Variations In Surface Water Quality Research Articles

Assessment of Surface Water Quality in the Big Sunflower River Watershed of Mississippi Delta Using Nonparametric Analysis.

Assessment of surface water quality in the Mississippi Delta is essential to quantify the eutrophication of the Gulf of Mexico. This study estimated the characteristics and variations of surface water quality at three study sites in the Big Sunflower River Watershed (BSRW) within the Mississippi Delta using Kruskal-Wallis, Dunn, Mann-Kendall, and Pettitt tests. In general, contents of some water quality constituents such as nitrate-nitrogen (NO 3 - N) and total phosphorus (TP) in the BSRW varied from site to site each year, whereas variations of other constituents such as pH and dissolved oxygen (DO) each year were basically not significant. The highest median concentrations were found in spring for NO 3 - Nand total nitrogen (TN); in summer for specific conductance (SC), Na, and Cl; and in winter for DO. Mann-Kendall trend analysis revealed that there was an increasing annual trend at Leland but a decreasing annual trend at Merigold for NO 3 - Nconcentrations even though such changes were very small, whereas there was no annual trend for TP at any of the three study sites. Pettitt's test further identified that the NO 3 - N concentrations had an abrupt increase in February 2009 at the median value of 0.44 mg L-1 in Leland and an abrupt decrease in June 2012 at the median value of 3.65 mg L-1 in Merigold. A very good linear correlation exited between total dissolved solid (TDS) and magnesium (Mg) in the BSRW, which could be used to estimate TDS from Mg concentrations for this watershed when the data for TDS are absent.

Water quality and sediment contamination assessment of Pazarsuyu Stream, Turkey using multivariate statistical methods and pollution indicators

The spatio-temporal variations of surface water quality and the level of metal contamination in the sediment of Pazarsuyu Stream were determined by using multivariate statistical techniques and pollution indicators, respectively. The water and sediment samples were collected monthly from four different stations along the Pazarsuyu Stream between June 2014 and May 2015 and analyzed using standard methods. Results were assessed according to national and international water quality criteria and the water quality parameters in some stations were found to be over these criteria levels. The present study showed that statistical techniques such as Pearson Correlation Index (PCI), Principal Component Analysis (PCA), Cluster Analysis (CA), One Way ANOVA need to be used to get better information for the studies on the water quality of surface waters. PCA identified six factors accounting for 82.88% of the total variation in the data. The abundance of sediment heavy metals was in the order of Fe > Mn > Zn > Pb > Cu > Cr > Co > Cd. The heavy metal contamination in the sediments was evaluated by applying the enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo) and potential ecological risk index (PERI). Pb was heavily contaminated and has a significant enrichment of contamination according to calculated EF values. Therefore, the stream water can be used for irrigation with precaution but extensive treatment required before using for domestic purposes to prevent adverse public health effects.

The assessment and prediction of temporal variations in surface water quality-a case study.

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Evaluating the Importance of Non-Unique Behavioural Parameter Sets on Surface Water Quality Variables under Climate Change Conditions in a Mesoscale Agricultural Watershed

The parameter uncertainty in the eco-hydrological model Soil and Water Assessment Tool (SWAT) was estimated using non-unique parameter sets for the Altmühl watershed (Bavaria, Germany). The Sequential Uncertainty Fitting Algorithm (SUFI-2) was used to calibrate SWAT. The non-unique parameter sets found were subsequently applied to SWAT concurrently with climate change simulations to determine the variables of streamflow, nitrate nitrogen (NO3−-N) and total phosphorus (TP). A suite of seven bias corrected climate change simulations provided reference (1970–2000) and future (2041–2070) climate data. The non-unique behavioural parameter sets that met an objective function of NSE >0.6 during calibration were applied to SWAT with the reference climate and with the future climate simulations. The best parameter set was also propagated through SWAT with each reference and future climate simulation in turn. Combining the non-unique behavioural parameter sets for estimating uncertainty bounds with an ensemble of climate change simulations led to a wider mean monthly spread (difference between maximum and minimum) of simulated NO3−-N and TP than using the best run with the future climate simulations. More monthly data was considered using the non-unique approach, resulting in statistical significances for more months of the year and overall lower interquartile ranges. The study quantifies the non-unique behavioural parameter set contributions to the modelling prediction, which assists in making more informed decisions based on available knowledge, with its limitations, of the future simulations. We outline a simple approach that can easily be replicated for similar hydrological modelling studies.

