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 (Vd) of fine (PM2.5)-particles having a diameter of ≤ 2.5 Î1/4m), coarse (PM2.5−10-particles having a diameter of 2.5-10 Î1/4m), and giant (PM10+-particles having a diameter of > 10 Î1/4m) atmospheric particles. The algorithm is developed from an empirical fit of Vd data calculated using the size-resolved Vd scheme of Zhang et al. (2001) with assumed lognormal size distributions of PM2.5, PM2.5−10 and PM10+. In the new algorithm, the surface deposition velocity (Vds) is parameterized as a simple linear function of friction velocity (u*) for PM2.5 and as a polynomial function of u* for both PM2.5−10 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 Vds of PM2.5−10 and PM10+ over 9 of the 26 LUCs that have variable LAI. Constant gravitational settling velocities are provided for PM2.5, PM2.5−10 and PM10+. Aerodynamic resistance between a reference height and the surface can be calculated using available analytical formulas from the literature. The bulk Vd of PM2.5, PM2.5−10 and PM10+ at the reference height can then be calculated by combining the gravitational settling velocity, aerodynamic resistance and the parameterized Vds. Vd 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 Vd 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 Vd of bulk aerosol particles. Vd 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. © 2014 Author(s).