Estimating coal mine methane emissions using ground-based FTIR spectrometry, WRF driven Lagrangian dispersion modelling, and a regularized inversion approach

2021 ◽  
Author(s):  
Andreas Luther ◽  
Ralph Kleinschek ◽  
Julian Kostinek ◽  
Mila Stanisavljevic ◽  
Alexandru Dandocsi ◽  
...  
Keyword(s):  
Coal Mine ◽  
Dispersion Model ◽  
Mine Ventilation ◽  
Particle Dispersion ◽  
Point Sources ◽  
Hard Coal ◽  
The European Union ◽  
Inventory Data ◽  
North East ◽  
Area Of Interest

<p>Methane (CH<sub>4</sub>) emissions from coal production are one of the main sources of anthropogenic CH<sub>4</sub> in the atmosphere. Poland is the second largest hard coal producer in the European Union with the Polish area of the Upper Silesian Coal Basin (USCB) as a part of it. Emission estimates for CH<sub>4</sub> from USCB for individual coal mine ventilation shafts range between 0.03kt CH<sub>4</sub>/yr and 25.9kt CH<sub>4</sub>/yr, amounting to a basin total of roughly 465kt CH<sub>4</sub>/yr (E-PRTR database, 2014). During CoMet (Carbon Dioxide and Methane Mission 2018) four ground-based, portable FTIR (Fourier transform infrared) spectrometers EM27/SUN were deployed in the USCB. We arranged these instruments in fixed locations in the North, East, South, and West of the USCB in approx. 50km distance to the center of the basin. This set-up ensures both, upwind and downwind measurements of CH<sub>4</sub> for the prevailing wind directions. Subtracting upwind from downwind XCH<sub>4</sub> observations gives the net methane enhancement of the region in between two selected instruments. These enhancements are also modeled with the Lagrangian particle dispersion model Flexpart. The model is driven by WRF wind simulations calculated in a nested domain using data assimilation of 3D wind-lidar data measured at three locations in the area of interest. The residuals between modeled and measured enhancements are minimized with a Phillips-Tikhonov regularized, non-negative least squares approach using the E-PRTR inventory data as a-priori information. The regularization parameters are graphically chosen via L-curve determination. Simulation uncertainty is expressed through an ensemble of different model runs, each with altered, basic meteorological parameters. The model generally matches the E-PRTR inventory data within it's error range for a small number (6 to 10) of coal mine ventilation shafts, whereas it suggests higher emission rates than the E-PRTR for more involved point sources (>30).</p>

scholarly journals Quantifying CH<sub>4</sub> emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry

2019 ◽  
Vol 12 (10) ◽  
pp. 5217-5230 ◽  
Author(s):  
Andreas Luther ◽  
Ralph Kleinschek ◽  
Leon Scheidweiler ◽  
Sara Defratyka ◽  
Mila Stanisavljevic ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Coal Mine ◽  
Mine Ventilation ◽  
Hard Coal ◽  
Spatial And Temporal Variability ◽  
The European Union ◽  
Cross Sectional ◽  
Ch4 Emissions ◽  
Fourier Transform Spectrometry ◽  
Relative Errors

Abstract. Methane (CH4) emissions from coal production amount to roughly one-third of European anthropogenic CH4 emissions in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish side of the Upper Silesian Coal Basin (USCB) as the main part of it. Emission estimates for CH4 from the USCB for individual coal mine ventilation shafts range between 0.03 and 20 kt a−1, amounting to a basin total of roughly 440 kt a−1 according to the European Pollutant Release and Transfer Register (E-PRTR, http://prtr.ec.europa.eu/, 2014). We mounted a ground-based, portable, sun-viewing FTS (Fourier transform spectrometer) on a truck for sampling coal mine ventilation plumes by driving cross-sectional stop-and-go patterns at 1 to 3 km from the exhaust shafts. Several of these transects allowed for estimation of CH4 emissions based on the observed enhancements of the column-averaged dry-air mole fractions of methane (XCH4) using a mass balance approach. Our resulting emission estimates range from 6±1 kt a−1 for a single shaft up to 109±33 kt a−1 for a subregion of the USCB, which is in broad agreement with the E-PRTR reports. Three wind lidars were deployed in the larger USCB region providing ancillary information about spatial and temporal variability of wind and turbulence in the atmospheric boundary layer. Sensitivity studies show that, despite drawing from the three wind lidars, the uncertainty of the local wind dominates the uncertainty of the emission estimates, by far exceeding errors related to the XCH4 measurements themselves. Wind-related relative errors on the emission estimates typically amount to 20 %.