Improving water quality in China: Environmental investment pays dividends

This study highlights how Chinese economic development detrimentally impacted water quality in recent decades and how this has been improved by enormous investment in environmental remediation funded by the Chinese government. To our knowledge, this study is the first to describe the variability of surface water quality in inland waters in China, the affecting drivers behind the changes, and how the government-financed conservation actions have impacted water quality. Water quality was found to be poorest in the North and the Northeast China Plain where there is greater coverage of developed land (cities + cropland), a higher gross domestic product (GDP), and higher population density. There are significant positive relationships between the concentration of the annual mean chemical oxygen demand (COD) and the percentage of developed land use (cities + cropland), GDP, and population density in the individual watersheds (p < 0.001). During the past decade, following Chinese government-financed investments in environmental restoration and reforestation, the water quality of Chinese inland waters has improved markedly, which is particularly evident from the significant and exponentially decreasing GDP-normalized COD and ammonium (NH4+-N) concentrations. It is evident that the increasing GDP in China over the past decade did not occur at the continued expense of its inland water ecosystems. This offers hope for the future, also for other industrializing countries, that with appropriate environmental investments a high GDP can be reached and maintained, while simultaneously preserving inland aquatic ecosystems, particularly through management of sewage discharge.

Spatio-temporal variations of water quality in Nova Ponte Reservoir, Araguari River Basin, Brazil

Multivariate techniques, like cluster analysis (CA) and principal component analysis (PCA), were used to evaluate the spatial and temporal variability of surface water quality in a large neotropical hydroelectric reservoir (Nova Ponte Reservoir). The dataset, obtained during the period of 1995–2011 from the Energy Company of Minas Gerais State (CEMIG), contains 14 parameters surveyed quarterly in 14 sites, at different depths. The CA grouped the 14 sites in three main groups: lotic sites, half of the photic zone sites and bottom sites. Statistical tests showed that only three parameters (total dissolved solids, nitrate and chemical oxygen demand) have no significant difference between cluster groups. The PCA results showed temporal changes of the water quality in all groups, illustrating modifications to the importance of the parameters over time. PCA also revealed the major causes of water deterioration from 1995 to 2005 were related to agricultural and livestock activities. Currently, the water quality parameters related to organic pollution are also highlighted. Generally, this study shows that possible optimization of the monitoring network should consider temporal variation of water quality parameters.

Temporal Variation of Surface Water Quality in the Swan River Catchment in Himachal Pradesh India

As industrial growth in the lower Shiwalik hills has risen in past two decades, the last 10 years in the Una district has seen a rapid development in industrial and urban growth due to grant of industrial package by the central government of India. As a result, several production plants have sprung up within the Swan River catchment, threatening the water quality of this area. However, the actual effects on water quality are heretofore unknown. In this paper, we assess the water quality of the Swan River catchment by calculating the National Sanitation Foundation Water Quality Indicators (NSFWQI) and Overall Index of Population (OIP) between 2003-2012. Data on monitored cross sections were collected from State Pollution Control Board of Himachal Pradesh, India. The results indicate that there has been recent (within five years) considerable improvement in the water quality due to enforcement of proper pollution control technologies. The relationship between economic growth (GDP) and water quality was also studied.We carried out regression analysis of the water quality data to determine significant parameters as independent variables and WQI and OIP as dependent variables. The regression analysis further identified that the contribution of each variable with significant values r = 0.733, R2 = 0.695. The study further suggests that sustainable development is possible through adoption of proper treatment technologies, enforcement of formal legislation, and preparation of remedial action plans to reduce the environmental stresses.HYDRO Nepal JournalJournal of Water Energy and EnvironmentIssue: 20Page: 55-61

Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA) and principal component analysis (PCA). Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge.

Change-point detection in environmental time series based on the informational approach

In this study, the Schwarz Information Criterion (SIC) is applied in order to detect change-points in the time series of surface water quality variables. The application of change-point analysis allowed detecting change-points in both the mean and the variance in series under study. Time variations in environmental data are complex and they can hinder the identification of the so-called change-points when traditional models are applied to this type of problems. The data seasonality structure is incorporated through a linear modeling approach. The assumptions of normality and uncorrelation are not present in some time series, and so, a simulation study is carried out in order to evaluate the methodology’s performance when applied to non-normal data and/or with time correlation.

