The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?

Abstract

2019) The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?, Tellus B: Chemical and Physical Meteorology, 71:1, 1550324, ABSTRACT Dry deposition is an important loss process for atmospheric particles and can be a significant part of total deposition estimates calculated for critical loads analyses. However, algorithms used in large-scale air quality and atmospheric chemistry models to predict particle deposition velocity as a function of particle size are highly uncertain. Many of these algorithms, although derived from a common heritage, predict vastly different particle deposition velocities for a given particle diameter even under identical environmental conditions for major land use classes. Even more problematic, for vegetated landscapes (forests, in particular) the algorithms do not agree very well with available measurements. In this work, we perform a sensitivity study to estimate how significant the uncertainties in particle deposition algorithms may be in an air quality model's predictions of ground-level fine particle concentrations, particle deposition and overall total deposition of nitrogen and sulfur. Our results suggest that fine particle concentration predictions at the surface may vary by 5-15% depending on the choice of particle deposition velocity algorithm, while particle dry deposition is affected to a much greater extent with differences among algorithms >200%. Moreover, if accumulation mode particle dry deposition measurements over forests are correct, then dry particle deposition and total elemental deposition to these landscapes may be much larger than is typically simulated by current air quality and atmospheric chemistry models, calling into question commonly available estimates of total deposition and their use in critical loads analyses. Since accurate predictions of atmospheric particle concentrations and deposition are critically important for future air quality, weather and climate models and management of pollutant deposition to sensitive ecosystems, an investment in new dry deposition measurements in conjunction with integrated modelling efforts seems not only justified but vitally necessary to advance and improve the treatment of particle dry deposition processes in atmospheric models.

Revisiting particle dry deposition and its role in radiative effect estimates

“…The Zhang parameterization fails to represent recent observational evidence, including our own observations. Newer parameterizations ( 18 – 22 ) are more consistent with these observations, but GEOS-Chem and other chemical transport models still typically implement Zhang et al’s parameterization ( 12 ). The newer parameterizations vary from completely empirical ( 21 ) to incorporating additional, unknown loss processes ( 22 )––but are rarely incorporated into global models, likely due to the complexity of updated parameterizations.…”

Deposition and Resuspension Mechanisms Into and From Tree Canopies: A Study Modeling Particle Removal of Conifers and Broadleaves in Different Cities

“…If not coupled to a separate vegetation model, such air chemistry models simulate deposition velocity by estimating a number of resistances that do not depend on tree species properties (Khan and Perlinger, 2017). Thus, the uncertainty related to these simplified assumptions is very high, which has recently been criticized (Saylor et al, 2019). The results presented here may help to mend this problem by providing at least type specific dependencies as has been suggested by Hicks et al (2016).…”

Comparison of Methods for the Estimation of Total Inorganic Nitrogen Deposition to Forests in Germany

“…Considering these challenges to correctly estimate TIN WD, the average deviation of −0.8 kg N ha −1 a −1 compared to wet-onlycorrected bulk deposition measurements for 1,237 plot-years in Germany is remarkably low. For dry deposition estimation Saylor et al (2019) pointed out that the algorithms used in atmospheric chemistry models to predict particle deposition velocity are highly uncertain. In particular, estimates for forests show a weak agreement with available measurements (Saylor et al, 2019).…”

“…The accurate quantification of DD fluxes to forests is still challenging due to a large variety of N species, their chemical reactivity, a high uncertainty in the estimation of deposition velocities (Saylor et al, 2019) and different deposition pathways including bi-directional fluxes (Wichink Kruit et al, 2012). Currently, micrometeorological methods (e.g., eddy covariance and gradient techniques) are regarded as the most accurate approaches to quantify DD and OD (Marques et al, 2001;Mohr et al, 2005;Schmitt et al, 2005;Brümmer et al, 2020).…”

“…In Germany, the EBM approach integrates emission inventories and a large number of local measurements of wet or bulk deposition to TIN deposition estimates with complete spatial coverage. Major challenges lie in the accuracy and spatiotemporal resolution of emission data and the parametrization of receptor-specific deposition processes with respect to DD and OD (Saylor et al, 2019).…”