Spatial characteristics of surface water quality in the Haicheng River (Liao River basin) in Northeast China

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.     

Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria

This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons.     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

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.     

Water quality assessment of the Jinshui River (China) using multivariate statistical techniques

Multivariate statistical techniques have been widely utilized to assess water quality and evaluate aquatic ecosystem health. In this study, cluster analysis, discriminant analysis, and factor analysis techniques are applied to analyze the physical and chemical variables in order to evaluate water quality of the Jinshui River, a water source area for an interbasin water transfer project of China. Cluster analysis classifies 12 sampling sites with 22 variables into three clusters reflecting the geo-setting and different pollution levels. Discriminant analysis confirms the three clusters with nine discriminant variables including water temperature, total dissolved solids, dissolved oxygen, pH, ammoniacal nitrogen, nitrate nitrogen, turbidity, bicarbonate, and potassium. Factor analysis extracts five varifactors explaining 90.01% of the total variance and representing chemical component, oxide-related process, natural weathering and decomposition processes, nutrient process, and physical processes, respectively. The study demonstrates the capacity of multivariate statistical techniques for water quality assessment and pollution factors/sources identification for sustainable watershed management.     

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

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.     

Water and sediment quality assessment in the mid-Black Sea coast of Turkey using multivariate statistical techniques

The aim of this study was to investigate the water and sediment quality in the mid-Black Sea coast of Turkey. The samples were collected from six stations during 2007. Investigated parameters were total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC), ammonium-nitrogen (NH₄-N), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), total phosphorus (TP), sulphate, total hardness, methylene blue active substances (MBAS), phenol, adsorbable organic halogens (AOX), dissolved oxygen (DO), pH and electrical conductivity (EC) in water samples and TC, TIC, TOC, TP, pH, electrical conductivity (EC), redox potential (Eh) and water content (WC) in sediment samples. Different multivariate statistical techniques were used to evaluate variations in surface water and sediment quality. Principal component analysis helped in identifying the factors or sources responsible for water and sediment quality variations. Five factors were found responsible for 87.63% of the total variance in the surface waters. In sediments, three factors explained 84.73% of the observed total variance. Cluster analysis classified the monitoring sites into two groups based on similarities of water and sediment quality characteristics.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

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.     

Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

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.     

Geochemical and isotopic evolution of groundwater in the Wadi Watir watershed,Sinai Peninsula,Egypt

The Wadi Watir delta in the Wadi Watir watershed is a tourist area in the arid southeastern part of the Sinai Peninsula, Egypt, where development and growth of the community on the delta are constrained by the amount of groundwater that can be withdrawn sustainably. To effectively manage groundwater resources in the Wadi Watir delta, the origin of groundwater recharge, groundwater age, and changes in groundwater chemistry in the watershed needs to be understood. Mineral identification, rock chemistry, water chemistry, and the isotopes of hydrogen, oxygen, and carbon in groundwater were used to identify the sources, mixing, and ages of groundwater in the watershed and the chemical evolution of groundwater as it flows from the upland areas in the watershed to the developed areas at the Wadi Watir delta. Groundwater in the Wadi Watir watershed is primarily from recent recharge while groundwater salinity is controlled by mixing of chemically different waters and dissolution of minerals and salts in the aquifers. The El Shiekh Attia and Wadi El Ain areas in the upper Wadi Watir watershed have different recharge sources, either from recharge from other areas or from different storm events. The downgradient Main Channel area receives groundwater flow primarily from the El Shiekh Attia area. Groundwater in the Main Channel area is the primary source of groundwater supplying the aquifers of the Wadi Watir delta.     

Pattern recognition techniques for evaluating the spatial impact of copper mining on surface waters

In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources.     

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain,Iran)

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.     

Geochemistry of Bizerte lagoon affluent water, Northern Tunisia: principal component analysis

The monthly geochemical study of Bizerte lagoon principal affluent water consists in characterizing the water geochemical facies and their inorganic pollution degree by nutrients. The major elements analysis shows calcium sulfate to chloride calcium balanced facies. The geochemical analysis of water nutritive salts shows generally a good to excellent quality. Wadi Guenniche is considered more polluted as we recorded the highest nutrients contents. The principal component analysis of the connections between the physicochemical and geochemical parameters of Bizerte lagoon affluent water show that the low salinities, the turbidity, and the low contents of major sodium, chloride ions, and nutritive elements are the major factors of the environment controlling the good quality of this fresh water.     

