A New Aerosol Dry Deposition Model for Air Quality and Climate Modeling

Abstract

Dry deposition of aerosols from the atmosphere is an important but poorly understood and inadequately modeled process in atmospheric systems for climate and air quality. Comparisons of currently used aerosol dry deposition models to a compendia of published field measurement studies in various landscapes show very poor agreement over a wide range of particle sizes. In this study, we develop and test a new aerosol dry deposition model that is a modification of the current model in the Community Multiscale Air Quality (CMAQ) model. The new model agrees much better with measured dry deposition velocities across particle sizes. The key innovation is the addition of a second inertial impaction term for microscale obstacles such as leaf hairs, microscale ridges, and needleleaf edge effects. The most significant effect of the new model is to increase the mass dry deposition of the accumulation mode aerosols in CMAQ. Accumulation mode mass dry deposition velocities increase by almost an order of magnitude in forested areas with lesser increases for shorter vegetation. Peak PM2.5 concentrations are reduced in some forested areas by up to 40% in CMAQ simulations. Over the continuous United States, the new model reduced PM2.5 by an average of 16% for July 2018 at the Air Quality System monitoring sites. For summer 2018 simulations, bias and error of PM2.5 concentrations are significantly reduced, especially in forested areas.

Influence of More Mechanistic Representation of Particle Dry Deposition on 1850–2000 Changes in Global Aerosol Burdens and Radiative Forcing

“…(2020) and Pleim et al. (2022), but also impacts changes in aerosol loads between two periods (in our case, 1850–2000). This may be due to the fact that there are 1850–2000 changes in deposition velocities in both old and new DD and they differ between old and new DD (Figure S2 in Supporting Information ), and/or nonlinear responses of mass concentrations to changes in deposition velocities.…”

“…Figure 1 indeed shows that new DD largely captures the observed relationship between particle deposition velocities and particle size across forests, grassland, land ice/snow, as well as open water, in contrast to old DD. In particular, like Emerson et al (2020) and Pleim et al (2022), our new scheme better captures observed minima in the relationship. The new scheme's skill is more apparent in MATRIX (Figure 1) than OMA (Figure S1 in Supporting Information S1), which does not represent Aitken mode particles.…”

“…For example, there are ways of capturing bulk observed variations in deposition velocities with particle size other than the aerosol dry deposition scheme proposed and used here. More specifically, while in some cases our scheme has similar equations as the schemes by Emerson et al (2020) and Pleim et al (2022), in other cases the equations differ. We thus amplify the need for more aerosol flux data using state-of-the-art techniques and complementary measurements and modeling studies targeting specific pathways (e.g., Farmer et al, 2021;Hicks et al, 2016), which can inform model intercomparison studies.…”

“…Further, model intercomparison studies highlight the need to perform analyses investigating different dry deposition schemes (Galmarini et al., 2021), especially how particle size distributions play a role in differences (Textor et al., 2006). In addition, recent research suggests that uncertainty in dry deposition contributes to uncertainty in aerosols' impacts on climate (Carslaw et al., 2013, 2017; Emerson et al., 2020; Fanourgakis et al., 2019; Y. Feng et al., 2022; Johnson et al., 2020; Lee et al., 2013; Wu et al., 2018; Zeng et al., 2020), especially related to cloud condensation nuclei and the cloud albedo effect, as well as aerosol optical depth (Palacios‐Peña et al., 2020) and air pollution (Itahashi, 2018; Pleim et al., 2022; Saylor et al., 2019; Shu et al., 2022). For example, perturbed parameter ensembles find dry deposition of accumulation mode particles to be a key uncertainty in aerosol radiative forcing (Carslaw et al., 2013; Johnson et al., 2020).…”

Anthropogenic secondary organic aerosol and ozone production from asphalt-related emissions

“…Meteorological and non-asphalt emission inputs, including wildland res, are retrieved from the U.S. EPA's Air QUAlity TimE Series (EQUATES) project 25 and all simulations feature a 10 days spin-up beginning in December 2017 to minimize the inuence of initial conditions. Additional modeling options used here include: (1) an updated parameterization of aerosol deposition in the M3DRY model, 26 including bidirectional ammonia exchange, 27 (2) biogenic emissions generated inline using the Biogenic Emission Inventory System (BEIS) v4, 28 (3) runtime emissions processing using the Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, 29 (4) and gas-and aerosol-phase chemistry simulated using the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0 chemical mechanism. 16 The impact of asphalt-related emissions on SOA and ozone is quantied by taking the difference between two photochemical modeling simulations.…”