Preprints
https://doi.org/10.5194/acp-2021-661
https://doi.org/10.5194/acp-2021-661

  10 Sep 2021

10 Sep 2021

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Limitations of the Radon Tracer Method (RTM) to estimate regional Greenhouse Gases (GHG) emissions – a case study for methane in Heidelberg

Ingeborg Levin1, Ute Karstens2, Samuel Hammer1,3, Julian DellaColetta1,3, Fabian Maier1,3, and Maksym Gachkivskyi1 Ingeborg Levin et al.
  • 1Institut für Umweltphysik, Heidelberg University, INF 229, 69120 Heidelberg, Germany
  • 2ICOS Carbon Portal, Lund University, Geocentrum II, Sölvegatan 12, 22362 Lund, Sweden
  • 3ICOS Central Radiocarbon Laboratory, Heidelberg University, Berliner Straße 53, 69120 Heidelberg, Germany

Abstract. Correlations of night-time atmospheric methane (CH4) and 222Radon (222Rn) observations in Heidelberg, Germany, were evaluated with the Radon Tracer Method (RTM) to estimate the trend of annual CH4 emissions from 1996–2020 in the catchment area of the station. After an initial 30 % decrease of emissions from 1996 to 2004, no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg. We show that the reliability of total CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rate from soils in the catchment area of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gases (GHG) fluxes as large as a factor of two. RTM-based GHG flux estimates also depend on the parameters chosen for the night-time correlations of CH4 and 222Rn, such as the night-time period for regressions as well as the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station/observation footprint as their emissions are not captured by the RTM method. Long-term representative 222Rn flux observations in the catchment area of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

Ingeborg Levin et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-661', Alastair Williams, 23 Sep 2021
  • RC2: 'Comment on acp-2021-661', Claudia Grossi, 27 Sep 2021
    • RC3: 'corrigendum to the report of R2', Claudia Grossi, 30 Sep 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-661', Alastair Williams, 23 Sep 2021
  • RC2: 'Comment on acp-2021-661', Claudia Grossi, 27 Sep 2021
    • RC3: 'corrigendum to the report of R2', Claudia Grossi, 30 Sep 2021

Ingeborg Levin et al.

Ingeborg Levin et al.

Viewed

Total article views: 410 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
308 88 14 410 2 4
  • HTML: 308
  • PDF: 88
  • XML: 14
  • Total: 410
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 10 Sep 2021)
Cumulative views and downloads (calculated since 10 Sep 2021)

Viewed (geographical distribution)

Total article views: 452 (including HTML, PDF, and XML) Thereof 452 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2021
Download
Short summary
The so-called Radon Tracer Method is applied on long-term methane and radon observations from the Upper Rhine valley (DE) to estimate methane emissions from that region. Comparison of our top-down (TD) results with bottom-up (BU) inventory data requires high-resolution footprint modelling and representative radon flux data. Also then point source emissions are not captured in the TD approach. Still, trends of emissions can be estimated for our region showing no significant decrease after 2005.
Altmetrics