REVIEW ON AIR QUALITY MONITORING SYSTEM USING ARTIFICIAL INTELLIGENCE

Abstract

Air pollution poses a serious threat to human health and the environment. As cities grow and industrial emissions increase, monitoring air quality becomes essential. Traditional systems rely on physical sensors, which can be costly to install and maintain. To overcome these limitations, we propose an AI-based air quality monitoring system that uses API-based data collection instead of physical sensors.

Our system gathers real-time data from government databases, satellite services, weather forecasts, and historical pollution records using secure API keys. By eliminating the need for hardware, this method reduces infrastructure costs, minimizes maintenance, and enhances scalability across multiple locations—ideal for smart city deployment.

Using artificial intelligence and machine learning algorithms, the system analyzes pollution trends, identifies hazardous pollutant levels, and predicts future air quality. It also generates automatic alerts to warn users and authorities when pollution levels exceed safety thresholds. This helps government agencies, environmental bodies, and urban planners make informed decisions and act proactively to reduce pollution levels.

The platform is integrated with mobile apps and cloud services, offering users real-time air quality information, historical insights, and personalized forecasts. Individuals can access pollution alerts, recommendations, and safety precautions—such as using air purifiers, wearing masks, or avoiding outdoor exposure during high pollution hours.

Our API-driven approach provides an affordable, accurate, and highly accessible solution to monitor air quality. It empowers both the public and policymakers with the tools needed to address pollution in real time, contributing to healthier communities and more sustainable urban development.

Introduction

Air pollution continues to be a growing global issue, affecting both environmental quality and public health. Rapid urbanization, industrial activities, and traffic congestion have significantly increased pollutant levels in the atmosphere. As a result, monitoring air quality is now more important than ever.

Traditional monitoring systems rely heavily on physical sensors. While effective, these setups often come with high installation and maintenance costs. They can also be limited in accuracy due to hardware degradation or environmental interference.

To address these challenges, this study presents an AI air quality monitoring system that replaces physical sensors with API-based data collection. Our solution pulls data from trusted sources such as government air quality indexes, weather services, and satellite feeds. This not only improves accuracy but also eliminates the cost and complexity of maintaining physical hardware.

Artificial intelligence plays a key role in processing the incoming data. Machine learning models detect dangerous pollution levels, analyze historical trends, and forecast future air quality. With this insight, authorities can take timely action—like restricting traffic, regulating emissions, or issuing public safety alerts.

Additionally, our system is designed to integrate with cloud platforms and mobile applications. This ensures that users receive real-time updates, daily forecasts, and preventive guidance directly on their devices. By making air quality data more accessible, our system supports public awareness and personal health decisions.

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