Water Quality Monitor

Overview The Water Quality Monitor is a system designed to measure and analyze various water parameters to ensure safety and quality. The system utilizes sensors and microcontrollers to collect data, process it, and display relevant information for monitoring purposes.

Features • Real-time monitoring of water quality parameters • Sensors for measuring pH, turbidity, temperature, and TDS (Total Dissolved Solids) • Data visualization on an LCD display or a web dashboard • Alerts for abnormal water conditions • Logging and storage of water quality data • User-friendly interface

Components • Microcontroller (Arduino/Raspberry Pi/ESP8266) • pH Sensor • Turbidity Sensor • Temperature Sensor (DS18B20 or similar) • TDS Sensor • LCD Display or Web Dashboard • Power Supply • Connecting Wires and Other Accessories

Installation & Setup

1. Hardware Setup: • Connect the sensors to the microcontroller as per the circuit diagram. • Power the system using a regulated power supply.

2. Software Requirements: • Arduino IDE/Python Environment (depending on the microcontroller) • Required libraries: • Wire.h (for I2C communication) • OneWire.h and DallasTemperature.h (for temperature sensor) • Adafruit_GFX.h and Adafruit_SSD1306.h (for OLED display, if used)

3. Code Upload: • Upload the firmware to the microcontroller using the Arduino IDE or relevant software. • Ensure correct calibration of sensors before deployment.

4. Data Visualization: • Display data on an LCD screen or send it to a web dashboard. • Configure alerts for predefined thresholds.

Usage

1. Power on the system.
2. The sensors will start measuring water quality parameters.
3. Data is displayed on the LCD/web dashboard in real-time.
4. If any parameter crosses the defined limit, an alert is triggered.
5. The system logs data for future analysis.

Applications • Drinking water quality assessment • Industrial water monitoring • Agricultural water management • Aquatic ecosystem health tracking • Remote water monitoring systems

Future Enhancements • Integration with IoT for remote access • Mobile application support • AI-based anomaly detection for early warnings • Cloud storage for data analysis and history tracking

Contributors • Aditya S. Tawde (Lead Developer) • Neel Belsare (Lead hardware & main)

License This project is licensed under the MIT License - see the LICENSE file for details.

Contact For any queries or collaborations, feel free to reach out to Aditya S. Tawde at adityatawde9699@gmail.com.