Seasonal and annual precipitation-weightedmean concentrations and deposition values were alsocomputed for both networks and compared. Statisticallysignificant between-network biases were found to existin the weekly results for most of the measuredvariables, particularly standard gauge depth, sampledepth, pH, H⁺, NO₃^-,NH₄⁺,Na⁺; the NADP/NTN values were consistently lowerthan those of CAPMoN with the exception of pH andNa⁺. The magnitude of the biases was less than35% of the median CAPMoN weekly value for the 7 yr. For most of the measured parameters, thevariability of the between-network differencesrepresented less than 20% of the median CAPMoN weeklyvalue. Both the between-network biases andvariabilities were functions of several physicalparameters, the most dominant being the sample depthand the ionic concentration. For seasonal and annualdeposition values, statistically significantbetween-network biases were found for H⁺,SO₄^2-, NO₃^-,Ca²⁺,NH₄⁺ for both periods; for Mg²⁺ andK⁺ for seasonal data; and Cl^- for yearlydata, with the NADP/NTN deposition values being lowerthan those of CAPMoN. The relative biases ranged from7 to 37%. Part of the between-network bias in thedeposition estimates was directly attributable to astrong bias in the standard gauge depths of the two networks.     

A water quality monitoring network design methodology for the selection of critical sampling points: Part II

In order to resolve the spatial component of the design of a water quality monitoring network, a methodology has been developed to identify the critical sampling locations within a watershed. This methodology, called Critical Sampling Points (CSP), focuses on the contaminant total phosphorus (TP), and is applicable to small, predominantly agricultural-forested watersheds. The CSP methodology was translated into a model, called Water Quality Monitoring Station Analysis (WQMSA). It incorporates a geographic information system (GIS) for spatial analysis and data manipulation purposes, a hydrologic/water quality simulation model for estimating TP loads, and an artificial intelligence technology for improved input data representation. The model input data include a number of hydrologic, topographic, soils, vegetative, and land use factors. The model also includes an economic and logistics component. The validity of the CSP methodology was tested on a small experimental Pennsylvanian watershed, for which TP data from a number of single storm events were available for various sampling points within the watershed. A comparison of the ratios of observed to predicted TP loads between sampling points revealed that the model's results were promising.     

辽河流域水环境监测网络优化技术研究

以辽河流域2009—2011年监测数据为基础,针对北方季节性河流,在制定了水环境监测网络优化原则的前提下,分别运用最优分割法、变异系数与水质类别相结合方法、连续3年未检出判断法进行了辽河流域水环境质量监测断面、监测频次和监测项目的优化。结果表明,辽河流域优化后的监测断面为18个,比优化前减少8个。在监测频次上,干旱少雨的月份中,1、5、10月应保持每个月2次的监测频次,2—4月、11—12月每月监测1次;湿润多雨的6—9月只监测1~2次即可,全年监测频次分布更合理。监测项目分为必测项目和选测项目,必测项目为12~20项,选测项目为3~11项。     

典型产业集聚区地下水优先控制污染物筛选

为明确地下水污染高风险区环境管控目标,快速识别优先控制污染物,于2022年以典型产业集聚区为研究对象,利用污染源产排污状况和地下水基础环境状况调查结果,将污染评价和环境风险相结合,优选污染源超标率、地下水环境污染等级、生物毒性参数和环境迁移转化参数作为筛选因子,利用层次分析法和综合分析法,计算各污染指标综合得分,建立筛选地下水优先控制污染物的方法体系。筛选结果显示,该产业集聚区的优先控制污染物相关指标包括：硝酸盐、耗氧量、石油类、总硬度、亚硝酸盐、镍、锌、硫酸盐、甲苯、氨氮等。筛选结果与园区主导行业产排污特征和地下水质量现状相符,具有一定的可靠性,可为科学、精准管控高风险区地下水质量提供依据。     

A Water Quality Monitoring Network Design Methodology for the Selection of Critical Sampling Points: Part I

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.     

对《水质急性毒性的测定发光细菌法》的改进和探讨

以国标（GB/T 15441—1995）方法中推荐使用的明亮发光杆菌T₃小种（Photobacterium phosphoreum T₃ spp.）为测试生物.针对《水质 急性毒性的测定 发光细菌法》（GB/T 15441—1995）,从数据处理、参比毒物和测定方法3个角度,对国标方法进行了改进。应用Origin 7.5程序对测试数据进行拟合,并选择出拟合效果较为理想的Sigmoidal函数与国标中使用的线性函数进行了拟合对比。针对国标中使用的剧毒性物质HgCl₂,选择了毒性较低的ZnSO₄ ·7H₂O作为替代参比物,并对2种金属离子的毒性测试数据进行了比较。因为使用的生物毒性测定仪的国标方法要求严格的操作并且步骤繁琐,选择利用酶标仪进行测定,并对2种方法进行了对比研究,验证了酶标仪使用的可行性。     

An Analysis of the Daily Radial Activity of 7 Boreal Tree Species, Northwestern Quebec

