Siting background towers to characterize incoming air for urban GHG estimation: a case study in the Washington DC/Baltimore Area

Published: June 05, 2019

Author(s)

Kimberly L. Mueller, Vineet Yadav, Israel Lopez Coto, Anna Karion, Sharon M. Gourdji, Cory R. Martin, James R. Whetstone

Abstract

There is increased interest in understanding urban greenhouse gas (GHG) emissions. However, to accurately estimate city emissions, the influence of exurban 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 sources and sinks. The use of observations from upwind locations to represent GHG urban inflow has had limited success in estimating city emissions using inversion methods. We present a coupled Bayesian Information Criteria (BIC) and geostatistical regression approach to help site four background locations that best explain CO2 variability due to exurban fluxes as observed at twelve NEC-B/W urban towers. The method employs a pseudo-data analysis using an atmospheric transport and dispersion model coupled with two different flux inventories to evaluate fifteen candidate tower locations along the urban domain for February and July 2013. On average, the analysis shows that the influences of exurban fluxes are 29% (February) and 43% (July) of the total signal observed at the NEC-B/W urban towers. The variability in observed NEC-B/W CO2 mole fractions is impacted by fluxes as far away as Cleveland, Ohio. The method selects background towers whose observations generally capture the variability CO2 enhancements observed at the NEC-B/W urban towers due to the spatio-temporal distribution of exurban fluxes. However, errors associated with representing background air can be up to 10 ppm for any given observation suggesting that more sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.
Citation: Journal of Geophysical Research-Atmospheres
Pub Type: Journals

Keywords

greenhouse gas, urban, inverse modeling, carbon cycling
Created June 05, 2019, Updated June 05, 2019