Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO 2 emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO 2 emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO 2 boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO 2 emissions based on the ensemble mean and quartiles (5.26-5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.
The Indianapolis Flux Experiment (INFLUX) aims to develop and assess methods for quantifying urban greenhouse gas emissions. Here we use CO 2 , 14 CO 2 , and CO measurements from tall towers around Indianapolis, USA, to determine urban total CO 2 , the fossil fuel derived CO 2 component (CO 2 ff), and CO enhancements relative to background measurements. When a local background directly upwind of the urban area is used, the wintertime total CO 2 enhancement over Indianapolis can be entirely explained by urban CO 2 ff emissions. Conversely, when a continental background is used, CO 2 ff enhancements are larger and account for only half the total CO 2 enhancement, effectively representing the combined CO 2 ff enhancement from Indianapolis and the wider region. In summer, we find that diurnal variability in both background CO 2 mole fraction and covarying vertical mixing makes it difficult to use a simple upwind-downwind difference for a reliable determination of total CO 2 urban enhancement. We use characteristic CO 2 ff source sector CO:CO 2 ff emission ratios to examine the contribution of the CO 2 ff source sectors to total CO 2 ff emissions. This method is strongly sensitive to the mobile sector, which produces most CO. We show that the inventory-based emission product ("bottom up") and atmospheric observations ("top down") can be directly compared throughout the diurnal cycle using this ratio method. For Indianapolis, the top-down observations are consistent with the bottom-up Hestia data product emission sector patterns for most of the diurnal cycle but disagree during the nighttime hours. Further examination of both the top-down and bottom-up assumptions is needed to assess the exact cause of the discrepancy.
Urban environments are the primary contributors to global anthropogenic carbon emissions. Because much of the growth in CO 2 emissions will originate from cities, there is a need to develop, assess, and improve measurement and modeling strategies for quantifying and monitoring greenhouse gas emissions from large urban centers. In this study the uncertainties in an aircraft-based mass balance approach for quantifying carbon dioxide and methane emissions from an urban environment, focusing on Indianapolis, IN, USA, are described. The relatively level terrain of Indianapolis facilitated the application of mean wind fields in the mass balance approach. We investigate the uncertainties in our aircraft-based mass balance approach by (1) assessing the sensitivity of the measured flux to important measurement and analysis parameters including wind speed, background CO 2 and CH 4 , boundary layer depth, and interpolation technique, and (2) determining the flux at two or more downwind distances from a point or area source (with relatively large source strengths such as solid waste facilities and a power generating station) in rapid succession, assuming that the emission flux is constant. When we quantify the precision in the approach by comparing the estimated emis-sions derived from measurements at two or more downwind distances from an area or point source, we find that the minimum and maximum repeatability were 12 and 52 %, with an average of 31 %. We suggest that improvements in the experimental design can be achieved by careful determination of the background concentration, monitoring the evolution of the boundary layer through the measurement period, and increasing the number of downwind horizontal transect measurements at multiple altitudes within the boundary layer.
Abstract. We performed an atmospheric inversion of the CO 2 fluxes over Iowa and the surrounding states, from June to December 2007, at 20 km resolution and weekly timescale. Eight concentration towers were used to constrain the carbon balance in a 1000×1000 km 2 domain in this agricultural region of the US upper midwest. The CO 2 concentrations of the boundaries derived from CarbonTracker were adjusted to match direct observations from aircraft profiles around the domain. The regional carbon balance ends up with a sink of 183 Tg C±35 Tg C over the area for the period June-December, 2007. Potential bias from incorrect boundary conditions of about 0.55 ppm over the 7 months was corrected using mixing ratios from four different aircraft profile sites operated at a weekly time scale, acting as an additional source of uncertainty of 24 Tg C. We used two different prior flux estimates, the SiBCrop model and the inverse flux product from the CarbonTracker system. We show that inverse flux estimates using both priors converge to similar posterior estimates (20 Tg C difference), in our reference inversion, but some spatial structures from the prior fluxes remain in the posterior fluxes, revealing the importance of the prior flux resolution and distribution despite the large amount of atmospheric data available. The retrieved fluxes were compared to eddy flux towers in the corn and grassland areas, revealing an improvement in the seasonal cycles between the two compared to the prior fluxes, despite large absolute differences due to representation errors. The uncertainty of 34 Tg C (or 34 g C m 2 ) was derived from the posterior uncertainty obtained with our reference inversion of about 25 to 30 Tg C and from sensitivity tests of the assumptions made in the inverse system, for a mean carbon balance over the region of −183 Tg C, slightly weaker than the reference. Because of the potential large bias (∼24 Tg C in this case) due to choice of background conditions, proportional to the surface but not to the regional flux, this methodology seems limited to regions with a large signal (sink or source), unless additional observations can be used to constrain the boundary inflow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.