Real-time Water Quality Monitoring System Research Articles

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

Designing of Real-time Communication Method to Monitor Water Quality using WSN Based on IOT

Data accuracy has always been the essential and foremost requirement of any communication. A Real-time Water quality-monitoring system (WQMS) needs high-level data accuracy to process the water perfectly for the desired usage in any specific purpose.  Exponential growth in man-made processes, human activities, Industrialization, and economic growth along with depleting safe water resources has made the water pollution issue a foremost threat to human survival and human civilisation as a whole. The Conventional WQMS lacks in providing data accuracy while testing and analysing the water samples at sites due to improper data transmission, human intervention, instrument node working and calibration issues. Therefore, monitoring the quality of the water is essential with a prime focus on data accuracy through proper testing, data analysis and data transmission methods to provide real-time data accuracy. In this research work, an IOT-based wireless sensor network (WSN) is proposed that uses mesh networking to connect the sensor nodes and message queuing telemetry transfer (MQTT) protocol to send the acquired data to a cloud server ADDA Fruit IO. ESP32 standalone microcontroller with in-built Wi-Fi is used as a transceiver on sensor nodes and master nodes. As far as sensor and master node power is concerned, a self-adapting power generation system is incorporated using solar power and water energy harvesting techniques. The sensor nodes are calibrated as per WHO standards using deionized water and buffer capsules. Five random samples are collected from river water, pond water, Borewell water, R.O water and Municipal committee water to analyse the proposed system’s accuracy. The accuracy test and analysis is done using statistical tools on water sample measurements by the proposed sensor node, and the same is compared with the actual certified instrument, hardware manual-based measurement, Laboratory value check and transmitted value on Cloud Server. The proposed WQMS is designed to measure various WQM parameters i.e. Total Dissolve Solid (TDS), pH, Temperature and turbidity and to ensure data accuracy.

Field measurements of the carbon to chlorophyll-a ratio using Imaging FlowCytobot: Implications for dissolved oxygen modeling

In subtropical coastal waters around Hong Kong, dissolved oxygen (DO) can undergo significant diurnal changes (up to 40%–50% saturation) during algal blooms. The carbon to chlorophyll-a ratio (CCHL) – a key parameter in water quality models – is variable and depends on factors that include light intensity and temperature. Despite numerous laboratory and field investigations, the prediction of CCHL and DO during high productivity conditions remains a difficult problem; the CCHL during algal blooms has also not been measured.The phytoplankton and DO dynamics are studied in a semi-enclosed tidal inlet using a submerged Imaging FlowCytobot (IFCB) together with a real-time water quality monitoring system. A diurnal pattern of algal abundance is observed with peaks around 4pm. Based on carbon estimates from the bio-volumes of the algal images and direct measurements of chlorophyll-a, the CCHL ratio is monitored continuously during an algal bloom event. The CCHL is found to vary significantly over a bloom cycle—in the range of 10 to several hundred and generally much greater than the constant values (in the order of 10) assumed in industry standard water quality models. A first order theoretical estimate of the CCHL as a function of algal growth rate and light saturation intensity is supported by field data. Using a simple oxygen budget model, the diurnal DO changes during an algal bloom can be satisfactorily predicted by integrating IFCB data with water quality data. The findings help to improve environmental and ecosystem modeling and mariculture management.

Design and Analysis Performance of IoT-Based Water Quality Monitoring System using LoRa Technology

Water quality is one of the most important variables impacting human life. Generally, water quality measurements must be conducted on-site. If the region to be investigated is large, several test locations will be required. Repeated evaluations of water quality will be complicated and time-consuming. Therefore, a real-time water quality monitoring system is required to protect and monitor the water in order to take proactive measures for contamination. This project focuses on the aforementioned concerns and uses LoRa technology and the Node-RED application to develop environmental sensors that monitor and display water quality. It is the measurement and collection of data on water quality parameters, including temperature, electric conductivity, pH, air quality, and turbidity, according to the region requiring analysis. The microcontroller processes the sensor data before transmitting it via the wireless network to the database, where it is displayed on the Node-RED dashboard. The experimental results demonstrated that a range of 2.0 km can be used to transmit information in areas where LoRa technology encounters obstacles. Furthermore, the IoT-based monitoring system is able to monitor water quality in real time and display a Node-RED dashboard. It was determined that usability assessments were more efficient and convenient.

