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Airborne particulate matter accumulation on common green wall plants.
Paull, Naomi J; Krix, Daniel; Irga, Peter J; Torpy, Fraser R.
Afiliação
  • Paull NJ; Plants and Environmental Quality Research Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, Australia.
  • Krix D; Plants and Environmental Quality Research Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, Australia.
  • Irga PJ; School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
  • Torpy FR; Plants and Environmental Quality Research Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, Australia.
Int J Phytoremediation ; 22(6): 594-606, 2020.
Article em En | MEDLINE | ID: mdl-31814450
ABSTRACT
In order to better design greening systems for effective particulate matter (PM) removal, it is important to understand the impact leaf traits have on PM deposition. There are however, inconsistences amongst the leaf traits that have previously been correlated with PM accumulation. The aim of this paper was to identify vegetation characteristics of green wall plants that were associated with the accumulation of particulate matter. To determine patterns associated with different leaf morphologies, eleven common ornamental plant species were sampled across 15 sites, over a 6 month duration. PM deposition was determined gravimetrically and its associated size fractions determined microscopically. Linear mixed models were used to identify statistical patterns relating to differences in PM deposition across plant species. PM deposition and the relative frequencies of particle size fractions were found to be statistically different among species, sites and months. Green wall plants were shown to be effective at PM accumulation as all of the assessed plant species had equivalent PM removal efficiency, with minimal evidence of influential leaf characteristics that could enhance PM removal.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article