Abstract

Effective forecasting of industrial emissions is critical for ensuring regulatory compliance and advancing environmental sustainability. This study presents a multi-variable engineering model designed to predict emissions levels from EPA-regulated industrial facilities using integrated operational, environmental, and technological parameters. The model incorporates real-time process variables such as fuel type, combustion efficiency, operating temperature, load factor, and emission control system performance. Meteorological inputs, including ambient temperature, humidity, and wind speed, are also integrated to reflect environmental influences on dispersion and chemical transformation of pollutants. Advanced statistical techniques and machine learning algorithms, such as multiple linear regression (MLR), random forest, and artificial neural networks (ANNs), are utilized to identify complex interdependencies and improve forecast accuracy. Historical emissions data from multiple sectors such as power generation, petrochemicals, and manufacturing are employed to train and validate the model under the framework of the U.S. Environmental Protection Agency’s (EPA) National Emissions Inventory (NEI) and Clean Air Markets Division (CAMD) databases. The model is benchmarked against existing EPA emission estimation methods, demonstrating a significant improvement in predictive accuracy, especially for key pollutants such as NOx, SO₂, CO, PM₂.₅, and VOCs. Furthermore, the model supports scenario-based forecasting, enabling facility managers and regulators to assess the impact of operational changes, technological upgrades, and seasonal variations on emissions profiles. A sensitivity analysis identifies the most influential variables affecting emissions, providing actionable insights for engineering design, operational optimization, and regulatory planning. This research offers a robust decision-support tool that bridges the gap between engineering operations and environmental compliance, facilitating proactive emissions management in line with EPA standards. The model’s adaptability across sectors and its compatibility with real-time monitoring systems position it as a valuable resource for predictive environmental management, policy formulation, and sustainable industrial development.