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

Abstract

<p><strong>Abstract.</strong> The majority of anthropogenic CO<sub>2</sub> 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 CO<sub>2</sub> 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 BEACO<sub>2</sub>N network, in combination with an inverse model based on 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 km<sup>2</sup>. The model uses an hourly 1 × 1 km<sup>2</sup> emission inventory and 1 × 1 km<sup>2</sup> 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 ppm 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 at reducing uncertainty in CO<sub>2</sub> emissions. We examine uncertainty in estimated CO<sub>2</sub> fluxes at 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). 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 could estimate weekly CO<sub>2</sub> emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.</p>

The BErkeley Atmospheric CO<sub>2</sub> Observation Network: initial evaluation

“…We simulate hourly CO 2 concentrations (ŷ) at each site in the network using the Stochastic Time-Inverted Lagrangian Transport model (STILT; Lin et al, 2003) coupled to the Weather Research and Forecasting model (WRF; Skamarock et al, 2008). The coupled model is known as "WRF-STILT" (Nehrkorn et al, 2010) and the setup used here follows that of Turner et al (2016; see their Sect. S1 for details of the WRF setup).…”

“…The trajectories of these 500 particles are then used to construct footprints for each observation that represent the sensitivity of the observation to a perturbation in emissions from a given location. The footprints can be represented in matrix form (H) and multiplied by a set of gridded emissions (x, from the high-resolution bottom-up CO 2 inventory in Turner et al 2016) to compute the CO 2 enhancement at each site due to …”

“…Future work will focus on constructing inferred emissions patterns and trends at this scale from the body of observations. In an initial effort in this regard, Turner et al (2016) constructed and applied a WRF-STILT inverse model to synthetic observations with density similar to BEACO 2 N. For an area source the size of the Oakland metropolitan area, emissions were estimated to within 18 % accuracy; for a freeway-sized line source to within 36 %; and to within 60 % for the sum of six industrial point sources -consistently outperforming a smaller hypothetical network (three sites) with significantly better precision. Using week-long averages, the BEACO 2 Nlike network was able to further reduce the uncertainty in the integrated urban area source to < 2 %, a significant improvement over the citywide emissions estimates provided by real and proposed ∼ 10 site sensor networks described by Lauvaux et al (2016) (25 % uncertainty in five day averages), Kort et al (2013) (> 10 % uncertainty in monthly averages), and Wu et al (2016) (11 % uncertainty in monthly totals).…”

“…In addition, further characterization of the trace gas and particulate matter sensors will allow for more specific source attribution via the exploitation of emissions factors unique to various combustion activities (e.g., Ban-Weiss et al, 2008;Harley et al, 2015), while providing public-health-relevant air quality information as well. There is also potential for fine-grained verification of space-based observations or even of personal sensors when their inherent mobility brings them within the geographical area well represented by the fixed BEACO 2 N network.…”

Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales

“…SCIAMACHY and GOSAT demonstrated the capability for high-precision (< 1 %) measurements of methane from space (Buchwitz et al, 2015), but SCIAMACHY had coarse pixels (30 × 60 km 2 in nadir) and GOSAT has sparse coverage (10 km diameter pixels separated by 250 km). Inverse analyses have used observations from these satellitebased instruments to estimate methane emissions at ∼ 100-1000 km spatial resolution (e.g., Bergamaschi et al, 2009Bergamaschi et al, , 2013Fraser et al, 2013;Monteil et al, 2013;Wecht et al, 2014a;Cressot et al, 2014;Kort et al, 2014;Turner et al, 2016a;Alexe et al, 2015;Tan et al, 2016;Buchwitz et al, 2017;Sheng et al, 2018a, b). But such coarse resolution makes it difficult to resolve individual source types because of spatial overlap (Maasakkers et al, 2016).…”