Simon Heather, Valin Luke C, Baker Kirk R, Henderson Barron H, Crawford James H, Pusede Sally E, Kelly James T, Foley Kristen M, Owen R Chris, Cohen Ronald C, Timin Brian, Weinheimer Andrew J, Possiel Norm, Misenis Chris, Diskin Glenn S, Fried Alan
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
J Geophys Res Atmos. 2018 Mar 27;123(6):3304-3320. doi: 10.1002/2017jd027688.
Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO relationships to constrain emission inventories. We derive ambient and modeled ΔCO:ΔNO ratios that have previously been interpreted to represent CO:NO ratios in emissions from local sources. Modeled and measured ΔCO:ΔNO are similar; however, measured ΔCO:ΔNO has much more daily variability than modeled values. Sector-based tagging shows that regional transport, on-road gasoline vehicles, and nonroad equipment are the major contributors to modeled CO mixing ratios in the Baltimore area. In addition to those sources, on-road diesel vehicles, soil emissions, and power plants also contribute substantially to modeled NO in the area. The sector mix is important because emitted CO:NO ratios vary by several orders of magnitude among the emission sources. The model-predicted gasoline/diesel split remains constant across all measurement locations in this study. Comparison of ΔCO:ΔNO to emitted CO:NO is challenged by ambient and modeled evidence that free tropospheric entrainment, and atmospheric processing elevates ambient ΔCO:ΔNO above emitted ratios. Specifically, modeled ΔCO:ΔNO from tagged mobile source emissions is enhanced 5-50% above the emitted ratios at times and locations of aircraft measurements. We also find a correlation between ambient formaldehyde concentrations and measured ΔCO:ΔNO suggesting that secondary CO formation plays a role in these elevated ratios. This analysis suggests that ambient urban daytime ΔCO:ΔNO values are not reflective of emitted ratios from individual sources.
来自社区多尺度空气质量模型的源归因信息与2011年从与空气质量相关的柱面和垂直分辨观测中获取地表条件信息的巴尔的摩实地研究的环境数据相结合。我们评估源贡献,并评估利用飞机测量的一氧化碳(CO)和一氧化氮(NO)关系来约束排放清单的效用。我们得出了环境和模型的ΔCO:ΔNO比值,这些比值此前被解释为代表本地源排放中的CO:NO比值。模型和测量的ΔCO:ΔNO相似;然而,测量的ΔCO:ΔNO的日变化比模型值大得多。基于部门的标记显示,区域传输、道路汽油车辆和非道路设备是巴尔的摩地区模型CO混合比的主要贡献者。除了这些源之外,道路柴油车辆、土壤排放和发电厂也对该地区模型的NO有很大贡献。部门组合很重要,因为排放源之间的排放CO:NO比值相差几个数量级。在本研究中,模型预测的汽油/柴油比例在所有测量地点保持不变。将ΔCO:ΔNO与排放的CO:NO进行比较受到环境和模型证据的挑战,即自由对流层的卷入和大气处理使环境ΔCO:ΔNO高于排放比值。具体而言,在飞机测量的时间和地点,标记移动源排放的模型ΔCO:ΔNO比排放比值高出5% - 50%。我们还发现环境甲醛浓度与测量的ΔCO:ΔNO之间存在相关性,这表明二次CO形成在这些升高的比值中起作用。该分析表明,城市白天环境ΔCO:ΔNO值不能反映单个源的排放比值。