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Abstract
Methane (CH4) is a powerful greenhouse gas. Increasing attention has recently fallen on the reduction of CH4 emissions. During COP26 in Glasgow, 105 countries agreed to intensify their efforts towards lowering CH4 emissions and improving the accuracy of the existing inventory methodologies regarding CH4 emission estimation and reporting. Landfill gas emissions constitute the major contributor of anthropogenic CH4 from the waste sector, as the anaerobic decomposition of organic waste generates landfill gas for several decades after landfilling. CH4 emission monitoring from landfills, as well as upscaling to annual predictions, can be difficult due to their spatial and temporal variability. Several quantification techniques have been developed to quantify CH4 emissions from landfills, and yet none of them is able to address emission inhomogeneity in space and time.
The overall aim of this PhD study was to investigate the environmental factors behind the temporal variability of CH4 emissions from landfills and to develop a method that is able to address both spatial and temporal variability when assessing landfill emissions. The primary objective was to develop an empirical model to estimate annual landfill CH4 emissions more accurately than current methods. A secondary objective was to evaluate the performance of a passive biocover system through the use of the empirical model.
In order to identify the environmental factors that affect landfill CH4 emissions, two complementary quantification techniques were used in two Danish landfills, each differing in terms of age, size, waste amounts and categories. Skellingsted landfill represents an old closed municipal solid waste landfill, whereas AV Miljø landfill represents an active modern landfill receiving noncombustible waste. The techniques were applied simultaneously, measuring CH4 emissions discontinuously, using the tracer gas dispersion method, and continuously, using the eddy covariance method. The first method provided whole-site emission measurements, while eddy covariance measured fluxes from a limited landfill area, but it was ideal for long- and short-term studies. Whole-site CH4 emissions, measured using the tracer gas dispersion method, ranged from 0 to 93 kg h−1 for Skellingsted and from 3 to 36 kg h−1 for the AV Miljø landfill. Under decreasing barometric pressure conditions, the highest CH4 rates were observed, while under increasing barometric pressure emissions were suppressed almost to zero. The eddy covariance method demonstrated similar emission dynamics despite differences in spatial representation and temporal resolution. We concluded that the rate of change in barometric pressure had a significant impact on the landfills’ emission variability, and therefore advection was the dominant transport mechanism at the two investigated sites. No clear relationship was established between emissions and other meteorological parameters (wind speed, precipitation and temperature).
An empirical, non-linear model was developed, using a discrete number of field emission measurements, in order to address short-term emission variability induced by pressure variations. The modelled emissions were tested against eddy covariance fluxes, thereby proving that the empirical model can represent short-term variability as precisely as the eddy covariance method. The model, using barometric pressure data as an input parameter, performed annual upscaling of CH4 emissions more accurately than extrapolation discrete field measurements. Estimated annual CH4 emissions were 69 ± 4 tonnes and 80 ± 4 tonnes for the Skellingsted and AV Miljø landfills, respectively.
A passive biocover system was constructed at Skellingsted landfill, as part of Denmark’s national strategy for reducing greenhouse gas emissions. The performance of the system was assessed by employing the empirical model. More specifically, predicted annual methane emissions before and after biocover system implementation were compared, and overall methane oxidation efficiency was estimated at 51%. However, this was considered a conservative estimate, due to the pronounced seasonal variability observed in the system. Moreover, the performance of an individual biowindow was investigated based on a series of in-situ campaigns and eddy covariance data. The results showed that the performance of the passive biocover system was highly influenced by pressure variations. During periods of decreasing barometric pressure, estimated total efficiency declined to 20%, while under increasing barometric pressure nearly 100% oxidation was achieved. The findings from the individual biowindow displayed a similar pattern regarding oxidation efficiency. In addition, CH4 screenings and flux measurements on the surface of the biowindow revealed a significant level of spatial emission variability, created by an uneven load distribution and causing a reduction in the efficiency of the overloaded areas. The results revealed the challenge of using current approaches to estimate accurately the performance of a passive biocover system, due to the large spatial
and short-term variability of CH4 emissions.
