Quantification and relative comparison of different types of uncertainties in sewer water quality modeling

Quantifiable sources of uncertainty have been identified for a case study of integrated modeling of a sewer system with a more downstream wastewater treatment plant and storage sedimentation tank. The different sources were classified in model input and model-structure-related uncertainties. They were quantified and propagated towards the uncertainty in the event-based prediction of sewer emissions (flow, and physico-chemical water quality concentrations and loads). Based on the concept of variance decomposition, the total prediction uncertainty was split into the contributions of the various uncertainty sources and the different submodels. Although the results strongly depend on the water quality variable considered, it is in most general terms concluded that the uncertainty contribution by the water quality submodels is an order of magnitude higher than that for the flow submodels. Future model improvement should therefore mainly focus on water quality data collection, which would reduce current problems of spurious model calibration and verification, but also of knowledge gaps in in-sewer processes.     

Assessment of the integrated urban water quality model complexity through identifiability analysis

Urban sources of water pollution have often been cited as the primary cause of poor water quality in receiving water bodies (RWB), and recently many studies have been conducted to investigate both continuous sources, such as wastewater-treatment plant (WWTP) effluents, and intermittent sources, such as combined sewer overflows (CSOs). An urban drainage system must be considered jointly, i.e., by means of an integrated approach. However, although the benefits of an integrated approach have been widely demonstrated, several aspects have prevented its wide application, such as the scarcity of field data for not only the input and output variables but also parameters that govern intermediate stages of the system, which are useful for robust calibration. These factors, along with the high complexity level of the currently adopted approaches, introduce uncertainties in the modelling process that are not always identifiable. In this study, the identifiability analysis was applied to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two WWTPs and has a small river as the RWB. The system was simulated by employing an integrated model developed in previous studies. The main goal of the study was to assess the right number of parameters that can be estimated on the basis of data-source availability. A preliminary sensitivity analysis was undertaken to reduce the model parameters to the most sensitive ones. Subsequently, the identifiability analysis was carried out by progressively considering new data sources and assessing the added value provided by each of them. In the process, several identifiability methods were compared and some new techniques were proposed for reducing subjectivity of the analysis. The study showed the potential of the identifiability analysis for selecting the most relevant parameters in the model, thus allowing for model simplification, and in assessing the impact of data sources for model reliability, thus guiding the analyst in the design of future monitoring campaigns. Further, the analysis showed some critical points in integrated urban drainage modelling, such as the interaction between water quality processes on the catchment and in the sewer, that can prevent the identifiability of some of the related parameters.     

Implementing integrated and systems approaches to water quality management considering data uncertainty

The paper emphasises the need for a system analysis approach at all scales in a water system considering all elements, subsystems and their interactions. In order to support this process in developing countries, the paper presents best management practices of monitoring and regulation of water resources. Furthermore, it recommends the integration of the modelling, decision and information support tools with the corresponding monitoring practices, regulatory instruments and management activities in a closed-loop cycle. Estimation and implications of monitoring data uncertainties are illustrated by two case study examples based on water quality monitoring data in the Southern African region. One is related to compliance with regulatory instruments and the other to pollution load assessments. Implications of monitoring data characteristics and uncertainty are discussed. Considering the conditions in developing countries, characterised by shortage of resources and lack of data, the application of adaptive management and modelling strategies in the water resources management practice is recommended.     

Applying global sensitivity analysis to the modelling of flow and water quality in sewers

While several approaches for global sensitivity analysis (GSA) have been proposed in literature, only few applications exist in urban drainage modelling. This contribution discusses two GSA methods applied to a sewer flow and sewer water quality model: Standardised Regression Coefficients (SRCs) using Monte-Carlo simulation as well as the Morris Screening method. For selected model variables we evaluate how the sensitivities are influenced by the choice of the rainfall event. The aims are to i) compare both methods concerning the similarity of results and their applicability, ii) discuss the implications for factor fixing (identifying non-influential parameters) and factor prioritisation (identifying important parameters) and iii) rank the important parameters for the investigated model. It was shown that both methods lead to similar results for the hydraulic model. Parameter interactions and non-linearity were identified for the water quality model and the parameter ranking differs between the methods. For the investigated model the results allow a sound choice of output variables and rainfall events in view of detailed uncertainty analysis or model calibration. We advocate the simultaneous use of both methods for a first model assessment as they allow answering both factor fixing and factor prioritisation at low computational cost.     

