๐ค AI Summary
This study addresses the significant interference caused by drone rotor downwash on in situ gas sensors, which compromises accurate mapping of volcanic gas distributions. To overcome this challenge, the authors propose an integrated approach combining open-path remote sensing, gas tomography, and a Lagrangian atmospheric dispersion model to effectively compensate for wind-induced advection and mitigate downwash effects. This methodology enables, for the first time, high-precision three-dimensional reconstruction of volcanic COโ emissions from a drone-based platform. Field experiments conducted at the Salinelle dei Cappuccini mud volcano demonstrate excellent agreement between the reconstructed emissions and manual in situ measurements, confirming the methodโs reliability and accuracy. The work thus establishes an innovative technical pathway for remote sensing and mapping of volcanic gas emissions.
๐ Abstract
Volcanoes emit large amounts of CO2, directly influencing human lives. Mapping volcanic gas emissions helps to forecast eruptions and understand the impact of volcanoes on climate and the environment. Drone-based gas sensing significantly reduces risks in volcanic monitoring but faces technical limitations when measuring gas, as rotor downwash disperses the gas plume before detection. Gas Tomography using remote gas sensing addresses this challenge. At the Salinelle dei Cappuccini mud volcanoes, we demonstrate that while drone-mounted in-situ sensors failed to detect CO2 emissions due to aerodynamic disturbance, open-path sensing successfully enabled remote gas distribution mapping. We present a novel model-based gas tomographic reconstruction approach that incorporates a Lagrangian model to compensate for wind-induced advection. The resulting gas distribution maps align with manually collected in-situ measurements, confirming that model-based gas tomography effectively overcomes downwash limitations and enables accurate mapping of volcanic emissions.