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

Abstract

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.

Keywords: embedded sensors; water pollution and sensing methods; water quality monitoring system.

Figure 1

River Water Quality Trends from 2008 until 2020 year versus percentage number of rivers (%) in Malaysia [13]. Blue bar chart refers to unpolluted rivers; yellow bar chart refers to slightly polluted rivers; red bar chart refers to polluted rivers; green line shows total number of rivers.

Figure 2

Three virtual sensor (VS) constellations: (a) VS based entirely on physical sensor (PS), (b) VS based only another VS and (c) VS based on both virtual and physical sensors [32]. Virtual sensor intercorrelates with data captured by physical sensors which are embedded into software applications to implement the algorithmic analytics from all the data sets given.

Figure 3

An overview of virtual sensing development steps [32].

Figure 4

Machine learning (ML) Techniques used the most from 2019–2021 [32]. ANN refers to artificial neural network; RF refers to random forest; MLR refers to multiple linear regression; SVM refers to support vector machine; Adaboost refers to adaptive boosting; kNN refers to k-nearest neighbor and NM refers to numerical models.

Figure 5

Overall Architecture of CPS [66]. Reproduced with permission from Mohamed, M.A.; Kardas, G.; Challenger, M; 2021.

Figure 6

The heterogeneous components of CPS that connected through wired and wireless communication [64].

Figure 7

Control system of closed loop in CPS [64].

Figure 8

Membership function (MF) plots based on fuzzy rule for water quality parameters (a) pH and (b) DO [63].

Figure 9

Graphical user interface (GUI) developed for water quality monitoring system [17].