AI-Based Pollution Monitoring in Bio-Energy Production Chains: Methods, Applications, and Gaps

The rapid growth of bio-energy production is closely aligned with global sustainability agendas, particularly the Sustainable Development Goals (SDGs) related to Affordable and Clean Energy (SDG 7), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Effective pollution monitoring across the bio-energy production chain is essential to ensure that renewable energy expansion does not lead to unintended environmental burdens. Current research largely treats artificial intelligence (AI) applications in environmental monitoring and bio-energy systems as separate domains, creating a research gap in integrated, process-wide frameworks that connect emission sources, sensor networks, data pipelines, and AI models across all production stages. The objective of this study is to critically review AI-based pollution monitoring approaches for bio-energy systems and to assess their capability to support sustainable and responsible energy production in line with SDG targets. The methodology involves a structured synthesis of recent literature on sensing technologies, data acquisition and preprocessing, machine learning and deep learning models, and hybrid physics-informed approaches applied from biomass handling to biofuel refining. The key findings show that AI-enabled monitoring improves real-time emission estimation, early detection of abnormal events, and short-term forecasting, supporting cleaner production pathways. At the same time, challenges related to sensor drift, data scarcity, model transferability, and interpretability limit large-scale adoption. The implications of this review highlight the need for open benchmark datasets, robust calibration strategies, and explainable AI models to strengthen regulatory trust, promote sustainable industrial practices, and contribute directly to achieving SDG-linked environmental and energy objectives.