scholarly journals Towards verifying CH<sub>4</sub> emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry

2019 ◽  
Author(s):  
Andreas Luther ◽  
Ralph Kleinschek ◽  
Leon Scheidweiler ◽  
Sara Defratyka ◽  
Mila Stanisavljevic ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Coal Mine ◽  
Mine Ventilation ◽  
Hard Coal ◽  
Spatial And Temporal Variability ◽  
The European Union ◽  
Cross Sectional ◽  
Ch4 Emissions ◽  
Fourier Transform Spectrometry ◽  
Relative Errors

Abstract. Methane (CH4) emissions from coal production are one of the primary sources of anthropogenic CH4 in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish side of the Upper Silesian Coal Basin (USCB) as the main part of it. Emission estimates for CH4 from the USCB for individual coal mine ventilation shafts range between 0.03 kt/a and 20 kt/a, amounting to a basin total of roughly 440 kt/a according to the European Pollutant Release and Transfer Register (E-PRTR, http://prtr.ec.europa.eu/, 2014). We mounted a ground-based, portable, sun-viewing FTS (Fourier Transform Spectrometer) on a truck for sampling coal mine ventilation plumes by driving cross-sectional stop-and-go Patterns at 1 to 3 km distance to the exhaust shafts. Using a mass balance approach, several of these transects allowed for estimating CH4 emissions based on the observed enhancements of the column-averaged dry-air mole fractions of methane (XCH4). Our resulting emission estimates range from 6 ± 1 kt/a for a single shaft up to 109 ± 33 kt/a for a subregion of the USCB, which is in broad agreement with the E-PRTR reports. Three wind lidars were deployed in the larger USCB region providing ancillary information about spatial and temporal variability of wind and turbulence in the atmospheric boundary-layer. Sensitivity studies show that, despite drawing from the three wind lidars, the uncertainty of the local wind dominates the uncertainty of the emission estimates, by far exceeding errors related to the XCH4 measurements itself. Wind-related relative errors on the emission estimates typically amount to 20 %.

Ground-based CO2 monitoring network design over India to constrain the regional terrestrial fluxes

2020 ◽  
Author(s):  
Nalini Krishnankutty ◽  
Sijikumar Sivaraman ◽  
Vinu Valsala ◽  
Yogesh Tiwari ◽  
Radhika Ramachandran
Keyword(s):  
Dispersion Model ◽  
Monitoring Network ◽  
Surface Flux ◽  
Northeast India ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Monitoring Network Design ◽  
North East ◽  
Observational Uncertainty ◽  
Flux Estimation

&lt;p&gt;The present study aims to design an optimal CO&lt;sub&gt;2&lt;/sub&gt; monitoring network over India to better constrain the Indian terrestrial surface fluxes using Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Prior and posterior cost functions are calculated using potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm. A total of 73 regular grid cells are identified over the Indian land mass in 2&amp;#176;x2&amp;#176; latitude by longitude resolution assuming each cell can hold a potential site. Further, using incremental optimization methodology, the effectiveness of CO&lt;sub&gt;2&lt;/sub&gt; observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified. The study is carried out in three parts. Firstly, we evaluated the existing stations over India in terms of reduction in uncertainty brought out by them in the surface flux estimation over the Indian landmass. This provides a unique opportunity for the representative stations to restart the observational programs based on their role in the flux estimation. In second part, we devised a methodology to design an extended network by adding a few more potential stations to the existing stations. Thirdly, we identified a completely new set of optimal stations for measuring atmospheric CO&lt;sub&gt;2&lt;/sub&gt; over India, which do not have any liabilities of pre-existing stations. The study depicts that the existing stations could bring down the uncertainty in the range of 18% to 36%. Among the existing stations, Kharagpur, Sagar, Shadnagar, Kodaikanal and Pondicherry are the best stations, which are indeed adding value to the CO&lt;sub&gt;2&lt;/sub&gt; flux inversions by reducing the uncertainty in the range of 4% to 13%. Addition of five new stations to the base network formed an extended network, which could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. The new stations are mainly located over the east and north-east India with few exceptions during post-monsoon where stations are identified over the west and south India as well. The study identified 12 stations for each season and formed a &amp;#8216;new network&amp;#8217; that could achieve the equivalent uncertainty reduction as compared with the 14 stations in the &amp;#8216;extended network&amp;#8217;. From this, an &amp;#8216;optimal network&amp;#8217; and the best network consisting of 17 stations were identified that could best represent flux scenario and transport over India in all the four seasons. In northeast India, flux uncertainty is quite large, also the prevailing westerly wind in most parts of the year contributes to the surface CO&lt;sub&gt;2&lt;/sub&gt; signature of India to that location, demanding requirement of CO&lt;sub&gt;2 &lt;/sub&gt;observations throughout the year. The study highlights a major zone of CO&lt;sub&gt;2&lt;/sub&gt; &amp;#8216;observational void&amp;#8217; that exists in potential locations near east and northeast parts of India. Immediate requirement of CO&lt;sub&gt;2&lt;/sub&gt; monitoring initiative in these areas is highly recommended.&lt;/p&gt;

