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.     

Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China

Water quality is the critical factor that influence on human health and quantity and quality of grain production in semi-humid and semi-arid area. Songnen plain is one of the grain bases in China, as well as one of the three major distribution regions of soda saline-alkali soil in the world. To assess the water quality, surface water and groundwater were sampled and analyzed by fuzzy membership analysis and multivariate statistics. The surface water were gather into class I, IV and V, while groundwater were grouped as class I, II, III and V by fuzzy membership analysis. The water samples were grouped into four categories according to irrigation water quality assessment diagrams of USDA. Most water samples distributed in category C1-S1, C2-S2 and C3-S3. Three groups were generated from hierarchical cluster analysis. Four principal components were extracted from principal component analysis. The indicators to water quality assessment were Na, HCO₃, NO₃, Fe, Mn and EC from principal component analysis. We conclude that surface water and shallow groundwater are suitable for irrigation, the reservoir and deep groundwater in upstream are the resources for drinking. The water for drinking should remove of the naturally occurring ions of Fe and Mn. The control of sodium and salinity hazard is required for irrigation. The integrated management of surface water and groundwater for drinking and irrigation is to solve the water issues.     

Assessment of seasonal variations in surface water quality

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 16 physical and chemical parameters collected from 22 monitoring stations in a river during the years from 1998 to 2001 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons.     

Treated water quality assurance and description of distribution networks by multivariate chemometrics

Throughout the year 2007, 89 treated water samples from three water treatment plants (WTPs) of the Athens Water Supply and Sewerage Company (EYDAP S.A.) and 180 samples from network tanks (NWTs) were analyzed for electrical conductivity (EC), alkalinity (TA), pH, aluminium (Al), total hardness (TH), chloride (Cl⁻), residual chlorine (free Cl), calcium (Ca²⁺) and magnesium (Mg²⁺). The results regarding the WTPs were subjected to a principal component analysis (PCA) with 75% of the total variance being explained. A stepwise linear discriminant analysis (LDA) model constructed from the 89 treated water samples was used to predict class membership of the samples from the NWTs with a view to estimating the propagation of a possible water quality deterioration originating from the WTPs. The model utilized Cl⁻, Al and EC and yielded a 96% correct classification of the training dataset, whereas the cross-validation yielded a 94% correct classification. Network tank samples were 95% correctly classified with regard to their theoretically expected origin. The stepwise discriminant analysis based on separate covariance matrices of the canonical discriminant functions yielded a 98% correct classification of both the training dataset and the network tank samples. The classification and regression tree (C&RT) algorithm showed that the main parameters used in the discrimination of the WTP samples were EC and Al. The post-hoc classification of the training dataset was 99%, whereas 88% of NWT samples were correctly classified.     

Influence of water quality on the presence of off-flavour compounds (geosmin and 2-methylisoborneol)

Geosmin and 2-methylisoborneol are off-flavour compounds related to poor organoleptics and a decrease in consumer satisfaction with drinking water. The relationship between these off-flavour compounds and 39 parameters of water quality (chemical, physical and biological) for three different surface waters from lotic systems that supply potable water to Quebec City and Lévis (Canada) was studied using principal component analysis. The objective of this study was to show that a multidimensional approach with principal component analysis using the component matrix serves to differentiate the processes involved in the appearance of the olfactory compounds from those not involved or little involved and to extract the most representative data of these processes. Our study shows that the presence of geosmin can be explained, in the case of the water studied, by a process associated primarily with the eutrophication of water and, to a lesser degree, by an allochthonous origin. However, the presence of 2-methylisoborneol may be linked to two processes with different origins, but of equal importance: a winter origin – most likely allochthonous – involving anthropic contributions and an autochthonous bacterial origin.     

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.     

Monitoring of water quality from roof runoff: Interpretation using multivariate analysis

The quality of harvested rainwater used for toilet flushing in a private house in the south-west of France was assessed over a one-year period. Temperature, pH, conductivity, colour, turbidity, anions, cations, alkalinity, total hardness and total organic carbon were screened using standard analytical techniques. Total flora at 22 °C and 36 °C, total coliforms, Escherichia coli and enterococci were analysed. Overall, the collected rainwater had good physicochemical quality but did not meet the requirements for drinking water. The stored rainwater is characterised by low conductivity, hardness and alkalinity compared to mains water. Three widely used bacterial indicators - total coliforms, E. coli and enterococci - were detected in the majority of samples, indicating microbiological contamination of the water. To elucidate factors affecting the rainwater composition, principal component analysis and cluster analysis were applied to the complete data set of 50 observations. Chemical and microbiological parameters fluctuated during the course of the study, with the highest levels of microbiological contamination observed in roof runoffs collected during the summer. E. coli and enterococci occurred simultaneously, and their presence was linked to precipitation. Runoff quality is also unpredictable because it is sensitive to the weather. Cluster analysis differentiated three clusters: ionic composition, parameters linked with the microbiological load and indicators of faecal contamination. In future surveys, parameters from these three groups will be simultaneously monitored to more accurately characterise roof-collected rainwater.     

