A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Chironomidae and Oligochaeta for water quality evaluation in an urban river in southeastern Brazil

Considering the importance of benthic macroinvertebrates for diagnosis of variations in the ecological conditions of aquatic habitats, the aim of this study was to investigate the structure of the Chironomidae and Oligochaeta assemblages along an organic pollution gradient. The fauna specimens were obtained with the use of artificial substrates, and the environmental variables were recorded at five sites of the São Lourenço River, during 12 months. Metrics of the assemblage and detrended correspondence analysis were used to verify the response of the fauna to the pollution gradient. Procrustes analysis was used to verify whether the data on the Chironomidae and Oligochaeta assemblages, as well as the taxonomic and numerical resolution of these groups, provide similar results in relation to the pollution gradient. The richness, evenness, and taxonomic composition of the Chironomidae and Oligochaeta assemblages varied significantly among the collection sites, with distinct conservation conditions. Genera of the subfamilies Orthocladiinae and Tanypodinae were associated with the sites upstream of the urban area, where the dissolved oxygen levels are higher. Species of Oligochaeta and the genus Chironomus were associated with more organically polluted sites. No concordance was observed in the response of the Chironomidae and Oligochaeta assemblages in relation to the environmental variables, indicating the need to use both groups in biomonitoring studies. On the other hand, both the data on composition (presence or absence) and those on the lowest taxonomic resolution (abundance of subfamilies) were effective to diagnose the pollution gradient in the river studied. Therefore, when the environmental conditions along a river’s gradient are contrasting, we suggest the use of the lowest taxonomic resolution of Chironomidae and Oligochaeta in biomonitoring. That procedure considerably reduces the assessment time, besides being a method that can be used by people not specializing in the taxonomy of groups.     

A multi-variate methodology for analyzing pre-existing lake water quality data

Environmental agencies are given the task of monitoring water quality in rivers, lakes, and other bodies of water, for the purpose of comparing the results with regulatory standards. Monitoring follows requirements set by regulations, and data are collected in a systematic way for the intended purpose. Monitoring enables agencies to determine whether water bodies are polluted. Much effort is spent per monitoring event, resulting in hundreds of data points typically used solely for comparison with regulatory standards and then stored for little further use. This paper devises a data analysis methodology that can make use of the pre-existing datasets to extract more useful information on water quality trends, without new sample collection and analysis. In this paper, measured lake water quality data are subjected to statistical analyses including Principal Component Analysis (PCA) to deduce changes in water quality spatially and temporally over several years. It was found that the lake as a whole changed temporally by season, rather than spatially. Storm events caused the greatest shifts in water quality, though the shifts were fairly consistent across sampling stations. This methodology can be applied to similar datasets, especially with the recent emphasis by the U.S. EPA on protection of lakes as water sources. Water quality managers using these techniques may be able to lower their monitoring costs by eliminating redundant water quality parameters found in this analysis.     

Quantitative assessment of eutrophication: A scoring system for characterising water quality in coastal marine ecosystems

A scoring system based on nutrient concentrations was developed to assess coastal water quality according to the trophic level. Three nutrient data sets from eutrophic, mesotrophic and oligotrophic waters were used as the reference information for setting up a semi quantitative water quality scale (from 0 to 5) to express different nutrient loadings. The validity and sensitivity of the method was applied to a number of stations spaced out along the coastal area of Rhodes (Greece). A score for each nutrient/sampling site was calculated and the scorecard formed, was the data matrix used for numerical classification of the stations. The results showed (a) good discrimination between cutrophic, mesotrophic and oligotrophic waters (b) nitrate among the nutrients showed the maximum sensitivity in characterising pollution levels. The reference data sets used for assessing eutrophication levels ensured the objectivity of the method. The proposed method is described step-by-step and it is suggested that the method can be further adapted to describe other forms of pollution becoming a useful quantitative technique in coastal management practices.     

