Title
Multi layered modeling of particulate matter removal by a growing forest over time, from plant surface deposition to washoff via rainfall Multi layered modeling of particulate matter removal by a growing forest over time, from plant surface deposition to washoff via rainfall
Author
Faculty/Department
Faculty of Sciences. Biology
Publication type
article
Publication
Easton, Pa ,
Subject
Chemistry
Biology
Source (journal)
Environmental science and technology / American Chemical Society. - Easton, Pa
Volume/pages
48(2014) :18 , p. 10785-10794
ISSN
0013-936X
ISI
000341801500032
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Airborne fine particulate matter (PM) is responsible for the most severe health effects induced by air pollution in Europe. Vegetation, and forests in particular, can play a role in mitigating this pollution since they have a large surface area to filter PM out of the air. Many studies have solely focused on dry deposition of PM onto the tree surface, but deposited PM can be resuspended to the air or may be washed off by precipitation dripping from the plants to the soil. It is only the latter process that represents a net-removal from the atmosphere. To quantify this removal all these processes should be accounted for, which is the case in our modeling framework. Practically, a multilayered PM removal model for forest canopies is developed. In addition, the framework has been integrated into an existing forest growth model in order to account for changes in PM removal efficiency during forest growth. A case study was performed on a Scots pine stand in Belgium (Europe), resulting for 2010 in a dry deposition of 31 kg PM2.5 (PM < 2.5 ?m) ha(-1) yr(-1) from which 76% was resuspended and 24% washed off. For different future emission reduction scenarios from 2010 to 2030, with altering PM2.5 air concentration, the avoided health costs due to PM2.5 removal was estimated to range from 915 to 1075 euro ha(-1) yr(-1). The presented model could even be used to predict nutrient input via particulate matter though further research is needed to improve and better validate the model.
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