The importance of ammonia for springtime atmospheric new particle formation and aerosol number abundance over the United States

New particle formation (NPF) and subsequent growth can contribute upwards of 50 % of the global cloud condensation nuclei (CCN) budget. It is also a significant source of ultrafine aerosols (PM_0.1) with health implications. Ammonia (NH₃) can play a significant role in enhancing NPF and contributing to the growth of nucleated particles. Understanding these processes are vital for air quality and climate. Here, we examine the role of NH₃ in NPF and consequent effects on aerosol number concentrations (including CCN) and size distributions during springtime over the United States (US). We use the GEOS-Chem chemistry transport model coupled with the size-resolved Advanced Particle Microphysics (APM) Model. We also employ measurements of particle number size distributions, CN10 (condensation nuclei > 10 nm), CCN0.4 (CCN at 0.4 % supersaturation), and aerosol composition (SO₄, NO₃, NH₄, Organics) at the Southern Great Plains site (SGP). The impact of NH₃ in ion-mediated nucleation is the improved capturing of the occurrence of almost all springtime (March-April) NPF events observed at SGP during 2015-2020. Furthermore, this brings the magnitude and temporal variations of particle number concentrations in stronger agreement with observations; mean fractional bias for modeled CN10(CCN0.4) reducing from -1.26 to -0.27 (-0.75 to -0.54) and overall good-agreement (∣FractionalBias ∣ < 0.6) improving from 8.5 to 54 % (31 to 42 %). The contribution of NH₃ in new particle formation is important for springtime abundance of ultrafine aerosols (explaining 63 ± 15 % of CN10) and CCN (16 ± 10 % of CCN0.4) over the US. Our analysis shows that the deviation of CCN0.4 is strongly correlated with PM₁-NH₄⁺ deviations, suggesting the importance of improved model representation of ammonium for more accurate quantification of potential cloud forming particles.

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study

We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM2.5 mass and sulfate (SO4) concentrations in both the eastern and western United States, and nitrate (NO3) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM2.5 species. This method captures the overall shapes of the size distributions of PM2.5 mass and SO4 particles in the east and west, and NO3 particles in the west. However, the estimated PM2.5 and SO4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.