Project Overview:

The AI-Based Environmental Monitoring System is an IoT + AI/ML-powered solution that continuously monitors environmental conditions such as air quality (AQI), temperature, humidity, and gas levels in real-time. Using AI models, it analyzes and predicts pollution levels, generates health-based alerts, and assists in early identification of harmful environmental trends.

This project is ideal for use in urban areas, schools, factories, and smart cities to ensure a safe and healthy environment.

Technologies Used:

Hardware:

• Microcontroller: ESP32 / NodeMCU / Raspberry Pi

• Sensors:

  • MQ135 / MQ2 – For air pollution (CO2, NH3, Benzene, etc.)

  • DHT11 / DHT22 – Temperature and humidity

  • BMP280 – Atmospheric pressure (optional)

• Wi-Fi Module: Built-in with ESP32 or Raspberry Pi

• (Optional) GPS module – For location tagging

Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Chart.js for real-time graphs, Map integration for pollution hotspots)

Backend:

• Option 1: Node.js + MongoDB

• Option 2: PHP + MySQL

• Option 3: Java Spring Boot + PostgreSQL

AI/ML:

• Python (pandas, scikit-learn, TensorFlow)

• Models:

  • Regression model for AQI prediction

  • Classification model for pollution level categorization (e.g., Good, Moderate, Poor)

System Architecture:

1. Sensors collect environmental data in real time.

2. Microcontroller sends sensor data to the backend via Wi-Fi.

3. Backend saves data in a cloud database.

4. A Python ML model processes historical and live data to:

   • Predict future AQI

   • Classify pollution level (good/moderate/poor/very poor)

5. The web dashboard displays:

   • Live environmental conditions

   • Color-coded AQI meter

   • Health-based alerts

   • Graphs showing trends over time

6. (Optional) If GPS is used, display pollution levels on a map view.

ML Model Description:

AQI Prediction (Regression):

• Input: Current and past gas readings, temperature, humidity

• Output: Predicted AQI value for the next hour/day

• Algorithms: Linear Regression, Random Forest, LSTM (for time-series)

Air Quality Classification:

• Based on AQI thresholds:

  • 0–50: Good

  • 51–100: Moderate

  • 101–200: Unhealthy

  • 200: Hazardous

• Algorithm: Decision Tree / Logistic Regression