Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system

Andersen, Truls; Vinkovic, Katarina; Vries, Marcel de; Kers, Bert; Necki, Jaroslaw; Swolkień, Justyna; Roiger, Anke; Peters, Wouter; Chen, Huilin

doi:10.1016/j.aeaoa.2021.100135

Cited by 20 publications

(23 citation statements)

References 32 publications

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“…(1). Andersen et al (2021) also developed an inverse Gaussian approach to quantify CH 4 emissions from coal mine ventilation shafts based on the same flights. Firstly, the Gaussian dispersion is built as…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Shi

Han

et al. 2022

Atmos. Chem. Phys.

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Abstract. There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH4-emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH4-emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h−1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h−1 on 21 August 2017. Results show that CH4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH4-emission rates from strong point sources with high accuracy.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Shi

Han

et al. 2022

Atmos. Chem. Phys.

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“…Approximately 33% of the CH4 emissions from coal mining (43 TgCH4yr-1) are estimated to come from the total fossilfuel-related emissions during 2008-2017 (Saunois et al, 2020). CH4 is released primarily to the atmosphere via ventilation shafts located at the surface during the production and processing of the coal (Saunois et al, 2020;Andersen et al 2021). The largest contribution of CH4 emission related with the coal mining activities in Europe is from southern Poland-the Upper Silesian Coal Basin (USCB) (Luther et al, 2019;Krautwurst et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Quantifying hard coal mines CH<sub>4</sub> emissions from TROPOMI and IASI observations using high-resolution CAMS forecast data and the wind-assigned anomaly method

Schneider

Hase

et al. 2022

Preprint

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Abstract. Intensive coal mining activities are in the Upper Silesian Coal Basin (USCB) in southern Poland, resulting in large amounts of methane (CH4) emissions. Annual CH4 emission reached to 448 kt according to the European Pollutant Release and Transfer Register (E-PRTR, 2017). As a CH4 emission hot spot in Europe, it is of importance to investigate its emission sources and accurate emission estimates. In this study, we use satellite-based column-averaged dry-air molar fraction observations of CH4 (XCH4) from the TROPOspheric Monitoring Instrument (TROPOMI) and tropospheric XCH4 (TXCH4) from the Infrared Atmospheric Sounding Interferometer (IASI), together with the high-resolution model forecast XCH4 and TXCH4 from the Copernicus Atmosphere Monitoring Service (CAMS) to estimate the CH4 emission rate averaged over three years in the USCB region (49.3°–50.8° N and 18°–20° E). Using the CAMS inventory as the a priori knowledge of the sources, together with ERA5 wind at 330 m, the wind-assigned XCH4 anomalies for two opposite wind directions are calculated, which yields an estimated CH4 emission of 9.6E26 ± 1.4E25 molec./s for CAMS XCH4 and 9.1E26 ± 1.2E25 molec./s for CAMS TXCH4. These values are very close to the total emission of the CAMS inventory (9.7E26 molec./s). Very good agreements between CAMS and the wind-assigned model results (R2 = 0.89 for XCH4 and TXCH4) indicate that our wind-assigned method is quite robust. The similar estimates of XCH4 and TXCH4 also imply that for a strong source, the dynamically induced variations of the CH4 mixing ratio in the upper troposphere and lower stratosphere region is of secondary importance. This wind-assigned method is further applied to the TROPOMI XCH4 and TROPOMI+IASI TXCH4 with using the Carbon dioxide and Methane (CoMet) inventory performed in 2018. The calculated averaged total CH4 emission over the USCB region is about 5.7E26 ± 4.9E24 molec./s for TROPOMI XCH4 and 5.2E26 ± 2.2E25 molec./s for TROPOMI+IASI TXCH4. These results are very close and thus in agreement to the emissions given in the E-PRTR inventory (5.33E26 molec./s) and the CoMet inventory (6.6E26 molec./s). Since the wind speed is increasing with altitude, sensitivity tests show that higher CH4 emission strengths are yielded with increasing altitude and vice versa. About 23 % lower and 13 % higher estimates are obtained when using lower wind information at 10 m and higher wind information at 500 m instead of 330 m, respectively. When using different wind coverage and different wind segmentation, an uncertainty of 4.2 % and −2.1% is obtained, respectively. These results suggest that our wind-assigned method is quite robust and might also serve as a simple method to estimate CH4 or CO2 emissions for other regions.

