Open AccessWintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique
Author(s) -
Israel López-Coto,
X. Ren,
O. E. Salmon,
A. Karion,
P. B. Shepson,
Russell R. Dickerson,
Ariel Stein,
Kuldeep Prasad,
J. R. Whetstone
Publication year - 2020
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.9b06619
Subject(s) - greenhouse gas , environmental science , sampling (signal processing) , spatial variability , atmospheric sciences , metropolitan area , estimation , meteorology , statistics , climatology , mathematics , geography , engineering , ecology , geology , archaeology , filter (signal processing) , systems engineering , electrical engineering , biology
Since greenhouse gas mitigation efforts are mostly being 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 5 days within an inverse model to estimate emissions from the DC-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.