Sensor-Driven Localisation of Airborne Contaminant Sources via the Sandpile-Advection Model and (1+1)-Evolution Strategy

The primary aim of this study is to develop an effective decision-support system for managing crises involving the release of hazardous airborne substances. Such scenarios often result from industrial accidents or intentional releases and require rapid contaminant source identification to facilitate timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection with the (1+1) Evolution Strategy to solve the inverse problem of source localisation. The initial part of the paper reviews existing methods for simulating atmospheric dispersion and reconstructing the source location. In the subsequent sections, we describe the proposed system architecture, modelling assumptions, and the experimental framework. A key feature of the presented method is that it relies solely on concentration measurements reported by a distributed network of sensors and does not require any prior knowledge about the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments supported model calibration and refinement, followed by formal tests to evaluate localisation accuracy and robustness. Each test case was completed in under 20 minutes on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1+1)-ES Sandpile model can effectively reconstruct source parameters within the resolution limits of the sensor grid. The system’s speed, simplicity, and exclusive reliance on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications.