Water Quality Assessment Research Articles

Assessment of water quality and trophic state using a multipara-metric index-based approach for Lake Guineo, Lake Carraizo and Lake Cidra, Puerto Rico

To ensure a safe drinking water supply, it is necessary to protect water quality. The proposed project aims to understand water quality data analyzed from a temporal and spatial perspective on the following physiochemical parameters: DO, pH, temperature, Secchi disc turbidity, conductivity, nitrate, chlorophyll α & phosphate. The results provide information to evaluate the status of the water quality and its suitability for human consumption. These variables related to the general characteristics of water quality and trophic level were used for calculation of a water quality index (WQI) and trophic state index (TSI) to assess 3 lakes in Puerto Rico: Lake Cidra, Lake Carraizo and Lake Guineo. Presence or absence of macrophytes were monitored during six months in the three reservoirs in Puerto Rico as well as the parameters mentioned above. The objective of this investigation is to discuss the trophic state and water quality in 3 different trophic state reservoirs in Puerto Rico. Results were correlated with the density of the macrophyte species, Eichhornia crassipes. Variations of water in situ multiparametric sampling were recorded for 6 months. Thus, integrating components that define the status of reservoirs using multiparameter measurements from various perspectives; a better diagnosis is expected regarding the water quality assessment. This investigation aims to undertake a comprehensive study of water quality by implementing the WQI and TSI indices, considering physical, chemical, and biological parameters with respect to eutrophication and ecological considerations for three reservoirs in the Puerto Rico, providing information regarding the quality of water and trophic state of the chosen reservoirs. Overall WQI results revealed that the water quality improved after the Hurricane Fiona’s occurrence. Trophic State Index after the hurricane showed slight fluctuations in the reservoirs.

Water quality assessment of groundwater resources in rural areas of Karachi, Pakistan

Water quality assessment of groundwater resources in rural areas of Karachi, Pakistan

Chemical Characteristics and Water Quality Assessment of Groundwater in the Dongjiang-Hanjiang River Basin, China

Groundwater plays an important role in the rapid economic development of the Dongjiang-Hanjiang River Basin. This paper uses the Gibbs model, ionic ratio coefficient analysis, factor analysis, water quality index method and human health risk assessment model to estimate the chemical properties and quality of groundwater in the Dongjiang-Hanjiang Basin. The ion concentration and proportion of SO42- water in the Hanjiang River Basin are higher than those in the Dongjiang River Basin. The groundwater ions come mainly from the dissolution of carbonate rocks, silicate rocks and saline rocks, some of which come from human activities such as industry and mining. Groundwater composition is controlled by rock dissolution, alternating cation adsorption, and human activities. The proportion of rock dissolution is the greatest, but the influence of human activities is obvious. The intensity of human activities such as industry, mining and agriculture is greater in the Hanjiang River Basin than in the Dongjiang River Basin. The overall groundwater quality is poor, and the Dongjiang River Basin is better than the Hanjiang River Basin. The above-standard factors are mainly pH, I−, NO3−, NH4+, As and Pb2+. Nitrate pollution in groundwater is harmful to health and the health risk of children from nitrate is higher than that of adults. The health risk in the Hanjiang River Basin is higher than that in the Dongjiang River Basin. This can serve as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention and control.

Environmental and Hydrological Synergies Shaping Phytoplankton Diversity in the Hetao Irrigation District

