CarbonLens

Urban carbon emission hotspot mapping and preventive action recommendation system for Bhubaneswar.

Overview

CarbonLens is a full-stack web application that identifies, visualizes, and analyzes carbon emission hotspots in urban areas. The platform helps city planners, environmental agencies, and citizens understand emission patterns and take preventive measures.

⚠️ Important: This application currently uses MOCK DATA for demonstration purposes. All emission data, risk classifications, and preventive measures are simulated.

Tech Stack

• Frontend: Next.js 14 (App Router) + TypeScript + Tailwind CSS
• Backend: Next.js API Routes (Node.js)
• Database: MongoDB Atlas (via Mongoose)
• Mapping: Leaflet / react-leaflet
• Data Processing: Python (mock logic, ready for real models)

Project Structure

carbonlens/
├── frontend/          # Next.js frontend application
│   ├── app/          # App Router pages
│   ├── components/   # React components
│   └── lib/         # API helpers
├── backend/         # Backend API and models
│   ├── models/      # Mongoose schemas
│   ├── lib/         # MongoDB connection
│   └── app/api/     # API routes
├── data/            # Mock data files
├── python/          # Python data processing scripts
└── package.json     # Dependencies

Features

• 🗺️ Interactive Map View: Visualize emission hotspots on an interactive map with color-coded risk levels
• 📊 Data Analysis: View aggregate statistics, risk distributions, and source type breakdowns
• 🔍 Hotspot Filtering: Filter hotspots by risk level and explore detailed information
• 🛡️ Preventive Measures: Access recommended preventive measures for each hotspot

Installation

Prerequisites

• Node.js 18+ and npm
• MongoDB Atlas account (or local MongoDB)
• Python 3.8+ (for data generation scripts)

Setup Steps

1. Clone the repository

   git clone <repository-url>
   cd CarbonLens

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env

   Edit .env and add your MongoDB Atlas connection string:

   MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/carbonlens?retryWrites=true&w=majority
   

4. Generate mock data (optional, if you want to regenerate)

   cd python
   python generate_mock_data.py
   cd ..

5. Seed the database

   npm run seed

6. Start the development server

   npm run dev

7. Open your browser Navigate to http://localhost:3000

Usage

Home Page (/)

• Project overview and introduction
• Key statistics dashboard
• Quick navigation to map and hotspots

Map Page (/map)

• Interactive Leaflet map centered on Bhubaneswar
• Color-coded markers (Red: High, Amber: Medium, Green: Low)
• Click markers to view hotspot details
• Risk level legend

Hotspots Page (/hotspots)

• List view of all emission hotspots
• Filter by risk level (All, High, Medium, Low)
• View detailed information and preventive measures

API Endpoints

• GET /api/hotspots - Get all hotspots (supports ?risk_level= and ?source_type= filters)
• GET /api/hotspots/:id - Get single hotspot by zone_id
• GET /api/stats - Get aggregate statistics

Replacing Mock Data with Real Data

1. Replace CSV Files

1. Export your real sensor data as CSV
2. Modify backend/scripts/seed.js to read CSV files:

   const csv = require('csv-parser');
   const fs = require('fs');
   // Parse CSV and convert to hotspot documents

3. Run npm run seed to import real data

2. Replace Python Logic

1. Train your ML model for risk classification
2. Update python/process_emissions.py:

   # Replace mock thresholds with:
   risk_level = model.predict([[pm25, co2]])

3. Integrate with your data pipeline

3. Replace MongoDB Seed Data

1. Set up a data ingestion pipeline
2. Create scheduled jobs to update MongoDB
3. Replace data/mock_hotspots.json with real-time data sources

Why MongoDB + Next.js?

• MongoDB Atlas: Cloud-hosted, scalable, flexible schema for emission data
• Next.js: Full-stack framework with built-in API routes, server-side rendering, and excellent developer experience
• Modular Architecture: Easy to swap mock data/logic with real implementations

Development

Scripts

• npm run dev - Start development server
• npm run build - Build for production
• npm run start - Start production server
• npm run seed - Seed database with mock data
• npm run lint - Run ESLint

Code Structure

• Modular Design: All logic is separated into modules for easy replacement
• Type Safety: Full TypeScript support throughout
• Comments: Extensive comments explaining where real data would replace mocks

Future Enhancements

• Real-time sensor data integration
• Machine learning model integration
• Historical trend analysis
• User authentication and personalized dashboards
• Mobile app support
• Advanced filtering and search
• Export functionality (PDF, CSV)
• Alert system for high-risk zones

Contributing

This is a capstone project. For production use, ensure:

• Data validation and sanitization
• Error handling and logging
• Security best practices
• Performance optimization
• Accessibility compliance

License

MIT License - see LICENSE file for details

Disclaimer

This application uses MOCK DATA for demonstration purposes. All emission values, risk classifications, and preventive measures are simulated and should not be used for real-world decision-making without proper validation and real data integration.