R-mod factor analysis, a popular multivariate statistical technique to evaluate water quality in Khaf-Sangan basin, Mashhad, Northeast of Iran

Water quality data are required in order to compare chemical water analyses and identify water masses. R-mode factor analysis, a popular multivariate statistical tool, has been effectively used for groundwater quality studies. In this paper, the R-mode factor analysis was applied to 50 groundwater samples collected from pumping wells in the Sangan-Khaf basin which is located in the southeast of Mashhad, northeast Iran. The groundwater samples were analysed for chemical parameters. The factor analysis was then performed on the chemical data set. It can be suggested that four factors in R-mode analysis explain more than 94.31% of the total variance. The contribution of each factor at sample points, factor score, was calculated. The spatial distribution of the factor scores for each factor was mapped separately. Since the Sangan iron mine south of the study area probably affects groundwater aquifer, therefore, such studies can be used to manage the groundwater quality in the study area.     

Spatial–temporal characteristics of surface water quality in the Taihu Basin,China

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 COD_Mn, 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.     

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (?370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.     

Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges,India

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.     

Preliminary groundwater quality assessment in the central region of Ghana

Insufficient knowledge of the hydrogeochemistry of aquifers in the Central Region of Ghana has necessitated a preliminary water quality assessment in some parts of the region. Major and minor ions, and trace metal compositions of groundwater have been studied with the aim of evaluating hydrogeochemical processes that are likely to impair the quality of water in the study area. The results show that groundwater in the area is weakly acidic with mean acidity being 5.83 pH units. The dominant cation in the area is Na, followed by K, Ca, and Mg, and the dominant anion is Cl^?, followed by HCO₃ ^? and SO₄ ^2?. Two major hydrochemical facies have been identified as Na–Cl and Na–HCO_3, water types. Multivariate statistical techniques such as cluster analysis (CA) and factor analysis/principal component analysis (PCA), in R mode, were employed to examine the chemical compositions of groundwater and to identify factors that influenced each. Q-mode CA analysis resulted in two distinct water types as established by the hydrochemical facies. Cluster 1 waters contain predominantly Na–Cl. Cluster 2 waters contain Na–HCO₃ and Na–Cl. Cluster 2 waters are fresher and of good quality than cluster 1. Factor analysis yielded five significant factors, explaining 86.56% of the total variance. PC1 explains 41.95% of the variance and is contributed by temperature, electrical conductivity, TDS, turbidity, SO₄ ^2?, Cl^?, Na, K, Ca, Mg, and Mn and influenced by geochemical processes such as weathering, mineral dissolution, cation exchange, and oxidation–reduction reactions. PC2 explains 16.43% of the total variance and is characterized by high positive loadings of pH and HCO₃ ^?. This results from biogenic activities taking place to generate gaseous carbon dioxide that reacts with infiltrating water to generate HCO₃ ^?, which intend affect the pH. PC3 explains 11.17% of the total variance and is negatively loaded on PO₄ ^3? and NO₃ ^? indicating anthropogenic influence. The R-mode PCA, supported by R-mode CA, have revealed hydrogeochemical processes as the major sources of ions in the groundwater. Factor score plot revealed a possible flow direction from the northern sections of the study area, marked by higher topography, to the south. Compositional relations confirmed the predominant geochemical process responsible for the various ions in the groundwater as mineral dissolution and thus agree with the multivariate analysis.     

Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine,Dinajpur, Bangladesh

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⁺, Fe^total, 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.     

基于多元统计方法的河流水质空间分析

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(D_link/D_max)×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。     

Evaluation of water quality trends in the Maroon River Basin,Iran, from 1990 to 2010 by WQI and multivariate analyses

This research aimed to investigate the long-term spatiotemporal changes of surface water quality of the Maroon River by implementing Water Quality Index (WQI) and multivariate statistical analyses such as non-metric multidimensional scaling and cluster analyses, as complementary tools to investigate spatial variations in water quality parameters and also delineate areas in terms of water quality conditions in the period under study. The other purposes of this study were to evaluate the physicochemical properties of the Maroon River water and assess the effects of each water quality parameter on the WQI values. Relationship between quality scale of hydrochemical parameters and the resulting WQI scores was determined employing linear regression analysis. Moreover, the suitability of water quality was evaluated for irrigation purposes using conventional indices, electrical conductivity (EC), sodium adsorption ratio (SAR), and percent sodium (Na%). The monitoring stations were placed in high and very high categories according to the assessment of irrigation water quality with EC. Considering WQI, the upper (S1, S2, and S4) and lower (S3, S5, and S6) monitoring stations of the Maroon River distributed in category C3 (high salinity) and C4–C5 (very high salinity), respectively. The findings of WQI presented an increasing trend from upstream toward downstream in the Maroon River. The findings of the linear regression analysis showed no significant correlation between WQI scores with pH and SO₄ ^2? concentrations even though the relationship is weak. These results suggest that pH and SO₄ ^2? concentrations could be the secondary driving parameters behind the variations in WQI scores. It can be inferred that the Maroon River water is appropriate for irrigation based on Na% and SAR. However, it also exhibits high EC. Therefore, for mitigating the adverse impacts of polluted water authors recommend multidimensional management practices such as transferable discharge permit programs in the study area.