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.

State of knowledge of aquatic ecosystem and fisheries of the Lake Edward System, East Africa

“…For water quality, methods and sensitivity of instruments for some parameters may have changed in response to technological advances (Zainurin et al, 2022). As result, temporal comparisons should be made cautiously.…”

“…In these fishery‐independent surveys, efforts should be made to design the methods in a way that facilitates the comparison of results with those from studies in the past. For water quality, methods and sensitivity of instruments for some parameters may have changed in response to technological advances (Zainurin et al., 2022). As result, temporal comparisons should be made cautiously.…”

LOWEL AI Using technology and machine learning algorithms to detect harmful bacteria present in water

“…Despite the strides made in water quality monitoring, the accurate and timely detection of bacteria in water remains a global challenge. Conventional microbial testing methods often requires time-consuming and culturing processes which hinders the fast assessment of water quality [3]. Researchers have recognized the need for rapid and precise bacterial detection techniques to address this gap.…”

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

“… 119 While ML is a powerful tool that is especially suited to the prediction of HAB formation from complex data, it frequently encounters gaps and defects in the data, 120 which make its implementation challenging. For example, improved sensing technologies and new platforms for data access are being developed, 121 and they may not always provide enough historical data for HAB predictions. Similarly, detailed ground truth data sets with confirmed positive and negative HAB events are necessary but remain insufficiently available, especially for freshwater systems (HAEDAT, 122 Environmental Working Group, EWG 123 ).…”