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Development of an Air Quality Monitoring Mobile Application with Predictive Analytics
  1. case
  2. Development of an Air Quality Monitoring Mobile Application with Predictive Analytics

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Development of an Air Quality Monitoring Mobile Application with Predictive Analytics

10clouds.com
Environmental Services
Health & Fitness
Information technology

Environmental Health Awareness Challenge

Urban air pollution frequently exceeds statutory limits, particularly in winter, posing significant health risks to residents—especially those with respiratory conditions. Existing solutions lack predictive capabilities and real-time actionable insights to mitigate exposure.

About the Client

A technology company focused on creating human-centric solutions to address environmental and health challenges through innovative software development.

Key Development Goals

  • Raise public awareness of air pollution levels and health impacts
  • Enable real-time tracking of pollutants (e.g., carbon monoxide, nitrogen dioxide)
  • Integrate machine learning models to predict future air quality trends
  • Provide users with personalized alerts and protective action recommendations
  • Ensure accessibility through a lightweight, intuitive mobile interface

Core System Functionalities

  • Real-time air pollution tracking with geolocation support
  • Machine learning-driven pollution level predictions
  • Customizable health-based alerts for vulnerable users
  • User-friendly interface with visualizations of pollutant data
  • Integration with public health guidelines for actionable advice

Technology Stack

Mobile app frameworks (e.g., React Native, Flutter)
Machine learning libraries (e.g., TensorFlow, PyTorch)
Cloud services for model deployment (e.g., AWS, Google Cloud)
Data visualization tools (e.g., D3.js, Chart.js)

External System Integrations

  • Air quality APIs (e.g., OpenWeatherMap, AirNow)
  • Geolocation services (e.g., Google Maps API)
  • Push notification services (e.g., Firebase Cloud Messaging)

Non-Functional Requirements

  • Scalability to handle high user volumes during pollution peaks
  • Low-latency real-time data updates
  • Data privacy compliance (e.g., GDPR)
  • Cross-platform compatibility (iOS/Android)
  • Energy-efficient ML model execution on mobile devices

Expected Business and Social Impact

Empower users to proactively manage health risks from air pollution, drive public engagement with environmental issues, and establish the client as a leader in health-focused sustainability technology. The app is projected to improve quality of life in urban areas while supporting data-driven policy advocacy.

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