2022/11/02 by Arka Daw, Kyongmin Yeo, Daw, Arka +5
Chemistry · Environmental Science · #Air Quality Monitoring and Forecasting #Atmospheric and Environmental Gas Dynamics #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Spectroscopy and Laser Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.00864
openalex publication_date 2022/11/02 · openalex created_date 2022/11/08 · openalex updated_date 2026/07/28
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.