Intelligence frameworks for environmental pollution assessment: a review on air and water quality monitoring systems

Environmental pollution monitoring is essential to human health protection, biodiversity preservation, and ecological degradation minimization. Traditional methods of establishing air and water quality are scientifically valid but normally suffer from flaws such as excessive operational cost, low spatial-temporal resolution, slow processing, and low scalability. Advances in machine learning have introduced superior alternatives to escape these flaws. Particularly noteworthy are supervised learning paradigms such as random forests, support vector machines, and deep neural networks, which have demonstrated immense success in pollutant forecasting, anomaly detection, and low-cost sensor array calibration. Deep learning architectures, e.g., convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have also enabled high-accuracy spatiotemporal estimation, with the capability to capture nuanced environmental dynamics at increased spatial and temporal resolutions. The integration of machine learning with IoT sensors and satellite remote sensing platforms has further facilitated the development of scalable, autonomous environmental intelligence systems for real-time, continuous monitoring across diverse landscapes. This study contributes by addressing critical gaps in current ML-based environmental monitoring systems, particularly sensor drift, data sparsity, and model generalizability. It proposes strategic directions such as explainable AI (XAI), multimodal data fusion, and domain adaptation to enhance system performance and applicability. The innovative potential of this study lies in not only advancing environmental monitoring capabilities but also in laying a roadmap for future intelligent stewardship systems that will be scalable, transparent, and effective post-2025. This article aims to create a general synthesis of current breakthroughs, highlight key limitations, and propose future research directions for the evolution of intelligent environmental monitoring systems.