Iot And ML Based Real-time water Quality Monitoring System

Iot And ML Based Real-time water Quality Monitoring System

Design of a real–time water quality monitoring and control system using Internet of Things (IoT)

United Nations (UN) sustainable development goal 6 focuses on clean water and sanitation for all. To date, one-third of the world’s population lack access to clean, safe water. The traditional water purification methods cannot monitor and control water quality parameters from the early stages. In this paper, Internet of Things (IoT) is used to develop a real-time water quality monitoring and control system for a water treatment plant. A microcontroller is developed and interfaced with sensors to detect any deviations in the water quality parameters from the World Health Organization (WHO) standards. Every stage of the water treatment process is tested for quality, and deviations from the WHO standard triggers an autonomous control measure. A simulation model and a working prototype are developed. The research produces the expected results: accurate monitoring and control of water parameters.

IoT-Based Real-Time Aquaculture Health Monitoring System

Aquaculture fastest growing business worldwide especially in developing countries. Fisheries are marine species and required an oceanic environment where fisheries could grow and live naturally. Off-shore aquaculture businesses need a real-time water quality monitoring system. So, aquafarmers could maintain the required environment for a sustainable and profitable business. This work represents an IoT-based realtime health management system designed for aquaculture and considered the most required health metrics for aquaculture. The proposed system used four primary sensors: water level, temperature, pH, and dissolved oxygen. Sensors connected with microcontroller Arduino Uno R3 and ESP 8266 wi-fi module are used for data transmission to the IoT source ThingSpeak. The designed system could access online through the web interface and phone App for aquafarmers. The sensor data was accurate, and the system worked as designed.

Real-time water quality monitoring of River Ganga (India) using internet of things

Achieving and maintaining suitable water quality is one of the important parameters to ensure health and well-being of the human as well as ecosystems. Among the various aquatic ecosystems, riverine ecosystems are more prone to pollution and therefore needs to be monitored frequently and on regular time intervals. In this context, real-time water quality monitoring system offers excellent opportunity to keep track of the water quality on a continuous basis; which not only helps to identify the affected location and pollution source, but also creates alert enabling the authorities to take immediate action. One such real-time water quality monitoring system was installed in the River Ganga (India), considering the fragility and significance of the Gangetic ecosystem. In this paper, we have presented the details of the real-time water quality monitoring system installed in River Ganga and results obtained through it for various parameters. The results have also been compared with the standard values. Additionally, based on this preliminary investigation, limitations and recommendations have also been presented to further enhance the utility of the system.

Design and analysis of water quality monitoring and filtration system for different types of water in Malaysia.

Water pollution always occurs in Malaysia due to industrial, construction, agriculture, and household activities. River pollution can disturb water supply that eventually affects business and household activities. Thus, water quality monitoring system is needed to detect contaminated water. We developed a water quality monitoring and filtration system controlled by Arduino. The proposed system was designed in Proteus software and ThingSpeak platform was used for real-time monitoring. The main objective of the study was to compare water quality of river, lake and tap water in terms of pH, temperature, turbidity, electrical conductivity and oxidation–reduction potential. If the water quality was not satisfied, the water sample would be filtered through filtration system. Water turbidity level, pH, temperature, electrical conductivity, and oxidation–reduction potential for filtered and nonfiltered water were compared and analyzed according to international and national water quality standard. Besides that, statistical analysis such as box plot and one-way analysis of variance test was applied to validate data from the system. The real-time water quality monitoring system was implemented through data storage, data transfer, and data processing. The system was connected to wireless fidelity whereas the output data was sent to the user and monitored by ThingSpeak. The system can be further upgraded and scaled up to be applied in the main tank at our home or factory. The outcome of this research can be used as a reference for further study on lake and river pollution monitoring system.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13762-022-04192-x.