河流水质预测模型的应用研究

河流水质预测模型使用的参数未引入预测断面现状监测结果 ,使预测结果可能与现状监测结果发生矛盾。针对这一情况 ,通过对预测模式的推导转化 ,在模型中引入污染源现状源强和预测断面现状监测浓度 ,将预测结果同现状监测结果联系起来 ,使预测结果更贴近实际     

The Niagara River: A water quality management overview

The Niagara River constitutes part of the Laurentian Great Lakes and St. Lawrence River system which represents approximately 80% of North America's supply of surface fresh water. The river is a major source of water for industry, municipalities, recreation and power generation and is the link between Lakes Erie and Ontario. The river forms part of the Canada-U.S. border and falls under the jurisdiction of both countries.The massive industrialization of the region surrounding the river has led to a typical resource use conflict situation in which pollution of the river continues to be a major public concern.A number of constitutional, institutional and jurisdictional factors make the management of the Niagara River an involved and complicated matter. The interests, intent, philosophies, laws and regulations are not necessarily the same among the numerous jurisdiction involved. Despite these differences, however, Canada and the United States have succeeded in developing and implementing a model cooperative international management plan for the river. An overview of the main international aspects relating to the development and implementation of this plan, the Niagara River Toxics Management Plan, is presented.     

River water quality management under incomplete information: application of an N-person iterated signaling game

One of the important issues in river quality management is to provide pollution control strategies which are acceptable for all stakeholders. When there is only one water quality checkpoint in a reach of a river which receives pollution loads of several dischargers and dischargers are penalized for any water quality violation, the game theory can be used for modeling the natural process of bargaining among load dischargers considering the assimilative capacity of a river. There are also some types of uncertainties in river water quality management which should be incorporated throughout the bargaining process. Signaling games can be utilized for modeling the bargaining among dischargers and developing perfect Bayesian equilibrium (PBE) strategies for pollution control. In this paper, a new methodology called N-person iterated signaling game is developed for river quality management considering the existing uncertainties in pollution loads of dischargers. The methodology can provide the stable PBE waste load allocation strategies. The practical utility of the proposed methodology is illustrated by applying it to a reach of the Zarjub River in Iran. This reach includes seven pollution load dischargers.     

Efficiency of a standardized artificial substrate for biological monitoring of river water quality

Before using macroinvertebrates in water quality assessment in the Chusovaya River (Russia, the Urals, 50°55N, 60° E), preliminary results of three sampling methods were compared: handnet, circular shovel and a standardized artificial substrate sampler. The artificial substrate consisted of glass marbles ( 20 mm). To compare the efficiency of these sampling methods the total numbers of taxa found at each location per sampling data were considered to be 100%. The highest efficiency was reached with the artificial substrate sampler. 75–100% of the taxa at the different locations were collected with this sampler. Only 5–19% and 10–20% of the taxa at each location per sampling date were collected with the circular shovel in the sand and gravel substrate respectively, being the lowest efficiency. Intermediate results were obtained with the hand net. 23–38% of the taxa were collected with this net. Based on these results and requirements placed upon sampling methods in general, the standardized artificial substrate sampler has been considered to be an optimal sampling device for macroinvertebrates in biological monitoring.     

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.     

Coupling upland watershed and downstream waterbody hydrodynamic and water quality models (SWAT and CE-QUAL-W2) for better water resources management in complex river basins

Effective water resources management programs have always incorporated detailed analyses of hydrological and water quality processes in the upland watershed and downstream waterbody. We have integrated two powerful hydrological and water quality models (SWAT and CE-QUAL-W2) to simulate the combined processes of water quantity and quality both in the upland watershed and downstream waterbody. Whereas the SWAT model outputs water quality variables in its entirety, the CE-QUAL-W2 model requires inputs in various pools of organic matter contents. An intermediate program was developed to extract outputs from SWAT at required subbasin and reach outlets and converts them into acceptable CE-QUAL-W2 inputs. The CE-QUAL-W2 model was later calibrated for various hydrodynamic and water quality simulations in the Cedar Creek Reservoir, TX, USA. The results indicate that the two models are compatible and can be used to assess and manage water resources in complex watersheds comprised of upland watershed and downstream waterbodies.     