Assessment of surface water quality of Ain Zada dam (Algeria) using multivariate statistical techniques

ABSTRACTMultivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis, have been applied for the assessment of temporal variations of surface water quality in Ain Zada dam, Algeria, for 10 years by monitoring 16 parameters. The different parameters indicate that the data are homogeneous. As against this record an annual variation is more important than the monthly change in connection with climate change. The facies of these waters is Cl–Na especially in connection with human actions. Values of the Water Quality Index classified the surface water as medium to good quality. The Pearson correlation analysis revealed a significant positive relationship between salinity and all variables and negative relationship between water volume of dam and all variables. The CA in R mode grouped the 16 variables into 4 clusters of similar water quality characteristics and in Q mode, 160 sampling are grouped into 2 statistically groups where total dissolve solids and capacity seem to be major distinguishing factors between variables and years. The CA has classified the data into two groups, one formed by the dry years and the other formed by wet years. The PCA and the FA applied to the datasets have resulted in two significant factors which represent 69.92% of total variance. The first factor as salinization factor explained 58.68% of the total variance. The second factor, can be called organic pollution factor, explained 11.24% of the total variance. The results of discriminant analysis showed only 11 parameters were necessary in the temporal variations analysis, affording more than 90% correct assignations.

Discriminant analysis application in spatiotemporal evaluation of water quality in South Florida

The discriminant analysis (DA) method was used to differentiate and classify the water quality of three major rivers in South Florida. In this study, DA was used to assess the water quality and evaluate the spatial and temporal variations in surface water quality in South Florida. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. It was found by the stepwise DA that five variables (chl-a, dissolved oxygen (DO), total Kjeldahl nitrogen (TKN), total phosphorus (TP) and water temperature) are the most important discriminating water quality parameters responsible for temporal variations. Spatial variation in water quality was also evaluated and identified five variables (TKN, TP, ammonia-N, magnesium, and sodium) and seven variables (chl-a, DO, TKN, TP, ammonia-N, magnesium, and chloride) as the most significant discriminating variables in the wet and dry season, respectively of three selected rivers in South Florida. It is believed that the results of apportionment could be very useful to the local authorities for the control and management of pollution and better protection of important riverine water quality.

Characterizing and explaining spatio-temporal variation of water quality in a highly disturbed river by multi-statistical techniques.

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran’s index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.

Assessment of Seasonal Variations in Surface Water Quality of Cooum River in Chennai, India – A Statistical Approach

&lt;div&gt; &lt;p&gt;Rapid development in industrial sectors and population expansion has adverse impact on the &lt;em&gt;Cooum &lt;/em&gt;river basin, especially Chennai region. The present study focuses on the assessment of seasonal variation in surface water quality of &lt;em&gt;Cooum &lt;/em&gt;river basin. The samples were collected seasonally and are categorized as pre-monsoon, monsoon and post-monsoon during March 2013 to March 2014. Eighteen physicochemical parameters were assessed for eleven different samples collected along the channel of C&lt;em&gt;ooum&lt;/em&gt; river basin. Statistical tools such as correlation analysis scatter plots, box plot and multivariate tools such as cluster analysis and principal component analysis were applied to categorize the &lt;em&gt;Cooum&lt;/em&gt; river water quality. From the data sets, the ionic concentration, organic loads exhibits positive correlation (R&lt;sup&gt;2&lt;/sup&gt;&amp;gt;0.7) for all three seasons. Also, box plot and scatter plot results revealed that during post-monsoon season the ionic concentration along with organic and inorganic levels were slightly higher than monsoon and pre-monsoon. Similarly, multivariate statistical tools such as principle component analysis (PCA) indicate that the ionic concentrations and organic load contributed more than 50% of variance while cluster analysis (CA) reveals that nature of pollutant load among the sites.&lt;/p&gt; &lt;/div&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin, China

Multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River (SSHR) basin in China, obtained during two years (2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites. The results showed that most of physicochemical parameters varied significantly among the sampling sites. Three significant groups, highly polluted (HP), moderately polluted (MP) and less polluted (LP), of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics. DA identified pH, F, DO, NH3-N, COD and VPhs were the most important parameters contributing to spatial variations of surface water quality. However, DA did not give a considerable data reduction (40% reduction). PCA/FA resulted in three, three and four latent factors explaining 70%, 62% and 71% of the total variance in water quality data sets of HP, MP and LP regions, respectively. FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities (point sources: industrial effluents and wastewater treatment plants; non-point sources: domestic sewage, livestock operations and agricultural activities) and natural processes (seasonal effect, and natural inputs). PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters (about 80% reduction) as the most important parameters to explain 72% of the data variation. Thus, this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and, in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.

Assessment of temporal and spatial variations in surface water quality using multivariate statistical techniques: A case study of Nenjiang River basin, China

Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years (2012–2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis (HCA) grouped 12 months into three periods (LF, MF and HF) and classified 10 monitoring sites into three regions (LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis (PCA)/factor analysis (FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.

