Why is IoT Used in Real-time Water Quality Monitoring?

Monitoring water quality in real-time using the Internet of Things (IoT) can save corporations millions in product recalls, reputational damage, and penalties. In the United States, violation of the Clean Water Act can lead to penalties running between $25000 and $50000 per day. IoT devices enable chemistry control personnel to collect and monitor water quality data in real-time, thereby avoiding negative financial consequences.

Industries associated with food and beverage, pharmaceutical, and chemical manufacturing are directly linked to human consumption and health, which makes them the most critical sectors from a water quality monitoring perspective.

The introduction of IoT in water quality monitoring is a game-changer, unlocking capabilities such as wireless data transmission and cloud-based data storage. These features lead to operational cost reduction for remote sites where 24-hour manning is not feasible. Business owners and environmental monitoring organizations can visualize data in real time to ensure regulatory compliance. In this article, we will explore how the introduction of IoT-based sensors enhances monitoring capabilities, their operation, benefits, challenges, and future trends.

Purpose of Using IoT in Water Quality Monitoring

★   The Need for Continuous Monitoring

In a classic approach, the chemistry control requires sampling the water intermittently at a frequency. The interval between each reading can go from 1 hour to several hours. The change in the chemistry of water between these intervals will go unnoticed until the sample results are obtained. Regulators are transitioning to continuous monitoring of water, resulting in enhanced regulatory control.

These sensors, combined with live and continuous monitoring, can trigger actions in real-time, controlling the elements that cause degradation in water quality.

★  Real-time Decision Making

IoT-based water quality systems allow immediate actions in case of contamination. Degrading water quality conditions can be rapidly detected, and the source can be identified and cut off. The turbidity, pH, chlorine, and dissolved oxygen sensors can collectively provide a complete picture of the water quality. Alarm value for each of the parameters can be set using control systems that monitor the data.

★  Data-Driven Water Management

Having a large dataset to analyze, combined with modern AI tools, can facilitate an in-depth examination of the system. It can lead to predictive and preventive actions that can be taken before water quality failure occurs. Any degradation is detected head-on. It can also allow long-term analysis, such as identifying seasonal trends and recurring pollution sources.

Alongside quick actions, it can also immediately help point out the behaviour of a fouling sensor. It can also predict failures of equipment before they even occur.

What is an IoT Water Quality Monitoring System?

1. Components of an IoT-Based System

The practical implementation of an IoT-based system for a particular application requires a combination of components. All these components work together to form a working system.

● Sensors (turbidity, pH, DO, conductivity, TDS)

Monitoring water quality involves analyzing various physical and chemical parameters of the sample. These sensors are usually installed in tanks and pipes with moving or static water. Turbidity, pH, DO, conductivity, and TDS collectively allow analysis of quality.

• • Turbidity: The presence of suspended material in water can cause it to become cloudy or hazy. The quantity of this suspended material is detected using turbidity sensors compatible with modern IoT systems. For example, the Rika Turbidity Sensors (RK500-07 Series) can detect tiny changes of 0.01 NTU.
  • pH: Measuring pH gives insight into the acidity and alkalinity of water. Lower pH means the water is acidic. The Rika RK500-12 offers a detection accuracy of 0.01 pH, suitable for most water quality monitoring systems.
  • DO: Microbial activity can change levels of dissolved oxygen (DO) in water. Moreover, the presence of oxygen in power plants can cause damage to metal. Therefore, optical sensors such as RK500-04 can provide live values of DO levels in water to maintain proper chemistry as per application requirements.
  • Conductivity: The Presence of ionic content and salinity of water is detectable using the conductivity meter. A 0–2 mS/cm of conductivity is suitable for drinking water. Therefore, an EC detector, such as the RK500-13, with a range of 0–200 mS/cm (±1–2% accuracy), is crucial for IoT systems to function efficiently.
  • TDS: Total dissolved solids should be <500 mg/L (drinking water, WHO guideline), up to 2000 mg/L (irrigation), and >10,000 mg/L (brine/industrial). Detecting it is crucial to ensure regulatory compliance. Typically, an EC sensor like the RK500-13 EC/Salinity Sensor can also detect TDS through conversion.

