Technical Note: An empirical algorithm estimating dry deposition velocity of fine, coarse and giant particles

Abstract. An empirical algorithm is developed for calculating bulk dry deposition velocity (V_d) of fine (PM_2.5) – particles having a diameter of ≤ 2.5 μm), coarse (PM_2.5−10 – particles having a diameter of 2.5–10 μm), and giant (PM₁₀₊ – particles having a diameter of > 10 μm) atmospheric particles. The algorithm is developed from an empirical fit of V_d data calculated using the size-resolved V_d scheme of Zhang et al. (2001) with assumed lognormal size distributions of PM_2.5, PM_2.5−10 and PM₁₀₊. In the new algorithm, the surface deposition velocity (V_ds) is parameterized as a simple linear function of friction velocity (u_*) for PM_2.5 and as a polynomial function of u_* for both PM_2.5−10 and PM₁₀₊ over all the 26 land use categories (LUCs). An adjustment factor as an exponential function of u_* and leaf area index (LAI) is also applied to V_ds of PM_2.5−10 and PM₁₀₊ over 9 of the 26 LUCs that have variable LAI. Constant gravitational settling velocities are provided for PM_2.5, PM_2.5−10 and PM₁₀₊. Aerodynamic resistance between a reference height and the surface can be calculated using available analytical formulas from the literature. The bulk V_d of PM_2.5, PM_2.5−10 and PM₁₀₊ at the reference height can then be calculated by combining the gravitational settling velocity, aerodynamic resistance and the parameterized V_ds. V_d values calculated using the new algorithm are within ±20% of those using the original size-resolved scheme for fine, coarse and giant particles. Uncertainties in V_d values from the new algorithm due to the pre-assumed size distributions are on the order of 20% for fine particles and on the order of a factor of 2.0 for coarse and giant particles. The new algorithm provides an alternative approach for calculating V_d of bulk aerosol particles. V_d of any particulate species can be simply estimated using this scheme as long as the mass fractions in fine, coarse and giant particles are known or can be assumed.