Improved Water Quality Monitoring Research Articles

Centralized or Onsite Testing? Examining the Costs of Water Quality Monitoring in Rural Africa.

Rural water systems in Africa have room to improve water quality monitoring. However, the most cost-effective approach for microbial water testing remains uncertain. This study compared the cost per E. coli test (membrane filtration) of four approaches representing different levels of centralization: (i) one centralized laboratory serving all water systems, (ii) a mobile laboratory serving all systems, (iii) multiple semi-centralized laboratories serving clusters of systems, and (iv) decentralized analysis at each system. We employed Monte Carlo analyses to model the costs of these approaches in three real-world contexts in Ghana and Uganda and in hypothetical simulations capturing various conditions across rural Africa. Centralized testing was the lowest cost in two real-world settings and the widest variety of simulations, especially those with water systems close to a central laboratory (<36 km). Semi-centralized testing was the lowest cost in one real-world setting and in simulations with clustered water systems and intermediate sampling frequencies (1-2 monthly samples per system). The mobile lab was the lowest cost in the fewest simulations, requiring few systems and infrequent sampling. Decentralized testing was cost-effective for remote systems and frequent sampling, but only if sampling did not require a dedicated vehicle. Alternative low-cost testing methods could make decentralized testing more competitive.

Estrogenic activity in wastewater treatment plants through in vitro effect-based assays: Insights into extraction phase

Effluents of wastewater treatment plants can abundantly spread endocrine disrupting chemicals in the environment. To improve water quality monitoring, the use of effect-based tools that measure estrogenic activity has been suggested, however their results could be influenced by different factors.This study compared the estrogenic activity of wastewater samples extracted with two stationary phases and tested with two in vitro effect-based assays to investigate whether and how stationary phases and assays could influence biomonitoring data.During four seasonal periods, the effluents of six WWTPs located in northern Italy were sampled. After the extraction using two different stationary phases (HLB, C18), the samples (n = 72) were tested using two effect-based assays: a gene reporter luciferase assay on mammalian cells (MELN) and yeast estrogen screen assay (YES).The results showed that estrogenic activity of HLB extracts was significantly different from the activity of C18 extracts, suggesting that extraction phase can influence biomonitoring data. Moreover, the estrogenic activity was overall higher using gene reporter MELN assay than using YES assay, suggesting that, due to difference in cell membrane permeability and metabolic activation, the applied cell model can affect the biomonitoring results. Finally, from the comparison between the activity of the final effluent and the environmentally safe estrogenic levels in surface waters, MELN data suggested that the activity of this effluent may pose an environmental risk, while YES data showed that it should not be considered a threat to the receiving surface waters.This study pointed out that a standardized approach is needed to assess the estrogenic activity of waters; it reported important data to select the most suitable stationary phase for samples extraction (samples extracted with C18 sorbent showed higher estradiol equivalent concentration values) and the most appropriate bioassay (gene reporter luciferase MELN assay was more sensitive than YES assay) to assess the environmental risk, thus protecting human health.

Inter and transdisciplinarity strategies for evaluating and improving water quality monitoring systems: Uruguay as a study case

Developing robust systems for monitoring and evaluating water quality is crucial for assessing ecosystem integrity and the impacts of human activities on nature. It also enables the assessment of water management effectiveness, governance systems, and the design and evaluation of public policies. However, designing such monitoring programs is complex due to multiple constraints like eco-hydrological knowledge, economic resources, human capital availability, and governance dynamics. This study combines quantitative and qualitative analyses (virtual watershed methodologies, empirical modeling, and theoretical frameworks of water governance) to evaluate the robustness of water quality monitoring systems and identify strengthening alternatives (including institutional design and public policy). The inter and transdisciplinary strategy is tested in the evaluation of eutrophication processes in Uruguay. Major spatial patterns of water quality at a national scale were identified, highlighting the influence of land use, soil types, point sources of pollution, and livestock on nitrogen and phosphorus concentrations. Current monitoring efforts and spatial coverage fall short of adequately addressing water management needs, especially in Uruguay's socio-economic context. Based on the weaknesses identified, an increase in the number of stations (and their spatial distribution) is proposed to have a better representation of biogeophysical and socio-economic conditions diversity. The challenge involves an important transformation (i.e. establishing a network system of public institutional nodes at national and regional levels) due to the country's centralism, fragmented water governance system, and scarce economic assets.

PathoSense: a rapid electroanalyticaldevice platform for screening Salmonella in water samples.

Salmonella contamination is a major global health challenge, causing significant foodborne illness. However, current detection methods face limitations in sensitivity and time, which mostly rely on the culture-based detection techniques. Hence, there is an immediate and critical need to enhance early detection, reduce the incidence and impact of Salmonella contamination resulting in outbreaks. In this work, we demonstrate a portable non-faradaic, electrochemical sensing platform capable of detecting Salmonella in potable water with an assay turnaround time of ~ 9min. We evaluated the effectiveness of this sensing platform by studying two sensor configurations: one utilizing pure gold (Au) and the other incorporating a semiconductor namely a zinc oxide thin film coated on the surface of the gold (Au/ZnO). The inclusion of zinc oxide was intended to enhance the sensing capabilities of the system. Through comprehensive experimentation and analysis, the LoD (limit of detection) values for the Au sensor and Au/ZnO sensor were 0.9 and 0.6CFU/mL, respectively. In addition to sensitivity, we examined the sensing platform's precision and reproducibility. Both the Au sensor and Au/ZnO sensor exhibited remarkable consistency, with inter-study percentage coefficient of variation (%CV) and intra-study %CV consistently below 10%. The proposed sensing platform exhibits high sensitivity in detecting low concentrations of Salmonella in potable water. Its successful development demonstrates its potential as a rapid and on-site detection tool, offering portability and ease of use. This research opens new avenues for electrochemical-based sensors in food safety and public health, mitigating Salmonella outbreaks and improving water quality monitoring.

High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.

Longitudinal dissolved oxygen patterns in Atlantic salmon aquaculture sites in British Columbia, Canada

Dissolved oxygen (DO), an important water-quality parameter required to support aquatic life, is a critical factor for determining the general biological health of the aquatic ecosystem, and the concentration of DO is a critical factor in determining salmon growth and welfare. This study used longitudinal DO concentration, recorded hourly from 21 aquaculture sites, each with loggers in three separate cages, in four areas in British Columbia, Canada, between 2015 and 2017. The measurements were evaluated based on the recommended DO concentrations for protection of salmonids from hypoxia. Using a two-stage time-series analysis, we described variations in DO concentrations measured over the study period and their associations with environmental factors. Based on the water quality criteria for DO concentration, 42.3, 56.5, and 1.2% of the hourly DO data from the overall 21 aquaculture sites were classified as ‘optimal’, ‘sub-optimal’, and ‘stressed’, respectively. The frequency of hypoxic episodes differed substantially among seasons, aquaculture sites and even among cages within sites. The effects of environmental variables on DO concentration had markedly different patterns depending on the season. Significant associations with DO concentrations were observed for temperatures in the summer and winter months, and both wind direction and remotely-sensed estimated absorption from phytoplankton [aph(443)] in the winter months. The time-series regression model results showed overall (year-round) associations of temperature, wind speed, and aph(443) with DO concentrations. Describing DO measurements at these aquaculture sites provided an understanding of how much they deviated from the recommended DO concentrations, as well as provide baseline information for future water resource planning, including continued and improved water quality monitoring in aquaculture areas.

Harmonizing Environmental Protection Integration of Monitoring and purification Systems

Real-time water quality monitoring has advanced significantly with the integration of IoT-enabled pH and temperature sensors and the creation of a mobile application. This initiative intends to improve environmental management efforts by regularly measuring the temperature and pH of water bodies and giving stakeholders access to data that can be used to make decisions. Real-time pH and temperature data is collected by the monitoring system using Internet of Things (IoT) technology, and it is then sent to a web-based interface for analysis and visualization. The web interface is complemented with a mobile application that gives stakeholders access to temperature and pH data in real time, lets them get warnings, and lets them submit observations from their mobile devices.In order to further improve water quality monitoring and management techniques, future additions will involve the integration of advanced analytics, remote calibration and maintenance capabilities, and community monitoring networks. All things considered, this initiative advances environmentally responsible water management techniques and environmental care.

