Fully Integrated, Simple, and Low-Cost Electrochemical Sensor Array for in Situ Water Quality Monitoring

The Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from an influx of contaminants, leading to hypoxia, fish kills, and eutrophication. Conventional in situ water quality monitoring methods are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color-producing agents (CPAs), including chlorophyll-a, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. In this study, field, laboratory, and historical land use land cover (LULC) data were collected during the summers of 2002, 2011, 2015, and 2016. The results indicated higher levels of chlorophyll, ranging from 2.94 to 12.19 μg/L, and TSM values were from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. A model was developed using multivariate, partial least squares regression (PLSR) to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.49; RMSE = 1.8 μg/L) and TSM (R2 = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. The results from this study provide a strong foundation for the future of water quality monitoring and for the protection of biodiversity in the ACE basin.

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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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Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11141674 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1674 ◽

Cited By ~ 11

Author(s):

Fangling Pu ◽

Chujiang Ding ◽

Zeyi Chao ◽

Yue Yu ◽

Xin Xu

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Quality Data ◽

Inland Lakes ◽

Quality Classification ◽

Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

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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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Development of ceramic electrochemical sensor based on Bi2Ru2O7+x−RuO2 sub-micron oxide sensing electrode for water quality monitoring

Ceramics International ◽

10.1016/j.ceramint.2010.07.013 ◽

2010 ◽

Vol 36 (8) ◽

pp. 2407-2413 ◽

Cited By ~ 18

Author(s):

Serge Zhuiykov

Keyword(s):

Water Quality ◽

Electrochemical Sensor ◽

Water Quality Monitoring ◽

Quality Monitoring

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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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