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Water quality control using IoT

Water quality control using IoT

Water is one of the most abundant substances in nature. Almost 71% of the Earth’s surface is covered with water. Of this, as much as 97% is the water of the seas and oceans. The remaining 3% of water is trapped in glaciers, permanent snow cover and permafrost, forms rivers and water reservoirs, and is also found in groundwater, soil, air and is a component of organisms [1,2].

Water is an essential element for the life of plants, animals and humans. Water is the most important component of organisms, for example, it makes up about 60-70% of the human body weight. It is necessary for its proper functioning: it is involved in regulating body temperature, transporting nutrients, metabolic products and in all biochemical reactions taking place in the body [3].

In the water present on Earth, substances that have found their way to its circulation are dissolved. Therefore, natural waters may contain components both desirable and harmful to living organisms, the so-called pollution.

Water pollution is the unfavorable change in the physicochemical and biological properties of water, caused by the introduction of a large amount of organic and inorganic substances and heat. This situation causes changes in the aquatic ecosystem, negatively affecting aquatic organisms, and also results in deterioration of water quality, which limits or prevents the use of water for drinking and economic purposes [4].

Due to its polar properties, water is very susceptible to contamination as it is a very good solvent and can dissolve more substances than any other liquid [5]. Types of pollution of natural waters caused by human activity are presented in Fig. 1.

Fig 1. Anthropogenic sources of water pollution.

The scarcity of fresh water available to humans, combined with its high consumption, especially in urban areas, industrial plants, agriculture, as well as climate change and a large human population has led to a water crisis. Water pollution is one of the greatest threats in recent times. Polluted water can be a source of various diseases, both in humans and animals, and also has a negative impact on the environment and the economy. Detecting water pollution at an early stage allows you to take a variety of actions to prevent dangerous situations. To ensure that the water is of good quality, it must be tested in real time. This is possible thanks to innovative methods in the use of sensors, communication and Internet of Things (IoT) technology. Intelligent IoT solutions for monitoring water pollution are becoming increasingly important these days. The objectives of the intelligent water quality monitoring system are shown in Fig. 2 [6]

Fig.2. The objectives of a smart water quality monitoring system

In traditional methods of water quality monitoring, the procedure consists in manual sampling of water from the tested reservoir, then tests and analyzes are carried out in the laboratory (Fig.3).

Fig.3. Traditional method of water quality testing

The traditional method, although widely used, has some limitations:

-Specialized equipment and qualified personnel are needed

-No real-time data

-No trend forecasting

-The properties of collected water samples may change during transport to the laboratory

-It takes time to prepare a water quality report

The process of traditional water quality measurement is therefore laborious and time-consuming. Designing and implementing a real-time water monitoring system using IoT technology can help combat problems related to aquatic ecosystems, such as water pollution and the risk of flooding, and thus improve the quality of life for people and other organisms (Fig.4).


Fig.4. Examples of the use of IoT in water monitoring (based on


The general water quality monitoring system consists of sensors that measure various parameters (physical, chemical, microbiological). As shown in Fig. 5, all sensors are connected to the main controller, which collects data from them and compares them with threshold values. Then the obtained values are sent to the end user or the authorities via a wireless network- if the values are higher than the threshold value, appropriate action can be taken quickly, such as preventing the spread of pollutants to other reservoirs or evacuating people from the area at risk of flooding.

Fig.5. General scheme of water quality monitoring system [7]

Water Quality Index (WQI)

The water quality index (WQI) is one of the most commonly used tools to determine water quality. It is based on the measurement of various water quality parameters [8]. The most commonly used parameters are described in the table below [6,9]

Selected intelligent water pollution monitoring systems


1. Smart Water Quality Monitoring System

This method is based on the use of IoT technology. It enables real-time water monitoring with portable sensors, digital computing devices, communication media, web services, and applications.



-Real-time water monitoring

-Water monitoring is carried out from anywhere in the world using sensors, digital devices, internet services

-Ability to monitor water in households, agriculture, aquaculture, lakes, and rivers, among others

-Low costs

-No space restrictions

-Ability to store data in the cloud

-Low energy requirements

-Water quality forecasting


An example of such a method of water monitoring is the work by Pasika & Gandla 2020 [10] – the authors use a monitoring system based on sensors to measure such parameters as water turbidity, pH, water level in the tank, ambient humidity, and water temperature. The sensors connect to a microcontroller (MCU) and additional processing is performed by a personal computer (PC). The data is then routed to the cloud using the ThinkSpeak IoT-based water quality monitoring application [6].

It is also worth noting the work of Mukta et al. 2019 [11], where water quality was measured based on the following parameters: pH, temperature, turbidity, and electrical conductivity. Quality parameters are detected by sensors connected to the Arduino Uno. The data collected from the sensors are sent to the application and then tested by the developer’s SWQM model, using a fast binary classifier to classify the tested water in terms of suitability for consumption.

2. WSN-Based Water Quality Monitoring

With this method, an embedded microprocessor-based node reads specific water properties using sensors installed in a specific body of water. Then, it processes the data and sends it to the main station (e.g. to a computer server) using a wireless network (e.g. Wi-Fi, ZigBee, LoRaWAN), where data processing and analysis are then carried out. The data is updated and the results are communicated to the relevant consumers or authorities [6].

Such a monitoring system is used, for example, in India, where water-related threats are the main causes of humanitarian crises. To prevent this, rapid emergency control is needed through frequent monitoring of water and air quality. Water monitoring systems using wireless sensor networks (WSN) help to combat water threats such as pollution that is caused by industrial, sewage, or agricultural wastewater that is discharged into rivers or lakes [12].

However, the WSN system has several disadvantages:

– High energy costs

– Slow communication speed

– Storage problems

– High installation and maintenance costs


3. The method of a smart water quality monitoring system using remote sensing and IoT technology

This method has been used in Fiji, and the parameters used to analyze the water are the oxidation and reduction potential (ORP) and the pH value. The implementation of this system will enable early warning of water pollution and can be used at many monitoring stations [13].

4. Machine learning techniques

It may be difficult to consider all parameters when calculating the WQI, e.g. due to the lack of a given sensor. However, most of the parameters needed to calculate the WQI show strong correlations. Thanks to this, it is possible to use fewer sensors and, based on the obtained data, draw conclusions about other important parameters, e.g. through mathematical models. Machine learning is regarded as a subset of artificial intelligence (AI). Based on the obtained data, machine learning algorithms create a mathematical model capable of making decisions or making predictions [6].


In short, water is an essential resource for life. Water quality is determined by the water quality index (WQI), which is measured by a variety of parameters. Traditional methods used to assess water quality are based on laboratory analysis and are costly and time-consuming. An alternative to these methods is an IoT-based monitoring system that allows real-time water monitoring. Such solutions as, for example, the Smart Water Quality Monitoring System or Machine Learning help to examine selected water parameters in a faster and cheaper way, calculate/predict WQI, and even detect dangers early (e.g. water pollution, rising water levels in the river) and send a warning to the consumer or local authorities.


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“Iot based smart water quality monitoring system

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