Constraints on Carbon Monoxide Emissions Based on Tall Tower Measurements in the U.S. Upper Midwest
详细信息 查看全文
- 作者：Su Youn Kim ; Dylan B. Millet ; Lu Hu ; Michael J. Mohr ; Tim J. Griffis ; Deyong Wen ; John C. Lin ; Scot M. Miller ; Marcos Longo
- 刊名：Environmental Science & Technology (ES&T)
- 出版年：2013
- 出版时间：August 6, 2013
- 年：2013
- 卷：47
- 期：15
- 页码：8316-8324
- 全文大小：475K
- 年卷期：v.47,no.15(August 6, 2013)
- ISSN：1520-5851
文摘
We interpret a full year of high-frequency CO measurements from a tall tower in the U.S. Upper Midwest with a time-reversed Lagrangian Particle Dispersion Model (STILT LPDM) and an Eulerian chemical transport model (GEOS-Chem CTM) to develop top-down constraints on U.S. CO sources in 2009. Our best estimate is that anthropogenic CO emissions in the U.S. Upper Midwest in 2009 were 2.9 Tg, 61% lower (a posteriori scale factor of 0.39) than our a priori prediction based on the U.S. EPA鈥檚 National Emission Inventory for 2005 (NEI 2005). If the same bias applies across the contiguous U.S., the inferred CO emissions are 26 Tg/y, compared to the a priori estimate of 66 Tg/y. This discrepancy is significantly greater than would be expected based solely on emission decreases between 2005 and 2009 (EPA estimate: 23% decrease). Model transport error is an important source of uncertainty in the analysis, and we employ an ensemble of sensitivity runs using multiple meteorological data sets and model configurations to assess its impact on our results. A posteriori scale factors for the U.S. anthropogenic CO source from these sensitivity runs range from 0.22 to 0.64, corresponding to emissions of 1.6鈥?.8 Tg/y for the U.S. Upper Midwest and 15鈥?2 Tg/y for the contiguous U.S. The data have limited sensitivity for constraining biomass + biofuel burning emissions and photochemical CO production from precursor organic compounds. Our finding of a NEI 2005 overestimate of CO emissions is consistent with recent assessments for individual cities and with earlier analyses based on the NEI 1999, implying the need for a better mechanism for refining such bottom-up emission estimates in response to top-down constraints.