Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.     

Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River Basin (Cordoba-Argentina)

We report a comparative study using three different chemometric techniques to evaluate both spatial and temporal changes in Suquía River water quality, with a special emphasis on the improvement obtained using discriminant analysis for such evaluation. We have monitored 22 parameters at different stations from the upper, middle, and beginning of the lower river basin during at least two years including 232 different samples. We obtained a complex data matrix, which was treated using the pattern recognition techniques of cluster analysis (CA), factor analysis/principal components (FA/PCA). and discriminant analysis (DA). CA renders good results as a first exploratory method to evaluate both spatial and temporal differences, however it fails to show details of these differences. FA/PCA needs 13 parameters to point out 71% of both temporal and spatial changes, consequently data reduction from FA/PCA in this case is not as considerable as expected. However, FA/PCA allows to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall change in water quality, specially during the analysis of temporal changes. DA technique shows the best results for data reduction and pattern recognition during both temporal and spatial analysis. DA renders an important data reduction using 6 parameters to afford 87% right assignations during temporal analysis. Besides, it uses only 5 parameters to yield 75% right assignations during the spatial analysis of four different basin areas. DA allowed us to greatly reduce the dimensionality of the starting data matrix, pointing out to a few parameters that indicate the biggest changes in water quality as well as variation patterns associated with seasonal variations, urban run-off, and pollution sources, presenting a novel approach for water quality assessments.     

Evaluation of river water quality monitoring stations by principal component analysis

The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.     

Using chemical and physical parameters to define the quality of Pardo River water (Botucatu-SP-Brazil)

This paper aims to study the ecological system of the Pardo River, at the source and lower-order passages, which are in the Botucatu area, S?o Paulo State, Brazil. This study was carried out to determine water quality with some chemical-physical indicators, coliforms, and chemical species of samples taken monthly, 1995/02-1996/01, from eight sampling stations sited along the Pardo River. The results in the river monitoring are discussed based on annual averages, analysis of variance, and compared to Tukey's Studentized Range--HSD, and principal component analysis (PCA) was applied to normalize data to assess association between variables. We can conclude that the variables used are very efficient for identifying and that the dry season shows the worst water quality. These were caused by organic matter, nutrients (originate) from anthropogenic sources (spatial sources) and mainly municipal wastewater, affecting the quality and hydrochemistry of the river water, which have been differentiated and assigned to polluting sources. Meanwhile, the degree of degradation of the Pardo River is low (sewage treatment carried out by the city of Pardinho is efficient), leaving the water of the river suitable for use by the population of Botucatu, after conventional treatment (Conama, Resolu??o No. 20, CONAMA, Brazilia DF, 09-23, 1986--the water of the Pardo river is classified as level 03).     

Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques

Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish ‘sentry’ sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment.     

Statutory Water Quality Objectives on the River Cam

The Government has announced its wishes to gradually introduce statutory water quality objectives in pilot catchments, commencing in late 1993. The River Cam is one of the catchments under consideration.
The paper describes the main physical features of the River Cam and its uses, which include salmonid and cyprinid fisheries, agricultural and industrial abstraction, and recreational pursuits.
Significant sewage and industrial discharges in the catchment, and their impact on water quality, are discussed, and current river quality is compared with established river quality objectives, EC requirements and proposed statutory water quality objectives. Procedures for setting the latter objectives are considered, together with monitoring and compliance assessment. Future effluent discharge standards are proposed, and options to achieve long-term water quality targets are suggested as part of a catchment management action plan.     

Assessment of water quality using principal component analysis: A case study of the river Ganges

In present study multivariate statistical approaches are used; interpretation of large and complex data matrix obtained during a monitoring of the river Ganges in Varanasi. 16 physicochemical and bacteriological variables have been analyzed in water samples collected every three months for two years from six sampling sites where river affected by man made and seasonal influences. The dataset was treated using Principal Component Analysis (PCA) to extract the parameters that are most important in assessing variation in water quality. Four Principal Factor were identified as responsible for the data structure explaining 90% of the total variance of the dataset, in which nutrient factor (39.2%), sewage and feacal contamination (29.3%), physicochemical sources of variability (6.2%) and waste water pollution from industrial and organic load (5.8%) that represents total variance of water quality in the Ganges River. The present study suggests that PCA techniques are useful tools for identification of important surface water quality parameters.     

Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques

Understanding the spatial distribution and apportioning the sources of water pollution are important in the study and efficient management of water resources. In this work, we considered data for 13 water quality variables collected during the year 2004 at 46 monitoring sites along the Qiantang River (China). Fuzzy comprehensive analysis categorized the data into three major pollution zones (low, moderate, and high) based on national quality standards for surface waters, China. Most sites classified as “low pollution zones” (LP) occurred in the main river channel, whereas those classified as “moderate and high pollution zones” (MP and HP, respectively) occurred in the tributaries. Factor analysis identified two potential pollution sources that explained 67% of the total variance in LP, two potential pollution sources that explained 73% of the total variance in MP, and three potential pollution sources that explained 80% of the total variance in HP. UNMIX was used to estimate contributions from identified pollution sources to each water quality variable and each monitoring site. Most water quality variables were influenced primarily by pollution due to industrial wastewater, agricultural activities and urban runoff. In LP, non-point source pollution such as agricultural runoff and urban runoff dominated; in MP and HP, mixed source pollution dominated. The pollution in the small tributaries was more serious than that in the main channel. These results provide information for developing better pollution control strategies for the Qiantang River.     

长江与黄河两大流域水生态问题剖析

对流域水生态的研究由传统单一的水质评价逐渐转变为河流生态质量的评价,基于河流健康保障的流域生态保护与发展已成为国际性趋势。长江、黄河两大流域内的生态保护与发展也是重大国家战略。以长江、黄河两大流域为研究对象,对两大流域水生态环境问题进行分区概述,并从河流湖库问题治理的关键是注重流域的整体性的角度,分析为维系两大流域水生态健康可开展的研究工作,最后将生态修复与景观设计结合考虑,提出注重景观价值的流域生态保护策略。     

The environmental chemistry of Mahaweli river,Sri Lanka

The water pollution levels of Mahaweli River, the longest river in Sri Lanka, the basin of which covers one sixth of the Island, were monitored to probe the impacts of the urban environment in a developing country. It was observed the chemical quality is largely controlled by natural factors. From among the metals however, vanadium, zinc and copper showed higher concentrations. Pb and Cd showed a correlation co‐efficient of r = 0.58 for each other, and Co showed a highly significant correlation of r = 0.98 with Cu. The lack of correlation of Pb and Cd with the total dissolved solids (TDS) indicates an anthropogenic input of Pb and Cd into the aquatic environment. In general, the chemical quality of the water in the Mahaweli river is satisfactory for most purposes, none of the major dissolved constituents and nutrients exceeding the limit suggested by the WHO for potable water.     

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.     

Water quality modeling for load reduction under uncertainty: a Bayesian approach

A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH(4)(+)) were selected as WQM variables. The first-order decay rate (k(i)) and load (L(i)) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (L(m)) of NH(4)(+) and BOD for each river segment was determined based on DSM and the estimated parameters of k(i). The results showed that for most river segments, the historical loading was beyond the L(m) threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (C(i-1)) and water velocity (v(i)) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different C(i-1) and v(i) scenarios.     

Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

An indicator model for evaluating trends in river quality using a two-stage fuzzy set theory to condense efficiently monitoring data is proposed. This candidate data reduction method uses fuzzy set theory in two analysis stages and constructs two different kinds of membership degree functions to produce an aggregate indicator of water quality. First, membership functions of the standard River pollution index (RPI) indicators, DO, BOD(5), SS, and NH(3)-N are constructed as piecewise linear distributions on the interval [0,1], with the critical variables normalized in four degrees of membership (0, 0.33, 0.67 and 1). The extension of the convergence of the fuzzy c-means (FCM) methodology is then used to construct a second membership set from the same normalized variables as used in the RPI estimations. Weighted sums of the similarity degrees derived from the extensions of FCM are used to construct an alternate overall index, the River quality index (RQI). The RQI provides for more logical analysis of disparate surveillance data than the RPI, resulting in a more systematic, less ambiguous approach to data integration and interpretation. In addition, this proposed alternative provides a more sensitive indication of changes in quality than the RPI. Finally, a case study of the Keeling River is presented to illustrate the application and advantages of the RQI.     

Pollution studies of River Khan (INDORE) India—I. Biological assessment of pollution

Pollution studies of the River Khan, Indore (India) have been made, with an emphasis on the biological assessment of water quality. The study covers a critical analysis and testing of various European methods for the biological monitoring of water pollution, under the Indian conditions in the River Khan. The river has been divided into different zones of pollution. The biological data have been correlated with the chemical data, and the practical implications of the European saprobity system, under the Indian conditions have been discussed. The importance of benthic macro-invertebrates in the assessment of water quality in the river has also been discussed.     

