GENERATIVE ARTIFICIAL INTELLIGENCE BASED SMART  AIR QUALITY MONITORING SYSTEM

Abstract : Air pollution is a growing problem that affects both human health and the environment. To tackle this issue, we have developed an AI-based air quality monitoring system that uses API keys to gather real-time data from various sources instead of relying on physical sensors. The system collects information from government air quality databases, weather reports, and historical pollution data to provide a complete picture of air quality. Using AI and machine learning, the system analyzes the data to detect pollution trends, identify dangerous pollutant levels, and predict future air quality. This helps government agencies, environmental organizations, and city planners make better decisions to reduce pollution. The system also includes automated alerts, so people can stay informed about changes in air quality and take necessary precautions. To make air quality information easily accessible, the system is connected to mobile apps and cloud platforms, allowing users to check real-time pollution levels, view past data, and receive forecasts. Since it relies on API-based data collection, it reduces costs and maintenance compared to traditional sensor-based monitoring. Additionally, this approach makes it easier to expand the system to different locations, making it ideal for use in smart cities.

Keyword Artificial Intelligence, Air Quality Monitoring, API-Based Data Collection, Pollution Prediction, and Real-Time Environmental Analysis.