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1.
Spatial variations of the water quality in the Haicheng River during April and October 2009 were evaluated for the national monitoring program on water pollution control and treatment in China. The spatial autocorrelation analysis with lower Moran’s I values displayed the spatial heterogeneity of the 12 physicochemical parameters among all the sampling sites of the river. The one-way ANOVA showed that all variables at different sampling sites had significant spatial differences (p < 0.01). Based on the similarity of water quality characteristics, cluster analysis grouped the 20 sampling sites into three clusters, related with less polluted, moderately polluted and highly polluted sites. The factor analysis extracted three major factors explaining 76.4 % of the total variance in the water quality data set, i.e., integrated pollution factor, nitrogen pollution factor and physical factor. The results revealed that the river has been severely polluted by organic matter and nitrogen. The major sources leading to water quality deterioration are complex and ascribed to anthropogenic activities, e.g., domestic and industrial wastewater discharges, agricultural runoff, and animal rearing practices.  相似文献   

2.
Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.  相似文献   

3.
A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (r), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO3 type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO3, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl? and SO4 2? ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K+, Fetotal, SO 4 2 , As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.  相似文献   

4.
The current study presents the application of selected chemometric techniques—hierarchical cluster analysis (HCA) and principal component analysis (PCA)—to evaluate the spatial variation of the water chemistry and to classify the pollution sources in the Langat River. The HCA rendered the sampling stations into two clusters (group 1 and group 2) and identified the vulnerable stations that are under threat. Group1 (LY 1 to LY 14) is associated with seawater intrusion, while group 2 (LY 15 to LY 30) is associated with agricultural and industrial pollution. PCA analysis was applied to the water datasets for group 1 resulting in four components, which explained 85 % of the total variance while group 2 extracted six components, explaining 88 % of the variance. The components obtained from PCA indicated that seawater intrusion, agricultural and industrial pollution, and geological weathering were potential sources of pollution to the study area. This study demonstrated the usefulness of the chemometric techniques on the interpretation of large complex datasets for the effective management of water resources.  相似文献   

5.
Located in the northeastern part of Tunisia, Wadi El Bey drains the watershed through farmland, industrial, and urban areas of the region. It serves to discharge treated wastewater of different types. In this work, the variations of the water quality of Wadi El Bey were studied and evaluated, during 2 years (2012–2013), using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA). In addition, the similarities or dissimilarities among the sampling points were as well analyzed to identify spatial and temporal variations. The results obtained based on the cluster analysis, led to identify three similar water quality zones: relatively polluted (LP), moderately polluted (MP), and highly polluted (HP). The inorganic and organic parameters, temperature, conductivity, dissolved oxygen, chemical oxygen demand, salmonella, and enterococcus, seemed to be the most significant parameters of water quality. Three factors were identified as responsible for the data structure, explaining 60.95% of the total variance. The first factor is the physical and non-organic chemical parameters explaining 23.48% of the total variance. The second and third factors are, respectively, the microbiological (21.26%) and organic-nutrient (16.2%).This study shows that multivariate statistical methods can help the water managers to understand the factors affecting the water quality.  相似文献   

6.
Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg2+) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.  相似文献   

7.
Taihu Basin is one of the most developed and industrialized regions in China. In the last two decades, rapid development of economy as well as an increase in population has resulted in an increase of pollutants produced and discharged into rivers and lakes. Much more attention has been paid on the serious water pollution problems due to high frequency of algal blooming. The dataset, obtained during the period 2001–2002 from the Water Resources Protection Bureau of the Taihu Basin, consisted of eight physicochemical variables surveyed monthly at 22 sampling sites in the Taihu Basin, China. Principal component analysis (PCA) and cluster analysis (CA) were used to identify the characteristics of the surface water quality in the studied area. The temporal and spatial variations of water quality were also evaluated by using the fuzzy synthetic evaluation (FSE) method. PCA extracted the first two principal components (PCs), explaining 86.18% of the total variance of the raw data. Especially, PC1 (73.72%) had strong positive correlation with DO, and was negatively associated with CODMn, COD, BOD, NH4 +–N, TP and TN. PC2 (12.46%) was characterized by pH. CA showed that most sites were highly polluted by industrial and domestic wastewater which contributed significantly to PC1. The sites located in the west of Lake Taihu were influenced by farmland runoff which may contribute to nitrogen pollution of Lake Taihu, whereas the monitoring sites in the eastern of Lake Taihu demonstrated that urban residential subsistence and domestic wastewater are the major contaminants. FSE indicates that there is no obvious variance between 2001 and 2002 among most sites. Only several sites free from point-source pollution appear to exhibit good water quality through the studied period.  相似文献   

8.
Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995–2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.  相似文献   

9.
The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO_3 is more positively explained in the spring period by Ca and SO_4 and negatively explained by pH and HCO_3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.  相似文献   

10.
In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.  相似文献   

11.
The River Ganges being the most sacred river and lifeline to millions of Indians in serving their water requirements is facing excessive threat of pollution. Under various river management and conservation strategies for its protection, the assessment of water quality of its main tributary Ramganga River is lacking. This study focuses on assessment of physicochemical and heavy metal pollution of the Ramganga River by application of multivariate statistical techniques. Sampling of Ramganga River at sixteen sampling sites was carried out in three seasons (summer, monsoon and winter) of 2014. The collected water samples were analyzed for physicochemical parameters and heavy metals. Results from cluster analysis (CA) of the data divided the whole stretch of the river into three clusters as elevation from 1304 to 259 m as less polluted, from 207 to 154 m as moderately polluted and from elevation 154 to 139 m as high-polluted stretches with anthropogenic as main sources of pollution in high-polluted stretch. Principal component analysis of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72–8% of total variance with strong loadings (>0.75) of PC1 on fluoride (F?), chloride (Cl?), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3 ?), total dissolved solids and electrical conductivity. Temporal variation by one-way ANOVA (Analysis of Variance) showed significant seasonal variation was in the pH, chemical oxygen demand, biochemical oxygen demand, turbidity, HCO3 ?, F?, Zn, cadmium (Cd) and Mn (p < 0.05). Turbidity showed approximately a twofold increase in monsoon season due to rainfall in the catchment area and subsequent flow of runoff into the river. Concentration of HCO3 ?, F? and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summers compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters.  相似文献   

12.
基于多元统计方法的河流水质空间分析   总被引：15，自引：0，他引：15       下载免费PDF全文
基于聚类分析和判别分析探讨了河流水质空间分析方法,旨在识别采样点的空间相似性与差异性,从而为水质监测网络优化提供支持。该方法首先利用kurtosis和Skewness检验数据分布特征和进行数据对数转化与标准化处理;然后利用聚类分析进行空间相似性分析,确定空间尺度分类情况;最后利用判别分析识别显著性污染指标,以此反映上述空间尺度分类的差异性。以香港后海湾水质管制区为例,结果表明:①通过对数转化显著改善数据分布特征,使绝大部分污染指标呈正态或接近正态分布;②该区域采样点在个案链锁距离与最大链锁距离之比(Dlink/Dmax)×100<35处明显分为3类,它们分别代表轻度、中度、重度污染3种类型,且后两者属于采样点主要属于营养盐和重金属污染类型,需要控制其生活污水、畜牧污染、工业污染和地表径流污染;③后退式判别分析具有良好的指标降维能力,仅需7个显著性污染指标(pH,NH3-N,NO3-N,F.coil,Fe,Ni和Zn)可以反映整体水质的空间差异性,且具有90.65%的正确判别能力;④归纳起来,从3类采样点中选择一个或多个、监测7个显著性污染指标即可全面反映后海湾水质管制区的水质空间特征,实现水质监测网络优化。  相似文献   

