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.     

Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis

22 Physico-chemical variables have been analyzed in water samples collected every three months for two and a half years from three sampling stations located along a section of 25 km of a river affected by man-made and seasonal influences. Exploratory analysis of experimental data have been carried out by box plots, ANOVA, display methods (principal component analysis) and unsupervised pattern recognition (cluster analysis) in an attempt to discriminate sources of variation of water quality. PCA has allowed the identification of a reduced number of “latent” factors with a hydrochemical meaning: mineral contents, man-made pollution and water temperature. Spatial (pollution from anthropogenic origin) and temporal (seasonal and climatic) sources of variation affecting quality and hydrochemistry of river water have been differentiated and assigned to polluting sources. An ANOVA of the rotated principal components has demonstrated that (i) mineral contents are seasonal and climate dependent, thus pointing to a natural origin for this polluting form and (ii) pollution by organic matter and nutrients originates from anthropogenic sources, mainly as municipal wastewater. The application of PCA and cluster analysis has achieved a meaningful classification of river water samples based on seasonal and spatial criteria.     

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。     

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.     

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.     

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.     

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.     

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.     

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.     

Long-term water quality monitoring of the Sejnane reservoir in northeast Tunisia

The Sejnane reservoir in northeast Tunisia provides drinking and irrigation water. Long-term water quality monitoring data including precipitation, evaporation, temperature, pH, conductivity, dissolved oxygen, turbidity, total suspended solids, major anions and cations, fluoride, BOD₅, NO₃ ⁻, NO₂ ⁻, NH₄ ⁺, P _tot, fecal coliform bacteria, boron and heavy metals (Fe, Zn, Cu, Ni, Pb, Cr and Cd) are reported. The appropriateness for irrigation was estimated by the SAR and Na percentage and the water quality assessed using the Canadian Water Quality Index as good to excellent, which confirmed its suitability for drinking, aquatic life and irrigation purposes.      

Spatial and temporal variations in the occurrence of cocaine and benzoylecgonine in waste- and surface water from Belgium and removal during wastewater treatment

Estimates of cocaine consumption are currently resulting from population surveys, consumer interviews and crime statistics. A new approach (“sewage epidemiology”) based on the analysis of cocaine (COC) and its metabolite, benzoylecgonine (BE), in water samples was applied to 10 river sites and 30 wastewater treatment plants (WWTPs) in Belgium. Each river site was sampled twice, during the summer of 2007 and the winter of 2007-2008, while each WWTP was sampled on a Sunday and a Wednesday, during the summer-autumn of 2007 and the winter of 2007-2008. This sampling strategy allowed for the evaluation of spatial and seasonal variations in the occurrence of COC and BE in waste- and surface water. WWTP Brussel-Noord was sampled for 19 consecutive days to evaluate daily and weekly variations in the presence of COC and BE in wastewater. For 7 WWTPs, influent and effluent water samples were collected to investigate the removal of COC and BE during the wastewater treatment process. Analysis of water samples was performed using solid-phase extraction and liquid chromatography coupled to tandem mass spectrometry. Measured concentrations were further converted into an amount of used cocaine, called cocaine equivalents, as previously described in the literature. Results showed no significant difference in cocaine use between the investigated seasons. A constant cocaine consumption was observed during the week (Monday-Friday) with peaks during the weekend for WWTP Brussel-Noord. The COC/BE ratio in water samples was significantly higher during winter, most probably due to a slower hydrolysis of cocaine in low-temperature water. COC and BE were removed in the investigated WWTPs with a removal efficiency of >93%.     

应用综合水质指数法评价沙澧河漯河段的水质

以沙澧河漯河段3个断面2006-2009年的水质监测数据为基础,应用综合水质指数法,对沙澧河漯河段水质状况进行了分析与评价.将参与评价的项目分为3类:对人体危害严重且难以消除的污染物(第一类)、处理后容易达标的污染物(第二类)、其他污染物(第三类).评价结果表明,澧河水质优良并保持相对稳定;沙河水质稍差,但由尚好逐渐向...     