利用长江水作热泵系统冷热源的技术分析

介绍了江水源热泵系统的形式，分析了长江重庆段的水温和水质情况。研究表明，长江水作为冷热源适于发展水源热泵技术，应优先选择壳管式直接利用热泵系统。此外，针对长江水泥沙含量高、夏季悬浮物和微生物数量多的问题，探讨了江水的净化处理、增强传热和除垢、热泵系统工况分析、取水方式以及江水二次利用等关键技术。     

Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis

Exploratory data analysis such as hierarchical cluster analysis and principal component analysis were applied to water quality dataset of the Kaduna River, obtained during 3 years (2008–2010), monthly monitoring of eight key different sampling sites for 19 parameters to extract correlations and similarities between variables and to classify river sampling sites in groups of similar quality. Hierarchical cluster analysis grouped eight sampling sites into three statistically significant clusters of similar water composition. Six varifactors were obtained after varimax rotation of initial principal components using principal component analysis. These techniques gave an insight into the sources of pollution. Anthropogenic influence (municipal, industrial wastewater and agricultural run‐off) was the major source of river water pollution.     

Impact of mass bathing on the water quality of the Ganga River at Haudeshwarnath (Pratapgarh), India--a case study

This paper reports on the impact of mass bathing on Ganga River water quality on the occasion of the Maha-Shivaratri festival at Haudeshwarnath Ghat situated on the left bank of the River Ganga in the district of Pratapgarh, India. Water samples collected from three sampling points before and after mass bathing were analysed for a total of 17 physico-chemical and seven biological parameters. It was concluded from the results that mass bathing causes a significant change in water quality of the river, which may represent a health hazard to users of the river water.     

Water quality index as a simple indicator of watersheds pollution in southwestern part of Iran

The Karun River is the most important watershed in the southwestern region of Iran. Water quality parameters were sampled from October 2006 through July 2007 at three sites along a 4 km gradient, covering both the wet and the dry season. Surface water was collected at three different stations per site (close to the banks and in the middle of the river) and analysed for 14 parameters and heavy metals. The values of 1300, 196.8 and 4042.9 ppm for chemical oxygen demand, biochemical oxygen demand and chloride, respectively, were higher than the standards limits. water quality index (WQI) values were very useful for the classification of the waters monitored. The averaged WQI was low (47%), and quality declined significantly during the dry season [analysis of variance (ANOVA, P < 0.05)]. The annual WQI values of 54.60, 40.29 and 45.71 from sites 1, 2 and 3 correspond to medium, bad and bad water qualities, respectively.     

Long-term behaviour and cross-correlation water quality analysis of the River Elbe, Germany

This study analyses weekly data samples from the river Elbe at Magdeburg between 1984 and 1996 to investigate the changes in metabolism and water quality in the river Elbe since the German reunification in 1990. Modelling water quality variables by autoregressive component models and ARIMA models reveals the improvement of water quality due to the reduction of waste water emissions since 1990. The models are used to determine the long-term and seasonal behaviour of important water quality variables. Organic and heavy metal pollution parameters showed a significant decrease since 1990, however, no significant change of chlorophyll-a as a measure for primary production could be found. A new procedure for testing the significance of a sample correlation coefficient is discussed, which is able to detect spurious sample correlation coefficients without making use of time-consuming prewhitening. The cross-correlation analysis is applied to hydrophysical, biological, and chemical water quality variables of the river Elbe since 1984. Special emphasis is laid on the detection of spurious sample correlation coefficients.     

Neural network modeling of salinity variation in Apalachicola River.

Salinity is an important indicator for water quality and aquatic ecosystem in tidal rivers. The increase of salinity intrusion in a river may have an adverse effect on the aquatic environment system. This study presents an application of the artificial neural network (ANN) to assess salinity variation responding to the multiple forcing functions of freshwater input, tide, and wind in Apalachicola River, Florida. Parameters in the neural network model were trained until the model predictions of salinity matched well with the observations. Then, the trained model was validated by applying the model to another independent data set. The results indicate that the ANN model is capable of correlating the non-linear time series of salinity to the multiple forcing signals of wind, tides. and freshwater input in the Apalachicola River. This study suggests that the ANN model is an easy-to-use modeling tool for engineers and water resource managers to obtain a quick preliminary assessment of salinity variation in response to the engineering modifications to the river system.