13.
Groundwater availability depends on its accessibility, as well as on its quality. Factor analysis (FA) has been used to analyze quality problems and provide strategies for water resources exploitation. The present study demonstrated the use of factor analysis to evaluate temporal variations in groundwater quality and find latent sources of water pollution in coastal areas of Ramanathapuram District, Tamil Nadu, India. The data set included data of eleven water quality parameters viz., pH, electrical conductivity, salinity, total dissolved solids, total alkalinity, calcium hardness, magnesium hardness, total hardness, chloride and fluoride for two different seasons (pre- and post-monsoon) in 2012. FA of the two seasons resulted in two latent factors accounting for 80.38 % of total variance for pre-monsoon (summer) and 73.03 % for post-monsoon (winter) in the water quality data sets. The results obtained from FA prove that the groundwater quality in winter is better than that of summer. Langelier Saturation Index was used to find out scaling and corrosive tendency of the groundwater samples for the study area. Karl Pearson correlation matrix was used to study the correlation between the studied water quality parameters. Hence, the analysis suggests that FA techniques are useful tools for identification of influence of various quality parameters on overall nature of the groundwater.  相似文献   

14.
The hydrochemistry of a perennial river has been investigated with multivariate cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA). The aim was to investigate parameters responsible for spatial and temporal variations of river water quality. Water quality was monitored along the river basin at 20 different sites over a period of 1 year from July, 2008 to June, 2009. Multivariate statistics revealed that Ca2+, Mg2+, Na+, K+, HCO3 ?, Cl?, H4SiO4, SO4 2?, NO2 ?, and PO4 3? were influenced by seasonal and spatial variations and that water quality was in the first place determined more by natural weathering processes than by anthropogenic activities. We could prove by (a) Box and Whisker plot, (b) matrix scatter score mean plot, (c) ternary plot, and (d) Gibbs plot that the chemistry of river water is controlled by lithogenic weathering processes. The higher concentration of dissolved silica during summer and the pre-monsoon season is explained by natural and tropical climatic conditions of the environment.  相似文献   

15.
Principal components analysis (PCA) is a multivariate data analysis tool that can be used to recombine the variables of a large multivariate dataset in such a way that the first few variables of the reconstructed dataset account for the majority of the variance in the data. Application of PCA in marine geochemistry has become quite common in recent years. In this study, we illustrate the use of PCA through examples that arose while investigating the geochemistry of sinking particles during the MedFlux project. The examples presented do not simply repeat the analyses of the original study, but instead extend them in the context of simultaneous application of PCA and cluster analysis. Our results show that constructing a one dimensional (1D) “degradation index” using only the first principal component (PC) is in most cases oversimplified, and that constructing 2D or 3D “degradation trajectories” with the first 2 or 3 PCs is more informative. Use of the first three PCs is indicated when the variance explained by the third PC is comparable in magnitude to that explained by the second PC in the reconstructed dataset. We also discuss the use of scree plots and cluster analysis in helping decide whether the third PC is needed to capture the essential information in the dataset.  相似文献   

16.
River pollution data are characterized by high variability. Multivariate statistical methods help to determine a complex set of these multidimensional data and to extract latent information (e.g. differently polluted areas, discharges). The chemometric methods can handle interactions between different pollutants and relationships among various sampling locations. This study presents an application of multivariate data analysis in the field of environmental pollution. The dataset consists of As, Cd, Cr, Cu, Fe, Ni, Mn, Pb and Zn contents of sediment samples collected in the upper and middle Odra River (Poland) in three sampling campaigns (November 1998, June 1999, and May 2000). As chemometric tools cluster analysis (CA), multivariate analysis of variance and discriminant analysis (MVDA) and factor analysis (FA) were used to investigate the matrix of 60 sampling points.  相似文献   

17.
Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca2+, and Mg2+. Therefore, the first dominant factor was hardness. In the second PC, Cl-, SAR, and Na+ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.  相似文献   

