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

## TL;DR

This paper introduces a new system using sensor data and advanced algorithms to quickly locate sources of airborne contaminants, aiding in emergency response.

## Contribution

The novel integration of a modified Sandpile model with advection and the (1 + 1)-Evolution Strategy for real-time contaminant source localization.

## Key findings

- The system successfully reconstructs contaminant source parameters using only sensor concentration measurements.
- The proposed method achieves high computational efficiency, completing test cases in under 20 minutes on a standard laptop.
- Validation with synthetic data confirms the model's accuracy and robustness within sensor grid resolution limits.

## Abstract

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of 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 were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min 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, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications.