Preprints
https://doi.org/10.5194/acp-2022-180
https://doi.org/10.5194/acp-2022-180
 
07 Jun 2022
07 Jun 2022
Status: this preprint is currently under review for the journal ACP.

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

Tianqi Shi1, Zeyu Han2, Ge Han3, Xin Ma1, Huilin Chen4,5, Truls Andersen5, Huiqin Mao6, Cuihong Chen6, Haowei Zhang1, and Wei Gong1,7 Tianqi Shi et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
  • 2School of Mathematics and Statistics, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
  • 4Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • 5Centre for Isotope Research, Energy and Sustainability Institute Groningen (ESRIG), University of Groningen, Groningen, Netherlands
  • 6Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing, China
  • 7Electronic Information School, Wuhan University, Luoyu Road No.129, Wuhan 430079, China

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.

Tianqi Shi et al.

Status: open (until 19 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-180', Anonymous Referee #1, 27 Jun 2022 reply
  • RC2: 'Comment on acp-2022-180', Anonymous Referee #2, 27 Jun 2022 reply

Tianqi Shi et al.

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Short summary
CH4 works as the second most important greenhouse gas, its reported emission inventories are far less than CO2. In this study, we developed a self-adjusted model to estimate the CH4 emission rate from strong point sources by UAV-based Aircore system. This model would reduce the uncertainty of CH4 emission rate quantification accused by errors in measurements of wind and concentration. Actual measurements on Pniówek coal demonstrate high accuracy and stability of our developed model.
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