Lakhasly

Water pollution, stemming from industrial waste, sewage, and other pollutants like fertilizers and plastics, contaminates water sources, harming biodiversity and human health. This necessitates effective monitoring. A smart water quality monitoring system, leveraging IoT technology and various sensors (pH, turbidity, temperature, conductivity, etc.), is proposed. This system measures key water parameters, analyzes data in real-time, and transmits information to the cloud. The system's hardware comprises sensors, an Arduino microcontroller (ATMEGA328) for data conversion, an LCD display, and a Wi-Fi module for cloud connectivity. Testing with different water samples demonstrated the system's ability to classify water potability. While advancements exist, future work focuses on incorporating advanced sensors and communication technologies for improved water safety.

Water pollution, caused by the influx of harmful substances into water bodies, poses a threat to ponds, rivers, lakes, seas, and oceans, impacting water quality and purity. Industrial waste and urban sewage are major culprits, while secondary sources include pollutants from soils and the atmosphere. Common water pollutants encompass viruses, bacteria, fertilizers, pharmaceuticals, pesticides, and plastics, affecting biodiversity and leading to a scarcity of drinkable water. The consequences include health issues, infant mortality, and economic setbacks.

The smart water quality monitoring system aims to measure critical metrics like physical, chemical, and microbial properties, detect deviations in metrics promptly, and provide real-time sensor data analysis for corrective actions. User involvement and considerations for hygiene, environmental sanitation, disposal, and storage are integral in maintaining water quality.

Several studies propose IoT-based water quality monitoring systems, employing various sensors and technologies. Many researchers leverage IoT for continuous water quality assessment. The systems use diverse sensors, connectivity options, and analytical approaches to enhance monitoring efficiency.

Methods:

In general water quality monitoring system consists of various sensors such a pH sensor, turbidity sensors, temperature sensors, conductivity sensors, humidity sensors and many other sensors. Fig. 1 shows the general block diagram of smart water quality monitoring system.

Fig. 1. Taxonomy diagram of water quality monitoring system.

Fig. 1. Taxonomy diagram of water quality monitoring system.

With the advances in IoT technology, the water quality monitoring system is becoming smarter with reduced power consumption and ease of operation. Fig. 2 shows the operating flow chart of smart water quality monitoring system.

The core controller is integrated with various sensors such as pH sensor, conductivity sensor, temperature sensor, turbidity sensor and many sensors. The sensor leads are placed in the water to be tested. The sensor values will be processed by ADC and the core controller reads the value and it will be uploaded on the cloud.

Fig. 2. Working of smart water quality monitoring system.

Fig. 2. Working of smart water quality monitoring system.

Results and discussion:

The schematic diagram of the proposed work is as shown in Fig. 3(a). The work consists of two parts, the first one is hardware & second one is software. The hardware part has sensors which help to measure the real time values, another one is Arduino ATMEGA328 converts the analog values to digital and LCD shows the displays output from sensors, Wi-Fi module gives the connection between hardware and software. ATMEGA328 has inbuilt ADC and Wi-Fi modules. The water quality parameters are checked by one by one and updated in the cloud server as well as the values are displayed in the LCD display. Fig. 3(b) shows the developed model of smart water quality monitoring system.

Fig. 3. (a) Schematic diagram of water quality monitoring system, (b) Developed model of smart water quality monitoring syste Fig. 3. (a) Schematic diagram of water quality monitoring system, (b) Developed model of smart water quality monitoring syste

Fig. 3. (a) Schematic diagram of water quality monitoring system, (b) Developed model of smart water quality monitoring system.

The developed model is tested with three different water samples and the results are tabulated in Table 2. From the analysis, water sample 1 is drinkable and other two samples are not drinkable.

Table 2 Water quality parameters for different samples

Table 2 Water quality parameters for different samples

Conclusion:

Water pollution poses a significant threat, impacting health, economy, and biodiversity. This study provides insights into the causes and effects of water pollution, reviews various water quality monitoring methods, and proposes an efficient IoT-based approach. Despite notable advancements, the research landscape remains challenging. The developed model is cost-effective, user-friendly, and capable of classifying water drinkability. Future directions involve incorporating advanced sensors, wireless communication standards, and IoT for enhanced water quality monitoring and rapid response to ensure water safety.