Continuous CH4 and δ13CH4 Measurements in London Demonstrate Under-Reported Natural Gas Leakage

o te d on 2 N ov 2 2 — C C -B Y -N C 4 — h tt p s: / d o .o rg / 0 1 0 / ss o r. 1 5 5 73 .1 — T h is a p re p ri n t a d h a n o b ee n p ee r re v ie w ed . D a a m ay b e p re li m in a y. Continuous CH4 and δ13CH4 Measurements in London Demonstrate Under-Reported Natural Gas Leakage Eric Saboya, Giulia Zazzeri, Heather Graven, Alistair J. Manning, and Sylvia Englund Michel Imperial College London UK Met. Office Institute of Arctic and Alpine Research, University of Colorado, Boulder


INTRODUCTION
Urban areas are hotspots of greenhouse gas emissions accounting for 21 % of global anthropogenic methane (CH ) emissions .
The London region comprises 0.65 % of the UK's land area, yet 2.7 % of the UK's annual CH emissions, and 9.1 % of the UK's annual fugitive CH emissions.
According to inventory estimates, CH from the waste sector accounts for half of London's emissions and, fossil-fuel sources (e.g. natural gas pipe leaks) of CH make up 38 % of London's CH emissions .
Isotopic measurements of C/ C in CH (δ CH ) are an established means for dinstinguishing between sources . Fossil-fuel sources typically have δ CH > -50 ‰.
The aim of this study is to develop the use of δ CH measurements for assessing regional-scale CH emissions and sources. We use a mass-balance framework for comparing regional-scale simulations of δ CH and CH to observations.

ATMOSPHERIC MEASUREMENTS OF CH₄ IN LONDON
Continuous measurements of atmospheric CH₄ mole fractions and δ CH have been made at Imperial College London (ICL) using a Picarro G2201-i isotopic analsyer, which has a sample frequency of 1 min, since early 2018.
The Graven lab at ICL where we measure CH and CO An Allan standard deviation was calculated to measure the noise response of the instrument over different averaging intervals. A precision of 0.2 ‰ (1σ) was achieved for 20-minute data.
In this study we use 20-minute averaged data. Ambient air is sampled from an inlet mounted on a 2 m mast on the southeast corner of the Huxley building roof (26 magl, 51.4999 N, 0.1749 W).

Potential Local sources
There is an on-campus natural-gas fired power station located ~200 m east of the air inlet There are four main roads nearby There is a large sewage works and one waste facility within 4 km south of ICL Sources in the surrounding area of ICL with the NAEI emissions superimposed.

MEASUREMENTS SUGGEST A PREDOMINANCE OF NATURAL GAS CH₄
Mole fractions measured between 13:00-17:00 in central London were on average 225 ppb higher than background measurements at the Mace Head observatory.
Observed δ CH at ICL was both higher and lower than the Mace Head background δ CH during 2019-2020. Pollution events with both higher and lower δ CH can be seen.

Identifying CH sources by Keeling Plot analysis
We created an algorithm for automatically applying the Keeling Plot technique to measurements of δ CH and CH to identify regional and local sources.
Regional sources were found by considering 13:00-17:00 data within 3-day and 7-day moving windows. Local sources used data from all hours in 12-hour moving windows.
Wind speed and wind direction measurements at ICL were used to try constrain the origins of different pollution events.
We did not find consistent patterns between the wind direction and isotopic source values. This reflects the collocation and heterogeneity of sources in London.
Some events with low isotopic signatures and wind directions southerly or southwesterly may be influenced by the London Wetland Centre of by the sewage or landfill sites south of south-west of the ICL

SIMULATIONS INDICATE INVENTORIES UNDERESTIMATE NATURAL GAS CH₄
We compared CH measurments at ICL to the simulated excess CH mole fractions by subtracting the daily Mace Head background values from our measurements.
Simulations that used EDGAR emissions over the UK typically overestimated ICL measurements.

Simulated δ CH values
Measured δ CH values were compared to simulated values. The EDGAR and NAEI emissions provide sectoral estimates. Simulated CH for the different sectors, and the background values were multiplied by their UK isotopic signature, summed and then divided by the total simulated CH to simulate δ CH at ICL to compare the inventory source distributions to our measurements.
Large excursions towards more-negative δ CH values are seen in the simulations. Higher δ CH values (indicative of fossil-fuel sources of CH ) were not seen in the simulations. These results suggest there is a large amount of waste/agricultural sources in the inventories, or a significant absence of fossil-fuel sources for the region.
In comparison to the measurements, no correlation between the measured and simulated δ CH values were found: Whilst positive CH4 obs.-sim. correlations were found, the lack of δ CH obs.-sim. correlation suggests discrepancies in the inventory source apportionments.

SIMULATING EXCESS CH₄ MOLE FRACTIONS WITH NAME AND BOTTOM-UP INVENTORIES
Simulations of CH mole fractions above the background level of the modelling domain (i.e. excess CH ) are obtained by combining back-trajectories of air-masses arriving at ICL with global and UK emissions inventories.
Three sets of hourly footprints were generated using the Langrangian dispersion model: NAME. Each set of footprints has a different horizontal spatial resolution: 30 km, 10 km, 2 km.
Footprints were combined with anthropogenic emissions from the NAEI and EDGAR inventories to form four different sets of simulations.
On the left are the high-resolution NAEI emissions for the London region. On the right are the NAEI emissions gridded at 30 km subtracted from the EDGAR emissions for London. Over the UK, NAEI emissions are generally higher but in London EDGAR emissions are greater than the NAEI.
2. EDGAR-10km: 10 km footprints over Europe nested in 30 km footprints over the rest of the modelling domain all combined with EDGAR.
3. NAEI-30km: NAEI emissions for the UK and EDGAR emissions for rest of the domain.
4. NAEI-2km: 2 km footprints combined with NAEI emissions for the UK nested in 10 km footprints for Europe, nested in 30 km footprints.
In each set of simulations we considered wetland contributions by combining the 2015 WetCHARTs mean extended ensemble emissions with the 30 km footprints.

CONCLUSION
Measurements at Imperial College London found a predominance of natural-gas CH for the London region.
Measured mole fractions were observed to be higher than the background measurements at Mace Head, and δ CH values were seen the deviate above and below the δ CH background measurements at Mace Head.
Simulations of CH for the same time period were found to be in good agreement with the ICL observations. We found higherresolution simulations to be in better agreement with the measurements.
When simulating δ CH , no correlation between the measurements and simulations were found for any of the simulations. This suggests discrepancies in the source apportionment of the inventories, where waste emissions are likely being overestimated and natural gas emissions are being under-reported.
Previous measurement campaigns in London found natural gas emissions were underestimated in the national inventory. It is likely leaks from natural gas pipes were not entirely accounted.
We have demonstrated that δ CH measurements can be used to infer sources of CH at a regional-level, and are an effective method for comparing the source allocation in emissions inventories.