Published Paper Details:

Air pollution has emerged as one of the most pressing environmental challenges of the 21st century, with significant implications for public health, climate change, and ecosystem integrity. Traditional air quality monitoring systems, while effective, are often limited by high costs, sparse deployment, and lack of real-time data accessibility. The integration of Internet of Things (IoT) technology with air quality monitoring presents a transformative approach to environmental surveillance, enabling continuous, cost-effective, and geographically distributed monitoring of atmospheric pollutants. This paper presents a comprehensive review and analysis of IoT-based air quality monitoring systems, examining their architecture, sensor technologies, communication protocols, data analytics approaches, and real-world applications. Through systematic analysis of existing implementations and research findings, this study demonstrates that IoT-enabled monitoring systems can achieve measurement accuracies comparable to conventional equipment while offering superior spatial coverage and public accessibility. The paper discusses various sensor types for detecting particulate matter, carbon monoxide, nitrogen dioxide, ozone, and other pollutants, along with wireless communication technologies including Wi-Fi, LoRaWAN, and cellular networks. Furthermore, this research explores cloud computing platforms for data storage and analysis, machine learning algorithms for predictive modeling, and visualization techniques for public awareness. The findings indicate that IoT-based air quality monitoring systems represent a viable solution for smart cities, enabling informed decision-making for pollution control and public health protection