Advances in on-line drinking water quality monitoring and early warning systems

Significant advances have been made in recent years in technologies to monitor drinking water quality for source water protection, treatment operations, and distribution system management, in the event of accidental (or deliberate) contamination. Reports prepared through the Global Water Research Coalition (GWRC) and United States Environment Protection Agency (USEPA) agree that while many emerging technologies show promise, they are still some years from being deployed on a large scale. Further underpinning their viability is a need to interpret data in real time and implement a management strategy in response. This review presents the findings of an international study into the state of the art in this field. These results are based on visits to leading water utilities, research organisations and technology providers throughout Europe, the United States and Singapore involved in the development and deployment of on-line monitoring technology for the detection of contaminants in water.     

Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO₂), respirable suspended particulates (RSP) and nitrogen dioxide (NO₂), collected in monitoring network of Hong Kong from January 2001 to December 2007.     

In-pipe water quality monitoring in water supply systems under steady and unsteady state flow conditions: a quantitative assessment

Monitoring the quality of drinking water from the treatment plant to the consumers tap is critical to ensure compliance with national standards and/or WHO guideline levels. There are a number of processes and factors affecting the water quality during transmission and distribution which are little understood. A significant obstacle for gaining a detailed knowledge of various physical and chemical processes and the effect of the hydraulic conditions on the water quality deterioration within water supply systems is the lack of reliable and low-cost (both capital and O & M) water quality sensors for continuous monitoring. This paper has two objectives. The first one is to present a detailed evaluation of the performance of a novel in-pipe multi-parameter sensor probe for reagent- and membrane-free continuous water quality monitoring in water supply systems. The second objective is to describe the results from experimental research which was conducted to acquire continuous water quality and high-frequency hydraulic data for the quantitative assessment of the water quality changes occurring under steady and unsteady-state flow conditions. The laboratory and field evaluation of the multi-parameter sensor probe showed that the sensors have a rapid dynamic response, average repeatability and unreliable accuracy. The uncertainties in the sensor data present significant challenges for the analysis and interpretation of the acquired data and their use for water quality modelling, decision support and control in operational systems. Notwithstanding these uncertainties, the unique data sets acquired from transmission and distribution systems demonstrated the deleterious effect of unsteady state flow conditions on various water quality parameters. These studies demonstrate: (i) the significant impact of the unsteady-state hydraulic conditions on the disinfectant residual, turbidity and colour caused by the re-suspension of sediments, scouring of biofilms and tubercles from the pipe and increased mixing, and the need for further experimental research to investigate these interactions; (ii) important advances in sensor technologies which provide unique opportunities to study both the dynamic hydraulic conditions and water quality changes in operational systems. The research in these two areas is critical to better understand and manage the water quality deterioration in ageing water transmission and distribution systems.     

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.     

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.     

再生水水质综合评价的主成分分析方法

采用主成分分析方法评价再生水综合水质,利用R软件强大的统计功能,对某再生水厂供水实测数据进行了定量化评价。结果表明,主成分分析方法是一种切实可行的水质综合评价方法,值得进一步研究,并能为其他相关行业的质量评价提供参考。     

Comparing in situ particle monitoring to microscopic counts of plankton in a drinking water reservoir

In a one-year study, the multispecies assemblages of phytoplankton (picoplankton to microplankton) within a drinking water reservoir were counted, determined and evaluated in their size fractions using microscope enumeration (MC). The manual counts were compared with the size evaluation obtained by a light obscuring particle counter (PC) in order to evaluate its use for the monitoring practice of a drinking water reservoir. With this multispecies comparison we present a novel approach for the evaluation of automated counting systems.The picoplankton clearly remained uncounted by the PC even though its lower size limits imply an adequate match. The highest and most consistent count numbers of plankton (nano- and microplankton) and particles were obtained during the spring mass development. However, from the middle of the year onwards, the measured particle concentration surpassed the counted plankton abundances by two- to threefold indicating the rise of seston within the water column. This fraction would be missed if counted solely by MC. Further, the PC consistently undersized the biological counts, but not the minerogenic fraction represented by the manganese oxidising bacteria. Consequently, the rise and decline of Metallogenium bacteria was reliably monitored with the PC.The PC provides additional size information compared to other bulk optical sensors (turbidity, chlorophyll-a). The correlation of particles with probe measurement always exceeded the plankton coefficient, but all combinations of plankton, particle and probe measurement revealed significant linear regressions. However, the redundancy of the chlorophyll-a probes was also shown in order to explain plankton abundances.Our results indicate that background knowledge of the monitored system and cautious interpretation of data is required to allocate and understand automated particle counts. Therefore, only in combination with MC, the PC enables phytoplankton or minerogenic particle counts under frequent real-time monitoring conditions. As such it may serve as a helpful tool for example in critical situations in the management of drinking water reservoirs.     

