Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin,Brazil

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.     

Evaluation of surface water quality characteristics by using multivariate statistical techniques: A case study of the Euphrates river basin, Turkey

The surface water quality of the Euphrates river basin in Turkey are evaluated by using the multivariate statistical techniques known as factor analysis (FA) and multidimensional scaling (MDS) analysis. When FA was applied to the water quality data obtained from the 15 different surface water quality monitoring stations, two factors were identified, which were responsible from the 86.02% of the total variance of the water quality in the Euphrates river basin. The first factor called the urban land use factor explained 44.20% of the total variance and the second factor called the agricultural use factor explained 41.81% of the total variance. MDS technique showed that electrical conductivity (EC), percent sodium (Na%) and total salt are the most important variables causing difference in the water quality analysis.     

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering

Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers, along with critical pollutants and locations using an advanced hierarchical cluster and multivariate statistical analysis. A systematic approach has been developed by agglomerating both R-mode and Q-mode analysis, which develops monoplots, two-dimensional biplots, rotated component matrices, and dendrograms (using “SPSS” and “Analyse It” software) to reveal relationships among various quality parameters to identify the pollutant sources along with clustering of critical sampling sites and pollutants. A case study of the Ganges River Basin of India has been considered to demonstrate the efficacy and usefulness of the model by analyzing 85 open drains. Both organic and metallic pollutants are analyzed simultaneously as well as separately to get a holistic understanding of all the relationships and to broaden the perspective of water characterization. Results provide a comprehensive guidance to the policy makers and water managers to optimize corrective efforts, minimize further damage, and improve the water quality condition to ensure sustainable development of the river basin.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

自动监控系统在太湖流域水质变化预警中的作用

以2011年太湖流域水质自动监控系统捕捉到的水质异常数据为基础,对太湖流域水质异常预警的特征进行分析。结果表明,太湖流域水质异常预警具有明显的时空变化特征,水质异常频次较高的断面多位于丹阳、武进、宜兴交界处,且以泥炭河、漕桥河、中干河等河流为主。流域水质异常预警主要发生在枯水期,导致水质异常的主要污染指标以氨氮、总磷为主。典型河流水质预警情况分析表明,流域水质变化具有明显的上下游响应关系。水质变化与水文气象、工厂生产周期等外界因素的关系较明显。     

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.     

Design of a new monitoring network and first testing of new biological assessment methods according to water framework directive

In most European member states, more or less completely new monitoring networks and assessment methods had to be developed as basic technical tools for the implementation of the EU Water Framework Directive (WFD). In the river basin of the Stever, the largest tributary to the river Lippe (River Rhine, Northrhine-Westphalia, Germany), a WFD-monitoring network was developed, and new German biological methods for rivers, developed for the purposes of the WFD, have been applied. Like most rivers in the German lowland areas, nearly all the river courses of the Stever system are altered by hydro-morphological degradation (straightening, bank fixation, lack of canopy etc.). In 2005 and 2006, the biological quality components of macroinvertebrates, fish and macrophytes were investigated and evaluated for the assessment of the ecological status of about 50 surface water bodies within the whole Stever system. Basic physical and chemical parameters, as well as priority substances, have been analysed in the same period. In this contribution, the design of the new monitoring network, the core principles of the German biological methods, and the most important results of the pilot monitoring will be presented. As main impacts with severe effects on the faunal and floral communities, the many migration barriers and the bad quality of the river morphology could be stated. Organic pollution is no more a severe problem in the Stever. The pilot project was successfully conducted in close collaboration with the water authorities (District Government Münster) and the water association Lippeverband.     

Defining chemical status of a temporary Mediterranean River

Although the majority of rivers and streams in the Mediterranean area are temporary, no particular attention is being paid for such systems in the Water Framework Directive (WFD). A typical temporal Mediterranean river, draining an intensively cultivated basin, was assessed for its chemical status. Elevated concentrations of nitrates and salts in river water as well as nutrients and heavy metals in river sediments have been attributed to agricultural land uses and practices and point sources of organic pollution. A scheme for the classification of the river's chemical status (within the ecological quality classification procedure) was applied by combining pollution parameters in groups according to related pressures. In light of the temporal hydrological regime and anthropogenic impacts, sediment chemical quality elements were considered, in addition to hydrochemical ones. Despite the extensive agricultural activities in the basin, the majority of the sites examined showed a good quality and only three of them were classified as moderate. For the classification of the chemical quality of temporary water bodies, there is a need to develop ecologically relevant salinity and sediment quality standards.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

突发性水污染事故预警体系建设综述

随着流域水污染事件的频发,突发性水污染事故预警平台的科学建设成为流域应急管理的重要内容。基于预警平台数据感知层、数据管理层、业务感知层的总体架构,综述了国内外突发性水污染事故预警平台的建设现状。从水质监测技术、突发事故预警模型、预警平台整体构建4个方面分析了近年来突发性水污染事故预警平台的研究进展与不足,以期为流域突发性水污染预警系统的建设提供参考。     

Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China

This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS Data) to monitor water quality parameters including chlorophyll-a, secchi disk depth, total phosphorus and total nitrogen at Chaohu Lake. In this paper, multivariate regression analysis, Back Propagation neural networks (BPs), Radial Basis Function neural networks (RBFs) and Genetic Algorithms-Back Propagation (GA-BP) were applied to investigate the relationships between water quality parameters and the MODIS bands combinations. The study results indicated that a simple, efficient and acceptable model could be established through multivariate regression analysis, but the model precision was relatively low. In comparison, BPs, RBFs and GA-BP were significantly advantageous in terms of sufficient utilization of spectra information and model reliance. The relative errors of BPs, RBFs and GA-BP were below 35%. Based on method comparison, it can be concluded that GA-BP is more suitable for simulation and prediction of water quality parameters by applying genetic algorithm to optimize the weight value of BP network. This study demonstrates that MODIS data can be applied for monitoring some of the water quality parameters of large inland lakes.     

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.     

Chemical monitoring of river water bodies in an EU outermost region: examples from the Azores archipelago (Portugal)

Chemical monitoring of water quality on a total of 16 rivers in the Azores archipelago (Portugal), since 2003, made it possible to identify the major pressures and spatial geochemical variations along main course of the rivers. River water pollution is to a large extent associated to point sources, namely domestic wastewater discharges, especially in urban areas, and diffuse sources, associated with pasture land, and explain the high values on BOD(5) and nutrients (P and N). Heavy metals and metalloids, as well as hydrocarbons and pesticides, are generally under the detection limits of the analytical methods. Generally, river water reflects pollution loads according to a simple model, derived from land use in the watershed: in the upper part conditions are pristine, in the intermediate portion of the basin pasture land dominates and near the coast urban discharges are increasingly important. Results stress the role that an approach based on the watershed scale, coupled with land use management measures, are crucial to water management procedures and a successful WFD implementation in small river basin districts like the Azores. The paper also shows the need for full compliance regarding EU directives on urban wastewater and nitrate pollution due to agriculture.     

Possible way to substantial improvement of early warning system in the International Odra (Oder) River Basin

Water ecosystems are threatened by accidental spills of pollution. Rapidity and trueness of information gathering the biological impact of accidental pollution is crucial for the efficiency of the minimisation of possible deterioration of ecosystems and for success in detecting the source of pollution. Due to the randomised occurrence of accidental spills the only way to quickly detect hazardous situations is to perform continuous monitoring of surface water quality. The current situation in the field of early warning in the International Odra (Oder) River basin is not satisfactory. The actual number of monitoring stations and list of routinely continuously monitored parameters are not able to meet the needs of sensitive and rapid detection of biological impact of accidental pollution spills. An effort to change this unfavourable situation was the reason for the offer survey, selection and a model operation of a commercially produced biological monitoring device. This apparatus was located on the border-line profile on the territory of the Czech Republic and represented the first and only one tool of continual biological monitoring of surface water quality in the International Odra (Oder) River Basin. The selected apparatus was the Daphnia Toximeter produced by the firm bbe Moldaenke (Kiel, Germany). This device exploited for rapid detection of changes of biological quality of surface water evaluation of behavioural response of monitoring organisms??daphnids. Five years of model operation proved its suitability for early warning purposes. The apparatus was reliable in function and sensitive enough to detect the deterioration of the biological quality of the river water. The given examples document its applicability not only for detection of accidental spills but also of illegal emissions of pollution, which are very often toxic.     

Spatial and seasonal characteristics of river water chemistry in the Taizi River in Northeast China

Anthropogenic activities have led to water quality deterioration in many parts of the world, especially in Northeast China. The current work investigated the spatiotemporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons. One-way analysis of variance indicated that the 20 measured variables (except pH, 5-day biological oxygen demand, permanganate index, and chloride, orthophosphate, and total phosphorus concentrations) showed significant seasonal (p?≤?0.05) and spatial (p?<?0.05) variations among the mainstream and major tributaries of the river. Hierarchical cluster analysis of data from the different seasons classified the mainstream and tributaries of the river into three clusters, namely, less, moderately, and highly polluted clusters. Factor analysis extracted five factors from data in the different seasons, which accounted for the high percentage of the total variance and reflected the integrated characteristics of water chemistry, organic pollution, phosphorous pollution, denitrification effect, and nitrogen pollution. The results indicate that river pollution in Northeast China was mainly from natural and/or anthropogenic sources, e.g., rainfall, domestic wastewater, agricultural runoff, and industrial discharge.