Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density

Abstract

Abstract. The majority of anthropogenic CO2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO2 Observation Network (BEACO2N) in California's Bay Area, in combination with an inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km2. The model uses an hourly 1 × 1 km2 emission inventory and 1 × 1 km2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO2 emissions. We examine uncertainty in estimated CO2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO2N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.

Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

“…accuracy and precision) must be relatively high to ensure accurate and precise urban inverse flux estimates. Some recent studies found that sensors with lower measurement quality, given sufficient numbers, serve as potentially useful tools for the detection of urban CO 2 emissions (Wu et al, 2016;Turner et al, 2016;Shusterman et al, 2016;Martin et al, 2017). These studies, however, only considered random error in the sensors, not sensor bias.…”

“…Top-down methods infer quantitative information on surface CO 2 fluxes from variations in atmospheric CO 2 concentrations through inverse modeling with atmospheric tracer transport models (Ciais et al, 2011), and may include isotope composition measurements to identify fossil fuel sources (Levin et al, 2003;Miller et al, 2012;Turnbull et al, 2015;Basu et al, 2016). Uncertainties in atmospheric transport models (Peylin et al, 2002;Lauvaux et al, 2009;Peylin et al, 2011;Isaac et al, 2014), limited density of atmospheric measurements (Gerbig et al, 2009;Turner et al, 2016) and uncertainties in prior fluxes (Peylin et al, 2005;Carouge et al, 2010;Lauvaux et al, 2016) all constitute sources of error in this method (Engelen et al, 2002).…”

“…Another challenge is minimization of uncertainty in high-resolution atmospheric transport models used to simulate trace gas transport in an urban setting, where complex boundary layer structures may be formed due to the land-use/land-cover change and intensive human activities (Wang et al, 2011;Sarmiento et al, 2017;Gaudet et al, 2017). In addition, the inverse estimation of CO 2 ff emissions is constrained by atmospheric CO 2 measurements, and the trade-off between measurement density and quality is an important emerging debate for urban GHG monitoring (Wu et al, 2016;Shusterman et al, 2016;Turner et al, 2016;Martin et al, 2017). Lastly, uncertain spatial structures in prior flux errors influence the precision of inverse flux estimates (Saide et al, 2011;Lauvaux et al, 2016).…”

Southern California Megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using remote sensing and a Lagrangian model

“…Turner et al (2016) describes this approach for an urban region. Here we follow the notation of Rodgers (2000).…”

“…Bayesian inversions are more complex, but can also improve information on the spatial distribution and intensity of fluxes (e.g., Turner et al, 2016;Lauvaux et al, 2016); they can be solved by analytical or adjoint methods (Rodgers, 2000;Kopacz et al, 2009 12 km model and the Bayesian inversion with the linear forward model.…”