● Gateways and PLC/SCADA Systems

The signals from all the water quality sensors are processed, and relevant actions are taken by the programmable logic controller (PLC). Then these individual PLCs gather data and send it to a centralized monitoring system, known as SCADA (Supervisory Control and Data Acquisition), which allows for operator intervention when needed.

● Communication Modules (LoRaWAN, cellular, Wi-Fi)

The centralized monitoring system can be near the field from where the data is collected, or it can be at a significant distance that requires wireless transmission. For this, there are 4G cellular SIM cards in the IoT device (V-box) to send PLC-collected data to the cloud via MODBUS TCP/IP. Other approaches for communication include:

• • Short Range: In the case of short range, WiFi is the best method that allows data transmission within a plant without the need for wires.
  • Long Range: For remote monitoring, LoRaWAN provides low-power, long-range communication, making it ideal for rural water sources and distributed monitoring points.

● Cloud Storage and Analytics Platform

All the data from the sensors, after passing through the communication module, is stored and analyzed. These now result in enhanced monitoring with predictive studies. Everything from preventive maintenance, sensor healthiness, seasonal effects, and related hurdles is highlighted at this stage.

2. Working Principle

The working principle, when we put all these components together, is conveniently put into a step-by-step flow:

• Step 1: Data collection
• Step 2: Transmission
• Step 3: Storage
• Step 4: Visualization
• Step 5: Alerts

Benefits of IoT in Real-time Water Quality Monitoring

✔  Continuous, Automated Data Collection

The combination of IoT with a real-time water quality monitoring system provides 24/7 surveillance. Through wireless transmission of data, a remote monitoring site with SCADA systems can receive real-time alerts and data from multiple sites. It also enables business owners to receive real-time alerts of the conditions through their mobile or web dashboards. Sensors from RIKA can provide a 1-sec response time for near-continuous measurement.

✔  Faster and Accurate Decision-Making

Having water quality sensors that provide data collection every second enables precise trending. The trending allows decision-making before any water parameter reaches the threshold condition. It can lead to lower downtime and on-time decision-making. For example, a PLC and IoT gateway setup enables real-time alarms if any parameter exceeds a threshold, such as turbidity greater than 5 NTU (WHO). 

✔  Cost Savings and Resource Efficiency

Manual sampling can be time-consuming, which reduces the chances of early detection of any abnormalities. According to the Journal of Soft Computing Exploration, TDS and turbidity sensors reduced the manual testing workload and provided accurate results with an average error of only 1.53%. It enables the optimization of chemical dosing and reduces energy needs by improving backwashing requirements for filters in the water treatment process.

✔  Integration with Control Systems

Incorporating IoT devices that continuously send data and perform actuations based on the data received from control centers enables automated control actions. These actions can include backwashing cycles, valve operations, chemical dosing, and membrane changes, among others. Using SCADA/PLC enables process automation.

✔  Regulatory Compliance and Reporting

Complying with the regulatory requirements of local government or international standards necessitates the use of real-time water quality monitoring. To avoid massive penalties, serious corporate implications, and financial problems, having a system that provides historical logs in cloud SCADA is key. It can make audit trails available and allow export of data for regulators.

Challenges and Considerations

IoT devices are exceptional for monitoring and control, but there are some challenges that come with these devices. Here are some of the challenges and considerations to realize before adapting to the modern way of water quality monitoring:

A. Power Supply for Remote Locations

In the event that the sensors are installed at a remote location. Their associated equipment, including a PLC and communication module, will need power. Off-grid situations may require the use of solar power with batteries. It may add to the initial capital but pay off in the long run.

B. Data Security and Privacy Concerns

Transmitting data over the internet or wirelessly makes it vulnerable to theft. In cases where data security is a key feature, such as encryption and use of the latest WiFi and cellular network signals (5G), it can prove beneficial.

C. Connectivity Limitations in Rural/Industrial Areas

There will always be places where cellular or wired internet connection is unavailable. In such cases, using satellite-based internet services is the only solution, which can be costly to set up and maintain.

