Exploring the impact of boundary layer dynamics on the Δ17O of reactive nitrogen oxides with the PACT-1D model

Atmospheric reactive nitrogen oxides including NOₓ and nitrate acquire oxygen isotope anomalies (Δ¹⁷O = δ¹⁷O - 0.52 × δ¹⁸O) through ozone-driven chemistry. Prior chemical transport modeling studies have investigated the spatiotemporal patterns of Δ¹⁷O of atmospheric nitrate, yet these approaches remain fundamentally localized as they neglect inter-grid transport effects (Alexander et al., 2020; Walters et al., 2024). Transport process can influence Δ¹⁷O both directly through mixing and indirectly by altering precursor concentrations, thereby modulating isotopic transfer during chemical reactions. However, integrating transport into Δ¹⁷O modeling has been hindered by the requirement to track multiple isotopologues per species, which would substantially increase the complexity in chemical mechanism and computational cost.

This study introduces a novel, computational efficient Δ¹⁷O modeling framework with the transport effect incorporated, in which Δ¹⁷O is treated directly as prognostic variable. The contributions of chemical and transport processes to Δ¹⁷O evolution are separated using operator splitting. The Δ¹⁷O transfer during chemistry is computed explicitly following the method of Morin et al. (2011). The Δ¹⁷O transport equations are solved using a similar numerical scheme for the Eulerian transport equation. We apply this framework within an adapted version of the PACT-1D model (Tuite et al 2021) to examine how boundary layer dynamics impact the Δ¹⁷O variability in reactive nitrogen oxides. In particular, modeled Δ¹⁷O values of atmospheric nitrate are evaluated against recent vertical profile observations. This comparison aims to improve our understanding of the controlling factors on nitrate Δ¹⁷O and to assess its utility as a proxy for atmospheric oxidation capacity.