The overall aim of this PhD study was to investigate the environmental factors behind the temporal variability of CH4 emissions from landfills and to develop a method that is able to address both spatial and temporal variability when assessing landfill emissions. The primary objective was to develop an empirical model to estimate annual landfill CH4 emissions more accurately than current methods. A secondary objective was to evaluate the performance of a passive biocover system through the use of the empirical model.
In order to identify the environmental factors that affect landfill CH4 emissions, two complementary quantification techniques were used in two Danish landfills, each differing in terms of age, size, waste amounts and categories. Skellingsted landfill represents an old closed municipal solid waste landfill, whereas AV Miljø landfill represents an active modern landfill receiving noncombustible waste. The techniques were applied simultaneously, measuring CH4 emissions discontinuously, using the tracer gas dispersion method, and continuously, using the eddy covariance method. The first method provided whole-site emission measurements, while eddy covariance measured fluxes from a limited landfill area, but it was ideal for long- and short-term studies. Whole-site CH4 emissions, measured using the tracer gas dispersion method, ranged from 0 to 93 kg h−1 for Skellingsted and from 3 to 36 kg h−1 for the AV Miljø landfill. Under decreasing barometric pressure conditions, the highest CH4 rates were observed, while under increasing barometric pressure emissions were suppressed almost to zero. The eddy covariance method demonstrated similar emission dynamics despite differences in spatial representation and temporal resolution. We concluded that the rate of change in barometric pressure had a significant impact on the landfills’ emission variability, and therefore advection was the dominant transport mechanism at the two investigated sites. No clear relationship was established between emissions and other meteorological parameters (wind speed, precipitation and temperature).
An empirical, non-linear model was developed, using a discrete number of field emission measurements, in order to address short-term emission variability induced by pressure variations. The modelled emissions were tested against eddy covariance fluxes, thereby proving that the empirical model can represent short-term variability as precisely as the eddy covariance method. The model, using barometric pressure data as an input parameter, performed annual upscaling of CH4 emissions more accurately than extrapolation discrete field measurements. Estimated annual CH4 emissions were 69 ± 4 tonnes and 80 ± 4 tonnes for the Skellingsted and AV Miljø landfills, respectively.
A passive biocover system was constructed at Skellingsted landfill, as part of Denmark’s national strategy for reducing greenhouse gas emissions. The performance of the system was assessed by employing the empirical model. More specifically, predicted annual methane emissions before and after biocover system implementation were compared, and overall methane oxidation efficiency was estimated at 51%. However, this was considered a conservative estimate, due to the pronounced seasonal variability observed in the system. Moreover, the performance of an individual biowindow was investigated based on a series of in-situ campaigns and eddy covariance data. The results showed that the performance of the passive biocover system was highly influenced by pressure variations. During periods of decreasing barometric pressure, estimated total efficiency declined to 20%, while under increasing barometric pressure nearly 100% oxidation was achieved. The findings from the individual biowindow displayed a similar pattern regarding oxidation efficiency. In addition, CH4 screenings and flux measurements on the surface of the biowindow revealed a significant level of spatial emission variability, created by an uneven load distribution and causing a reduction in the efficiency of the overloaded areas. The results revealed the challenge of using current approaches to estimate accurately the performance of a passive biocover system, due to the large spatial
and short-term variability of CH4 emissions.
Original language | English |
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Place of Publication | Kgs. Lyngby |
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Publisher | Technical University of Denmark |
Number of pages | 155 |
Publication status | Published - 2022 |
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Dive into the research topics of 'Landfill methane emission dynamics and the influence of barometric pressure'. Together they form a unique fingerprint.Projects
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Understanding landfill methane emission dynamics as a basis for emission quantification
Kissas, K. (PhD Student), Oonk, J. H. (Examiner), Imhoff, P. T. (Examiner), Scheutz, C. (Main Supervisor), Ibrom, A. (Supervisor) & Kjeldsen, P. (Supervisor)
01/12/2018 → 30/09/2022
Project: PhD