Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach

Khatri, Punit; Gupta, Karunesh Kumar; Gupta, Raj K.

doi:10.5194/dwes-12-31-2019

Cited by 26 publications

(26 citation statements)

References 22 publications

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“…The present paper endeavors to fill this gap. More specifically, we report on CNT-based electrical and electrochemical sensors, because they are particularly well-suited for online water monitoring applications [ 16 , 17 , 18 ]. The electrical transduction options for CNT-based chemical sensors are electrochemical, resistive, field-effect-based and electromechanical.…”

Section: Introductionmentioning

confidence: 99%

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

Cho

Azzouzi

Zucchi

et al. 2021

Sensors

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Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial technologies. In the present review, we show across 20 water monitoring analytes and 90 references that carbon nanotube-based electrochemical sensors, chemistors and field-effect transistors (chemFET) can meet these needs. A set of 126 additional references provide context and supporting information. After introducing water quality monitoring challenges, the general operation and fabrication principles of CNT water quality sensors are summarized. They are sorted by target analytes (pH, micronutrients and metal ions, nitrogen, hardness, dissolved oxygen, disinfectants, sulfur and miscellaneous) and compared in terms of performances (limit of detection, sensitivity and detection range) and functionalization strategies. For each analyte, the references with best performances are discussed. Overall, the most frequently investigated analytes are H+ (pH) and lead (with 18% of references each), then cadmium (14%) and nitrite (11%). Micronutrients and toxic metals cover 40% of all references. Electrochemical sensors (73%) have been more investigated than chemistors (14%) or FETs (12%). Limits of detection in the ppt range have been reached, for instance Cu(II) detection with a liquid-gated chemFET using SWCNT functionalized with peptide-enhanced polyaniline or Pb(II) detection with stripping voltammetry using MWCNT functionalized with ionic liquid-dithizone based bucky-gel. The large majority of reports address functionalized CNTs (82%) instead of pristine or carboxyl-functionalized CNTs. For analytes where comparison is possible, FET-based and electrochemical transduction yield better performances than chemistors (Cu(II), Hg(II), Ca(II), H2O2); non-functionalized CNTs may yield better performances than functionalized ones (Zn(II), pH and chlorine).

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Section: Introductionmentioning

confidence: 99%

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

Cho

Azzouzi

Zucchi

et al. 2021

Sensors

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“…SEM systems, including air quality evaluation, water pollution, and agricultural monitoring systems and subsidiary implementations of the three major studies, were investigated in this survey. The contributions included a broad range of SEM approaches, including air quality assessments, for various purposes [7,15,16,[42][43][44][45][46][47][48][49]; water pollution control methods [18][19][20][34][35][36][37][38][39][40][41]; and smart agriculture monitoring systems [17,[25][26][27][28][29][30][31][32][33].…”

Section: Discussion and Analysismentioning

confidence: 99%

“…In [48] presented a smart drinking water measurement method. The machine includes a Raspberry Pi board with several sensors.…”

Section: Agriculture Monitoring Based Iotmentioning

confidence: 99%

IoT for Smart Environment Monitoring Based on Python: A Review

Haji

Sallow

2021

AJRCoS

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Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

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“…Thus, current water quality assessments need to be improved. Khatri et al [ 17 ] proposed a water monitoring system using a Raspberry Pi-based hardware platform. The system used a python framework for the development of a graphical user interface (GUI) and fuzzy logic for decision making.…”

Section: Comparison Of Various Water Quality Monitoring Methodsmentioning

confidence: 99%

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Zainurin

Ismail

Mahamud³

et al. 2022

IJERPH

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Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

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Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach

Cited by 26 publications

References 22 publications

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

IoT for Smart Environment Monitoring Based on Python: A Review

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

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