Exploring the Confluence of Nanotechnology and Biotechnology in Water Sports: Innovative Solutions for Enhanced Performance and Sustainability

The popularity of water sports and swimming necessitates implementing efficient monitoring systems to guarantee water safety and purity. The current techniques used for water quality assessments need help delivering instantaneous, precise information for prompt decision-making within water sports. This requires the use of creative strategies that make use of cutting-edge technology. The research suggests using the Nanotechnology and Biotechnology-based Water Sports Monitoring System (NTBT-WSMS) to address these difficulties. This method combines the advanced disciplines of nanotechnology and biotechnology to improve water quality monitoring in areas specifically designated for water sports and swimming. The NTBT-WSMS incorporates nanosensors for immediate identification of pollutants, biotechnological techniques for evaluating microorganisms, and an intelligent data integration system for thorough surveillance. The proposed NTBT-WSMS yields remarkable average values for critical criteria, such as Flexibility (88.71%), Accuracy (94.55%), Processing Speed (502.56 fps), Integration Efficiency (88.1%), and Reliability (93.87%).

Quality index metrics with Bi-GRU-based water quality prediction

ABSTRACT The precise assessment of groundwater resources is significant in the face of growing water demands, environmental pollution, and degradation. However, traditional methods of water quality prediction are inadequate in dealing with large volumes of data or missing data. Machine learning-based prediction methods are being explored to address this issue. Currently, single-indicator techniques are commonly used, but they may not accurately forecast multimodal water quality or capture inter-indicator connections. In this study, a Bi-GRU is proposed to predicting water quality based on quality index data. To evaluate the performance of our proposed method, we utilize Kaggle Datasets, which provide a diverse range of water quality data. We compare our results with existing methods such as MLP, LSTM, and GRU, using metrics including accuracy, precision, recall, and f-score. Overall, our study demonstrates that the Bi-GRU model is highly effective in predicting water quality based on quality index data. The results of our experiments indicate that our proposed method surpasses traditional approaches like MLP, LSTM, and GRU in terms of accuracy, precision, recall, and f-score. These findings have significant implications for improving water quality monitoring and pollution prevention efforts, enabling better management of groundwater resources in the face of growing water demands and environmental challenges.

An IoT based smart water quality assessment framework for aqua-ponds management using Dilated Spatial-temporal Convolution Neural Network (DSTCNN)

Assuring the quality of water is crucial for the growth and survival of fish in aquaculture ponds. Traditional methods of water quality monitoring can be inefficient which makes real-time monitoring and decision is a challenging one. Some deep learning techniques have shown apparent in improving water quality monitoring and assessment process, but encounter some limitations like data-overfitting, interpretability, and finds difficulties in capturing complex spatial and temporal dynamics that have hindered their effectiveness. To overcome these challenges, we propose an enhanced Dilated Spatial-temporal Convolution Neural Network (DSTCNN) for water quality monitoring in aquaculture, which uses an IoT system setup for capturing real-time data inputs from aqua ponds. The water quality data captured through the IoT sensors is labeled as per the water quality index (WQI) standards for analysis. This labeled data is effectively classified into two categories by the proposed DSTCNN model based on their suitability for fish growth or potential to cause fish mortality. By the leveraging power of dilated convolutions, the DSTCNN architecture accurately handles the intricacies of both spatial and temporal data, enabling it to capture essential features and patterns across multiple snapshots. This capability empowers the model to truly comprehend the complex relationships inherent in spatiotemporal data. Furthermore, to address the concerns like overfitting due to complexity of data and enhance generalization, the proposed model employs a hybrid activation function that synergistically combines ReLU and sigmoid during the activation process. The proposed DSTCNN model has been implemented on real-time and public datasets and obtained 99.28% and 99.02% accuracy respectively, whereas the state-of-the-art PCR-GB model obtains 96.97% and 97.11% accuracy on real-time and public datasets respectively.

