Trees
and urban forests remove particulate matter (PM) from the
air through the deposition of particles on the leaf surface, thus
helping to improve air quality and reduce respiratory problems in
urban areas. Leaf deposited PM, in turn, is either resuspended back
into the atmosphere, washed off during rain events or transported
to the ground with litterfall. The net amount of PM removed depends
on crown and leaf characteristics, air pollution concentration, and
weather conditions, such as wind speed and precipitation. Many existing
deposition models, such as i-Tree Eco, calculate
PM2.5 removal using a uniform deposition velocity function
and resuspension rate for all tree species, which vary based on leaf
area and wind speed. However, model results are seldom validated with
experimental data. In this study, we compared i-Tree Eco calculations of PM2.5 deposition with fluxes determined
by eddy covariance assessments (canopy scale) and particulate matter
accumulated on leaves derived from measurements of vacuum/filtration
technique as well as scanning electron microscopy combined with energy-dispersive
X-ray spectroscopy (leaf scale). These investigations were carried
out at the Capodimonte Royal Forest in Naples. Modeled and measured
fluxes showed good overall agreement, demonstrating that net deposition
mostly happened in the first part of the day when atmospheric PM concentration
is higher, followed by high resuspension rates in the second part
of the day, corresponding with increased wind speeds. The sensitivity
analysis of the model parameters showed that a better representation
of PM deposition fluxes could be achieved with adjusted deposition
velocities. It is also likely that the standard assumption of a complete
removal of particulate matter, after precipitation events that exceed
the water storage capacity of the canopy (Ps), should be reconsidered
to better account for specific leaf traits. These results represent
the first validation of i-Tree Eco PM removal with
experimental data and are a starting point for improving the model parametrization and the estimate
of particulate matter removed by urban trees.