A multi-parameter integrated chip system for water quality detection

Water pollution has always been an important issue threatening human health. Water quality monitoring is an important step to ensure water quality safety. Water quality parameters such as pH, temperature and conductivity are important indicators in water quality monitoring. In this paper, a multi-parameter water quality detection integrated chip system is studied, and multiple sensors are integrated on the same chip to realize the monitoring of conventional parameters such as pH, temperature and conductivity in drinking water. The chip system was processed by MEMS technology. A ruthenium oxide (RuO2) film is used for pH detection, and the sensor exhibits a super-Nernst response (62.88 mV/pH) in the range of pH 1.92–11.74. The patterned micro Pt film is used as the temperature sensor, and the three-wire orthogonal structure is used for temperature detection. The sensitivity of this temperature sensor is 5.517 [Formula: see text]/[Formula: see text]C in the range of 6 to 80[Formula: see text]C. Conductivity was measured by a four-electrode system, and the electrode constant was 1.667 cm[Formula: see text] measured between 0.994 and 21.167 mS/cm. The test results show that the multi-parameter integrated chip system can realize the simultaneous detection of pH, temperature and conductivity in water, and has the characteristics of miniaturization and low cost, and provides an effective way for simultaneous detection of water quality.

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A Study on IOT for Smart Water Quality Monitoring Using MQTT Algorithm

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952243 ◽

2019 ◽

pp. 869-872

Author(s):

G. Vadivel ◽

A. P. Thangamuthu ◽

A. Priyadharshini

Keyword(s):

Water Quality ◽

Low Cost ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Quality Monitoring ◽

Smart Water ◽

Quality Detection ◽

Quality Of Water ◽

System Water

The decrease in quality of water resources has become a common problem. The standard methods of water quality surveillance include water sample manual collection from various locations. These water samples were tested in laboratory using intelligence capabilities. Such approaches take time and are no longer considered inefficient. The old method of water quality detection was time consuming, less accurate and expensive. By focusing on the above problems, IOT can be used to monitor water quality in real time, a low cost water quality monitoring system. Water quality parameters in the proposed system are measured by various sensors such as pH, temperature and dissolved oxygen to transfer data on a platform via a microcontroller system. Therefore, to meet these needs, you can use other technologies such as MQTT (Message Sorting Delimiter Transform), allowing the Sensor and End device rankings to publish and subscribe. And the number of data simultaneously between sensors and servers with the help of the MQTT algorithm.

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Real-Time Water Quality Monitoring For Small Aquatic Area Using Unmanned Surface Vehicle

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2661 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3959-3964

Author(s):

A. T. Demetillo ◽

E. B. Taboada

Keyword(s):

Water Quality ◽

Low Cost ◽

Water Quality Monitoring ◽

Relevant Information ◽

Quality Parameters ◽

Quality Monitoring ◽

Aquatic Area ◽

Monitoring Methods ◽

Coverage Area ◽

Unmanned Surface Vehicle

Most developing countries depend on conventional water quality monitoring methods which are usually expensive, complicated, and time-consuming. In recent years, stationary and portable water quality monitoring and a mobile surface vehicle have increased the utilization of on-site water measurements and monitoring. The first has the disadvantage of small coverage area while the second has its cost and operational complexity. This paper addresses these issues by placing materials and equipment used in fixed online water quality monitoring and using a customized and low-cost unmanned surface vehicle. The measurements are taken automatically on the equipment onboard the unmanned surface vehicle (USV), transmitted wirelessly to a PC-based remote station or nearby stations and saved there in a dedicated database. The overall system comprises a commercial water quality sensor, a GSM and Zigbee module for a wireless communication system, a low-cost mobility platform, and the location/positioning system. During testing, all captured data like water quality parameters, location, and other essential parameters were collated, processed and stored in a database system. Relevant information from the USV can be viewed on a smartphone or a computer. The USV was also tested to conduct unmanned water quality measurements using the pre-inputted navigation route which shows a good result in navigation and data transmission. Water bodies with calm water such as lakes and rivers can use the USV, in a stand-alone mode or as a part of a networked sensor system.