scholarly journals Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2

2005 ◽  
Vol 5 (9) ◽  
pp. 2461-2474 ◽  
Author(s):  
A. Stohl ◽  
C. Forster ◽  
A. Frank ◽  
P. Seibert ◽  
G. Wotawa
Keyword(s):  
Nuclear Power ◽  
Water Cycle ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Technical Note ◽  
Point Sources ◽  
Lagrangian Particle ◽  
Modeling And Analysis ◽  
Application Fields

Abstract. The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle). It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. In this note we provide a citeable technical description of FLEXPART's latest version (6.2).

scholarly journals Issues of photovoltaic installation size choice for a hard coal mine

2019 ◽  
Vol 123 ◽  
pp. 01014
Author(s):  
Sylwester Kaczmarzewski ◽  
Piotr Olczak ◽  
Artur Halbina
Keyword(s):  
Coal Mine ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Hard Coal ◽  
The European Union ◽  
Upper Silesian Coal Basin ◽  
Hourly Data ◽  
Hard Coal Mining ◽  
Demand Coverage

Poland is the leader in hard coal mining in the European Union and in generation of electricity on this basis, it is related also to low generation of energy from renewable energy sources, in particular photovoltaic installations. The paper analyses the potential of PV installations application for the needs of a selected hard coal mine from the Upper Silesian Coal Basin. Using the hourly data on its electricity consumption in 2018 various sizes of PV installations were selected, a simple payback period was calculated as well as the percentage of energy from the installation use for the current mine operations. It has been shown that in the case of a mine, having available 20 MW of ordered power and average consumption of approx. 14 MW, an installation of 20 MWp rating covers approx. 15% of the electricity demand per year, while for 1 o’clock p.m., i.e. the hour at which most frequently the peak consumption occurred, the share in electricity demand coverage by the PV installation of this power on average amounts to approx. 50% per year.

scholarly journals Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data

2013 ◽  
Vol 6 (1) ◽  
pp. 151-166 ◽  
Author(s):  
T. Krings ◽  
K. Gerilowski ◽  
M. Buchwitz ◽  
J. Hartmann ◽  
T. Sachs ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
Coal Mine ◽  
Emission Rate ◽  
Input Parameter ◽  
Mine Ventilation ◽  
Point Sources ◽  
Integral Approach ◽  
Anthropogenic Activity ◽  
Optical Remote Sensing

Abstract. The quantification of emissions of the greenhouse gas methane is essential for attributing the roles of anthropogenic activity and natural phenomena in global climate change. Our current measurement systems and networks, whilst having improved during the last decades, are deficient in many respects. For example, the emissions from localised and point sources such as landfills or fossil fuel exploration sites are not readily assessed. A tool developed to better understand point sources of the greenhouse gases carbon dioxide and methane is the optical remote sensing instrument MAMAP (Methane airborne MAPper), operated from aircraft. After a recent instrument modification, retrievals of the column-averaged dry air mole fractions for methane XCH4 (or for carbon dioxide XCO2) derived from MAMAP data have a precision of about 0.4% or better and thus can be used to infer emission rate estimates using an optimal estimation inverse Gaussian plume model or a simple integral approach. CH4 emissions from two coal mine ventilation shafts in western Germany surveyed during the AIRMETH 2011 measurement campaign are used as examples to demonstrate and assess the value of MAMAP data for quantifying CH4 from point sources. While the knowledge of the wind is an important input parameter in the retrieval of emissions from point sources and is generally extracted from models, additional information from a turbulence probe operated on-board the same aircraft was utilised to enhance the quality of the emission estimates. Although flight patterns were optimised for remote sensing measurements, data from an in situ analyser for CH4 were found to be in good agreement with retrieved dry columns of CH4 from MAMAP and could be used to investigate and refine underlying assumptions for the inversion procedures. With respect to the total emissions of the mine at the time of the overflight, the inferred emission rate of 50.4 kt CH4 yr−1 has a difference of less than 1% compared to officially reported values by the mine operators, while the uncertainty, which reflects variability of the sources and conditions as well as random and systematic errors, is about ±13.5%.