Rapid detection of live bacteria in water using nylon filter membrane-integrated centrifugal microfluidics

Water is one of the most indispensable elements for human beings. People can live without food for a couple of weeks but cannot live without water for a couple of days. Unfortunately, drinking water is not always safe around the world; in many areas, the water for drinking could be contaminated with various microbes. However, the total viable microbe count in water still relies on culture-based methods in laboratories. Therefore, in this work, we report a novel, simple, and highly efficient strategy to detect live bacteria in water via a nylon membrane-integrated centrifugal microfluidic device. A handheld fan and a rechargeable hand warmer were utilized as the centrifugal rotor and the heat resource for reactions, respectively. The bacteria in water can be rapidly concentrated >500-fold by our centrifugation system. After incubation with water-soluble tetrazolium-8 (WST-8), the color change of the nylon membranes can be visually interpreted directly by the naked eye or recorded with a smartphone camera. The whole process can be finished in 3 h, and the detection limit can reach 102 CFU/mL. The detection range ranges from 102 CFU/mL to 105 CFU/mL. The cell counting results of our platform are highly positively correlated with the results of cell counting by the conventional lysogeny broth (LB) agar plate approach or the commercial 3 M Petrifilm™ cell counting plate. Our platform provides a convenient and sensitive strategy for rapid monitoring. We highly anticipate that this platform can improve water quality monitoring in resource-poor countries in the near future.

Deep Learning Approaches for Numerical Modeling and Historical Reconstruction of Water Quality Parameters in Lower Seine

Water quality monitoring is essential for managing water resources and ensuring human and environmental health. However, obtaining reliable data can be challenging and costly, especially in complex systems such as estuaries. To address this problem, we propose a novel deep learning-based approach that uses limited available data to accurately estimate and reconstruct critical water quality variables, such as electrical conductivity, dissolved oxygen, and turbidity. Our approach included two tasks, numerical modeling and historical reconstruction, and was applied to the Seine River in the Normandy region of France at four quality stations. In the first task, we evaluated four deep learning approaches (GRU, BiLSTM, BiLSTM-Attention, and CNN-BiLSTM-Attention) to numerically simulate each variable for each station under different input data selection scenarios. We found that incorporating the quality data with the water level data collected at the various stations into the input data improved the accuracy of the water quality data simulation. Combining water levels from multiple stations reliably reproduced electrical conductivity, especially at stations near the sea where tidal fluctuations control saltwater intrusion in the area. While each model had its strengths, the CNN-BiLSTM-Attention model performed best in complex tasks with dissimilar input trends, and the GRU model outperformed other models in simple monitoring tasks with similar input-target trends. The second task involved automatically searching the optimal configurations for completing the missing historical data in sequential order using the modeling task results. The electrical conductivity data were filled before the dissolved oxygen data, which were in turn more reliable than the turbidity simulation. The deep learning models accurately reconstructed 15 years of water quality data using only six and a half years of modeling data. Overall, this research demonstrates the potential of deep learning approaches with their limitations and discusses the best configurations to improve water quality monitoring and reconstruction.

‘That water out there is no damn good for anybody’: Experiences with declining water quality in a First Nation community

In many Indigenous communities, the wellbeing of waterways correlates to the health of the population that it supports. However, current laws and water governance systems often fail to protect water sources and jeopardizes health and wellbeing, particularly in Indigenous communities. This study, curated by an Anishinaabe First Nations community located in Ontario on the Lake of the Woods (LOTW), was designed to detail the varying impacts of adverse water quality on people in the community. A community-based participatory research approach included interviews with Elders and key informants to understand lived experiences of adverse water quality, sources of pollution, and individual and community impacts. Key findings revealed changes in water quality within and between years, with water quality degrading over time. Further, changes in water quality were associated with changes in the community’s health, food sources, and activities. Finally, a paternalistic colonial history between Indigenous people and the Government of Canada continues to resonate and cause strained jurisdictional relations between the two groups. Opportunities and future water stewardship strategies require the active participation and inclusion of Indigenous people in policymaking, programming, and water management. As proposed by the LOTW community, this includes improving water quality monitoring, upgrading septic systems in the community, reintroducing wild rice to the shorelines, and creating water activities programming for Indigenous youth.

