🌍 AirSense – Multi-Agentic Air Quality Trends Analysis System

AirSense is a full-stack air quality monitoring and analytics platform designed to transform fragmented environmental data into actionable insights.
The system aggregates multi-source PM2.5 and PM10 data, performs comparative analytics, delivers AI-powered forecasts, and enables natural-language analytics through an LLM-based planning agent.

This project was developed as a collaborative group project at SLIIT for the Information Retrieval and Web Analytics (IT3041) module.

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🚀 Key Features

🌐 Multi-Source Data Aggregation

• Scrapes hourly air quality data from Open-Meteo, OpenAQ, IQAir, and WAQI
• Applies weighted aggregation with outlier trimming to ensure reliable data
• Persists clean, aggregated time-series data in MySQL

📊 Advanced Analytics

• Multi-city comparison with KPIs (mean, min, max PM levels)
• Best vs worst city ranking
• Part-to-whole and trend-based analysis

📈 AI-Powered Forecasting

• Time-series forecasting using SARIMAX
• Confidence intervals and backtesting (MAE, RMSE)
• Single-city and multi-city prediction support

🤖 LLM-Based Planning Agent (Enterprise Tier)

• Natural-language queries converted into executable analysis plans
• Uses a critic-based reflection pattern to ensure security and capability limits
• Transparent execution traces for explainability

🔐 Security & Tiered Access

• JWT-based authentication with bcrypt password hashing
• Subscription tiers: Free, Pro, Enterprise
• Plan-based enforcement of data windows, city limits, and forecast horizons

🧾 Professional Reporting

• Auto-generated PDF reports with charts and KPI tables
• Server-side rendering using ReportLab

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🧱 System Architecture

AirSense follows a four-layer architecture:

1. Presentation Layer – React SPA with interactive charts
2. Application Layer – FastAPI backend with modular routers
3. Data Layer – MySQL + SQLAlchemy ORM
4. Intelligent Agent Layer – LLM planner with MCP-style tool orchestration

This architecture enables scalability, security, and clear separation of concerns :contentReference[oaicite:1]{index=1}.

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🛠️ Tech Stack

• Frontend: React, Tailwind CSS, Recharts
• Backend: FastAPI (Python), Uvicorn
• Database: MySQL, SQLAlchemy
• AI / Analytics: SARIMAX, LLM (Ollama / Gemma), Agent Planning
• Security: JWT, bcrypt
• Reporting: ReportLab (PDF generation)

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🧠 Responsible AI Practices

• Fairness: Multi-source aggregation to reduce sensor bias
• Explainability: Interpretable SARIMAX models + execution traces
• Transparency: Visible data sources, KPIs, and agent steps
• Privacy: No personal location tracking; secure credential handling

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👥 Team & Leadership

Team Leader & Full-Stack Integration Architect:
Hirusha D G A D (IT23183018)

Key contributions include:

• AI forecasting engine & backtesting
• LLM agent design and orchestration
• Authentication & tier enforcement
• System-wide integration and documentation leadership

(Full contribution breakdown available in the final report) :contentReference[oaicite:2]{index=2}.

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🎯 Academic Context

• Institution: Sri Lanka Institute of Information Technology (SLIIT)
• Module: IT3041 – Information Retrieval and Web Analytics
• Year: 2025
• Project Type: Group Project (Industry-oriented system)

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📌 Future Enhancements

• Real-time alerts for pollution thresholds
• Additional data sources & ML models
• Extended agent reasoning capabilities
• Cloud deployment and CI/CD pipelines

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📜 License

This project is released for academic and learning purposes.