Solar-Powered Fishpond Feeder with Real-Time Water Quality Monitoring System

Solar-Powered Fishpond Feeder with Real-Time Water Quality Monitoring System

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective.

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth.

Usability evaluation of a real-time water quality monitoring mobile application

To ensure that a mobile application is usable to a particular extent, a real-time water quality monitoring system should be carefully designed, especially as end users are a diverse group of people with varying requirements and demands. The main objective of this research was to investigate how participants, the fisher-folks respond to a mobile water quality monitoring app in terms of its effectiveness, efficiency and satisfaction, which together represent its usability. The results were obtained by performing usability evaluation through the use of a modified Goal Question Metric (GQM) model, including field testing and questionnaires. The evaluation findings show that the Real-Time Water Quality Monitoring app achieves a high level of success in terms of effectiveness and efficiency, and that users are satisfied with it. However, there were a few concerns that needed to be addressed.

Real time monitoring of water Quality using IoT and Deep learning

Access to safe drinking water is one of the most pressing issues facing many developing countries. Water must meet Environmental Protection Agency (E.P.A.) requirements. The normal method of measuring physico-chemical parameters is to take samples manually and send them to the laboratory to check the water quality. In this paper, we proposed a new intelligent design of a real-time water quality monitoring system using Deep Learning technology. This system is composed of several sensors that allow us to measure water parameters (physico-chemical parameters), bacteriological parameters and organoleptic parameters) and to detect the presence of certain substances (undesirable substances, toxic substances) and of a single-board/mobile computer module, Internet and other accessories. Water parameters are automatically detected by the single-board computer. Raspberry Pi3 model B. The single board computer receives the data from the sensors and this data is sent to the web server using the Internet module. It is able to detect the water quality situation worldwide. The data will be analysed in real time. The application of deep learning to these areas has been an important research topic. The Long-Short Term Memory (LSTM) network has been shown to be well suited for processing and predicting large events with long intervals and delays in the time series. LSTM networks have the ability to retain long-term memory.

Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Assessment of Water Quality Parameters in Real-Time Environment

Assessment of drinking water quality has been an important issue nowadays as the water available is severely polluted and can be the cause of diseases like cholera, diarrhea, dysentery, etc. The traditional methods for water quality monitoring require a high-labor-cost and tine consumption as these methods include a sample collection followed by lab-based chemical testing. In addition, the chemicals used in the testing are toxic and of high-cost. So, there is a need for real-time monitoring and chemical-free testing of water quality parameters. This paper presents a real-time water quality monitoring system based on the Raspberry Pi 3 development board and a Python framework. The water quality parameters utilized for water quality monitoring are temperature, pH, oxidation reduction potential, electrical conductivity, and dissolved oxygen and E. coli. The water quality sensors were interfaced with the designed embedded platform. Finally, the acquired parameters were compared with the benchmark equipment for validation.

Analysis and prediction of big stream data in real-time water quality monitoring system

Large scale real-time water quality monitoring system usually produces vast amounts of high frequency data, and it is difficult for traditional water quality monitoring system to process such large and high frequency data generated by wireless sensor network. A real-time processing and early warning system framework is proposed to solve this problem, Apache Storm is used as the big data processing platform, and Kafka message queue is applied to classify the sample data into several data streams so as to reserve the time series data property of a sensor. In storm platform, Daubechies Wavelet is used to decompose the data series to obtain the trend of the series, then Long Short Term Memory Network (LSTM) model is used to model and predict the trend of the data. This paper provides a detailed description concerning the distribution mechanism of aggregated data in Storm, data storage format in HBase, the process of wavelet decomposition, model training and the application of mode for prediction. The application results in Xin’an River in Yantai City reveal that the prosed system framework has a very good ability to model big data with high prediction accuracy and robust processing capability.

