Spatial and temporal variability of urban fluxes of methane, carbon monoxide and carbon dioxide above London, UK

Abstract

Abstract. We report on more than 3 years of measurements of fluxes of methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2) taken by eddy-covariance in central London, UK. Mean annual emissions of CO2 in the period 2012–2014 (39.1 ± 2.4 ktons km−2 yr−1) and CO (89 ± 16 tons km−2 yr−1) were consistent (within 1 and 5 % respectively) with values from the London Atmospheric Emissions Inventory, but measured CH4 emissions (72 ± 3 tons km−2 yr−1) were over two-fold larger than the inventory value. Seasonal variability was large for CO with a winter to summer reduction of 69 %, and monthly fluxes were strongly anti-correlated with mean air temperature. The winter increment in CO emissions was attributed mainly to vehicle cold starts and reduced fuel combustion efficiency. CO2 fluxes were 33 % higher in winter than in summer and anti-correlated with mean air temperature, albeit to a lesser extent than for CO. This was attributed to an increased demand for natural gas for heating during the winter. CH4 fluxes exhibited moderate seasonality (21 % larger in winter), and a spatially variable linear anti-correlation with air temperature. Differences in resident population within the flux footprint explained up to 90 % of the spatial variability of the annual CO2 fluxes and up to 99 % for CH4. Furthermore, we suggest that biogenic sources of CH4, such as wastewater, which is unaccounted for by the atmospheric emissions inventories, make a substantial contribution to the overall budget and that commuting dynamics in and out of central business districts could explain some of the spatial and temporal variability of CO2 and CH4 emissions. To our knowledge, this study is unique given the length of the data sets presented, especially for CO and CH4 fluxes. This study offers an independent assessment of "bottom-up" emissions inventories and demonstrates that the urban sources of CO and CO2 are well characterized in London. This is however not the case for CH4 emissions which are heavily underestimated by the inventory approach. Our results and others point to opportunities in the UK and abroad to identify and quantify the "missing" sources of urban methane, revise the methodologies of the emission inventories and devise emission reduction strategies for this potent greenhouse gas.

Evaluating methane inventories by isotopic analysis in the London region

“…The seasonality of methane emissions caused by variation of the source strength is supported by the study of methane fluxes in central London by Helfter et al . 7 .…”

“…However, while many specific approaches have been developed for the estimate of total methane fluxes (e.g. refs 3–7), there is no established methodology that leads to a comprehensive partitioning of emissions by sources. Tracer methods based on Fourier Transform Infrared (FTIR) absorption spectroscopy 8 , flux chambers 9 and mobile ground surveys 10 are all used for the estimation of total emissions from a specific source area (e.g.…”

Atmospheric Methane Concentration Allows Estimating Natural Gas Leaks in Heating Systems in Tandil, Argentina

“…Higher built-up density could be directly associated with a large NG distribution pipeline, NG consumption, and high vehicular traffic (Sarak and Satman, 2003). In particular, Helfter et al (2016) showed that the differences in built-up density within the flux footprint explained up to 90% of the spatial variability of the annual CH 4 fluxes.…”

“…Each possible CH 4 source in the study site has a characteristic temporal emission. The mean atmospheric CH 4 concentration in an urban site results from an overlap between sources with either constant or time-varying emission rates (Helfter et al, 2016). A clear differentiation of sites and the influence of the different sources may be masked by seasonal variation, and, therefore, a further detailed temporal analysis is necessary.…”

“…Many studies have reported CH 4 emissions in urban areas in different places of the world, but most of them correspond to large cities (Kennedy et al, 2011;Sahay and Ghosh, 2013;Lamb et al, 2015;McKain et al, 2015;Helfter et al, 2016;Hopkins et al, 2016), leaving few that estimate CH 4 emissions from medium-sized and small centers where urbanization currently occurs most rapidly (Marcotullio et al, 2013). Urban centers contain residential, commercial, and industrial areas within their boundaries, with an ever-increasing population and consequently growing energy demand (Sahay and Ghosh 2013).…”