Quantifying methane emission of point-sources by integrating Aircore and DIAL-LiDAR measurements

Abstract

Accurately quantifying methane (CH 4 ) emissions from intense point sources at fine spatial and temporal scales remains a significant challenge for current monitoring techniques. In this study, we present an integrated methodology that combines UAV-based AirCore sampling with ground-based methane differential absorption light detection and ranging system (CH 4 -DIAL) measurements. By leveraging the high spatial resolution, sensitivity, and rapid scanning capabilities of lidar system, together with the flexibility and vertical profiling strengths of UAV-AirCore, our approach enables precise identification and quantification of methane emissions from localized sources. A hybrid estimation framework, incorporating genetic algorithms for initial source estimation and trust-region optimization for refinement, is developed to process the joint observational data. Field validation was conducted in two industrial areas in Dongying City. The results demonstrate that the integrated approach significantly enhances the accuracy and reliability of methane emission quantification for strong point sources, compared to conventional single-platform methods. This methodology offers a robust and scalable solution for atmospheric methane monitoring in complex industrial environments.