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Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

SSRN Electronic Journal ◽

10.2139/ssrn.3350297 ◽

2019 ◽

Author(s):

Jeba Anandh S ◽

Anandharaj M ◽

Aswinrajan J ◽

Karankumar G ◽

Karthik P

Keyword(s):

Water Quality ◽

Real Time ◽

Monitoring System ◽

Low Cost ◽

Water Quality Monitoring ◽

Quality Monitoring

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Water monitoring and analytic based thingspeak

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i4.pp3588-3595 ◽

2020 ◽

Vol 10 (4) ◽

pp. 3588

Author(s):

Abbas Hussien Miry ◽

Gregor Alexander Aramice

Keyword(s):

Water Quality ◽

Normal Values ◽

Water Quality Monitoring ◽

Lag Time ◽

Quality Parameters ◽

Quality Monitoring ◽

Proposed Model ◽

Response To Water ◽

Matlab Programming ◽

The Internet Of Things

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

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Pengendalian Kualitas Air untuk Tanaman Hidroponik Menggunakan Raspberry Pi dan Arduino Uno

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v7i1.3421 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Ryan Ganesha Calibra ◽

Irfan Ardiansah ◽

Nurpilihan Bafdal

Keyword(s):

Water Quality ◽

Growth And Development ◽

Low Cost ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Raspberry Pi ◽

Time Data ◽

Soil Nutrition ◽

Remote System ◽

Total Dissolved Solid

Water quality is very important for plant’s growth and development. Some of the important part of the water qualities are TDS(Total Dissolved Solid), EC(Electrical Conductivity), pH(Acidity). Cultivation inside a greenhouse provides some benefits but also have some deficiency, such as lack of soil nutrition because most plants inside greenhouse uses non soil growing media. To overcome the deficiency, An automated and remote system is needed to ease the controlling of water quality and nutrition feeding to the plant. This study aims to create low-cost greenhouse water quality monitoring that automatically display the real time data on a website. This research is done by using an engineering design methods. This system can be integrated with auto-pot watering system . The result shows that the system can adjust the TDS and pH as programmed, which are TDS= 1000-1200, and pH =5.5-6.5(these are recommended needs for Tomato plant). The TDS sensor in this reseach have the limitation of reading 0~1500ppm.

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Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽

10.1155/2020/9570789 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23 ◽

Cited By ~ 1

Author(s):

Yashon O. Ouma ◽

Clinton O. Okuku ◽

Evalyne N. Njau

Keyword(s):

Water Quality ◽

Linear Regression ◽

Dissolved Oxygen ◽

Multiple Linear Regression ◽

Pearson Correlation ◽

Multiple Linear Regression Model ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Water Quality Parameters ◽

Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

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A Low-Cost Water Quality Monitoring System for the Ayeyarwady River in Myanmar Using a Participatory Approach

Water ◽

10.3390/w11101984 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1984 ◽

Cited By ~ 4

Author(s):

Thanda Thatoe Nwe Win ◽

Thom Bogaard ◽

Nick van de Giesen

Keyword(s):

Water Quality ◽

Mobile Phone ◽

Low Cost ◽

School Teacher ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Quality Data ◽

Water Quality Data ◽

Lower Accuracy

Newly developed mobile phone applications in combination with citizen science are used in different fields of research, such as public health monitoring, environmental monitoring, precipitation monitoring, noise pollution measurement and mapping, earth observation. In this paper, we present a low-cost water quality mobile phone measurement technique combined with sensor and test strips, and reported the weekly-collected data of three years of the Ayeyarwady River system by volunteers at seven locations and compared these results with the measurements collected by the lab technicians. We assessed the quality of the collected data and their reliability based on several indicators, such as data accuracy, consistency, and completeness. In this study, six local governmental staffs and one middle school teacher collected baseline water quality data with high temporal and spatial resolution. The quality of the data collected by volunteers was comparable to the data of the experienced lab technicians for sensor-based measurement of electrical conductivity and transparency. However, the lower accuracy (higher uncertainty range) of the indicator strips made them less useful in the Ayeyarwady with its relatively small water quality variations. We showed that participatory water quality monitoring in Myanmar can be a serious alternative for a more classical water sampling and lab analysis-based monitoring network, particularly as it results in much higher spatial and temporal resolution of water quality information against the very modest investment and running costs. This approach can help solving the invisible water crisis of unknown water quality (changes) in river and lake systems all over the world.

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On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

Water ◽

10.3390/w12041096 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1096 ◽

Cited By ~ 2

Author(s):

Ramón Martínez ◽

Nuria Vela ◽

Abderrazak el Aatik ◽

Eoin Murray ◽

Patrick Roche ◽

...

Keyword(s):

Water Quality ◽

Wireless Sensor Networks ◽

Wastewater Treatment ◽

Sensor Networks ◽

Low Cost ◽

Treatment Plant ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Wireless Sensor ◽

Smart Water

The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.

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Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

Applied Water Science ◽

10.1007/s13201-019-1111-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 4

Author(s):

Prasad M. Pujar ◽

Harish H. Kenchannavar ◽

Raviraj M. Kulkarni ◽

Umakant P. Kulkarni

Keyword(s):

Water Quality ◽

Internet Of Things ◽

Real Time ◽

River Basin ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Quality Monitoring ◽

Quality Analysis ◽

Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

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