IoT-Based Air Pollution Monitoring System - Electronic Engineering Guide
1. Introduction
An IoT-Based Air Pollution Monitoring System enables real-time detection of environmental pollutants using sensors and transmits the data to a cloud platform. This project helps in understanding air quality trends and enables authorities to take timely action.
2. Objectives
• Measure key air quality parameters in real-time.
• Upload data to an IoT platform for remote monitoring.
• Alert users when pollution levels exceed safe thresholds.
• Provide accessible data visualization through web or mobile apps.
3. Components Required
• Microcontroller (e.g., NodeMCU ESP8266, ESP32)
• Air quality sensors: MQ135 (CO2/NOx), PMS5003 (PM2.5/PM10), DHT11/22 (temperature & humidity)
• OLED or LCD display (optional)
• Wi-Fi module (built-in on NodeMCU/ESP32)
• Buzzer/LED for alerts
• Power supply (battery or USB)
• Enclosure box (weatherproof for outdoor use)
4. System Architecture
Sensors collect air quality and climate data, which is processed by a microcontroller. The data is transmitted via Wi-Fi to an IoT platform (like ThingSpeak or Blynk), where it can be analyzed and visualized.
5. Sensor Integration
• MQ135 for CO2, ammonia, benzene, and other gases.
• PMS5003 for particulate matter (PM2.5 and PM10).
• DHT11 or DHT22 for temperature and humidity readings.
• Sensors are connected to analog or digital GPIO pins and powered with
regulated 5V/3.3V.
6. Microcontroller and IoT Platform
• ESP8266 or ESP32 handles sensor reading and Wi-Fi
communication.
• Platforms like ThingSpeak, Blynk, or Firebase are used to visualize data.
• Use MQTT or HTTP protocols to send data packets at intervals.
• Create dashboards for real-time visualization.
7. Data Acquisition and Processing
• Read values periodically from all sensors.
• Use filters or averaging to reduce sensor noise.
• Convert analog readings into meaningful values using sensor-specific formulas
or calibration curves.
• Store local copies for backup if internet is unavailable.
8. Cloud Connectivity and Visualization
• Use Wi-Fi to connect to a cloud platform.
• Upload sensor data with timestamps.
• Visualize trends using graphs or maps.
• Configure email/SMS alerts when pollution exceeds thresholds.
9. Power Supply and Enclosure
• Power via USB adapter, Li-ion battery, or solar panels.
• Use a buck converter for voltage regulation.
• House components in waterproof and UV-resistant enclosures for outdoor
installations.
10. Testing and Calibration
• Validate sensor readings with known air quality levels.
• Perform calibration using standard gases or reference meters.
• Monitor system stability over time.
• Adjust thresholds based on local air quality standards (e.g., AQI values).
11. Applications
• Urban air quality monitoring
• Smart city infrastructure
• Industrial emission tracking
• Educational and environmental research projects
12. Limitations and Future Enhancements
• Sensor accuracy may vary with temperature/humidity.
• Limited sensor lifespan and need for periodic calibration.
• Future improvements: GPS-based data mapping, AI-based anomaly detection,
solar-powered operation, mobile app integration.
13. Conclusion
The IoT-Based Air Pollution Monitoring System offers a scalable and real-time solution for environmental monitoring. By leveraging IoT platforms and low-cost sensors, the project provides valuable insights into air quality trends and contributes to public health awareness.