Abstract Title: | Using Observations to Understand Regional Methane Budgets |
Abstract Type: | Oral |
Session Choice: | Current capabilities and case studies |
Presenter Name: | Prof Neil Harris |
Company/Organisation: | Cranfield University |
Country: | United Kingdom |
Abstract Information :
There is a growing need for comparisons between emission estimates produced using bottom-up
and top-down techniques at high spatial resolution. In response to this, a proof of concept study
has been performed in which developed an inversion approach to estimate methane emissions for
a region (East Anglia) in the South East of the UK (˜100 x 150 km) at high spatial resolution. We
present results covering a 1-year period (June 2013 - May 2014) in which atmospheric methane
concentrations were recorded at 1-2 minute time-steps at four locations within the region of
interest. Precise measurements were obtained using gas chromatography with flame ionisation
detection (GC-FID) at three of the sites; the fourth used a PICARRO Cavity Ring-Down
Spectrometer (CRDS). These observations, coupled with the UK Met Office's Lagrangian particle
dispersion model, NAME, were used within the InTEM inversion system to produce the methane
emission fields. Realistic emissions estimates counties in East Anglia were produced, which
compare well with those of the UK National Atmospheric Emissions Inventory (NAEI).
In parallel a study of hot-spot emissions from a landfill near Cambridge was conducted with
reasonable agreement being found emission estimates using the WindTrax dispersion model, a
Gaussian Plume model and the NAME InTEM approach described above. The estimated emissions
from the three approaches were consistent within the associated uncertainties. Using the
Gaussian plume analysis of 3 years of measurements, we found strong evidence for a seasonal
cycle in methane emissions from the landfill with more being emitted in winter than in summer.
We suggest that this occurs as a result of more active methanogenic bacteria in the surface layer
of the landfill in the warmer summer months, indicating that further activation of the surface layer
could lead to reduced methane emissions. Further, the location of the landfill was identifiable in
the regional analysis, though large uncertainties were associated with the emission estimate. We
think that these uncertainties can be reduced, e.g. by using Bayesian approaches in which hot-spot
locations are included based on known landfill sites, etc. At the very least, the regional analysis
can identify high emitting point sources which can then be subjected to further study using the
new, lower cost sensors which are becoming available. This technique would be lower cost and
more easily deployable and so is suitable for on-going monitoring of point sources.