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Wintertime CO2, CH4 and CO emissions estimation for the Washington DC / Baltimore metropolitan area using an inverse modeling technique

Published

Author(s)

Israel Lopez Coto, Xinrong Ren, Olivia E. Salmon, Anna Karion, Paul B. Shepson, Russell R. Dickerson, Ariel Stein, Kuldeep R. Prasad, James R. Whetstone

Abstract

Since greenhouse gas mitigation efforts are being mostly implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4 and CO from aircraft over five days within an inverse model to estimate emissions from the D.C./Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e. 17% for CO2, 24% for CH4 and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.
Citation
Environmental Science & Technology

Keywords

Greenhouse gas, Inverse modeling, Daily variability, Urban emissions, Airborne measurements

Citation

Lopez, I. , Ren, X. , Salmon, O. , Karion, A. , Shepson, P. , Dickerson, R. , Stein, A. , Prasad, K. and Whetstone, J. (2020), Wintertime CO2, CH4 and CO emissions estimation for the Washington DC / Baltimore metropolitan area using an inverse modeling technique, Environmental Science & Technology, [online], https://doi.org/10.1021/acs.est.9b06619 (Accessed October 26, 2021)
Created February 11, 2020, Updated February 14, 2020