Dept. of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA; Pacific Northwest National Laboratory, Richland, WA 99354, USA.
Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Earth System Science, 3200 Croul Hall, University of California, Irvine, CA 92697-3100, USA.
Sci Total Environ. 2017 Oct 1;595:149-158. doi: 10.1016/j.scitotenv.2017.03.262. Epub 2017 Apr 4.
Isoprene and monoterpene emission rates are essential inputs for atmospheric chemistry models that simulate atmospheric oxidant and particle distributions. Process studies of the biochemical and physiological mechanisms controlling these emissions are advancing our understanding and the accuracy of model predictions but efforts to quantify regional emissions have been limited by a lack of constraints on regional distributions of ecosystem emission capacities. We used an airborne wavelet-based eddy covariance measurement technique to characterize isoprene and monoterpene fluxes with high spatial resolution during the 2013 SAS (Southeast Atmosphere Study) in the southeastern United States. The fluxes measured by direct eddy covariance were comparable to emissions independently estimated using an indirect inverse modeling approach. Isoprene emission factors based on the aircraft wavelet flux estimates for high isoprene chemotypes (e.g., oaks) were similar to the MEGAN2.1 biogenic emission model estimates for landscapes dominated by oaks. Aircraft flux measurement estimates for landscapes with fewer isoprene emitting trees (e.g., pine plantations), were about a factor of two lower than MEGAN2.1 model estimates. The tendency for high isoprene emitters in these landscapes to occur in the shaded understory, where light dependent isoprene emissions are diminished, may explain the lower than expected emissions. This result demonstrates the importance of accurately representing the vertical profile of isoprene emitting biomass in biogenic emission models. Airborne measurement-based emission factors for high monoterpene chemotypes agreed with MEGAN2.1 in landscapes dominated by pine (high monoterpene chemotype) trees but were more than a factor of three higher than model estimates for landscapes dominated by oak (relatively low monoterpene emitting) trees. This results suggests that unaccounted processes, such as floral emissions or light dependent monoterpene emissions, or vegetation other than high monoterpene emitting trees may be an important source of monoterpene emissions in those landscapes and should be identified and included in biogenic emission models.
异戊二烯和单萜烯排放率是模拟大气氧化剂和粒子分布的大气化学模型的重要输入。控制这些排放的生化和生理机制的过程研究正在提高我们的理解能力，并提高模型预测的准确性，但由于缺乏对生态系统排放能力的区域分布的限制，量化区域排放的工作受到了限制。我们在美国东南部的 2013 年 SAS（东南大气研究）期间使用基于机载小波的涡度协方差测量技术，以高空间分辨率对异戊二烯和单萜烯通量进行了特征描述。直接涡度协方差测量的通量与使用间接反演模型方法独立估算的排放物相当。基于飞机小波通量估算的高异戊二烯化学型（例如橡树）的异戊二烯排放因子与主要由橡树主导的景观的 MEGAN2.1 生物源排放模型估算值相似。对于具有较少异戊二烯排放树（例如松林种植园）的景观，飞机通量测量估算值比 MEGAN2.1 模型估算值低约 2 倍。这些景观中高异戊二烯排放物倾向于发生在阴影下层，那里依赖光照的异戊二烯排放减少，这可能解释了低于预期的排放。这一结果表明，在生物源排放模型中准确表示异戊二烯排放生物量的垂直分布非常重要。基于机载测量的高单萜烯化学型排放因子与以松树（高单萜烯化学型）为主的景观中的 MEGAN2.1 一致，但比以橡树（相对低单萜烯排放）为主的景观中的模型估算值高出 3 倍以上。这一结果表明，未被考虑的过程，例如花卉排放或依赖光照的单萜烯排放，或除高单萜烯排放树木以外的植被，可能是这些景观中单萜烯排放的重要来源，并且应该在生物源排放模型中加以识别和纳入。
