This book chapter explores the transformative role of Internet of Things (IoT) and Artificial Intelligence (AI) in revolutionizing pollution monitoring and environmental safety systems. With growing concerns over environmental degradation, conventional pollution management methods are increasingly inadequate in addressing real-time challenges. IoT-enabled sensor networks provide continuous, real-time data on various environmental parameters, such as air quality, water contamination, and hazardous emissions, facilitating more dynamic and efficient monitoring. By integrating AI algorithms, these systems enhance data analysis, enabling predictive insights and intelligent decision-making to mitigate pollution and optimize environmental control measures. The chapter discusses the integration of AI and IoT for pollution forecasting, early warning systems, and automated environmental safety measures, providing a holistic approach to managing pollution in urban and industrial settings. Furthermore, the impact of low-cost, high-performance sensors in large-scale deployments is highlighted, offering scalable solutions for global pollution challenges. Key challenges in data security, sensor calibration, and system interoperability are addressed, along with future research directions to further improve the efficacy of these technologies. This chapter provides a comprehensive overview of the evolving landscape of AI-powered IoT systems for pollution control, contributing to sustainable environmental management practices.
The escalating concerns regarding environmental pollution and its associated risks have become one of the most pressing global challenges in recent decades [1]. Industrialization, rapid urbanization, and unregulated waste disposal have contributed significantly to the degradation of air, water, and soil quality, leading to adverse effects on public health, biodiversity, and climate stability [2]. Traditional methods of pollution monitoring and control, which often rely on periodic sampling and manual interventions, have struggled to keep pace with the complexity and scale of contemporary pollution issues [3]. These conventional systems are typically limited in their ability to provide real-time data, predictive insights, and automated responses to pollution events, thus hindering timely decision-making and effective action [4]. As pollution levels increase and become more dispersed across large geographic areas, there is an urgent need for innovative solutions that can address these challenges and improve environmental safety [5].
The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) offers a promising solution to modern pollution monitoring and control systems [6]. IoT enables the deployment of a vast network of sensors capable of collecting real-time environmental data across urban, industrial, and rural landscapes [7]. These sensors monitor key pollutants, such as particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and volatile organic compounds (VOCs), along with other environmental parameters like temperature, humidity, and wind speed [8]. The continuous nature of data collection allows for the detection of pollution events as they unfold, providing valuable insights into the sources, distribution, and concentration of pollutants [9]. This shift to real-time monitoring represents a significant departure from traditional, limited sampling methods, making it possible to track environmental changes with unprecedented precision [10].
While IoT sensors provide the necessary infrastructure for data collection, the role of AI in processing and analyzing this data is equally critical [11]. AI algorithms, particularly machine learning (ML) and deep learning (DL), can efficiently process large volumes of sensor data, identifying patterns, correlations, and anomalies that would be difficult for humans to detect [12]. These algorithms can also predict future pollution levels by learning from historical data and incorporating factors such as weather conditions, traffic patterns, and industrial activities [13]. The predictive capabilities of AI can offer valuable foresight into potential pollution spikes or hazardous conditions, allowing for proactive interventions [14]. AI models can automatically adjust pollution control systems, such as air filtration, waste management, or traffic optimization systems, in response to changing environmental conditions. This dynamic, automated approach to pollution control enhances the effectiveness and efficiency of environmental safety measures [15].