The impact of new developments on river water quality from an integrated system modelling perspective

New housing areas are a ubiquitous feature of modern life in the developing and developed world alike built in response to rising social, demographic and economic pressures. Inevitably, these new developments will have an impact on the environment around them. Empirical evidence confirms the close relationship between urbanisation and ambient water quality. However, what is lacking so far is a detailed and more generalised analysis of environmental impact at a relatively small scale. The aim of this paper is to quantify the impact of new developments on river water quality within an integrated system modelling perspective. To conduct the impact analyses, an existing integrated urban wastewater model was used to predict water flow and quality in the sewer system, treatment plant and receiving water body. The impact on combined sewer overflow (CSO) discharges, treatment plant effluent, and within the river at various reaches is analysed by ‘locating’ a new development on a semi-hypothetical urban catchment. River water quality is used as feedback to constrain the scale of the new development within different thresholds in compliance with water quality standards. Further, the regional sensitivity analysis (RSA) method is applied to reveal the parameters with the greatest impact on water quality. These analyses will help to inform town planners and water specialists who advise them, how to minimise the impact of such developments given the specific context.     

Assessment of water quality management with a systematic qualitative uncertainty analysis

Uncertainty is an inevitable source of noise in water quality management and will weaken the adequacy of decisions. Uncertainty is derived from imperfect information, natural variability, and knowledge-based inconsistency. To make better decisions, it is necessary to reduce uncertainty. Conventional uncertainty analyses have focused on quantifying the uncertainty of parameters and variables in a probabilistic framework. However, the foundational properties and basic constraints might influence the entire system more than the quantifiable elements and have to be considered in initial analysis steps. According to binary classification, uncertainty includes quantitative uncertainty and non-quantitative uncertainty, which is also called qualitative uncertainty. Qualitative uncertainty originates from human subjective and biased beliefs. This study provides an understanding of qualitative uncertainty in terms of its conceptual definitions and practical applications. A systematic process of qualitative uncertainty analysis is developed for assisting complete uncertainty analysis, in which a qualitative network could then be built with qualitative relationship and quantifiable functions. In the proposed framework, a knowledge elicitation procedure is required to identify influential factors and their interrelationship. To limit biased information, a checklist is helpful to construct the qualitative network. The checklist helps one to ponder arbitrary assumptions that have often been taken for granted and may yield an incomplete or inappropriate decision analysis. The total maximum daily loads (TMDL) program is used as a surrogate for water quality management in this study. 15 uncertainty causes of TMDL programs are elicited by reviewing an influence diagram, and a checklist is formed with tabular interrogations corresponding to each uncertainty cause. The checklist enables decision makers to gain insight on the uncertainty level of the system at early steps as a convenient tool to review the adequacy of a TMDL program. Following the instruction of the checklist, an appropriate algorithm in a form of probability, possibility, or belief may then be assigned for the network. Consequently, the risk or evidence of the success of outcomes will be obtained. The incorporation of the systematic consideration of qualitative uncertainty into water quality management is expected to refine the decision-making process.     

Estimating the effect of water quality on surface water supplies

River quality models derived by regression analysis are often quick and cheap to produce. They can be used as a basis for forecasting river quality and to calculate the effects of water quality constraints on the operation of water supplies drawn from rivers and impoundments. Results for nitrate show that to operate intakes according to river quality is a poor method of controlling quality in impoundments.     