18.
The present study investigates the surface water quality of three important tributaries of Jakara Basin, northwestern Nigeria to provide an overview of the relationship and sources of physicochemical and biological parameters. A total of 405 water samples were collected from 27 sampling points and analyzed for 13 parameters: dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended solids (SS), pH, ammonia-nitrogen (NH3NL), dissolved solids (DS), total solids (TS), nitrates (NO3), chloride (Cl), phosphates (PO4), Escherichia coli (E. coli) and fecal coliform bacteria (FCB). Pearson’s product–moment correlation matrix and principal component analysis (PCA) were used to distinguish the main pollution sources in the basin. Four varimax components were extracted from PCA, which explained 84.86, 83.60, and 78.69 % of the variation in the surface water quality for Jakara, Tsakama, and Gama-Kwari Rivers, respectively. Strong positive loading included BOD5, COD, NH3NL, E. coli, and FCB with negative loading on DO attribute to a domestic waste water pollution source. One-way ANOVA revealed that there was no significant difference in the mean of the three water bodies (p?>?0.05). It is therefore recommended that the government should be more effective in controlling the point source of pollution in the area.  相似文献   

19.
Toxic organic compounds in wastewater are serious threats for both human and environment healthy states. This study investigates the potential sources of surface water, sediment and groundwater pollution by polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyl (PCBs) as discharged by wastewater into the River of Oued El bey in northeastern Tunisia. Analysis indicates that the concentration of PAHs and PCBs are high in wastewater and vary from 0.37 to 0.83 mg/L and from 0.28 and 1.18 mg/L, respectively. The spatial distribution of PAHs and PCB in surface water showed a variation between 0.37 to 9.91 mg/L and between 0.1 to 0.47 mg/L, respectively. However, the quality of surface water is changed after wastewater evacuation at Oued Tahouna. The determination of PAH and PCB pollutants in groundwater shows a great interest in the development of water resources. The Concentration of these pollutants varying from 0.0204 to 1.93 mg/L and from 0.0052 to 0.196 mg/L, respectively. For PAH, analysis reveals also that naphtelene, fluorene, anthracene and chrysene are the most detected PAHs compounds in water and sediment samples while benzo[b]fluoranthene and benzo[a]pyrene are less present and in trace level. Higher concentrations of PAHs and PCBs are found in samples taken close to industrial areas of Bouargoub and Soliman, and wastewater discharge locations in Soliman. Analysis of the spatial distribution of PAHs and PCBs clearly link their higher concentration in water and sediments to wastewater and manufacturing discharges in the study area. In surface sediment, the organic pollutants are present. The cluster analysis for organic pollutants in different state and different matrix highlight a relationship between the wastewater evacuation and the water qualities which confirmed the direct response of the pollution sources on the surface water and groundwater organic pollution quality.  相似文献   

20.
The lower Liaohe River Plain (LRP) is an economically and ecologically important area situated on an alluvial plain, where anthropogenic activities are very intensive. Field investigations were conducted in the LRP and 15 water quality parameters surveyed at 216 wells during September and October of 2009 and 2010. These showed significant variation in the hydrochemistry of groundwater throughout the plain. A Piper plot was used to identify the major geochemical processes occurring in the entire plain. Principal components analysis (PCA) was used to identify various underlying natural and anthropogenic processes that created these distinct water types. The Stuyfzand classification was used to subdivide and interpret the complex groundwater hydrochemistry of the Liaohe River delta. Five principal components (PCs) were extracted in terms of PCA, which can be invoked to explain 82% of the total variance in water quality parameters. The PCA results can be categorized by five major factors: (1) Holocene transgression and mixing; (2) surface water infiltration; (3) multi-factor processes; (4) rainfall and agricultural fertilizer contamination; and (5) Geogenic F enrichment. This study demonstrates that the great variation of groundwater hydrochemistry in the LRP should be attributed to both natural and anthropogenic processes.  相似文献   

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