Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique

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 CO 2 , CH 4 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 CO 2 , 24% for CH 4 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.