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.     

Assessment of physico-chemical and microbiological surface water quality using multivariate statistical techniques: a case study of the Wadi El-Bey River,Tunisia

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.     

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.     

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.     

Effects of discharge of municipal waste on water quality of the lower Mississippi River

The effects of discharge of municipal wastes on water quality within the lower Mississippi River below Old River have been reevaluated using published water quality data in the Louisiana reach of the river for the water years 1974–1984. A novel graphical technique has facilitated the evaluation of upriver controls on water quality and the identification of sources and sinks along the lower Mississippi. Comparison of calculated annual fluxes at different downstream monitoring stations has simplified some of the problems inherent in evaluating analyses of samples collected from different water masses during a typical sampling run. The absolute concentrations of chloride, nitrite plus nitrate, total phosphorous, dissolved oxygen, BOD, and COD are all strongly dependent on processes occurring upriver. Nonpoint influx of materials from agricultural wastes and natural plant debris may be the dominant upstream sources of N, P, BOD, and COD. Increases in chloride and phosphorous downstream within the Lower Mississippi appear to be caused by discharge of industrial wastes. Nitrogen fluxes decrease downriver, except where there is local discharge of high-N, high-P industrial waste water, possibly from fertilizer plants. Removal of N and increases in BOD may be due in part to biological uptake. High river discharge rates and efficient, natural processes of reaeration maintain high oxygen saturation levels. With the exception of an increase in bacterial count, the discharge of municipal waste into the Mississippi River in Louisiana appears to have had no significant effect on water quality, a finding consistent with the earlier U.S. Geological Survey study of Wells (1980). It would be highly desirable for future mass balance studies if existing water quality programs on the Mississippi River were to adopt a Lagrangian sampling approach.     

Application of multivariate statistical techniques in the assessment of water quality in Sakarya River,Turkey

In this paper, the surface water quality of the Sakarya River in Turkey is assessed by using multivariate statistical techniques. These techniques were applied to the chemical parameters obtained from the five different surface water quality observation stations. Factor and principal component analysis results reveal that the agricultural, anthropogenic and domestic pollution caused differences in terms of water quality. Cluster analysis revealed two different clusters of similarities between the stations, reflecting different chemical properties and pollution levels in the studied river. Surface water quality downstream of the river was different from the water quality upstream. Thus, this study shows the usefulness of multivariate statistical techniques for analysis and interpretation in the surface water quality problem.     

A Comparison of Water Quality Between Low- and High-Flow River Conditions in a Tropical Estuary, Hilo Bay, Hawaii

Effects of storms on the water quality of Hilo Bay, Hawaii, were examined by sampling surface waters at 6 stations 10 times during low-flow and 18 times during high-flow (storms) river conditions. The direction of a storm’s impact on water quality parameters was consistent among storms and most stations; however, direction of the impact varied with the parameter. High river flow conditions increased concentrations of nitrate and decreased those of dissolved organic nitrogen (N); effects on ammonium and particulate N were station specific. Storms also increased dissolved organic and particulate carbon (C) concentrations. Dissolved phosphorus (P) concentrations were not affected by high river flow events. Dissolved organic forms dominated the N, C, and P pools under both low- and high-flow river conditions. Soil-derived particles and fecal indicator bacteria increased during storms, while chlorophyll a concentrations and bacterial cell abundances decreased. Our results suggest that an increase in storms with global warming could impact water quality of tropical estuaries.     

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 the impact of industrial effluents on water quality of receiving rivers in urban areas of Malawi

A study was carried out in Malawi to assess the extent of chemical pollution in a receiving river as affected by industrial effluents. Both the effluents and the water at selected points in the river were analysed for pH, dissolved oxygen, biochemical oxygen demand, electrical conductivity, suspended solids, nitrate, alkalinity, hardness, chloride and phosphate in the dry and rainy seasons. The results showed that the effluents were acidic in both the dry season (range: 4.2 ± 0.02–6.5 ± 0.02) and in the rainy season (range: 4.2 ± 0.05–5.6 ± 0.01). While the levels of dissolved oxygen, biological oxygen demand, electrical conductivity, suspended solids, alkalinity and chloride were relatively high in the dry and rainy seasons, the concentration of phosphate and nitrate were low in both seasons. The water upstream was neutral (average pH, 7.40 ± 0.04) with high dissolved oxygen but low in the levels of the other parameters in both seasons. The water after the effluent receiving points was acidic and the levels of the other parameters were high, especially downstream. The results suggested that the water in the river was polluted and not good for human consumption. It is therefore recommended that the careless disposal of the wastes should be discouraged and although the values in some cases were lower than the allowable limits, the continued discharge of the effluents in the river may result in severe accumulation of the contaminants and, unless the authorities implement the laws governing the disposal of wastes, this may affect the lives of the people.     

