🌬️ Air Quality Intelligence System (AQIS)

AI-Powered Pollution Source Attribution & Geospatial Monitoring

AQIS is a comprehensive environmental monitoring solution that transforms raw particulate matter data into actionable intelligence. By using a custom AI Attribution Engine, the system identifies potential industrial sources of pollution spikes, enabling data-driven accountability.

---

Key Features

• Real-Time Monitoring: Live dashboard for PM2.5, PM10, and AQI across multiple urban centers (Ernakulam, Delhi, Mumbai).
• AI Source Attribution: Proprietary scoring engine that identifies "Suspect Sites" (factories, construction zones) based on pollution levels and proximity.
• Live Incident Map: Interactive Leaflet.js integration showing monitoring stations and flagged suspect sites with dynamic "Fly-To" navigation.
• Admin Governance Panel: Secure portal for environmental administrators to review crowdsourced reports and manage the suspect database.
• Incident History & Log: Persistent storage of past pollution violations for long-term trend analysis.

---

System Architecture

The project utilizes a Client-Server Architecture optimized for low-latency updates:

1. Data Layer: Fetches real-time sensor feeds via the OpenWeatherMap API.
2. Logic Layer (Node.js/Express): Processes raw data through an AI-scoring algorithm to calculate suspect confidence levels.
3. Persistence Layer: Uses localStorage for session history, incident logs, and admin moderation queues.
4. Presentation Layer: Responsive UI built with Tailwind CSS and dynamic mapping via Leaflet.js.

---

Tech Stack

• Frontend: HTML5, Tailwind CSS, JavaScript (ES6+), Leaflet.js
• Backend: Node.js, Express.js
• Data Source: OpenWeatherMap API
• Development AI: Google Gemini (Logic Optimization & Data Structuring)

---

⚙️ Installation

1. Clone the repository

git clone https://github.com/ASP-31/AirPro

2. Install dependencies

npm install

3. Start the server

node server.js

4. Launch the Dashboard Open dashboard.html in your preferred web browser.

---

📈 Future Roadmap

• Wind Correlation: Integrating real-time wind speed/direction to track plume trajectories.
• IoT Integration: Connecting custom hardware sensor meshes (ESP32/Arduino).
• Predictive AI: Implementation of LSTM models to forecast AQI trends 24 hours in advance.