Spatial and Temporal Variations in Surface Water Quality of Pithoragarh District, Uttarakhand (India)

The water quality of six drinking surface water sources of Pithoragarh district of hilly state Uttarakhand has been accessed. The spatial and temporal variations of water quality parameters have been explained with the help of Box and Whisker plots. The selected surface water sources of Pithoragarh district were evaluated on pre-monsoon and post monsoon basis for two consecutive years of 2010 and 2011. The analyzed water quality data was interpreted through various statistical tools and softwares viz. Box and Whisker Plots, Piper and Trilinear Diagram, Analysis of Variance (ANOVA), Pearson Correlation Coefficient and Water Quality Index (WQI). The seasonal variation in water quality parameters in surface water might be ascribed mainly due to anthropogenic and hydro-geological activities. Further, Pearson Correlation Coefficient revealed positive and negative correlations, which indicated the interrelationships and interactions between certain water quality parameters. The WQI values pointed out that some of the water sources fall under poor, very poor and unsuitable categories.

An integrative assessment of environmental degradation of Caveira abandoned mine area (Southern Portugal)

Since the end of the 1990s the Portuguese Iberian Pyrite Belt (IPB) is a typical European post-mining region with significant problems related to acid mine drainage (AMD), metal dispersion, mine waste management and unsafe mining infra-structures. The Portuguese government is providing particular attention on the sulphide abandoned mines and doing considerable investments on the mining recovery all over the country.The former Caveira mine was closed in the 1980s. It is considered an extremely impacted site due to the dimension of the areas affected by mining activities. Tailings, mine addicts and associated waste rock dumps, resulting from 129years of pyrite and Cu exploitation, are spread along the Grândola stream. Despite the semi-arid climatic conditions of the area, the tailings are considerably eroded by the surficial waters, particularly during rainfall events.The past mining and smelting activities have resulted in severe contamination of the Grândola stream and its tributary by AMD (pH<2) as well as degradation of surrounding stream sediments, soils and vegetation. In order to evaluate possible environmental risks, a sediment and surface water survey was carried out downstream the Caveira mine. The acidic effluent and mixed stream water show high Al, As, Ca, Cd, Cu, Fe, Hg, Mg, Mn, Ni, Pb and SO4 concentrations, with several of these contaminants exceeding local and/or surface water quality standards. The data show a strong seasonal variation of surface water quality with poorer water quality during the dry and rainy seasons caused by evaporation and efflorescent salt dissolution, respectively. The variable flow regime at the local streams causes dilution of AMD rich in trace metals reaching background within 14km downstream. The potential toxicity of stream metal concentrations was determined using cumulative criterion unit (CCU) scores and the modified AMD index (MAMDI), which highlighted As, Cd, Cu, Pb and Zn as the major sources of potential chronic stream toxicity, with emphasis on winter season. Although the threshold of the likely harm to aquatic life is exceeded at all sites, the two indexes highlight differences relating to the extension of contamination effects. The Average Index of Toxicity (AIT) showed that sediment contamination is very high even when the distance to mine promoted a decreasing in water metal concentrations, which are being precipitated in the sediments due to pH increase.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Assessment of Seasonal Variations in Surface Water Quality of Bageshwar District, Uttarakhand, India for Drinking and Irrigation Purposes

The seasonal variation in surface water quality of district Bageshwar, Uttarakhand (India) has been evaluated for 3 years from 2010 to 2012 for determining the suitability of water for drinking and irrigation needs. Water samples collected from different drinking water sources during pre-monsoon and post-monsoon seasons in each year were analysed for 23 water quality parameters including physico-chemical and metal analyses. The analysed water quality parameters show seasonal variation and low concentration in post-monsoon season compared to pre-monsoon season due to dilution effects. The Box and Whisker plots indicated the dominance of major cations and anions in order of Ca2+ > Mg2+ > Na+ > K+ and HCO3− > SO42− > Cl− in both seasons, respectively. Piper trilinear diagram showed that most of the water samples fall in Ca–Mg–HCO3 hydrogeochemical facies. The water quality index revealed deteriorated water quality at some of the sources during pre- and post-monsoon seasons. The Wilcox diagram and calculated sodium adsorption ratio, residual sodium carbonate and sodium percent values indicate that the water was suitable for irrigation purposes in both the seasons. The results concluded that water quality at some of the locations is deteriorating and needs proper monitoring to preserve and maintain its quality to reduce hazards to local population.