Water Quality Status of Jatigede Reservoir Sumedang Regency, West Java

With the captured fisheries sector as a livelihood for residents around Jatigede, it is feared that water quality will decline. Another function of the Jatigede Reservoir is to irrigate irrigation canals, control floods, source PLTA power, and raw water to serve the people of Sumedang, Indramayu, and Cirebon city. The intended purpose is to determine the quality of water in the Jatigede Reservoir. This research was carried out in Jatigede Reservoir from November 2020 to August 2021, using the survey method. The method of determining the location used a purposive sampling method, carried out at five stations representing Jatigede Reservoir. The water quality parameters used are temperature, pH, dissolved oxygen, and brightness. The results of observations of water quality in Jatigede Reservoir with temperatures ranging from 28°C-29°C, pH ranging from 6.67–8.35, dissolved oxygen ranging from 6.8–7.83 mg/l and brightness ranging from 70-93.8 cm indicate that the water quality of the Jatigede Reservoir is still categorized into good water quality and is ideal for capture fisheries sector activities in the Jatigede Reservoir. The results of the status of water quality in the waters of the Jatigede Reservoir are included in the category of Fulfill Quality Standards to Lightly Polluted, and the average Pollution Index value ranges from 1.75 to 3.01 in class II water quality standards and 0.86-1.94 in class III water quality standards. Water quality in Jatigede Reservoir during the research was influenced by the input of river flow in the form of organic and inorganic waste. Water pollution control strategies can be carried out by increasing the inventory and identification of pollutant sources, improving waste management, determining the capacity of the pollution load, increasing knowledge and community participation in waste management, increasing supervision of wastewater development, and improving water quality monitoring.

Data fusion system for monitoring water quality: Application to chlorophyll-a in Baltic sea coast

We present an operational system for multi-sensor data fusion implemented at the Finnish Environment Institute. The system uses Ensemble Kalman filter and smoother algorithms, which are often used for probabilistic analysis of multi-sensor data. Uncertainty and spatial and temporal correlations present in the available observation data are accounted for to obtain accurate and realistic results. To test the data fusion system, daily chlorophyll-a concentration has been modelled across northern shoreline of Gulf of Finland over the period of August 1st – October 31st 2011. Chlorophyll-a data from routine monitoring stations, ferrybox measurements, and data derived from Medium Resolution Imaging Spectrometer (MERIS) instrument on board the ENVISAT satellite has been used as input. The data fusion system demonstrates the use of existing and well-known Ensemble Kalman filtering and smoothing methods for improving water quality monitoring programs and for ensuring compliance with ecological standards.

A case study: The deployment of a novel in situ fluorimeter for monitoring biological contamination within the urban surface waters of Kolkata, India

The quality and health of many of our vital freshwater systems are poor. To tackle this with ever increasing pressures from anthropogenic and climatic changes, we must improve water quality monitoring and devise and implement more appropriate water quality parameters. Recent research has highlighted the potential for Peak T fluorescence (tryptophan-like fluorescence, TLF) to monitor microbial activity in aquatic systems. The VLux TPro (Chelsea Technologies Ltd., UK), an in situ real-time fluorimeter, was deployed in different urban freshwater bodies within Kolkata (West Bengal, India) during March 2019. This study is the first to apply this technology in surface waters within a densely populated urban area. Spot-sampling was also undertaken at 13 sampling locations enabling physicochemical analysis, bacterial enumeration and determination of nutrient (nitrate and phosphate) concentrations. This case study has demonstrated the ability of an in situ fluorimeter, VLux TPro, to successfully identify both biological contamination events and potential elevated microbial activity, related to nutrient loading, in complex surface freshwaters, without the need for expensive and time-consuming laboratory analysis.

Semi-Empirical Model of Remote-Sensing Reflectance for Chosen Areas of the Southern Baltic.

Coastal waters are the richest parts of ocean ecosystems characterised by dynamic changes in water biology, physical and chemical features. Establishing local relationships between water constituents and optical properties in these areas will help to develop successful ocean colour algorithms allowing a thorough understanding of complex coastal waters and improving water quality monitoring. In this paper, the authors present the use of optical and biogeochemical measurements in complex aquatic environments and aim to create a semi-empirical model of remote-sensing reflectance (Rrs(λi)) for four wavelengths (λi = 420 nm, 488 nm, 555 nm, and 620 nm) based on multiparameter algorithms of absorption (a(λi)) and backscattering (bb(λi)) coefficients. The bio-optical properties of water were determined based on empirical data gathered from aboard the r/v Oceania from April 2007 to March 2010 in chosen areas of the southern Baltic (Polish coast). The analyses reveal that Rrs(λi) in the studied area can be described with satisfactory accuracy using a five-parameter model. Positive results with a statistical error magnitude of Rrs(λi) of less than 50% were achieved for all four applied wavelengths. Bio-optical algorithms proposed by the authors enable evaluating biogeochemical characteristics of coastal areas in a broader context of ecosystem assessment and contribute significantly to the development of Earth and environmental sciences.