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“…The campaign combined active (lidar) and passive (spectrometer) remote sensors with in situ instruments installed on board a German research aircraft High Altitude and Long Range Research Aircraft (HALO, Gulfstream G550) and two smaller aeroplanes (Cessna 208 Grand Caravan, Cessna 207). Additional support was provided by stationary and mobile ground-based measurements, as well as drones (Luther et al, 2019;Fix et al, 2018b;Bovensmann et al, 2019;Andersen et al, 2021Andersen et al, , 2022.…”

Section: Introductionmentioning

confidence: 99%

Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission

Swolkień

Fix

Gałkowski³

2022

Atmos. Chem. Phys.

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Abstract. Methane is a powerful greenhouse gas responsible for around 20 % of radiative forcing (relative to the pre-industrial era) caused by all long-lived greenhouse gases (WMO, 2021). About 60 % of the global emissions are from anthropogenic sources, and coal mining is one of the largest contributors. Emissions are either estimated by bottom-up approaches (based on inventories) or top-down approaches (based on atmospheric measurements). Combining those with an accurate error estimation allows us to better characterise model errors e.g. caused by transport mechanisms. Here we provide a detailed description of factors influencing the coal mine methane emission variability. We use high-frequency (up to hourly) temporal data from seven coal mines in the Upper Silesian Coal Basin during the Carbon dioxide and Methane (CoMet 1.0) mission from 14 May to 13 June 2018. Knowledge of these factors for the individual ventilation shaft is essential for linking the observations achieved during the CoMet 1.0 mission with models, as most publicly available data in the bottom-up worldwide inventories provide annual emissions only. The methane concentrations in examined shafts ranged from 0.10 % to 0.55 %±0.1 % during the study period. Due to the changing scope of mining works performed underground, they were subjected to a significant variation on a day-to-day basis. The yearly methane average emission rate calculated based on 1 month's set of temporal data of the analysed subset of mines was of the order of 142.68 kt yr−1 (σ=18.63 kt yr−1), an estimate 27 % lower than the officially published State Mining Authority (WUG) data and 36 % lower than reported to the European Pollutant Release and Transfer Register (E-PRTR). We also found that emissions from individual coal mine facilities were over- and underestimated by between 4 % to 60 %, compared to the E-PRTR, when short-term records were analysed. We show that the observed discrepancies between annual emissions based on temporal data and public inventories result from (1) the incorrect assumption that the methane emissions are time-invariant, (2) the methodology of measurements, and lastly, (3) the frequency and timing of measurements. From the emission monitoring perspective, we recommend using a standardised emission measurement system for all coal mines, similar to the Methane Fire Teletransmission Monitoring System (SMP-NT/A). Legal safety requirements require all coal mines to implement this system. After an adaptation, the system could allow for gas flow quantification, necessary for accurate and precise estimations of methane emissions at a high temporal resolution. Using this system will also reduce the emission uncertainty due to factors like frequency and timing of measurements. In addition, it would be beneficial to separately identify the emissions from individual ventilation shafts and methane drainage stations. That would bridge the gap between bottom-up and top-down approaches for coal mine emissions. The intermittent releases of unutilised methane from the drainage stations are currently not considered when constructing regional methane budgets.

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Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system

Cited by 20 publications

References 32 publications

Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Quantifying hard coal mines CH<sub>4</sub> emissions from TROPOMI and IASI observations using high-resolution CAMS forecast data and the wind-assigned anomaly method

Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission

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Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system

Cited by 20 publications

References 32 publications

Retrieving CH4-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Retrieving CH4-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Quantifying hard coal mines CH&lt;sub&gt;4&lt;/sub&gt; emissions from TROPOMI and IASI observations using high-resolution CAMS forecast data and the wind-assigned anomaly method

Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission

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Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Retrieving CH₄-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Quantifying hard coal mines CH<sub>4</sub> emissions from TROPOMI and IASI observations using high-resolution CAMS forecast data and the wind-assigned anomaly method