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 index of the ground water of bitumen deposit impacted farm settlements using entropy weighted method

Ground water of the farm settlements in the bitumen deposit area of Western Nigeria were tested for pH, conductivity, turbidity, total dissolved solid, sulphate, phosphate, nitrogen nitrate, chloride, alkalinity, total hardness, calcium, magnesium, sodium, potassium and total coliform in the dry season (March) and rainy season (August) of years 2008 and 2009 using atomic absorption spectrometer and standard analytical methods. Conductivity, pH, turbidity, phosphate ions and total coliform values obtained in some of the wells were out of the recommended range for drinking water. Water quality index analysis was carried out using information entropy method. Water quality index of the wells when compared with the permissible limits of international standards, ranked the bore hole as ‘excellent’ in the dry season of year 2009 and just ‘good’ in the rainy season of the same year. Ground water sampling stations were ranked extremely poor at least once out of the four seasons considered. Water quality index ranking also showed that the quality of the wells declined over the years. The correlation coefficient matrix (p < 0.05) of water quality index and the parameters showed significant relationships between water quality index and total coliform (0.99), total dissolved solids and conductivity (0.96), hardness and Ca²⁺ (0.68), hardness and Mg²⁺ (0.75). Water quality index also showed moderate significant relationship with total dissolved solid, conductivity and N–NO₃ ^?. High concentration of total coliform in most of the shallow wells in the environment, due to bitumen deposit, renders them unfit for human consumption unless properly treated.     

水质站网规划研究中几个问题的探讨

在进行中国水质站网规划布设合理的性研究中，按河流水系将全国国土分为若干水质站网规划单元分区，然后运用主成分聚类方法进行单元分区类型的划分，作为水质站网规划的基础。在同一类型单元分区内，运用模糊集合聚类方法，将分区内的水质进行分类，以确定水质站的布设密度，并对每一水质参数的采亲频次进行分析研究，以指导确定其采样频次。     

Correlations between algae and water quality: factors driving eutrophication in Lake Taihu, China

Rapid population increase and economic growth in eastern China has lead to the degradation of many water bodies in the region, such as Lake Taihu, the third largest freshwater lake in China. Using data from recent investigations, the correlations between algae (measured as chlorophyll-a) and water quality indices in Lake Taihu were described by multivariate statistical analyses, and the key driving factors for the lake eutrophication were identified by principal component analysis. Results revealed strong spatiotemporal variation in the correlations between algae and water quality indices, suggesting that the limiting factor for the dominant algae growth depends on seasonality and location and it is necessary to reduce both nitrogen and phosphorus inputs for a long-term eutrophication control in this hyper-eutrophic system. Water temperature was another important controlling factor for algal growth in the lake. Using principal component analysis, nutrient contaminations from anthropogenic and natural inputs were identified as the key driving factor for the water quality problems of the lake. Moreover, five principal components were extracted and characterized with high spatial and seasonal variations in Lake Taihu. The key driving factors were believed to influence spatial variations including heavily polluted areas located in the northern and northwestern parts of the lake, where many manufacturing factories were built and wastewater from domestic and industrial plants was discharged. Based on this analysis, attention should be paid to effective land management, industrial wastewater treatment, and macrophytic vegetation restoration to reduce the pollutant loads and improve water quality. Principal component analysis was found to be a useful and effective method to reduce the number of analytical parameters without notably impairing the quality of information in this study.     

Investigation of water quality on Gökçekaya dam lake using multivariate statistical analysis,in Eskişehir,Turkey

Gökçekaya Dam is one of the dams located on the Sakarya River (Eski?ehir) in Turkey and is approximately 38 years old. No study regarding to the water quality of the dam lake has been hitherto made. This study aimed at determining the seasonal variations with physical, chemical, and biological parameters of the water quality of the dam lake. Depth-wise water samples were taken from five selected stations, in seasonal periods during 2005–2008, and anlayzed. The quality of water was classified in accordance with the results and taking into consideration many parameters. At the same time, number and type diversities of dissolved heavy metals and algae population were analyzed. Using principal component analysis (PCA) and classification analysis (CA) methods, differences between these parameters and samples from stations have been determined . The analysis showed that Gökçekaya dam lake, formerly an oligotrophic lake, has become a mesotrophic lake. And according to the results thereof the Lake has mezotrophic characteristics due to the sudden changes (including household waste water inflow and opening the Sar?yar dam lake shutters) in certain periods and in some stations. Hierarchical clustering analysis, revealed no significant difference between the measured parameters. And according to the Water Pollution Control Regulations in Turkey (WPCR) Gökçekaya dam lake is in the first class quality in terms of anions, cations, heavy metals, temperature, and pH values. However, the is of first class quality presence of nitrite reduces the water quality in the lake and causes this lake to be classified in the fourth class quality.     

