About Air Quality Trends Analysis Project
Air Quality Trends Analysis Project is an MCP server published by dyneth02 in the Developer Tools category: full-stack air quality analytics platform built with FastAPI, React, and MySQL. Aggregates multi-source PM2.5/PM10 data, performs multi-city comparison and time-series forecasting (SARIMAX), and integrates an LLM-based planning agent with tiered access, secure APIs, and PDF reporting. It has been installed 0 times through Conduid.
The repository has 21 stars and 6 forks, with the last commit 8 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.
Install
npx air-quality-trends-analysis-projectThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Ask AI
Ask AI about Air Quality Trends Analysis Project
Powered by Claude · Grounded in docs
Security checks
- ·README presentNot checked yet.
- ·License declaredNot checked yet.
- ·Tests presentNot checked yet.
- ·Dependencies pinnedNot checked yet.
- ·No dynamic code executionNot checked yet.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
README
🌍 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.
🚀 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
🧱 System Architecture
AirSense follows a four-layer architecture:
- Presentation Layer – React SPA with interactive charts
- Application Layer – FastAPI backend with modular routers
- Data Layer – MySQL + SQLAlchemy ORM
- 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}.
🛠️ 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)
🧠 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
👥 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}.
🎯 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)
📌 Future Enhancements
- Real-time alerts for pollution thresholds
- Additional data sources & ML models
- Extended agent reasoning capabilities
- Cloud deployment and CI/CD pipelines
📜 License
This project is released for academic and learning purposes.
README mirrored from the source repository 4 months ago. The original is authoritative.