Phytoplankton are crucial primary producers in freshwater ecosystems, driving matter and energy flow across trophic levels, essential for biodiversity and ecological balance. Most research emphasizes environmental factors shaping their diversity, while the role of hydrological connectivity remains poorly understood. This study collected 81 phytoplankton samples from the Hetao Irrigation District along a gradient from upstream to downstream and utilized high-throughput sequencing to evaluate the spatial distribution patterns of phytoplankton diversity. The study analyzed the impacts of environmental factors, hydrological connectivity (water surface ratio, Wp), and human activities (land-use intensity, LUI) on phytoplankton diversity. The results revealed that the phytoplankton community comprised 9 phyla, 158 families, 378 genera, and 1189 species. There were significant differences in phytoplankton diversity among different water bodies, with a gradual increase in phytoplankton diversity from west to east across the five major irrigation areas. Lake Ulansuhai had relatively low phytoplankton diversity. The ASV number, Chao1 index, and ACE index showed significant positive correlations with dissolved oxygen (DO), pH, and water temperature (WT). The Shannon index and Pielou’e evenness (Pielou_e) index showed significant positive correlations with the water surface ratio (Wp). The partial least squares model indicated that environmental factors directly influenced phytoplankton diversity. Hydrological connectivity indirectly affected phytoplankton diversity by altering environmental factors. We emphasize that hydrological connectivity is as important as environmental factors in driving phytoplankton diversity in the Hetao Irrigation District. This study provides key insights for water quality assessment and biodiversity conservation in the region.

Seasonal Variation of Physicochemical Properties in the Lower River Benue, Nigeria

The study investigates the impact of seasonal changes on water quality in the River Benue, Nigeria. Conducted over eighteen months from January 2020 to June 2021, the research focuses on three locations: Ibi, Lau, and Mayo-Ranewo. Key physicochemical parameters such as temperature, pH, turbidity, conductivity, Biochemical Oxygen Demand (BOD), hardness, dissolved oxygen (DO), fluoride, and nitrate were measured to assess water quality. The findings reveal significant seasonal variations in these parameters, influenced by rainfall patterns, land use, and anthropogenic activities. Temperature ranged from 25.56°C to 30.70°C, remaining within acceptable limits for tropical waters, supporting stable biological processes. However, turbidity levels exceeded recommended values, indicating the presence of suspended solids that could impair water quality and harm aquatic life. Total dissolved solids (TDS) and conductivity showed potential contamination risks, likely due to agricultural runoff and urban discharges. Dissolved oxygen (DO) levels were generally adequate, especially during the wet season, enhancing oxygen content through increased photosynthetic activity and water inflows. However, BOD spiked with rainfall, signalling organic pollution and potential eutrophication risks. Seasonal shifts in pH and water hardness reflected changes in runoff and photosynthesis, with values largely within acceptable ranges for aquatic life. Fluoride and nitrate concentrations increased in the wet season, primarily due to agricultural runoff, highlighting the influence of land-based activities on water quality.

Urban transformation of lakes and its impacts on a lake series: A case study of the yele mallappa shetty lake series Bengaluru, India.

The interconnected systems of lakes of Bengaluru, India, experience tremendous stress due to unsustainable urbanisation. Traditional lake-centric research approach struggles to capture and address this issue due to the cascaded nature of impacts in interconnected lake systems. Hence, this study assesses the impact of urbanisation on a lake series scale, focusing on the Yele Mallappa Shetty Lake series (YMSLS). Land use land cover change analysis (1993-2023) showed that in 1993, the YMSLS catchment area was dominated by open spaces (53.4%) and agriculture land and vegetation (35%), with built-up areas covering only 7.2%. By 2023, the built-up area has expanded to 34.6% and became the dominant land use. This rapid urbanisation has led to increased water surface area from wastewater influx, causing significant weed growth and water pollution in the lakes. Lake water quality assessment showed that faecal coliform, total coliform, dissolved oxygen, and biochemical oxygen demand levels exceeded standard limits with spatio-temporal variations driven by interconnectivity. Pollutant flushing during monsoon reduced pollution load in upstream lakes, but high wastewater inflow made downstream lakes, particularly the end lake, highly polluted but perennial. Increased water availability expanded the user base of the end lake, but traditional uses have declined and coping strategies of users, such as wastewater lift irrigation from the lake, became unsustainable. The study indicates that spatial interdependence and cascading effects necessitate a lake series approach for assessing the impacts of urbanisation on interconnected lake system, which is crucial for enhancing water security and preparedness for future extreme events.

