Inverse identification of a passive scalar source of pollution in large areas

This paper presents a novel and computationally efficient framework for the inverse identification of atmospheric pollution sources, with a focus on real-time applicability in complex environments. The proposed method combines stochastic modeling of pollutant dispersion with Higher-Order Singular Value Decomposition (HoSVD) and machine learning-based surrogate modeling. A case study centered on the Singapore region demonstrates the method’s ability to accurately reconstruct pollution fields from sparse sensor data over a 900-hour simulation window. The approach leverages a multi-objective optimization strategy, using Gaussian Process Regression (notably, the regression task is carried out in a parametric space of relatively low dimensionality) to approximate parametric modes and the Levenberg–Marquardt algorithm for source localization. Results show that the localization error remains within a fraction of the spatial grid resolution, and the entire computational pipeline—from simulation to optimization—can be executed in under 30 min on standard desktop hardware. This efficiency enables frequent or continuous deployment in operational monitoring systems. The framework’s modularity allows for extension to higher-dimensional parametric spaces, making it suitable for a wide range of environmental monitoring applications. • Hybrid Framework for Real-Time Inverse Identification The paper introduces a novel and computationally efficient framework that integrates stochastic pollutant dispersion modeling with Higher-Order Singular Value Decomposition (HoSVD) and machine learning-based surrogate modeling, specifically designed for real-time source identification in complex environments. • Low-Dimensional Parametric Space Optimization It employs a multi-objective optimization strategy using Gaussian Process Regression in a low-dimensional parametric space, enabling fast and accurate approximation of pollution fields and source localization via the Levenberg–Marquardt algorithm. • High Accuracy with Sparse Data and Fast Execution The method demonstrates high reconstruction accuracy from sparse sensor data over a long simulation window (900 hours), with localization errors within a fraction of the spatial grid resolution. The entire pipeline runs in under 30 minutes on standard desktop hardware. • Modular and Scalable Design for Environmental Monitoring The framework is modular and scalable, allowing extension to higher-dimensional parametric spaces, making it adaptable for various environmental monitoring applications beyond the case study in Singapore.