Using multi-criteria analysis to explore non-market monetary values of water quality changes in the context of the Water Framework Directive

The EU Water Framework Directive represents a major change in the management of water resources and sets ambitious ecological objectives for all European waters. In the Directive, the economic assessment of the non-market environmental benefits of water quality improvements plays a crucial role. Studies valuing these benefits are now appearing in the literature, applying stated preference valuation techniques. However, these techniques are often criticized for providing only narrow mono-criterion information to the decision-making process. The research presented here builds on a recent line of investigation that combines monetary stated preference tools, in this case a choice experiment, with multi-criteria analysis, in this case the Analytical Hierarchy Process (AHP). We argue that the AHP can contribute to a better understanding and interpretation of the choice experiment results by exploring the criteria involved in respondents' trade-off between the attributes. The AHP provides relevant insights for the application of use-based water quality ladders in the valuation of environmental benefits in the context of the WFD. Results also show the importance of the spatial dimension of preferences for water quality.     

Modified Lagrangian method for modeling water quality in distribution systems

Previous work has shown that Lagrangian methods are more efficient for modeling the transport of chemicals in a water distribution system. Two such methods, the Lagrangian Time-Driven Method (TDM) and Event-Driven Method (EDM) are compared for varying concentration tolerance and computational water quality time step. A new hybrid method (EDMNET) is developed which improves the accuracy of the Lagrangian methods. All the above methods are incorporated in an existing hydraulic simulation model. The integrated model is run for different network problems under varying conditions. The TDM-generated solutions are affected by both concentration tolerance and water quality time step, whereas EDM solutions are dependent on concentration tolerance. The EDMNET solutions are less sensitive to variations in these parameters. The threshold solutions are determined for all the methods and compared. The hybrid method simulates the nodal concentrations accurately with least maximum segmentation of network and reasonable computational effort as compared to the other Lagrangian methods.     

Contractors' perception of the use of costs of quality system in Malaysian building construction projects

While conforming on the findings of prior researches regarding quality cost system in building companies, current research successfully illustrates the contractors' perceptions on the importance of the quality cost system and the barriers that may constrain the implementation of the system for recording and collecting quality cost data. A postal and email surveys were undertaken on Malaysian building companies, focusing on the benefits and difficulties associated with the implementation of quality cost system. Statistical analyses based on Chi-Squared test and Relative Importance Index techniques were used to investigate the significance of the findings and determine the relative importance of the factors. The most important benefit of measuring quality costs is “getting management attention and increase quality awareness” as perceived by the sample of the study. The possible barriers that may affect the management's decision to implement quality cost system are identified and grouped into three categories, which are culture and knowledge; system; and company. The study suggests that the level of the site staff's knowledge should be as important as that of the management to successfully collect and record quality costs data. The findings of this research will raise the level of awareness and sensitize managers and those involved with building industry about the importance of quality cost system and collecting quality costs data.     

Rates of urbanisation and the resiliency of air and water quality

Global human population and urban development are increasing at unprecedented rates and creating tremendous stress on local, regional, and global air and water quality. However, little is known about how urban areas vary in their capacity to address effectively air and water quality impacts associated to urban development. There exists a need to better understanding the factors that mediate the interactions between urbanisation and variations of environmental quality. By synthesizing literatures on the relationship between urban development and air and water quality, we assess the amount of scholarship for each of these cities, characterize population growth rates in one hundred of the largest global cities, and link growth trends to changes in air and water quality. Our results suggest that, while there is a growing literature linking urbanisation and environmental quality, some regions of the globe are better represented than others, and that these trends are consistent with our characterization of population growth rates. In addition, the comparison between population growth rates and air and water quality suggest that multiple factors affect the environmental quality, and that approaching rates of urbanisation through the lens of ‘resiliency’ can be an effective integrative concept for studying the capacity of urban areas to respond to rapid rates of change. Based on these results we offer a framework for systematically assessing changes in air and water quality in megacities.     

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.     

斑马鱼生物学效应的研究进展及在水质监测中的应用

主要从不同污染物胁迫介绍了斑马鱼的呼吸行为、运动行为、群体行为等行为学效应,以及个体、器官、细胞、分子等多种水平的毒理效应,同时对斑马鱼行为学和毒理效应在水质监测和水环境评价方面的应用做出介绍和展望,得出综合斑马鱼的行为变化和毒理效应两个方面能更准确地表征水体污染类型及污染程度。