Identification of river water pollution characteristics based on projection pursuit and factor analysis

Human activities contribute different pollutants to receiving waters, often with significant variations in time and space. Therefore, integrating multiple parameters of water quality and their spatiotemporal variations is necessary to identify the pollution characteristics. Based on the water quality monitoring data with 12 parameters for 2 years at 22 sampling sites in the Cao-E River system, eastern China, the projection pursuit method was used to project all parameters and their temporal variations into a one-dimensional vector through two projections. Accordingly, we could easily assess the comprehensive water quality in different sampling sites and then classify their water pollution features. Factor analysis was then used to identify the pollution characteristics and potential sources. Results showed that all sampling sites for the river system could be classified into four groups: headwater sites (HS), agricultural nonpoint sources pollution sites (ANPS), point sources pollution sites (PSPS), and mixed sources pollution sites. Water quality in HS was good, containing only a few nutrients from the woodland runoff and soil erosion. For ANPS, the main pollutants were dissolved phosphorus, total P, and nitrate nitrogen (NO₃ ^?-N), mainly from farming land. For PSPS, ammonium nitrogen (NH₄ ⁺-N) and organic pollutants originated from industrial and municipal sewage. In HS and ANPS, NO₃ ^?-N was the main form of nitrogen, and a high ratio of NO₃ ^?-N/NH₄ ⁺-N was a remarkable characteristic, whereas NH₄ ⁺-N was the main form of nitrogen in PSPS. Except in HS, water quality in the other groups could not meet the local water quality control standard. Finally, suggestions were proposed for water pollution control for the different groups.     

Chemical behavior of the Salí River, Province of Tucumán, Argentina

Major ionic composition and other chemical parameters were determined at five sampling stations on the Salí River (Province of Tucumán, Argentina). The heavy human usage of the river causes increased levels of dissolved ions, from the dissolution of halite and gypsum, and from the weathering of basic sediments. Correlations demonstrate that sodium chloride and sulphate, and calcium carbonate are the main contributors to dissolved ions in the river. In the polluted region, south of the Celestino Gelsi dam, the main source of calcium and sulphate is the dissolution of gypsum. Large amounts of halite are also dissolved. Weathering of biotite, K-feldspar and albite are suggested by the data. Conductivity, dissolved oxygen and organic matter all indicate severe contamination by organic matter (mainly from sugar-cane processing) in the lower course. The data also demonstrate a substantial improvement in water quality before the discharge of the river at the Río Hondo dam. Data from the Colorado (a tributary of the Salí River) and Medina rivers are also analyzed and compared.     

Multivariate water quality assessment from the Tamiraparani river basin,Southern India

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.     

Classification of rivers based on water quality assessment using factor analysis in Taizi River basin, northeast China

A method for river classification based on water quality assessment (WQA) was introduced using factor analysis (FA) in this paper. Sixty-nine sampling sites and 20 water quality parameters in Taizi River basin were selected for monitoring and analysis. Five factors were determined in FA, denoted as general, hardness, trophic, nitrogen pollution, and physical factors. The total factor scores (TFSs) of the WQA results from all sampling sites were calculated by the eigenvalue and factor score of each factor. The TFSs of 69 sites were interpolated with the measure of inverse distance weighted in the river buffer zone generated by ArcGIS 9.2 software to form a continuous spatial distribution along river channels. All streams were divided into five classes marked “excellent”, “good”, “fair”, “poor”, and “seriously polluted”. The classification result showed that the water quality of Taizi River basin deteriorated gradually from the mountain area to the plain area. Sewage and intensive human activities contributed to the deterioration of water quality since towns and farmland were dotted densely along the river basin.     

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.     

Chemometric approach integrated with capillary electrophoresis for the investigation of public water system sources and quality

A chemometric approach coupled with capillary electrophoresis based on the hierarchical cluster analysis and principal component analysis has been applied for the investigation of the water quality in the Golcuk-Isparta region (Lake District of Turkey). In the research area, Egirdir Lake, Golcuk Lake and surrounding ground and domestic waters have been utilized as drinking water resources. Golcuk Lake is distinctive in terms of high fluoride content (3.50 ± 0.21 mg/mL) which is endemic in volcanic areas where the water flow through volcanic rocks and sediments. Based on the analysis of major anions chloride, sulfate, nitrate and fluoride with capillary electrophoresis, twenty-four drinking water sampling sites in the research area were classified into four classes using the hierarchical cluster and principal component analysis. Combining the research area investigation results of hierarchical cluster and principal component analysis, it was found that fluoride concentration is the major diagnostic variable to determine the quality of drinking waters, and all the other anions are the important classification factors to predict the resources of the drinking water samples, individually. To sum up, this study reveals the potential of the use of capillary electrophoresis in combination with chemometric techniques for the determination of the quality and origin of drinking waters.