Strong influence of deposition and vertical mixing on secondary organic aerosol concentrations in CMAQ and CAMx

Publication date: December 2017
Source:Atmospheric Environment, Volume 171
Author(s): Qian Shu, Bonyoung Koo, Greg Yarwood, Barron H. Henderson
Differences between two air quality modeling systems reveal important uncertainties in model representations of secondary organic aerosol (SOA) fate. Two commonly applied models (CMAQ: Community Multiscale Air Quality; CAMx: Comprehensive Air Quality Model with extensions) predict very different OA concentrations over the eastern U.S., even when using the same source data for emissions and meteorology and the same SOA modeling approach. Both models include an option to output a detailed accounting of how each model process (e.g., chemistry, deposition, etc.) alters the mass of each modeled species, referred to as process analysis. We therefore perform a detailed diagnostic evaluation to quantify simulated tendencies (Gg/hr) of each modeled process affecting both the total model burden (Gg) of semi-volatile organic compounds (SVOC) in the gas (g) and aerosol (a) phases and the vertical structures to identify causes of concentration differences between the two models. Large differences in deposition (CMAQ: 69.2 Gg/d; CAMx: 46.5 Gg/d) contribute to significant OA bias in CMAQ relative to daily averaged ambient concentration measurements. CMAQ's larger deposition results from faster daily average deposition velocities (VD) for both SVOC (g) (VD,cmaq = 2.15 × VD,camx) and aerosols (VD,cmaq = 4.43 × Vd,camx). Higher aerosol deposition velocity would be expected to cause similar biases for inert compounds like elemental carbon (EC), but this was not seen. Daytime low-biases in EC were also simulated in CMAQ as expected but were offset by nighttime high-biases. Nighttime high-biases were a result of overly shallow mixing in CMAQ leading to a higher fraction of EC total atmospheric mass in the first layer (CAMx: 5.1–6.4%; CMAQ: 5.6–6.9%). Because of the opposing daytime and nighttime biases, the apparent daily average bias for EC is reduced. For OA, there are two effects of reduced vertical mixing: SOA and SVOC are concentrated near the surface, but SOA yields are reduced near the surface by nighttime enhancement of NOx. These results help to characterize model processes in the context of SOA and provide guidance for model improvement.



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