In the `Des Vieux Arbres' ecological reserve situated within northwestern Québec, 40 band dendrometers were installedon 7 of the major boreal tree species. The late Spring–early Summer daily radial activity registered in 1997 was related todaily weather variables. For each tree species, the daily mean i) cumulative radial increment and ii) radial activity indexedseries obtained by first-difference standardization were analyzed. The results indicate the existence of strong similarities among the 7 species. All showed strong synchronousfluctuations in radius during late winter and early spring. Thisperiod ended with a short but sharp increase in radial increments that marked the passage of water into the stem. Thisinitial swelling, less obvious in Pinus species was followed by a prolonged period of little change in radial activity. Meteorological data indicated that air temperature waspositively related to stem swelling during the late winter–earlyspring period. Both air and soil temperatures became negatively related to radial expansion once the passage of water has occurred in the stem. Starting in early June, all species registered a sustained increase in radial increments possiblyassociated with active cell division. After this, radial expansion was negatively related to air temperature and positively to rainfall.     

重氮偶联法测定水中亚硝酸盐氮的改进

重氮偶联法作为测定亚硝酸盐氮的标准方法,因具有方法灵敏、选择性强、仪器较易获得等优点,被实验室广泛采用,但也存在测定上限较低,适用样品范围较窄等缺点。通过将显色剂中磷酸改为盐酸,把重氮试剂含量提高到国标法的3倍,明显提高亚硝酸盐氮的测定上限,即由原国标法上限的0.2 mg/L提高到0.6 mg/L,灵敏度与国标法接近,且准确度和精密度良好,6组标准溶液平行测定的相对误差为1.60%,相对标准偏差为1.34%,加标回收率为96.5%~104.9%,适用于生活污水、工业废水、地表水、养殖废水等水体的测定。     

海南省省控土壤环境监测基础点位布设思路与方法

为满足新时期土壤污染防治管理要求,掌握海南省土壤环境质量状况及变化趋势,海南省亟需加强省控土壤环境监测基础点位布设,完善土壤环境监测网。通过梳理海南省土壤环境监测工作基础和监测网络构建情况,形成了省控基础点位布设思路、原则和方法,并应用GIS技术完成点位布设。海南省省控土壤环境监测基础点位基本覆盖了海南省农用地主要土壤类型和成土母岩,监测结果可以反映海南省农用地土壤环境质量整体状况。     

分光光度法测定水中硼技术性因素的探讨

通过不同的分光光度法对水中硼元素的实际测定,进而对分光光度法测定水中硼的技术性因素进行了深入的分析和论述,并根据误差的形成原因提出了相关建议.     

水中单质磷测定方法的改进

探讨了《水质单质磷的测定磷钼蓝分光光度法(暂行)》(HJ593-2010)中存在的问题,并对方法进行了改进.确定测定目标组分为单质磷中的黄磷;改用酸性高锰酸钾氧化体系,去除了有机磷农药的干扰;改用抗坏血酸为还原剂,采用710 nm比色波长,提高了分析灵敏度;用10 mL甲苯一次萃取,对水中黄磷的检出限为0.002 mg/L.     

Multi N/C2100S总有机碳分析仪校准方法的建立

以德国Analytikjena公司Multi N/C 2100S分析仪为例,研究建立了合理可行的总有机碳分析仪校准方法.在总有机碳分析仪检定规程JJG 821-2005的基础上,增加零点漂移、直线性、标准曲线线性和检出限4项特性指标,改变了准确性特性的判定方法.根据仪器的使用频率和稳定性,建议校准周期不超过2年.     

加强环境监测点位管理的思考——以江苏省“十二五”环境监测网络调整为例

基于江苏省"十二五"环境质量监测网络优化调整工作,指出监测点位设置和管理工作中存在的问题,讨论了点位优化调整的原则和思路,总结监测网络调整结果,并开展点位优化调整的成效分析,提出进一步优化监测网络设置的建议。     

空气自动监测网络数据审核自动化实现的创新与实践

阐述了在城市空气质量自动监测网络的质量保证管理中,使用计算机数据审核系统对监测数据进行自动审核的设计思想及其采取的创新手段;详细介绍了该系统在山东省的使用情况。     

分光光度法测定水中六价铬的质量控制指标研究

通过国内多家实验室测定的大量数据,研究了分光光度法测定水中六价铬的质量控制指标,旨在为环境监测工作提供质量控制指标和依据.研究表明:样品质量浓度在0.04 ～0.5 mg/L范围内,标准样品室内相对标准偏差(RSD)≤4％,室间相对标准偏差(RSD′)≤15％,相对误差(RE)≤±10％(低浓度可适当放宽).实际样品质...     

不同水质评价方法在丹江口流域水质评价中应用比较

采用合理的水质评价方法,准确地描述河流水质状况,才能为水质管理提供治理方案。以丹江口水源地为例,选用单因子指数评价方法、综合污染指数评价方法、模糊综合评判法、主成分分析法、水污染指数法对丹江口水源地7个河流断面进行水质评价并对比评价效果。结果表明,水污染指数法操作简便、评价结果直观明了,具有广泛的应用范围。     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.