D. Cost of Implementation for Small Utilities

For small utilities, utilizing low-cost sensors and microcontrollers, avoiding expensive local servers, implementing in phases, and leveraging shared infrastructure (e.g., regional SCADA hubs) can make IoT viable even for small operators.

Future Trends in IoT Water Quality Monitoring

The rapid growth of the digital world is enabling corporations to enhance efficiency and improve productivity. To stay ahead of the growth curve, consider these key aspects and adapt them early to remain relevant.

● AI/ML-Based Predictive Analytics

Artificial Intelligence (AI) and machine learning (ML) are modern techniques for predictive analysis and behavioral studies. These algorithms can analyze the massive cloud-based database and identify patterns in sensor drift or pump current data, enabling the scheduling of maintenance before breakdowns, thereby reducing unplanned downtime.

● Integration With Smart Cities And Digital Twins

For cities that are already using IoT devices to control and monitor various city installations, such as streetlights, acoustic detectors, and cameras, they can integrate real-time water quality monitoring to enhance regulatory compliance from industries.

● Sustainability and Edge Computing For Faster Processing

These systems are leaning towards using a low-power IoT setup (29 W) designed for continuous operation with minimal energy footprint, aligning with sustainability goals. Reliable PLC-based systems require less manual intervention, reducing travel to remote sites (lowering carbon footprint). Moreover, PLCs and local IoT gateways can process alarms on-site, allowing decisions to be made without waiting for cloud processing — this is effectively edge computing. It can also ensure critical actions (like shutting a valve) are taken even if internet connectivity is lost.

Conclusion

The shift towards using IoT in water quality monitoring is inevitable. It is already adopted by Lake and River Monitoring Networks (Europe & US), Smart Water Cities (Singapore), and Dhaka WASA’s Pagla Sewage Treatment Plant. Their aim is to make monitoring efficient and sustainable. The addition of features like AI and ML for cloud data can provide deep insights that are otherwise challenging to spot through human intervention.

IoT sensors and devices are already making preventive maintenance, precise control, enhanced surveillance, and remote management possible. Brands like RIKA offer end-to-end services for an integrated solution regarding real-time water quality monitoring. Their sensors are of superior grade, with one of the highest accuracies and resolutions in the industry, as per industry standards.

FAQs (Frequently Asked Questions)

Q1: What parameters can IoT water quality monitoring measure?

IoT water quality monitoring mainly consists of turbidity, pH, DO, conductivity, and TDS parameter monitoring. They provide a complete picture of water quality conditions. International organizations typically have limits for these parameters. Monitoring them ensures regulatory compliance.

Q2: Is IoT monitoring suitable for drinking water systems?

The use of IoT in the monitoring of the drinking water system is highly recommended. It is the most advanced method for maintaining the properties of drinking water. A slight change is readily detected by the AI and ML algorithm. It can cause chemical dosing and source identification of contamination in real-time without delay.

Q3: Can IoT reduce water treatment costs?

Yes, using IoT devices can reduce costs by reducing the need for intermittent sampling through labor. For remote areas, frequent visits are drastically reduced. They can also reduce chemical consumption and enable predictive maintenance. Overall, it can reduce labor, operational, and maintenance costs for the water treatment industry.

Q4: What is the typical range of an IoT water quality sensor?

Typically, an IoT water quality sensor like the ones from Rika can offer turbidity sensors (RK500-07) that measure from 0-4000 NTU, pH sensors (RK500-12) typically measure 0–14 pH with 0.01 pH resolution, Dissolved Oxygen (DO) sensors (RK500-13) support 0–20 mg/L measurement range, Conductivity sensors (RK500-23) cover 0–20 mS/cm with multiple range options (customizable for low/high salinity water). TDS sensors are generally used up to 1000 ppm or more for water classification.

Q5: How secure is IoT data transmission?

Modern systems use encrypted communication protocols (e.g., TLS, HTTPS, VPN, or MODBUS over secure TCP/IP) and cellular networks with SIM authentication. It makes data theft challenging. Moreover, firewalls, intrusion detection systems, and regular firmware updates keep the systems up to date against modern threats. The advanced system will use AI agents to detect attacks.