| Abstract Title: | Characterizing Long-term Methane Emissions from Small-scale, Heterogenous, Variable Sources Using Satellite Remote Sensing – A Case Study at Wastewater Treatment Plant |
| Presenter Name: | Prof Ke Du |
| Co-authors: | Mr Seyed Mostafa Mehrdad Mrs Bo Zhang |
| Company/Organisation: | University of Calgary |
| Country: | Canada |
Abstract Information :
Satellite remote sensing technologies have been widely used to detect and quantify large scale emission sources of methane (CH4), such as oil and gas facilities, coal mines, agricultural lands, etc. Small emission sources, although the emission rate is low for each source, their total emission is significant. It was found that 70% of total oil/gas CH4 emissions in the continental US originate from facilities emitting < 100 kg/hr. However, such sources are poorly studied due to the requirement of high-resolution data, and the challenges from heterogeneity and variation in emission, contributing to large uncertainty in developing the emission inventory for these sources. This research evaluated the feasibility of studying the long-term CH4 emissions from a small-scale methane source, using a wastewater treatment (WWT) facility as the testbed, by analyzing high spatial resolution Sentinel-2 data. Satellite images of a WWT plant in Calgary, Canada, taken between 2019 and 2023, were processed to retrieve CH4 column concentration distributions. Digital image processing techniques were developed and used for extracting the time- and space-varying features of CH4 emissions, which revealed daily, monthly, seasonal, and annual variations. Emission hotspots were also identified and corroborated with ground-based measurements. Despite limitations due to atmospheric scattering, cloud cover, and sensor resolution that affect precise ground-level concentration assessments, the findings reveal the dynamic nature of fugitive GHG emissions from WWT, indicating the need for continuous monitoring. The results also show the potential of utilizing satellite images for cost-effectively evaluating fugitive CH4 emissions from small-scall sources.