An inexact chance-constrained programming model for water quality management in Binhai New Area of Tianjin, China

In this study, an inexact-chance-constrained water quality management (ICC-WQM) model is developed for planning regional environmental management under uncertainty. This method is based on an integration of interval linear programming (ILP) and chance-constrained programming (CCP) techniques. ICC-WQM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. Complexities in environmental management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method is applied to planning chemical-industry development in Binhai New Area of Tianjin, China. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various system-reliability constraints of water environmental capacity of pollutant. Tradeoffs between system benefits and constraint-violation risks can also be tackled. They are helpful for supporting (a) decision of wastewater discharge and government investment, (b) formulation of local policies regarding water consumption, economic development and industry structure, and (c) analysis of interactions among economic benefits, system reliability and pollutant discharges.     

Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis

The water quality in a distribution system is affected by many factors, including operational and environmental conditions as well as the condition in and around the distribution network. Lack of reliable data as well as knowledge gaps with respect to the impact of these factors on water quality make the quantification of water quality failure risk very challenging. Furthermore, the variability inherent in (sometimes) thousands of kilometers of distribution pipes presents added complexities. Major modes of water quality failures can be classified into intrusion of contaminants, regrowth of bacteria (biofilm), water treatment breakthrough, leaching of chemicals or corrosion products from system components, and permeation of organic compounds through plastic pipes. Deliberate contamination and negligence of operators have in recent years become an added concern. In earlier works by Sadiq et al. (2004 Sadiq, R., Kleiner, Y. and Rajani, B. 2004. Aggregative risk analysis for water quality failure in distribution networks. Journal of Water Supply Research and Technology: Aqua, 53(4): 241–261. [Web of Science ®] , [Google Scholar], 2007 Sadiq, R., Kleiner, Y. and Rajani, B. 2007. Water quality failures in distribution networks – risk analysis using fuzzy logic and evidential reasoning. Risk Analysis – An International Journal, 27(5): 1381–1394. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), an aggregative risk analysis approach using hierarchical structure was proposed to describe all possible mechanisms of contamination. In this paper a similar structure is used as a basis for a fault-tree approach. While fault-tree analysis is widely used for many engineering applications, in this paper we specifically explore how interdependencies among factors might impact analysis results. Two types of uncertainties are considered in the proposed analysis. The first is related to the likelihood of risk events, and the second is related to non-linear dependencies among risk events. Each basic risk event (input factor) is defined using a fuzzy probability (likelihood) to deal with its inherent uncertainty. The dependencies among risk events are explored using Frank copula and Frechet's limit. The proposed approach is demonstrated using two well-documented episodes of water quality failures in Canada, namely, Walkerton (ON) and North Battleford (SK).     

An optimization model for water quantity and quality integrated management of an urban lake in a water deficient city

This paper presents an optimization model for water quantity and quality integrated management of an urban lake in a water deficient city. A representative water quantity and quality safeguard system served urban lake, including multi-source water supply facilities, recirculating water purification facilities and surplus water discharge facilities, is widely used in Chinese water deficient cities. Because it is complicated, any mismanagement will result in water quality deterioration, water waste and high operation cost. The presented model attempts to achieve the objectives of controlling water pollution, reducing economic cost and improving water utilization efficiency through an optimized operating water safeguard system. The model is applied to Qingjing Lake in Tianjin, China. Results show that the model plays a more positive role for water quantity and quality integrated management.     

Calibration and validation of a dynamic model for water quality in combined sewer retention tanks

As the integrated management of urban wastewater systems becomes more and more popular, the development of wastewater management subsystem models appears essential to improve the understanding of the pollutant dynamics and their interactions. In such a context, a review of the literature reveals a lack of efficient models describing the dynamics of the water quality stored in off-line retention tanks. A model has thus been proposed based on the fractionation of suspended solids into three classes according to the particle settling velocity distribution measured in the field using the ViCAs settling test. In this paper, a calibration methodology is developed and full-scale field data sets from three different events are used for 1) calibrating this new dynamic retention tank model (two data sets); and 2) validating that model on the last data set. The results show a good agreement between observed and simulated data both for the total suspended solids and the total chemical oxygen demand.     

Assessment of the surface water quality in Northern Greece

The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is presented in this study. The dataset consists of analytical results from a 3-yr survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches. Twenty-seven parameters have been monitored on 25 key sampling sites on monthly basis (total of 22,350 observations). The dataset was treated using cluster analysis (CA), principal component analysis and multiple regression analysis on principal components. CA showed four different groups of similarity between the sampling sites reflecting the different physicochemical characteristics and pollution levels of the studied water systems. Six latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.     

A review of catchment management in the new context of drinking water safety plans

By managing a catchment effectively, the range and amount of contaminants entering waters used for public water supply can be reduced, with resultant benefits for both water companies and consumers. Consequently, catchment management is increasingly being recognised as being at the heart of the water environment. An overview of current and previous catchment management initiatives is given, along with the European Landscape Convention, which could be used as a vehicle for the implementation of a national catchment management strategy. The adoption of the drinking water safety plan approach is strongly advocated by the World Health Organisation, and effective catchment management underpins this approach. Effective management of our catchments will also make a significant contribution to meeting our obligations under the Water Framework Directive, by helping to address the serious issue of diffuse pollution from agriculture.     

Influence of water and sediment quality on benthic biota in an acidified river

Water and sediment quality and benthic biota were investigated in all seasons during three years in the River Akagawa that receives the effluent from a mine drainage treatment plant at its upstream site. The upper reaches kept the low pH, the comparatively high concentrations of metals and a large amount of iron deposited on the riverbed. The predominant macroinvertebrates were Protonemura sp., Capnidae, Nemoura sp. and Chironomidae in the upper and middle reaches. In the lowest reaches, the community structure of the macroinvertebrate changed into Chironomidae, Trichoptera (Hydropsychidae) and Ephemeroptera (Baetis sp.) as the pH was increased. From the results of multivariate analyses, it was found that the restoration of pH and attached algae and the increase in the concentrations of nutrients and organic matter promoted the inhabitation of Chironomidae and Hydropsychidae, whereas the dissolved metals in the river water inhibited the inhabitation of these families. Moreover, the sedimentation of metals would cause a severe damage to the inhabitation of Hydropsychidae compared with that of Chironomidae.     

采用BP神经网络模型对东北地区水库水水质的预测研究

采用BP神经网络建立了东北地区某水库水的浊度预测模型,同时针对该水库的地域特点,重点研究了冰封期对水源水水质预测的影响。结果表明,以浊度为预测对象,将水库的水质数据按照是否进入冰封期进行划分后建模,预测效果比划分之前有较明显的提高。     

River water quality of the River Cherwell: an agricultural clay-dominated catchment in the upper Thames Basin, southeastern England

The water quality of the River Cherwell and a tributary of it, the Ray, are described in terms of point and diffuse sources of pollution, for this rural area of the upper Thames Basin. Point sources of pollution dominate at the critical ecological low flow periods of high biological activity. Although the surface geology is predominantly clay, base flow is partly supplied from springs in underlying carbonate-bearing strata, which influences the water quality particularly with regards to calcium and alkalinity. The hydrogeochemistry of the river is outlined and the overall importance of urban point sources even in what would normally be considered to be rural catchments is stressed in relation to the European Unions Water Framework Directive. Issues of phosphorus stripping at sewage treatment works are also considered: such stripping on the Cherwell has reduced phosphorus concentrations by about a factor of two, but this is insufficient for the needs of the Water Framework Directive.     

新陈代谢GM（1,1）模型在河流水质预测中的应用

针对常规GM(1,1)模型存在的不足,运用灰色系统理论,建立了灰色新陈代谢GM(1,1)河流水质预测模型,对该模型的精度以及误差进行了分析,并利用该模型对某地区河流的水质进行了预测,预测结果显示:灰色新陈代谢GM(1,1)预测模型能够明显地提高预测精度,增加预测的可信度。