Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

Abstract

There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridor‐Baltimore/Washington, DC (NEC‐B/W), downwind of large fluxes. To help site background towers for the NEC‐B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, root‐mean‐square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.

Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018

“…In the temperate latitudes the CO 2 uptake of plants usually predominates in spring and summer. A modelling study using synthetic CO 2 data by Mueller et al (2018) showed, that for the Baltimore-Washington area a substantial fraction of ~3 5% of the CO 2 background variability is caused by biogenic sources and sinks in July. Therefore, we simulate the biogenic CO 2 flux with the Vegetation Photosynthesis and Respiration Model (VPRM, Mahadevan et al, 2008) coupled with WRF-GHG (Beck et al, 2013) in order to estimate the influence of the photosynthetic uptake and respiration on our assessment of the anthropogenic flux.…”

Greenhouse gas observations from the Northeast Corridor tower network

“…Sixteen of these station locations were chosen to be used for emissions estimation in a domain around Baltimore and Washington, D.C. (red boundary, Fig. 1) using inverse modelling techniques (Lopez-Coto et al, 2017;Mueller et al, 2018). Three others are in Mashpee, MA, Philadelphia, PA, and Waterford Works, NJ.…”

“…A second design study determined ideal 100 locations for background stations, i.e. observation station locations that would aid in the determination of background CO2 entering the analysis domain (Mueller et al, 2018). Four stations were identified as part of that study; an existing EN site in Bucktown, MD, serves as a fifth background station southeast of the analysis domain ( Fig.…”

A Comparative Analysis of Anthropogenic CO2 Emissions at City Level Using OCO‐2 Observations: A Global Perspective

“…In contrast, atmospheric carbon concentrations measured from surface stations or satellite sensors, together with inverse modeling (e.g., Hybrid Single-Particle Lagrangian Integrated Trajectory model by Stein et al, 2015), offer an alternative approach to evaluate carbon inventories and detect trends attributable to policy, regulation, or economic changes (Sargent et al, 2018;Stein et al, 2015). As such, carbon monitoring projects to build observational networks were initiated in many large cities such as Los Angeles (Feng et al, 2016), Salt Lake City (McKain et al, 2012), Paris (Bréon et al, 2015), Portland, Oregon (Rice & Bostrom, 2011), Boston (McKain et al, 2015, Indianapolis , and Washington, DC/Baltimore (Mueller et al, 2018). Their study highlighted the necessity to build multiple network stations in urban areas to monitor surface GHG concentrations in both spatial and temporal domains.…”

“…Their study highlighted the necessity to build multiple network stations in urban areas to monitor surface GHG concentrations in both spatial and temporal domains. As such, carbon monitoring projects to build observational networks were initiated in many large cities such as Los Angeles (Feng et al, 2016), Salt Lake City (McKain et al, 2012), Paris (Bréon et al, 2015), Portland, Oregon (Rice & Bostrom, 2011), Boston (McKain et al, 2015, Indianapolis , and Washington, DC/Baltimore (Mueller et al, 2018).…”