Escherichia coli virulence genes profile of surface waters as an indicator of water quality

We compared the presence of 58 known virulence genes (VGs) associated with Escherichia coli strains causing intestinal (InPEC) and extra-intestinal (ExPEC) infections in three estuarine, four brackish and 13 freshwater sites during the dry and wet seasons. The most common VGs observed in water samples during the dry season belonged to ExPEC (traT; 80% and ompA; 70%) whilst east1 (70%) gene was the most common among InPEC. More types of VGs were observed in water samples during wet season and included those found among InPEC (e.g. eaeA; 100%; fyuA, 90%; paa, 65%; cdt, 60%; and stx₂, 60%) and ExPEC (e.g. iroN_E.coli, 90%; iss, 90% and kpsMTII, 80%). Eight VGs were found exclusively in the wet season, of which four were found in all three water types indicating their association with storm-water run off. The number of VGs associated with ExPEC were significantly (P < 0.05) higher in only brackish and estuarine waters during the wet season compared to the dry season. There was no correlation between the number of E. coli and the presence of VGs in any of the water types in both seasons but we found similarities in VG profiles of sites with similar land uses.     

城市供水管网冲洗水水质变化特征与控制

针对供水管网存在的主要水质问题,分析了管道冲洗过程中水质的变化特征,并确定了合理的管道冲洗时间。结果表明,管道冲洗时,各类残渣量与铁锰含量的变化趋势相似,总残渣量、不可滤残渣量、可滤残渣量及铁、锰含量并非随冲洗时间持续下降,冲洗40 min时各指标均降到最低值,但是随着冲洗时间的延长,各指标均出现先升高后下降的趋势,并在80 min后逐渐达到稳定,此时可结束管道冲洗过程。     

Part I. Identifying anthropogenic markers in surface waters influenced by treated effluents: a tool in potable water reuse

In potable water reuse, treated wastewater becomes part of the drinking water supply. An important question associated with this practice is whether or not the organic quality of the treated wastewater is chemically different from that of non-human impacted water. This question was addressed in a case study of indirect potable water reuse where the organic matrix of the South Platte River was analyzed upstream and downstream of the discharge of treated wastewater effluent using conventional water quality parameters combined with pyrolysis-GC/MS. Effluent-derived organic material (EfOM) was found to be more aliphatic and had higher organic nitrogen and halogen content compared to organic material derived from "natural" (non-anthropogenic) sources (NOM). Seasonal changes that resulted from the change in the contributions of aquatic and terrestrial sources were not observed in EfOM; but they were strongly observed in NOM under the control of natural processes. Using principal component and factor analyses, the pyrolysis fragments of phenol, alkyl-phenols, and acetic acid were identified as the seasonal indicators for the NOM set of samples. In contrast, benzaldehyde, benzonitrile, chlorobutanoic acid, furancarboxaldehyde, and methylfurancarboxaldehyde were identified as the indicators for wastewater inputs for the EfOM set of samples. Overall, the results from conventional water quality parameters and pyrolysis-GC/MS revealed that: (1) EfOM bears a chemical signature distinct from NOM and (2) under the conditions of this study, EfOM discharged to the South Platte River persisted and controlled organic quality at downstream points.     

Analysis of long-term and seasonal river water quality changes in Latvia

An analysis of long-term historical records of the concentrations of major ions, TDS and nutrients for 20 river sites in Latvia is reported. Periods of water quality observations ranged from 15 to 43 years. A study of the quarterly adjusted time series showed that characteristic features of the data are non-normal distributions, seasonality, serial correlation and presence of significant trends that are mostly positive. The application of state-of-the-art software, based on non-parametric statistics such as the Seasonal Kendall slope estimator and the Seasonal Hodges-Lehmann estimator, made it possible to investigate these water quality records more accurately than other methods allow.Typical seasonal variations and concentration-discharge relationships were analyzed for different constituents. It was shown that fertilizer application and marsh land reclamation can cause widespread and intensive river water quality changes. Concentration increases of as much as 5–10 times that of background values were detected for NO₃⁻, Cl⁻, Na⁺ + K⁺ and SO₄²⁻. The main water quality changes took place in the 1960s and the early 1970s when fertilizer applications and reclamation works increased. After that, concentration increases for constituents other than NO₃⁻ and SO₄²⁻ were statistically insignficiant. The significant increases for NO₃⁻ and SO₄²⁻ were probably due to the additional impact of increased atmospheric deposition. The results of long-term changes of river loads entering the Baltic Sea and the Gulf of Riga from Latvian territory are examined.