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Species-specific efficiency in PM2.5 removal by urban trees: From leaf measurements to improved modeling estimates

Gaglio, Mattias; Pace, Rocco ORCID iD icon 1; Muresan, Alexandra Nicoleta; Grote, Rüdiger ORCID iD icon 1; Castaldelli, Giuseppe; Calfapietra, Carlo; Fano, Elisa Anna
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

The growing population in cities is causing a deterioration of air quality due to the emission of pollutants, causing serious health impacts. Trees and urban forests can contribute through the interception and removal of air pollutants such as particulate matter (PM). The dry deposition of PM by vegetation depends on air pollutant concentration, meteorological conditions, and specific leaf traits. Several studies explored the ability of different plant species to accumulate PM on leaf structures leading to the development of models to quantify the PM removal. The i-Tree Eco is the most used model to evaluate ecosystem services provided by urban trees. However, fine particulate matter (PM2.5) removal is still calculated with a poorly evaluated function of deposition velocity (which depends on wind speed and leaf area) without differentiating between tree species. Therefore, we present an improvement of the standard model calculation introducing a leaf trait index to distinguish the species effect on PM net removal. We also compared model results with measurements of deposited leaf PM by vacuum filtration. The index includes the effect of morphological and functional leaf characteristics of tree species using four parameters: leaf water storage, deposition velocity, resuspension rate and leaf washing capacity. ... mehr


Originalveröffentlichung
DOI: 10.1016/j.scitotenv.2022.157131
Scopus
Zitationen: 23
Web of Science
Zitationen: 21
Dimensions
Zitationen: 23
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2022
Sprache Englisch
Identifikator ISSN: 0048-9697
KITopen-ID: 1000148545
HGF-Programm 12.11.22 (POF IV, LK 01) Managed ecosystems as sources and sinks of GHGs
Weitere HGF-Programme 12.11.24 (POF IV, LK 01) Adaptation of natural landscapes to climate change
Erschienen in Science of The Total Environment
Verlag Elsevier
Band 844
Seiten Artkl.Nr.: 157131
Projektinformation GrüneLunge 2.0 (BMBF, 01LR2015A)
Vorab online veröffentlicht am 05.07.2022
Schlagwörter particulate matter; leaf traits; vacuum-filtration; i-Tree Eco; model parameters; nature-based solutions
Nachgewiesen in Dimensions
Scopus
Web of Science
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