Marais E A, Jacob D J, Kurosu T P, Chance K, Murphy J G, Reeves C, Mills G, Casadio S, Millet D B, Barkley M P, Paulot F, Mao J
Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Atmos Chem Phys. 2012 Jul;12(14):6219-6235. doi: 10.5194/acp-12-6219-2012. Epub 2012 Jul 18.
We use 2005-2009 satellite observations of formaldehyde (HCHO) columns from the OMI instrument to infer biogenic isoprene emissions at monthly 1 × 1° resolution over the African continent. Our work includes new approaches to remove biomass burning influences using OMI absorbing aerosol optical depth data (to account for transport of fire plumes) and anthropogenic influences using AATSR satellite data for persistent small-flame fires (gas flaring). The resulting biogenic HCHO columns (Ω) from OMI follow closely the distribution of vegetation patterns in Africa. We infer isoprene emission ( ) from the local sensitivity = ΔΩ / Δ derived with the GEOS-Chem chemical transport model using two alternate isoprene oxidation mechanisms, and verify the validity of this approach using AMMA aircraft observations over West Africa and a longitudinal transect across central Africa. Displacement error (smearing) is diagnosed by anomalously high values of and the corresponding data are removed. We find significant sensitivity of to NO under low-NO conditions that we fit to a linear function of tropospheric column NO. We estimate a 40% error in our inferred isoprene emissions under high-NO conditions and 40-90% under low-NO conditions. Our results suggest that isoprene emission from the central African rainforest is much lower than estimated by the state-of-the-science MEGAN inventory.
我们利用2005 - 2009年OMI仪器对甲醛(HCHO)柱的卫星观测数据,在非洲大陆以1×1°的月度分辨率推断生物源异戊二烯排放。我们的工作包括采用新方法,利用OMI吸收性气溶胶光学厚度数据消除生物质燃烧的影响(以考虑火羽流的传输),并利用AATSR卫星数据消除持续性小火(气体燃烧)的人为影响。OMI得出的生物源HCHO柱(Ω)与非洲植被模式的分布密切相关。我们利用GEOS - Chem化学传输模型,通过两种不同的异戊二烯氧化机制,从局部敏感性 = ΔΩ / Δ推断异戊二烯排放( ),并利用西非上空的AMMA飞机观测数据以及横跨非洲中部的纵向断面数据验证该方法的有效性。通过异常高的 值诊断位移误差(拖尾),并去除相应数据。我们发现在低NO条件下 对NO具有显著敏感性,我们将其拟合为对流层柱NO的线性函数。我们估计在高NO条件下推断的异戊二烯排放误差为40%,在低NO条件下为40 - 90%。我们的结果表明,中非雨林的异戊二烯排放远低于最新的MEGAN清单估计值。