Reply on AC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellites monitoring air pollutants (e.g., nitrogen oxides, NO_x = 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 NO_x 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 NO_x chemistry and fine-scale atmospheric transport (STILT-NO_x); and investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO₂ columns (tNO₂). Interpreting emission signals from tNO₂ 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 NO_x chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NO_x emissions to its concentrations. This NO_x 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 NO_x chemistry improves the model-data alignment in tNO₂ (a high bias of &le; 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 NO_x chemistry affects the relationship between NO_x and CO₂ in terms of the spatial and seasonal variability and (2) how assimilating tNO₂ can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NO_x and CO₂, which laid a foundation for a local-scale multi-tracer emission optimization system.