Retrieving coal mine CH₄ emissions using UAV-based AirCore observations and the GA-IPPF model

Abstract. The quantification of CH₄ emissions from coal mines has large uncertainty owing to the lack of effective monitoring methods. In this study, we developed a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rate of large point sources of CH₄ based on concentration sample. This model can provide optimized dispersion parameters and self-calibrate, thus lowering the requirements for auxiliary data accuracy. Meanwhile, we evaluated the influence of multiple parameters on retrieving CH₄-emission rate by the GA-IPPF, including the uncertainty of CH₄ concentration measurements, the number of CH₄ measurements, and the accuracy of meteorological data. Based on the atmospheric CH₄ concentration measurements from a UAV-based AirCore system and the GA-IPPF model, we retrieved the CH₄-emission rates from the Pniówek coal (Silesia coal mining region mine, Poland) ventilation shaft. Results show that, the CH₄ concentrations reconstructed by the model is highly consistent to the measured ones. And the CH₄-emission rates are variable even in a single day, ranging from 639.3±22.8 to 1415.5±68.5 kg/hour on August 18, 2017 and from 342.5±34.8 to 1449.8±57.1 kg/hour on August 21, 2017. The combination of the flexible UAV-based AirCore CH₄ measurements and the robust GA-IPPF model provides an effective means to quantify CH₄ emissions.