Improved water quality monitoring indicators may increase carbon storage in the oceans

Indicators related to organic matter are important when assessing aquatic environment quality. The chemical oxygen demand (COD) is widely used as a water quality reference. However, oxidizing agents used to determine the COD can oxidize refractory organic matter that is not pollutant and can persist in the ocean for thousands of years. This means the COD can misrepresent the water quality. The actual water quality can be indicated better by the biochemical oxygen demand (BOD) than the COD, but determining the BOD is time-consuming and gives variable results. In this study, the optical properties of dissolved organic matter in water samples from the Chinese coast that had been incubated for a long time or directly oxidized using COD oxidant were analyzed. The results indicated that the oxidizing agent rapidly oxidized 22.93% ± 4.96% of refractory dissolved organic matter (RDOM) that was resistant to microbial degradation, implying that RDOM made a marked contribution to the COD. Meanwhile, size-fractional fluorescence spectroscopy and COD measurements indicated that the COD of the >0.7 μm fraction and the fluorescence intensity of the protein-like component significantly positively correlated with the BOD of the bulk sample. This indicated that, for monitoring organic pollutants in coastal waters, the COD of the >0.7 μm fraction could be used as a proxy for the standard COD and that the fluorescence intensity of the protein-like component could be used as a convenient proxy for the BOD. The method can help retain recalcitrant organic matter in seawater to act as a carbon sink.

GAP FILLERS OR PIONEERS: INFORMAL WATER SUPPLIERS IN PERI-URBAN AREAS OF THE TECHIMAN MUNICIPALITY OF GHANA

There is continuous expansion and growth of urban and peri urban areas in Sub-Saharan Africa with projections indicating that this will double. Some peri-urban areas such as those in the Techiman municipality of Ghana are experiencing this trend. Nonetheless, the spate of growth of these areas outstrips the supply of utilities by municipal water distribution networks. As a result, some areas have little or no public provision. In order to address the water supply challenge, places with favourable hydrological settings are witnessing the emergence of informal water suppliers as “gap fillers” or “pioneers”, providing water services. Noteworthy, however, existing studies about informal water suppliers in peri-urban areas in Ghana lump them together, without considering the water source. This study differs in that it specifically examines the evolution and the features of informal water suppliers who privately and independently abstract and supply groundwater. Additionally, it attempts to understand the nature of informality of the suppliers and the possibility of formalisation. The paper is an exploratory study using the case of private mechanised borehole operators who supply water in their respective areas. The findings showed that in some peri-urban areas in the Techiman municipality, some dwellers constructed and operated mechanised boreholes, which provide in-situ water and utilities to others. They are mostly pioneer water suppliers in some of the areas. Their services are informal by nature because they are largely independent of the formal sector and apply informal arrangements in rendering their services. Seen also as business enterprises, they are not licenced. However, the boreholes are registered with the Municipal Assembly, which indicated some level of formalisation. The study recommended that efforts to formalise them further should focus on improving water quality monitoring for consumption and promoting sustainable abstraction.

An improved real-time water quality monitoring embedded system with IoT on unmanned surface vehicle

Water quality maintenance is crucial in strategizing control and pitching in measures to reduce water pollution and degradation of aquatic ecosystems. Periodic monitoring of water quality is an essential exercise that can help keep track of water bodies' hydrological information. This article presented a novel system that can make water quality monitoring more efficient to prevent ongoing pollution control trends. Here, we implemented a well-structured technique to collect the quality parameters, especially chemical indicators, and generate characteristic maps for each parameter about the particular water body under survey. The data collected were transferred to the cloud in real-time to keep track of the water body's quality and access real-time quality data of different chemical and biological indicators like pH, dissolved oxygen, total dissolved solvents, turbidity, etc. The parameters monitored through the prototype built are pH and Temperature. The proposed system solved major limitations of existing methods, thereby improving water quality monitoring efficiency and minimizing water pollution.