BreatheAhead is a state-of-the-art air quality monitoring and predictive dashboard designed to tackle the growing pollution crisis in India. By leveraging real-time satellite data and AI-driven forecasting, it provides citizens and governance with the tools needed to combat smog and protect public health.
To transition from a local prototype to a national-scale production system, the following Microsoft Azure services are proposed:
- Purpose: Global distribution of the dashboard UI.
- Scale: Automatically scales to handle millions of concurrent users during peak "smog seasons."
- Direct Integration: GitHub Actions for seamless CI/CD.
- Purpose: Instead of browser-side API calls, serverless Azure Functions will poll the Open-Meteo API (and other satellite sources like NASA/Sentinel) every 15 minutes.
- Efficiency: Decouples data collection from the user experience, ensuring a low-latency UI.
- Purpose: Store millions of historical JSON records across all Indian cities.
- Scalability: Horizontal scaling with multi-region replication ensures data is always available near the user.
- Purpose: If physical IoT sensors are integrated, Event Hubs will ingest millions of events per second, processing them via Azure Stream Analytics for immediate dashboard updates.
The ultimate goal of BreatheAhead is to predict rather than just react. Here is how we will transform our JSON history into a high-accuracy forecasting engine using Azure Machine Learning (Azure ML):
While we currently log AQI records, our training pipeline will include:
- Meteorological Data: Wind speed, humidity, temperature via Azure Open Datasets.
- Traffic Logs: Urban congestion patterns.
- Industrial activity: Seasonal data (e.g., crop residue burning schedules).
- Data Preparation: Convert historical JSON logs from Cosmos DB into structured datasets.
- Algorithm Selection: Use LSTM (Long Short-Term Memory) neural networks, which are highly efficient for time-series forecasting (predicting the next 24-48 hours of AQI).
- Automated ML (AutoML): Leverage Azure AutoML to iterate through thousands of models to find the highest accuracy version.
- Model Deployment: The trained model will be deployed as a Managed Online Endpoint in Azure ML.
- Inference: When a user opens the dashboard, the system sends current weather data to this endpoint.
- Output: The model returns a predicted AQI curve for the next 24 hours with >90% precision, allowing the government to implement GRAP measures before the pollution spikes.
The current version of BreatheAhead is built using a modern, scalable stack that integrates Data Science, Deep Learning, and Web Technologies.
- Static Layer: HTML5, Vanilla CSS3 (Custom Glassmorphism Design).
- Dynamic Logic: JavaScript (ES6+).
- Mapping UI: Leaflet.js for real-time interactive pollution heatmaps.
- Data Visualization: Chart.js for AQI trend analysis and forecasting curves.
- Multilingual Support: Custom internal localization engine for Hindi/English toggle.
- Deep Learning: PyTorch used for training the LSTM (Long Short-Term Memory) Neural Network (Time-Series Forecasting).
- Machine Learning: XGBoost for gradient-boosted decision tree regression.
- Data Science Stack: Pandas (Feature Engineering), NumPy (Mathematical ops), Scikit-learn (Preprocessing & Scaling).
- Models Trained: Ensemble of XGBoost and LSTM models focused on PM2.5 and PM10 pollutants.
- AQI Formula: Implemented US-EPA standard calculation algorithms.
- Framework: Flask (Python) serving as the REST API for model inference.
- Reverse Proxy: Nginx used for production-grade routing and static file serving.
- Containerization: Docker for unified packaging of the models, frontend, and backend.
- Automation: Shell scripts (
entrypoint.sh) for multi-service orchestration inside the container.
- Azure Static Web Apps: Hosts the unified frontend (HTML/JS) with global distribution and a managed API backend.
- Azure Functions (Serverless): Orchestrates periodic data fetching from satellite APIs and manages the asynchronous background logging.
- Azure Container Instances (ACI): Used for running short-lived data-processing jobs and model validation scripts.
- Azure Machine Learning (AML): Central hub for training, hyperparameter tuning, and deploying the LSTM AQI prediction models.
- Azure AI Services (Bot Service + Language): Powers the integrated "Pollution Bot" chatbot, enabling Natural Language Processing (NLP) to answer citizen queries.
- Azure Open Datasets: Provides integrated access to reliable historical weather and climate datasets from sources like NOAA.
- Azure Cosmos DB (NoSQL): The primary database for high-velocity pollution logs, providing millisecond latency and global scale.
- Azure Blob Storage: Stores large-scale raw satellite imagery and trained model artifacts (.pkl or .onnx files).
- Azure Event Hubs: Manages real-time data ingestion from distributed IoT air sensors across the country.
- Azure Key Vault: Secure management of API keys (Open-Meteo, Satellite providers) and database connection strings.
- Azure Entra ID (Formerly Active Directory): Secures the administrative dashboard for government officials to issue localized emergency alerts.
- GitHub Actions + Azure Pipelines: Automated CI/CD pipelines for zero-downtime deployments.
- Azure Monitor & Application Insights: Real-time tracking of app performance, API failure rates, and user engagement metrics.
- Azure Advisor: Continuous optimization for cost and performance across the entire cloud infrastructure.
For rapid deployment, BreatheAhead is containerized using Docker. This allows for consistent environments across development and production.
docker build -t breatheahead:v1 .
docker run -p 8080:80 breatheahead:v1- Azure Container Registry (ACR): Push the image to a private ACR for secure storage.
- Azure App Service for Containers: Deploy the Docker image directly to a managed web app for automatic scaling and SSL management.
- Azure Container Instances (ACI): Perfect for quick testing or isolation of specific versions.
Aligned with the National Clean Air Programme (NCAP) and the Viksit Bharat 2047 vision, BreatheAhead serves as a digital bridge between advanced cloud technology and life-saving environmental policy.
Created for the Microsoft Imagine Cup 2026