- 📖 About the Project
- 💻 Getting Started
- 👥 Authors
- 🔭 Future Features
- 🤝 Contributing
- ⭐️ Show your support
- 🙏 Acknowledgements
- 📝 License
AIR POLLUTION shows data about how clean or polluted the air is in different cities. It gets information from several sources and lets you see and sort the data in different ways.The website aims to help people understand air quality and make informed choices to support cleaner air efforts.
Back-End
DataBase
Hardware
Real-time monitoring and user-friendly dashboard for air quality data visualization.
Integration of sensors for accurate and reliable live data collection.
Educational content to raise awareness about health impacts of air pollution.
Discord bot for interaction with user queries and real-time updates on air quality.
Efficient data management and seamless communication through Node.js server.
In order to run this project you need:
- GitHub
- Code Editor
Clone this repository to your desired folder:
git clone https://github.com/Aryan9901/AIR_QUALITY_MANAGEMENT_SOFTWARES.git
Install this project with:
- npm install
Coming soon
You can deploy this project using: GitHub Pages or render
👤 ARYAN GUPTA
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GitHub: @Aryan9901 @dhruvsaboo1805 @hussainamaan87
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LinkedIn: Aryan Gupta Dhruv Saboo Amaan Hussain Akansha Seth
Enhanced Sensor Integration:
Incorporate advanced sensors such as the PMS7003 sensor for more accurate and comprehensive air quality monitoring. Integrate a GSM module for remote monitoring and data transmission, allowing users to access real-time air quality information from anywhere.
Interactive Mobile Application Development:
Develop interactive Android and iOS interfaces with intuitive user interfaces (UI) for seamless access to air quality data. Implement features for personalized settings, notifications, and alerts to keep users informed about air quality conditions in their area.
Integration of Machine Learning:
Utilize machine learning algorithms to analyze air quality data and provide personalized recommendations to users. Develop prediction models to forecast future air quality trends based on historical data, weather patterns, and other relevant factors.
Scalable Server Architecture:
Upgrade to a more scalable and robust server infrastructure to handle increased user traffic and data processing demands. Implement cloud-based solutions for improved reliability, scalability, and flexibility in managing and storing air quality data.
Enhancement of APIs:
Refine and optimize existing APIs for better performance and compatibility with a wider range of devices and platforms. Develop additional APIs to facilitate integration with third-party applications and services, expanding the project's reach and functionality.
Community Engagement and Partnerships:
Foster partnerships with environmental organizations, local governments, and research institutions to promote collaboration and data sharing. Engage with the community through outreach programs, workshops, and educational initiatives to raise awareness about air quality issues and sustainable practices.
Real-time Data Exchange with Socket.IO:
Implement Socket.IO for real-time communication between the server and client applications, enabling instant updates and synchronization of air quality data. Utilize WebSocket technology to establish persistent connections and facilitate efficient data exchange, providing users with up-to-the-second information on air quality conditions. Enable features such as live charts, graphs, and maps to visualize real-time data trends and fluctuations, enhancing the user experience and engagement with the platform.
This project is MIT licensed.