A branch scale analytical model for predicting the vegetation collection efficiency of ultrafine particles

The removal of ultrafine particles (UFP) by vegetation is now receiving significant attention given their role in cloud physics, human health and respiratory related diseases. Vegetation is known to be a sink for UFP, prompting interest in their collection efficiency. A number of models have tackled the UFP collection efficiency of an isolated leaf or a flat surface; however, up-scaling these theories to the ecosystem level has resisted complete theoretical treatment. To progress on a narrower scope of this problem, simultaneous experimental and theoretical investigations are carried out at the 'intermediate' branch scale. Such a scale retains the large number of leaves and their interaction with the flow without the heterogeneities and added geometric complexities encountered within ecosystems. The experiments focused on the collection efficiencies of UFP in the size range 12.6-102 nm for pine and juniper branches in a wind tunnel facility. Scanning mobility particle sizers were used to measure the concentration of each diameter class of UFP upstream and downstream of the vegetation branches thereby allowing the determination of the UFP vegetation collection efficiencies. The UFP vegetation collection efficiency was measured at different wind speeds (0.3-1.5 m s(-1)), packing density (i.e. volume fraction of leaf or needle fibers; 0.017 and 0.040 for pine and 0.037, 0.055 for juniper), and branch orientations. These measurements were then used to investigate the performance of a proposed analytical model that predicts the branch-scale collection efficiency using conventional canopy properties such as the drag coefficient and leaf area density. Despite the numerous simplifications employed, the proposed analytical model agreed with the wind tunnel measurements mostly to within 20%. This analytical tractability can benefit future air quality and climate models incorporating UFP. (C) 2012 Elsevier Ltd. All rights reserved