Rapid urbanization, industrialization, and population growth have significantly intensified environmental pollution, posing severe challenges to human health and ecosystem sustainability. Real-time monitoring, predictive analysis, and intelligent decision-making are essential to mitigate these impacts effectively. Integration of the Internet of Things (IoT) with machine learning (ML) offers a transformative approach for environmental intelligence, enabling continuous collection, analysis, and interpretation of multi-source pollution data. IoT-based sensor networks capture air, water, and soil quality parameters with high spatiotemporal resolution, while ML algorithms identify patterns, forecast pollutant concentrations, detect anomalies, and provide actionable insights for proactive interventions. Environmental Intelligence Systems (EIS) leverage this integration to facilitate decision support, early warning mechanisms, and policy implementation, ensuring timely responses to environmental hazards. Critical challenges include sensor calibration, data heterogeneity, energy efficiency, scalability of networks, and robustness of ML models under dynamic environmental conditions. Addressing these challenges requires interdisciplinary strategies combining IoT architecture, data analytics, and environmental science expertise. This chapter provides a comprehensive overview of IoT-enabled monitoring systems, ML-based predictive frameworks, and intelligent environmental management approaches, highlighting current research gaps and future directions for sustainable, data-driven pollution control.
Environmental pollution has emerged as one of the most critical global challenges, driven by the rapid growth of urban areas, industrialization, and expanding transportation networks [1]. Rising concentrations of air, water, and soil pollutants have led to significant health hazards, ecological degradation, and diminished quality of life in densely populated regions [2]. Conventional monitoring systems, which rely on manual sampling and laboratory analysis, often fail to provide timely information, lack high-resolution spatial coverage, and are labor-intensive [3]. The increasing complexity of environmental systems necessitates innovative monitoring solutions capable of real-time data collection, processing, and interpretation [4]. Continuous surveillance and dynamic assessment of environmental conditions have become essential for managing pollution, predicting hazards, and implementing effective mitigation strategies in urban and industrial ecosystems [5].
The integration of the Internet of Things (IoT) in environmental monitoring frameworks provides a robust mechanism for automated, continuous, and large-scale data acquisition [6]. IoT sensor networks enable real-time measurement of diverse environmental parameters, including particulate matter, greenhouse gases, chemical contaminants, and water quality indicators [7]. Distributed sensors can be deployed across urban, industrial, and rural areas, allowing comprehensive spatial and temporal coverage [8]. Wireless communication protocols such as LoRaWAN, NB-IoT, and Zigbee facilitate seamless transmission of data to centralized platforms for storage and processing [9]. Edge computing techniques optimize data handling by performing preprocessing locally, reducing network congestion and minimizing latency. The flexibility of IoT architectures allows for scalable, adaptive, and resilient monitoring networks capable of responding to environmental changes with high efficiency [10].
Machine learning (ML) algorithms enhance the utility of IoT-based environmental monitoring by enabling predictive analytics, anomaly detection, and pattern recognition [11]. Supervised and unsupervised learning techniques, along with deep learning models, can forecast pollution levels, identify emerging hotspots, and evaluate the impact of anthropogenic activities on ecosystems [12]. Advanced ML approaches allow multi-variable analysis, capturing interactions among multiple pollutants, weather patterns, and human activities [13]. The predictive capabilities of these models support proactive environmental management, allowing authorities to take timely preventive measures [14]. ML models also assist in generating actionable insights from large and heterogeneous datasets, ensuring that decision-making was evidence-based and targeted toward critical regions requiring immediate intervention [15].