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

Evaluation of water quality index for drinking purposes for river Netravathi, Mangalore, South India

An attempt has been made to develop water quality index (WQI), using six water quality parameters Dissolved oxygen (DO), Biochemical oxygen Demand (BOD), Most Probable Number (MPN), Turbidity, Total Dissolved Solids (TDS) and pH measured at eight different stations along the river basin. Rating curves were drawn based on the tolerance limits of inland waters and health point of view. Bhargava WQI method and Harmonic Mean WQI method were used to find overall WQI along the stretch of the river basin. Five point rating scale was used to classify water quality in each of the study areas. It was found that the water quality of Netravathi varied from Excellent to Marginal range by Bhargava WQI method and Excellent to Poor range by Harmonic Mean WQI method. It was observed that the impact of human activity was severe on most of the parameters. The MPN values exceeded the tolerable limits at almost all the stations. It was observed that the main cause of deterioration in water quality was due to the lack of proper sanitation, unprotected river sites and high anthropogenic activities.     

Assessing the impacts of water abstractions on river ecosystem services: an eco-hydraulic modelling approach

The provision of important river ecosystem services (ES) is dependent on the flow regime. This requires methods to assess the impacts on ES caused by interventions on rivers that affect flow regime, such as water abstractions. This study proposes a method to i) quantify the provision of a set of river ES, ii) simulate the effects of water abstraction alternatives that differ in location and abstracted flow, and iii) assess the impact of water abstraction alternatives on the selected ES. The method is based on river modelling science, and integrates spatially distributed hydrological, hydraulic and habitat models at different spatial and temporal scales. The method is applied to the hydropeaked upper Noce River (Northern Italy), which is regulated by hydropower operations. We selected locally relevant river ES: habitat suitability for the adult marble trout, white-water rafting suitability, hydroelectricity production from run-of-river (RoR) plants. Our results quantify the seasonality of river ES response variables and their intrinsic non-linearity, which explains why the same abstracted flow can produce different effects on trout habitat and rafting suitability depending on the morphology of the abstracted reach. An economic valuation of the examined river ES suggests that incomes from RoR hydropower plants are of comparable magnitude to touristic revenue losses related to the decrease in rafting suitability.     

The uncertainty effects of design flow on water quality management

Pollen grains in the atmosphere of Sivrihisar were studied for a continuous period of 2 years (1 January 2005–31 December 2006) using a Durham sampler. During this period, pollen grains belonging to 41 taxa were recorded, 24 of which belonged to arboreal plants and 17 to non-arboreal. From these, 23,219 were identified in 2005 and 34,154 in 2006. Of the total pollen grains, 90,46% were arboreal, 9,43% non-arboreal, and 0,1% unidentifiable. The majority of the investigated allergic pollen grains were from Pinaceae, Cupressaceae, Fraxinus spp., Cedrus spp., Artemisia spp., Poaceae, Chenopodiaceae/Amaranthaceae, Populus spp., Quercus spp., Urticaceae and Asteraceae, respectively. Pollen concentrations reached their highest levels in May. This information was then established into a calendar form according to the pollens determined in 2005–2006, in terms of annual, monthly and weekly numbers of taxa fall per cm². A comparison between the results and the meteorological factors revealed a close relationship between pollen concentrations in the air and meteorological conditions. An increase in pollination was also linked to increasing temperatures and the wind. It was therefore concluded that high temperatures and relative humidity were also effective in increasing the number of pollens in the air.     

Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam)

In this study, classification trees were combined with the Water Framework Directive (WFD)-Explorer, a modular toolbox that supports integrated water management in a river basin to evaluate the impact of different restoration measures on river ecology. First, the WFD-Explorer toolbox analysed the effect of different restoration options on the abiotic river characteristics based on the water and substance balance embedded in the simulation environment. Based on these abiotic characteristics, the biological index Biological Monitoring Working Party for Vietnam was then predicted by classification trees that were trained on biological and abiotic data collected in the Du river basin in northern Vietnam. The ecological status of streams in the basin ranged from nearly pristine headwaters to severely impacted river stretches. Elimination of point sources from ore extraction and decentralised domestic wastewater treatment proved to be the most effective measures to improve the ecological condition of the Du river basin. The combination of the WFD-Explorer results with data-driven models enabled model application in a situation where expert knowledge was lacking. Consequently, this approach appeared promising for decision support in the context of river restoration and conservation management.