Assessment of Water Quality in Pangani, Kayole and Dandora Boreholes in Nairobi County, Kenya

Assessment of Water Quality in Pangani, Kayole and Dandora Boreholes in Nairobi County, Kenya

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge. First, an appropriate function is fitted to the WQVs vertical profile, and second, the parameters of that function as representative of the WQVs vertical profile are estimated using machine learning techniques. The model's inputs are day, maximum depth of point, and remote sensing data. Finally, PAWN sensitivity analysis is applied to show the extent to which each input influences different parts of the vertical profile. This method is applied in the Wadi Dayqah Reservoir, the largest dam in Oman, to evaluate water temperature, dissolved oxygen, pH, and chlorophyll-a profile. The results show that the predicted profiles are properly representative of in situ measurements, with a mean absolute error of 0.28 °C, 0.25 mg/L, 0.052, and 0.33 μg/L on test data sets of water temperature, dissolved oxygen, pH, and chlorophyll-a, respectively. Finally, PAWN sensitivity analysis illustrates that satellite data not only influence the parameters representing surface WQVs but also contribute to the estimation of other curve parameters.

Water quality assessment for aquacultivation in Quang Ngai city, Quang Ngai province

Aquatic product development and growth are significantly influenced by the water supply used for aquaculture. Evaluating the water quality available for this activity is vital since Quang Ngai city has been a major hub for the growth of aquaculture in Quang Ngai province. Twelve surface water quality parameters were monitored and analyzed at eight sampling points in the river basins of An Nghia and Tinh Khe communes from May to September 2023. The Water Quality Index (WQI) was calculated, and methods including Principal Component Analysis (PCA), calculation of environmental variable weights based on principal components (PC), and Pearson correlation were used to assess the correlation of the investigated factors. In the river basin of An Nghia and Tinh Khe communes, 12 criteria for the assessment of surface water quality were tracked and examined using 8 sampling locations between May and September in 2023. The degree of linkage was assessed using principal component analysis (PCA), weighting environmental factors based on principal components (PC), and Pearson correlation. The water quality index (WQI) was calculated between the factors of the research. The findings show that: (1) The parameters exceeded the permitted limit: DO, Alkalinity, NH4+, TP, Vibrio SPP, Coliform, TOC, and TN. The two metrics with the greatest levels of pollution among them are Vibrio SPP and NH4+. The WQI index ranges from 52-74 and is within the average water quality range (51-75); (2) PCA analysis reveals that only the first two principal components, PC1 and PC2 can account for 74.39% of the variance, with NH4+ (0.089) having the highest weight; (3) NH4+ and Vibrio SPP content have a strong correlation (r = 0.95), indicating that both of these parameters can be used to evaluate water quality in the study area in general.

Intelligent water quality prediction system with a hybrid CNN–LSTM model

ABSTRACT Water pollution remains a longstanding challenge globally, prompting substantial investment in water quality protection. The integration of advanced machine learning models offers promising avenues for accurate water quality prediction, enabling proactive measures to safeguard water sources. Presently, water quality assessment relies predominantly on physical and chemical metrics. This study developed MultiLayer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and a hybrid CNN–LSTM model to forecast pH and dissolved oxygen (DO) levels. Results demonstrated the hybrid model's superior performance, with mean squared errors (MSEs) of 0.0015 and 0.0361 for pH and DO prediction, respectively. For Water Quality Classification (WQC), Random Forest (RF), k-Nearest Neighbors (kNNs), Support Vector Machine (SVM), and Light gradient Boosting Machine (Light GBM) were employed, with SVM achieving the highest accuracy at 88.75%. The research underscores the effectiveness of the CNN–LSTM model in predicting pH and DO levels. Leveraging these predictions as inputs to the SVM model offers valuable insights, particularly in regions where conventional monitoring methods face limitations. This streamlined approach, requiring only two parameters, signifies a significant advancement in accurate water quality prediction.

