AI-Based Air Quality Monitoring and Pollution Prediction System

Air pollution poses serious health and environmental risks worldwide. Conventional air quality monitoring systems provide limited real-time insights and lack predictive capabilities. This research-based project focuses on developing an AI-driven air quality monitoring and pollution prediction system.

Problem Statement

Existing monitoring systems are reactive and fail to predict pollution spikes, limiting preventive action.

Research Objectives

The objective is to analyze environmental sensor data and predict future air quality levels using machine learning models.

Methodology

Sensor data such as particulate matter, temperature, and humidity is collected and cleaned. Predictive models analyze trends and forecast pollution levels.

Technologies Used

Machine learning frameworks, IoT sensors, cloud platforms, and data visualization tools are utilized.

Expected Outcomes

The system enables early warnings, supports policy decisions, and improves public health awareness.

Conclusion

This project demonstrates how AI can contribute to environmental sustainability and smart urban planning.