Aerosol mass spectrometer constraint on the global secondary organic aerosol budget
Aerosol mass spectrometer constraint on the global secondary organic aerosol budget
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DOI：
10.5194/acp-11-12109-2011
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发表时间：
2011-12
影响因子：
6.3
通讯作者：
D. Spracklen;J. Jimenez;K. Carslaw;D. Worsnop;M. J. Evans;G. Mann;Q. Zhang;M. Canagaratna;
中科院分区：
文献类型：
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作者：
D. Spracklen;J. Jimenez;K. Carslaw;D. Worsnop;M. J. Evans;G. Mann;Q. Zhang;M. Canagaratna;
摘要。大气二次有机气溶胶（SOA）的收支非常不确定，近期的估计表明全球源强在每年12 - 1820太克（SOA）之间。我们使用了来自34个不同地表位置的气溶胶质谱仪（AMS）观测数据集来评估GLOMAP全球化学传输模型。标准模型模拟（仅包括来自单萜的SOA）低估了AMS观测到的有机气溶胶（OA），并且在重现数据集中的变异性方面能力较差。我们模拟了由生物源（单萜和异戊二烯）、综合人为源以及综合生物质燃烧挥发性有机化合物（VOCs）形成的SOA，并改变了每个前体源的SOA产率，以使模型与观测结果达到最佳的整体匹配。我们假设SOA基本上是非挥发性的，并且不可逆地凝结在现有气溶胶上。我们对SOA源强的最佳估计是每年140太克（SOA），但不确定性范围较大，我们估计为每年50 - 380太克（SOA）。我们发现当假设来自与人为污染在空间上匹配的源（我们称之为人为控制的SOA）有一个较大的SOA源（每年100太克（SOA））时，模型与AMS数据集之间的归一化平均误差（NME）最小。我们使用Bahadur等人（2009年）汇编的有机碳观测数据来评估我们估计的SOA源。我们发现具有较大人为SOA源的模型与这些观测结果最为一致，然而，与具有较大生物源SOA源（每年250太克（SOA））的模型相比，改进很小。我们使用来自农村地区的14C观测数据集来评估我们估计的SOA源。我们估计人为控制的SOA源中最多有每年10太克（SOA）（10%）可能来自化石（城市/工业）源。我们认为一个额外的人为源极有可能是由于人为污染增强了由生物源VOCs形成SOA。这样一个人为控制的SOA源将导致显著的气候强迫。我们估计人为控制的SOA的全球平均气溶胶直接效应为 - 0.26 ± 0.15瓦/平方米，间接（云反照率）效应为 - 0.6 + 0.24 - 0.14瓦/平方米。由于在受生物源和生物质源强烈影响的地区和时期，OA观测数量有限，因此通过这种分析，生物源和生物质SOA源没有得到很好的约束。为了通过这种方法进一步改善约束条件，需要在热带和南半球进行更多的OA观测。
Abstract. The budget of atmospheric secondary organic aerosol (SOA) is very uncertain, with recent estimates suggesting a global source of between 12 and 1820 Tg (SOA) a −1 . We used a dataset of aerosol mass spectrometer (AMS) observations from 34 different surface locations to evaluate the GLOMAP global chemical transport model. The standard model simulation (which included SOA from monoterpenes only) underpredicted organic aerosol (OA) observed by the AMS and had little skill reproducing the variability in the dataset. We simulated SOA formation from biogenic (monoterpenes and isoprene), lumped anthropogenic and lumped biomass burning volatile organic compounds (VOCs) and varied the SOA yield from each precursor source to produce the best overall match between model and observations. We assumed that SOA is essentially non-volatile and condenses irreversibly onto existing aerosol. Our best estimate of the SOA source is 140 Tg (SOA) a −1 but with a large uncertainty range which we estimate to be 50–380 Tg (SOA) a −1 . We found the minimum in normalised mean error (NME) between model and the AMS dataset when we assumed a large SOA source (100 Tg (SOA) a −1 ) from sources that spatially matched anthropogenic pollution (which we term antropogenically controlled SOA). We used organic carbon observations compiled by Bahadur et al. (2009) to evaluate our estimated SOA sources. We found that the model with a large anthropogenic SOA source was the most consistent with these observations, however improvement over the model with a large biogenic SOA source (250 Tg (SOA) a −1 ) was small. We used a dataset of 14 C observations from rural locations to evaluate our estimated SOA sources. We estimated a maximum of 10 Tg (SOA) a −1 (10 %) of the anthropogenically controlled SOA source could be from fossil (urban/industrial) sources. We suggest that an additional anthropogenic source is most likely due to an anthropogenic pollution enhancement of SOA formation from biogenic VOCs. Such an anthropogenically controlled SOA source would result in substantial climate forcing. We estimated a global mean aerosol direct effect of −0.26 ± 0.15 Wm −2 and indirect (cloud albedo) effect of −0.6 +0.24 −0.14 Wm −2 from anthropogenically controlled SOA. The biogenic and biomass SOA sources are not well constrained with this analysis due to the limited number of OA observations in regions and periods strongly impacted by these sources. To further improve the constraints by this method, additional OA observations are needed in the tropics and the Southern Hemisphere.