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

TLDR

Dry deposition is a key loss process for atmospheric particles, yet algorithms predicting particle deposition velocity vary widely and poorly match measurements, especially over forests. The study estimates how algorithmic uncertainties in particle deposition affect air‑quality model predictions of surface fine‑particle concentrations, deposition, and total nitrogen and sulfur deposition. We conduct a sensitivity analysis in an air‑quality model, varying particle deposition velocity algorithms to assess their impact on surface fine‑particle concentrations and deposition. The sensitivity study shows surface fine‑particle concentrations vary 5–15% across algorithms, but dry deposition differs by over 200%, and forest measurements imply that current models likely underestimate total deposition, challenging critical‑loads estimates.

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.