A Comprehensive Review of Water Quality Analysis

The effluent discharge of domestic wastewater and industrial effluents into watercourses has necessitated the development of monitoring systems with the purpose of characterizing levels of pollution. Substantial levels of pollution in emerging nations have been exacerbated by rapidly growing populations and inefficient enforcement of sustainable management initiatives. The Water Quality Index (WQI) can help fill the gap between water quality reporting and monitoring by offering a simple and effective way to assimilate and communicate results from a large amount of data. WQIs are the kind of communication tools that can facilitate knowledge sharing between scholars and the general public. The degradation of natural water resources, such as lakes, streams, and estuaries, is the most significant problem facing humanity. Water that is not clean has far-reaching effects on all aspects of life. Water resource management is therefore essential if it is to maximize water quality. If data are analyzed and water quality can be predicted in advance, water pollution may be efficiently addressed. Although this subject has been the subject of numerous earlier studies, additional research is still required to fully understand the effectiveness, reliability, accuracy, and application of the present approaches to water quality management. In this research work, a comprehensive review has been conducted on water quality assessment using artificial techniques. It reviews 75 research works on water quality analysis. The data collected in each research work have been analyzed. Moreover, the type of water resource surface water, groundwater, drinking water as well has been analyzed. The parameters considered in each work are “Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD), Total Dissolved Solids (TDS), and Chemical Oxygen Demand (COD), chloride, hardness, alkalinity, nitrate–nitrite”. In addition, the Artificial Intelligence technique utilized for water quality analysis is also assessed. Finally, the research gaps identified in water quality detection are exhibited.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

Spatial variation in water quality of the Burhi Gandak River: a multi-location assessment

The Burhi Gandak River, a significant tributary of the Ganga River and a vital water source in Bihar, India, is facing critical water quality degradation due to rapid population growth, urbanization, and industrial activities. This study conducts a detailed water quality assessment of the river, focusing on the effects of these anthropogenic factors across different locations, seasons, and industrial zones. Water samples were collected and analyzed for key parameters, including pH, total dissolved solids (TDSs), total hardness (TH), biological oxygen demand (BOD), and dissolved oxygen (DO). The results indicate significant spatial and temporal variations, with water quality deteriorating notably in areas like Samastipur and Khagaria, where industrial and urban activities are more concentrated. For instance, BOD levels increased from 7.5 mg/L to 9.5 mg/l as the river flows through urban Samastipur, signaling a decline in water quality. Additionally, sugar mills located along the river contribute to higher pollution levels during operational months, especially in Lauria and Sugauli. Seasonal analysis shows that the lean season experiences the highest levels of degradation, while monsoon floods increase suspended solids due to sediment inflow. These findings emphasize the urgent need for improved regulation of industrial discharge and urban waste management to protect the water quality of the Burhi Gandak River and the livelihoods dependent on it.

Using IoT for Cistern and Water Tank Level Monitoring

This paper proposes an experimental design to publish online the measurements obtained from four sensors: one sensor inside a cistern measures the level of drinking water, another sensor in a water tank monitors its level, a third sensor measures water flow or pressure from the pipes, and a fourth sensor assesses water quality. Several tank filling and emptying tests were performed. Experimental results demonstrated that if the cistern perceived that there was no water in the tank, it turned on the water pump to fill the tank to 100% of its storage capacity; while this was happening, the water level in the cistern and tank, the flow of water from the piped water, and the quality of the water could be visualized on a dashboard. In short, this proposal monitors water levels and flows through the Internet of Things. Data collected by sensors are posted online and stored in a database.

Water Quality Assessment and Prevalence of Waterborne Diseases within Chikun Local Government Area, Kaduna State-Nigeria

