4.3 Article

Predicting the dry deposition of aerosol-sized particles using layer-resolved canopy and pipe flow analogy models: Role of turbophoresis

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2009JD012853

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  1. University of Helsinki
  2. National Science Foundation [NSF-EAR 0628342, NSF-EAR 0635787, NSF-ATM-0724088]
  3. Binational Agricultural Research and Development (BARD) [IS-3861-06]

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A number of synthesis activities, mathematical modeling, and experiments on dry deposition of aerosol-sized particles over forested surfaces point to three disjointed findings: (1) deposition velocities measured over tall forests do not support a clearly defined minimum for particle sizes in the range of 0.1-2 mu m; (2) when measurements of the normalized deposition velocity (V-d(+)) are presented as a function of the normalized particle timescale (tau(+)(p)), where the normalizing variables are the friction velocity and air viscosity, a power law scaling in the form of V-d(+) similar to (tau(+)(p))(2) emerges in the so-called inertial-impaction regime for many laboratory and crop experiments, but none of the forest measurements fall on this apparent scaling law; and (3) two recent models with entirely different assumptions about the representation of the particle deposition process reproduce common data sets for forests. We show that turbophoresis, when accounted for at the leaf scale in vertically resolved or multilayer models (MLMs), provides a coherent explanation for the first two findings and sheds light on the third. The MLM resolves the canopy vertical structure and its effects on both the flow statistics and the leaf particle collection mechanisms. The proposed MLM predictions agree with a recent two-level particle-resolving data set collected over 1 year duration for a Scots pine stand in Hyytiala (southern Finland). Such an approach can readily proportion the particle deposition onto foliage and forest floor and can take advantage of recent advances in measurements of canopy structural properties derived from remote sensing platforms.

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