scholarly journals The Lagrangian particle dispersion model FLEXPART version 10.4

2019 ◽  
Vol 12 (12) ◽  
pp. 4955-4997 ◽  
Author(s):  
Ignacio Pisso ◽  
Espen Sollum ◽  
Henrik Grythe ◽  
Nina I. Kristiansen ◽  
Massimo Cassiani ◽  
...  
Keyword(s):  
Input Data ◽  
Dispersion Model ◽  
The United States ◽  
Particle Dispersion ◽  
Point Sources ◽  
Hazardous Substances ◽  
Small Scale ◽  
Forecast System ◽  
Lagrangian Particle ◽  
Meteorological Input

Abstract. The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

scholarly journals The Lagrangian particle dispersion model FLEXPART-WRF version 3.0

2013 ◽  
Vol 6 (3) ◽  
pp. 3615-3654 ◽  
Author(s):  
J. Brioude ◽  
D. Arnold ◽  
A. Stohl ◽  
M. Cassiani ◽  
D. Morton ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Vertical Gradient ◽  
Particle Dispersion ◽  
Point Sources ◽  
Lagrangian Particle ◽  
Convective Boundary ◽  
Meteorological Model ◽  
Computational Performance

Abstract. The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need has encouraged new developments in FLEXPART. In this document, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run and present special options and features that differ from its predecessor versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format with efficient data compression. In addition, test case data and the source code are provided to the reader as Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

scholarly journals The Lagrangian particle dispersion model FLEXPART-WRF version 3.1

2013 ◽  
Vol 6 (6) ◽  
pp. 1889-1904 ◽  
Author(s):  
J. Brioude ◽  
D. Arnold ◽  
A. Stohl ◽  
M. Cassiani ◽  
D. Morton ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Dispersion Model ◽  
Vertical Gradient ◽  
Particle Dispersion ◽  
Point Sources ◽  
Lagrangian Particle ◽  
Convective Boundary ◽  
Meteorological Model ◽  
Modeling And Analysis ◽  
Computational Performance

Abstract. The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such that occurring after an accident in a nuclear power plant. In the meantime, FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. A need for further multiscale modeling and analysis has encouraged new developments in FLEXPART. In this paper, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run this new model and present special options and features that differ from those of the preceding versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization, and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format, both of which have efficient data compression. In addition, test case data and the source code are provided to the reader as a Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

scholarly journals Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data

2012 ◽  
Vol 5 (5) ◽  
pp. 7383-7429
Author(s):  
T. Krings ◽  
K. Gerilowski ◽  
M. Buchwitz ◽  
J. Hartmann ◽  
T. Sachs ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
Coal Mine ◽  
Emission Rate ◽  
Input Parameter ◽  
Mine Ventilation ◽  
Point Sources ◽  
Integral Approach ◽  
Anthropogenic Activity ◽  
Optical Remote Sensing

Abstract. The quantification of emissions of the greenhouse gas methane is essential for attributing the roles of anthropogenic activity and natural phenomena in global climate change. Our current measurement systems and networks whilst having improved during the last decades, are deficient in many respects. For example, the emissions from localised and point sources such as landfills or fossil fuel exploration sites are not readily assessed. A tool developed to better understand point sources of the greenhouse gases carbon dioxide and methane is the optical remote sensing instrument MAMAP, operated from aircraft. After a recent instrument modification, retrievals of the column averaged dry air mole fractions for methane XCH4 (or for carbon dioxide XCO2) derived from MAMAP data, have a precision of about 0.4% or better and thus can be used to infer emission rate estimates using an optimal estimation inverse Gaussian plume model or a simple integral approach. CH4 emissions from two coal mine ventilation shafts in Western Germany surveyed during the AIRMETH 2011 measurement campaign are used as examples to demonstrate and assess the value of MAMAP data for quantifying CH4 from point sources. While the knowledge of the wind is an important input parameter in the retrieval of emissions from point sources and is generally extracted from models, additional information from a turbulence probe operated on-board the same aircraft was utilised to enhance the quality of the emission estimates. Although flight patterns were optimised for remote sensing measurements, data from an in-situ analyser for CH4 were found to be in good agreement with retrieved dry columns of CH4 from MAMAP and could be used to investigate and refine underlying assumptions for the inversion procedures. With respect to the total emissions of the mine at the time of the overflight, the inferred emission rate of 50.4 kt CH4 yr−1 has a difference of less than 1% compared to officially reported values by the mine operators, while the uncertainty, which reflects variability of the sources and conditions as well as random and systematic errors, is about ±13.5%.

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