| 23 Jan 2026
The Changing Sensitivity of Wintertime Particulate Nitrate to Precursor Emissions Diagnosed via Satellite Observations of Ammonia and Nitrogen Dioxide over the Midwestern United States
Abstract. Particulate nitrate (PN) is a critical component of fine particulate matter (PM2.5). During wintertime, the contribution of PN to PM2.5 over the Midwestern United States (MWUS), an agriculturally intensive region, has increased over the past decade and now contributes up to 40 % of the particle mass. PN formation is controlled by nitrogen oxides (NOx=NO + NO2), ammonia (NH3), and volatile organic compounds (VOCs). To best control wintertime PM2.5 burden, it is critical to determine PN formation sensitivity to precursor gases, but this is not well constrained. Prior efforts to diagnose PN sensitivity have been limited on both spatial and temporal scales. Satellite tropospheric column NH3/NO2 ratios cover large areas and long timeframes, and they have been shown to be effective in diagnosing PN sensitivity over East Asia, Europe, and the Eastern United States. Here, we expand this approach to quantify spatially and temporally resolved multidecadal wintertime PN formation sensitivity to NH3, NOx, and VOCs in the MWUS from 2007 to 2023 via satellite observations and GEOS-Chem sensitivity simulations. More than half of the total diagnosed pixels are classified as NOx-sensitive in 2007, and this increases to 89.0 % by 2023. VOCs do not control MWUS PN formation. The shift in PN formation sensitivity is explained by relatively flat trends in satellite NO2 column densities (0.48 ± 0.60 % yr-1) in combination with increases in satellite NH3 column densities (1.3 ± 0.3 % yr-1). Our work indicates that targeting NOx emissions is chemically effective for reducing wintertime PN and PM2.5 burden.
This manuscript investigates the interannual sensitivity of particulate nitrate formation to nitrogen oxide and ammonia emissions during wintertime over the Midwestern US during the period 2007-2023.
To do so, the authors utilize the chemistry transport model GEOS-Chem for simulating particulate nitrate formation in response to a fixed decrease of precursor emissions and deduce PN sensitivities. Satellite observations of NH3 and NO2 columns were also exploited to quantitatively define regime cutoffs. The investigation is also supported by ground-based measurements of gas concentrations, wet deposition and particle speciation.
Overall, I find that the manuscript highlights important findings regarding potential emission controls that can be implemented by policy makers in the agricultural intensive area of MWUS to efficiently mitigate winter PM pollution episodes. While the results are well communicated, I have major comments specifically in respect to the methodology.
I understand that the approach is similar to the one described in Dang et al., 2023. However, I am missing a basic explanation of how the species-sensitivities (from GEOS-Chem) are exploited to derive the regime cutoffs and if they are totally independent of the satellite observations. I am wondering if a schematic could help to clarify. In addition, I would suggest that the title also includes GEOS-chem or a reference to the simulated sensitivities.
In Fig. 3 I think the captions need to be revised and more precise regarding which data we are looking at (i.e., satellite columns/ simulated surface concentrations?).
I am not sure of the structure of the methodology and results, section 2.4 from Methods is called the same as 3.1 from results. Wouldn't it be more logical to also have the regime cutoffs definitions in the methodology?
While in Dang et al., 2023 we can clearly distinguish the 3 dominant regimes of PN sensitivity to precursors from the RMA, it is not obvious in the present work (Fig.3 a)) since NH3-sensitive and NOx-sensitive grid-cells overlap across the regression line. Any comments on that? Is it then really the best approach to use these equations for defining the regimes?
More specific comments: