Assessing urban methane emissions using column-observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework

Abstract

Abstract. Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions (<5 ppb downwind of the city). We found diffuse methane emissions of 73(±22) mol s−1, which is about 50 % of the urban total and 68 % higher than estimated from bottom-up methods, although it is somewhat smaller than estimates from studies using tower and aircraft observations. The measurement and model techniques developed here address many of the challenges present when quantifying urban greenhouse gas emissions and will help in the design of future measurement schemes in other cities.

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

“…For network deployments such as undertaken here, it is common practice to cross-calibrate the network nodes by side-byside measurements (Frey et al, 2015;Chen et al, 2016;Frey et al, 2019;Jones et al, 2021;Dietrich et al, 2021) in order to exclude spurious gradients when conducting upwind-downwind analyses. We calibrated the four instruments through side-byside measurements on 23 May and 26 May, 2018 at the southern location Pustelnik.…”

“…Here, we report on CH 4 emission estimates derived from measurements of four stationary, sun-viewing FTS of the type EM27/SUN arranged in a network-like pattern enclosing the USCB during the CoMet campaign activities. The setup largely mimics previous network deployments for quantifying urban greenhouse gas emissions in Berlin (Hase et al, 2015), Paris (Vogel et al, 2019), St. Petersburg (Makarova et al, 2020), Munich (Dietrich et al, 2021), Indianapolis (Jones et al, 2021) and other places. Our four EM27/SUN were positioned in the four cardinal directions at a distance of a few tens of kilometers to the center of the USCB.…”

Recovery of sparse urban greenhouse gas emissions

“…Understanding anthropogenic greenhouse gas (GHG) emissions is important for scientists and decision makers fighting climate change. Based on a growing amount of atmospheric observations, studies estimating emission fields of GHG sources and sinks from these observations have been performed on local (Chen et al (2016); Viatte et al (2017); Toja-Silva et al ( 2017)), metropolitan (Jones et al (2021); Turner et al (2020); Hase et al (2015)), country (Miller et al (2013); Shekhar et al (2020)), and global (Hirsch et al (2006); Mueller et al (2008); Turner et al (2015); Jacob et al (2016)) scale. One of the main reason for such studies is to verify and improve GHG emission inventories created by bottom up methods.…”

“…Due to a lack of measurements and high modeling and measurement uncertainties, estimating each grid cell of an emission field independently is not possible. Instead, sectors (Jones et al, 2021) or spatial correlations (Wesloh et al, 2020) are used to construct alternative parameterizations of emission fields to prevent overfitting.…”

“…Göckede et al (2010) have shown that on smaller domains, uncertainties in the background has a high influence on the estimation of the city emissions. Therefore, modern approaches, such as Jones et al (2021) and Klappenbach et al (2021), use the measurements acquired to additionally improve the certainty of background concentrations using a Bayesian approach. In this work, we ignore the background and make the assumption that it is known in full detail.…”