Machine Learning-Driven Source Apportionment of HONO in High-Ammonia Street Canyon Microenvironments

Gaseous nitrous acid (HONO) is a key precursor of atmospheric hydroxyl radicals (OH), yet its sources remain incompletely understood. While ammonia (NH3) is known to promote the heterogeneous reaction of NO2 forming HONO, its specific role in real-world high-NH3 environments remains unclear. This study conducted comprehensive observations at a street canyon site and an urban site. Results revealed significantly elevated concentrations of both HONO (2.0 times) and NH3 (1.7 times) within the street canyon compared to the urban site. Direct vehicle emissions contributed only ∼27.5% to the observed HONO, corresponding to an average ΔHONO/ΔNOx emission factor of 1.0%. A substantial unknown source (Punknown) contributed 1.9 ppbv h–1 to HONO formation in the street canyon. NH3 was identified as the primary driving factor of Punknown through machine learning. High NH3 enhances aerosol pH, thereby promoting the redox reaction between SO2 and NO2 to generate HONO, however, the contribution from this NH3-mediated reaction pathway was small (0.0002 ppbv h–1). Incorporating ambient NH3 concentrations to refine the NO2 uptake coefficient within an updated analytical framework proved critical, this refinement substantially reduced the estimated Punknown to 1.3 ppbv h–1 and demoted NH3 to a tertiary driver status. Crucially, this outcome validates the significant role of NH3 in facilitating heterogeneous NO2 reactions that drive Punknown within urban high-ammonia microenvironments.