An Inversion Framework for Optimizing Non-Methane VOC Emissions Using Remote Sensing and Airborne Observations in Northeast Asia During the KORUS-AQ Field Campaign

Abstract

We aim to reduce uncertainties in CH2O and other volatile organic carbon (VOC) emissions through assimilation of remote sensing data. We first update a three-dimensional (3D) chemical transport model, GEOS-Chem with the KORUSv5 anthropogenic emission inventory and inclusion of chemistry for aromatics and C2H4, leading to modest improvements in simulation of CH2O (normalized mean bias (NMB): −0.57 to −0.51) and O3 (NMB: −0.25 to −0.19) compared against DC-8 aircraft measurements during KORUS-AQ; the mixing ratio of most VOC species are still underestimated. We next constrain VOC emissions using CH2O observations from two satellites (OMI and OMPS) and the DC-8 aircraft during KORUS-AQ. To utilize data from multiple platforms in a consistent manner, we develop a two-step Hybrid Iterative Finite Difference Mass Balance and four-dimensional variational inversion system (Hybrid IFDMB-4DVar). The total VOC emissions throughout the domain increase by 47%. The a posteriori simulation reduces the low biases of simulated CH2O (NMB: −0.51 to −0.15), O3 (NMB: −0.19 to −0.06), and VOCs. Alterations to the VOC speciation from the 4D-Var inversion include increases of biogenic isoprene emissions in Korea and anthropogenic emissions in Eastern China. We find that the IFDMB method alone is adequate for reducing the low biases of VOCs in general; however, 4D-Var provides additional refinement of high-resolution emissions and their speciation. Defining reasonable emission errors and choosing optimal regularization parameters are crucial parts of the inversion system. Our new hybrid inversion framework can be applied for future air quality campaigns, maximizing the value of integrating measurements from current and upcoming geostationary satellite instruments.