IEEE VIS 2024 Content: Challenges in Data Integration, Monitoring, and Exploration of Methane Emissions: The Role of Data Analysis and Visualization

Challenges in Data Integration, Monitoring, and Exploration of Methane Emissions: The Role of Data Analysis and Visualization

Parisa Masnadi Khiabani - University of Oklahoma, Norman, United States

Gopichandh Danala - University of Oklahoma, Norman, United States

Wolfgang Jentner - University of Oklahoma, Norman, United States

David Ebert - University of Oklahoma, Oklahoma, United States

Room: Bayshore VI

2024-10-14T16:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T16:00:00Z
Exemplar figure, described by caption below
The image shows how integrating top-down and bottom-up approaches for methane leakage detection addresses methodological gaps, enhancing the detection and understanding of emission sources and rates. This integration enables cross-validation, which improves both top-down and bottom-up modeling. Every step contributes to visualization, yet data analysis and visual analytics are not only crucial for providing precise feedback for modeling but also integral in enhancing each step of the process. These tools are key for tackling challenges in data integration, effectively managing information, and uncovering hidden patterns, ensuring continuous improvement across all stages.
Abstract

Methane (CH4) leakage monitoring is crucial for environmental protection and regulatory compliance, particularly in the oil and gas industries. Reducing CH4 emissions helps advance green energy by converting it into a valuable energy source through innovative capture technologies. A real-time continuous monitoring system (CMS) is necessary to detect fugitive and intermittent emissions and provide actionable insights. Integrating spatiotemporal data from satellites, airborne sensors, and ground sensors with inventory data and the weather research and forecasting (WRF) model creates a comprehensive dataset, making CMS feasible but posing significant challenges. These challenges include data alignment and fusion, managing heterogeneity, handling missing values, ensuring resolution integrity, and maintaining geometric and radiometric accuracy. This study outlines the procedure for methane leakage detection, addressing challenges at each step and offering solutions through machine learning and data analysis. It further details how visual analytics can be implemented to improve the effectiveness of the various aspects of emission monitoring.