SWM technology for efficient water management in universities: the case of PUMAGUA, UNAM, Mexico City

ABSTRACT This case study details the establishment of a real-time water quality and quantity monitoring system at the National Autonomous University of Mexico (UNAM), Mexico’s largest university based in Mexico City. It is structured to outline the water challenges facing Mexico City and UNAM, the solutions implemented by PUMAGUA, the Program for the Management, Use and Reuse of Water at UNAM, and the lessons learned in the process which can be extended beyond the university.

Web-based system for visualisation of water quality index

Data visualisation is an essential tool for effective communication and interpretation of information. Visualisation of information using images is easier compared to numerical values. Google Earth provides a platform for a practical monitoring site visualisation system. A real-time web-based water quality monitoring system was deployed on Google Earth based on National Water Quality Index of Malaysia (NWQI). The system used graphical representation through vary shape and colour for easy interpretation of water quality status. NWQI index uses; pH, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, total suspended solids and ammonical nitrogen. The use of different colour, shades of a hue and shapes for the system can motivate public to participate in environmental conservation. The colour used to represent WQI value on the map is derived from a single hue scheme of different quantities which produce varying brightness and shades. Web-based WQI index can be used to identify pollution sources based on the graphical representation of water quality trend. The system is assessed by evaluating spatial variations water quality of a river in Peninsular Malaysia known as Langat River for 2005, 2010 and 2015. The water quality index status through visualisation can be accessed at the following URL – http://www.umlivinglabsystem.com/SiteViewer/SiteViewerAvgSL.

Impact of diffusion and dispersion of contaminants in water distribution networks modelling and monitoring

Abstract In recent years, there has been a need to seek adequate preventive measures to deal with contamination in water distribution networks that may be related to the accidental contamination and the deliberate injection of toxic agents. Therefore, it is very important to create a sensor system that detects contamination events in real time, maintains the reliability and efficiency of measurements, and limits the cost of the instrumentation. To this aim, two problems have to be faced: practical difficulties connected to the experimental verification of the optimal sensor configuration efficiency on real operating systems and challenges related to the reliability of the network modelling approaches, which usually neglect the dispersion and diffusion phenomena. The present study applies a numerical optimization approach using the NSGA-II genetic algorithm that was coupled with a new diffusive-dispersive hydraulic simulator. The results are compared with those of an experimental campaign on a laboratory network (Enna, Italy) equipped with a real-time water quality monitoring system and those of a full-scale real distribution network (Zandvoort, Netherlands). The results showed the importance of diffusive processes when flow velocity in the network is low. Neglecting diffusion can negatively influence the water quality sensor positioning, leading to inefficient monitoring networks.

Daphnia magna metabolic profiling as a promising water quality parameter for the biological early warning system

The inclusion of omics data into water quality monitoring programs is being considered to help alleviate the growing threat to water resources and ecosystem services. Despite the increasing need, the biological early warning system (BEWS), the widely used real-time water quality monitoring system, does not currently incorporate omics information, despite that metabolomics is a highly sensitive indicator of organism health and stress. We examined Daphnia magna metabolomics, which is the analysis of small molecules in living D. magna, as a potential water quality parameter for incorporation in the BEWS. The concentrations of 24 metabolites were measured with changes in water quality and variation of metabolite abundances was compared within and between conditions. Age-dependent monitoring revealed that matured individuals older than 8 days are appropriate model organisms for monitoring based on their low metabolomic variation as compared to younger daphnids. Hourly monitoring of metabolic variability and regulation under ambient and starved conditions demonstrated the rapid and sensitive detection of nutritional changes. Moreover, the metabolomic dysregulation due to exposure to the pollutant propranolol was also observed. By integrating all the observations, we found that the D. magna metabolome is a sensitive and useful parameter for detecting water quality changes and how these alter the function of keystone organisms. As such, this metabolomics-based framework is applicable to BEWS and highlights the beneficial advantages of integrating biomolecular and apical endpoint observations for enhanced performance in biomonitoring programs.