ABSTRACT
Air pollution represents a critical challenge in contemporary society, significantly worsened by the rapid industrialization and economic growth observed globally. This increase in emissions poses severe risks to public health, contributing to respiratory and cardiovascular disorders. Current regulatory frameworks for air quality management, while evolving, often lack the stringency required for effective pollution control, necessitating innovative solutions for monitoring and mitigation. This chapter explores the application of Internet of Things (IoT) technologies in air pollution monitoring and control, highlighting the potential of IoT to offer a comprehensive approach to real-time monitoring by integrating diverse data streams from sensors and devices for a holistic air quality assessment. The chapter examines the system architecture necessary for implementing IoT-based monitoring systems, emphasizing sensor networks, data acquisition, and communication protocols, and details the technologies involved, including advanced sensors for detecting particulate matter and gaseous pollutants. The chapter also explores the use of machine learning algorithms to enhance the predictive capabilities of IoT systems, enabling the development of models that forecast pollution trends and identify pollutant sources. The chapter discusses a few case studies and concludes with a discussion on the benefits and challenges of IoT adoption in air pollution control. The chapter emphasizes the transformative potential of IoT in creating smarter, more responsive, and effective environmental monitoring solutions.
