Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions

The Integrated Mass Enhancement (IME) method is among the most popular remote sensing method for estimating methane emissions from point sources, and it has gained significant popularity in recent years. In this study, we evaluated how key environmental and observational factors, namely wind speed, instrument noise, terrain topography, and the source of 10-meter wind speed (U₁₀) data, influence emission estimates derived from the IME method. Although landfills are typically area sources, we used a simplified point-source emission setup as a controlled case to systematically explore the sensitivity of IME to each of these factors. This approach allows us to isolate the impact of individual variables on the mass enhancement (ME), plume scale (L), and estimated emission rate (Q). Our results show that higher wind speeds reduce the detectable methane ME by as much as 85%, due to lower plume concentrations, and the instrument noise further reduces ME, especially for smaller emission rates. Noise also had a stronger effect on L than wind speed, L dropped by up to 40% under just 1% noise. In complex terrain, ME increased but plume shapes became more irregular, making detection more sensitive to noise and harder to interpret. We also compared different wind data sources and found that, in the 2-8 m/s range relevant for satellite IME, GEOS overestimated U₁₀ by 10-40%, while HRRR underestimated it by 10-30%. These differences can bias IME results and highlight the need for accurate, site-specific wind data. While remote sensing methods are effective for broad methane surveys, our results show that emission estimates at the facility level, especially for landfills, can be highly sensitive to wind speed, sensor noise, and terrain. To improve accuracy, location-specific calibration is essential for regulatory or operational use.Implications: Integrated Mass Enhancement is a widely used remote sensing algorithm for quantifying methane emissions from sources such as landfills. In this study, we demonstrated that the performance of the IME method can vary substantially under different meteorological and terrain conditions. Although remote sensing methods are highly effective for broad-scale methane surveys, our results indicate that facility-level emission estimates, such as those for individual landfills, can be significantly influenced by factors such as wind speed, sensor noise, and terrain characteristics. Therefore, developing location-specific calibration equations is essential for improving accuracy and ensuring reliable regulatory or operational use.