Study’s Excerpt Water quality and the prevalence of waterborne diseases in Chikun Local Government Area, Kaduna State, were assessed. Microbiological analyses revealed microbial, helminth and protozoan contamination in water sources while Physicochemical analyses revealed heavy metal contamination. There is a need for enhanced water treatment and community awareness for improving water safety and public health in the region. Full Abstract The health and well-being of individuals in a nation are intrinsically tied to the quality of water available for consumption. Access to high-quality water plays a vital role in supporting proper nutrition, ensuring food security, and preventing waterborne diseases. Therefore, this study was carried out to assess water quality and prevalence of waterborne diseases within Chikun Local Government Area, Kaduna State. A cross-sectional study was conducted between January and June 2024. Data were collected through the administration of questionnaires (400 respondents) and laboratory water analysis. Water samples were analyzed from various sources involving boreholes, rivers and wells through composite sampling and physicochemical and microbiological analyses. Results showed samples from Unguwan Maigero and Tricania possessed varying physicochemical properties, while water from Goningora and Nasarawa revealed high turbidity and heavy metal contamination. Chloride levels were below 500 ppm, and sulphate concentrations ranged from 10.76 to 378.3 ppm. Sodium levels ranged from 19.5 to 110 mg/l, and potassium levels exceeded the permissible limit in some samples. Total coliform counts exceeded national limits (zero coliform per 100ml of water), indicating significant microbial contamination. Parasitological analysis indicated helminths and protozoan contamination in non-borehole sources. Findings showed 41% prevalence of waterborne diseases with diarrhea and typhoid. The study indicates that community awareness, appropriate water treatment, surveillance, and monitoring are essentials for assuring the availability and utilisation of safe and high-quality drinking water in Chikun LGA, Kaduna State.

Automated Water Quality Assessment Using Big Data Analytics

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest accuracy at 66.77% after significant data preparation and training, followed by Gradient Boosting at 66.01% and SVM at 62.80%. This shows that Big Data analytics and machine learning algorithms can interpret complex water quality data for public health and natural resource management. The Random Forest classifier and SVM in this study accurately calculate water potability. Prediction algorithms consider water cleanliness data and may aid public safety and water resource monitoring.

Ecological Assessment of Phytoplankton Diversity and Water Quality to Ensure the Sustainability of the Ecosystem in Lake Maybalyk, Astana, Kazakhstan

Microalgae in planktonic communities are the main producers of biomass in lake ecosystems; however, their stability is influenced by many environmental factors. This study aims to assess the ecological state of Lake Maybalyk, located in Astana (Kazakhstan), based on the study of the taxonomic diversity and structure of phytoplankton, zooplankton, and the physico-chemical properties of the water. From 2019 to 2021, samples were taken for phytoplankton analysis, hydrochemical analysis of the water, zooplankton, and saprobiological analysis of the algocenosis. The study also investigated the main morphometric parameters of the lake, as well as the composition of hydrobionts, such as zooplankton, zoobenthos, and ichthyofauna. The analysis of phytoplankton revealed the presence of 97 species and intraspecific taxa of microalgae, with 71 types of microalgal indicators indicating water saprobity. The planktonic algoflora in Lake Maybalyk is predominantly composed of diatoms (Bacillariophyta) and green algae (Chlorophyta), which play a vital role in oxygen production and the food chain within the reservoir. Based on the Pantle–Buck saprobity index (2.15–2.5), the water quality in Lake Maybalyk is classified as moderately polluted. The assessment of the water quality, considering the number and composition of indicator phytoplankton species, places Lake Maybalyk in class III (β-mesosaprobic). The hydrochemical indicators align with the hydrobiological indicators, confirming the water quality as class III. The trophic status of the reservoir, during the study period, can be described as average. The obtained data on both the hydrobiological and hydrochemical indicators correlate, suggesting satisfactory water quality and the ability of the reservoir to purify itself. This study contributes to the sustainable management of water resources, by providing essential data on the ecological state of Lake Maybalyk. The results underscore the importance of continuous biomonitoring, with microalgae as indicators of water quality, which is crucial for developing effective ecosystem conservation strategies.

Identifying the pattern of shallow groundwater hydrochemistry and its driving factors in a typical estuarine delta of Poyang Lake watershed, China: Insights into water quality assessment

Study regionA typical estuarine delta of Gan−Xiu River within Poyang Lake watershed, situated in the north of Jiangxi Province, China Study focusAlthough groundwater is considered as a crucial water resource for social development and public health, it presents significant challenges in preventing the deterioration of groundwater quality in the seasonal floodplain regions. Hence, this study focuses on the spatiotemporal variations of ionic concentrations of shallow groundwater in an estuarine delta of Gan-Xiu River in Poyang Lake watershed, which is greatly complicated by complex hydrological regimes and intensive anthropogenic activities such as cropland fertilizer. The potential factors controlling the ionic concentrations were identified using self-organizing map clustering and Piper diagram approaches. Moreover, the overall water quality of shallow groundwater was assessed according to the ionic compositions. New hydrological insights for the region(1) Shallow groundwater chemistry was influenced by weathering silicate and carbonate from ionic and Gibbs plots, which is highly associated with the distribution of the carbonate rock fissure-karst aquifer in the study region. (2) The contribution of groundwater to river water varied from 53.2 % to 71.5 % via isotopic analysis, suggesting that groundwater dominate the component of river water. (3) Most samples from cluster types are suitable for drinking and irrigation use except for sites from Cluster 6, which were characterized by the higher concentrations of Cl−, NO3− and SO42−.

Analytical Examination of the Nutritional Status of Drinking Water Resources: A First Case Application in Africa

Water quality assessment is crucial for public and ecosystem health, yet traditional approaches often overlook the nutritional aspects of drinking water. This study introduces the Drinking Water Nutritional Quality Index (DWNQI) to Africa, presenting its first application in the continent. The research evaluated the nutritional status of drinking water resources in semi-urban areas of Southeast Nigeria, shifting from a contaminant-focused paradigm. Water samples were strategically collected from various drinking water sources. Five key elements (Na+, Mg2+, Ca2+, Mn2+, and Zn2+) that play crucial roles in both human health and environmental processes were considered. The DWNQI model revealed a concerning nutritional trend, with DWNQI scores ranging from 9.76 to 78.54 (average 32.02). The analysis showed that 73.33% of samples fell within the “Poor” category, 13.33% in “Marginal,” 6.67% in “Fair,” and 6.67% in “Good” categories, with no samples reaching the excellent range, indicating a low nutritional water quality trend in the area. Sensitivity analysis reveal that Mg2+and Ca2+ exerted the most influence on the DWNQI scores for the studied water samples. Removal of Mg2+ from the index resulted in a 23.77% decrease in the average DWNQI score, while removal of Ca2+ led to a 23.42% decrease. Key recommendations include implementing targeted mineral enrichment programs and developing public health initiatives to address potential mineral deficiencies. This research contributes to environmental sustainability and public health by providing a new perspective of water quality, potentially informing water management policies and guiding public health interventions in African settings and beyond.

Physicochemical and Biological Water Quality Assessment of Local Ponds of Tumakuru, Karnataka, India

Water is an essential component of life. The developments, urbanization and load of the various pollutant sources lead to the deterioration of the ponds. Therefore, the study was conducted to assess the physicochemical and biological characteristics of water from the regional ponds of Tumakuru, Karnataka, India. The water samples from six ponds were taken for the study. The physicochemical parameters showed that the pH of the ponds was found to be 7.35, 6.90, 7.21, 7.91, 7.16 and 8.10, respectively. EC was found to be 0.60, 0.19, 0.42, 0.22, 1.43 and 1.0, respectively. The high value of pH and EC shows its nutrient-rich and alkaline nature. Cl was found to be 4.0, 3.0, 4.4, 2.6, 2.4 and 6.8, respectively. Calcium hardness was observed as 1.9, 2.58, 2.34, 1.15, 1.11 and 12.19, respectively. The Biological oxygen demand (BOD) of the samples was found to be 0.1, 1.6, 0.8, 3.3, 1.2 and 0.4 mg/lit, respectively. Qualitative analysis through most probable number (MPN) method was employed. MPN index of water samples which tested satisfactory was < 2 per 100 mL and MPN index of water sample no. WS1, WS3 and WS5 respectively whereas unsatisfactory ranged from 38 to > 1600 per 100 mL of water sample no. WS2, WS4 and WS6, respectively showed that the water was not suitable for domestic uses having s high value of bacterial load. In order to stop the spread of intestinal illnesses, drinking water sources should undergo routine assessment of physicochemical and microbiological quality tests.