Work

Quantification of greenhouse gas emissions from a municipal solid waste incinerator using an uncrewed aerial vehicle

Year: 2025

Type: article

Abstract: The accurate quantification of greenhouse gas emissions from waste treatment facilities is critical for effective climate change mitigation and regulatory compliance. Measurement-based methods are increasingly emphasized as essential for addressing uncertainties in emission estimates, with uncrewed aerial vehicles (UAVs) recognized for their flexibility and ability to capture spatially resolved data. This study evaluated CO 2 emissions at a municipal solid waste incinerator using UAV monitoring with two quantification methods—the mass balance and inverse Gaussian methods. Ground-based wind data introduced significant uncertainty in CO 2 emission quantification. Therefore, this study proposed using a mounted anemometer to capture high-resolution spatially-resolved wind data. The performance of the proposed quantification methods was assessed by comparing UAV-derived fluxes to reference quantification data to calculate errors, which were then compared across methods to evaluate accuracy. The mass balance method, incorporating spatially-resolved wind data, achieved a mean absolute percentage error (MAPE) of 37.34%, which was a marked improvement compared to the 64.32% MAPE using spatially-averaged wind data. Similarly, the inverse Gaussian method showed a lower MAPE of 46.45% using spatially-resolved wind data, compared to 54.97% using spatially averaged wind data. Additionally, the advantages of each method under varying conditions of wind variability were evaluated. This study demonstrates that spatially-resolved wind measurements with a mounted anemometer improve the accuracy of CO 2 emission calculations. This approach highlights the importance of UAV-based monitoring of greenhouse gases emitted by waste management facilities. • Evaluated UAV-based CO 2 emissions with spatially-resolved wind data at an incinerator. • Improved emission estimates using high-resolution wind data over previous studies. • Mass balance method with spatially-resolved wind achieved the lowest emission error. • Mass balance had less error than inverse Gaussian method under high wind variability. • Highlighted wind variability’s critical role in emission quantification.

Source: Environment International

DOI: 10.1016/j.envint.2025.109396

Authors: Keun Taek Kim, Horim Kim, Sangjae Jeong, Young Su Lee, Eunhwa Choi, Jae Young Kim