A simplified non-linear chemistry transport model for analyzing NO₂ column observations: STILT-NO_x

Satellites monitoring air pollutants (e.g., nitrogen oxides, NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic "hotspots'". Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide, CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT-NOx); and investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO(2)). Interpreting emission signals from tNO(2) commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three US power plants and two urban areas across seasons. Using EPA-reported emissions for power plants with non-linear NOx chemistry improves the model-data alignment in tNO(2) (a high bias of <= 10 % on an annual basis), compared to simulations using either EDGAR or without chemistry (bias approaching 100 %). The largest model-data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO(2) can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.