Relationships between air particulate matter capture efficiency and leaf traits in twelve tree species from an Italian urban-industrial environment
Author(s)
Date Issued
2020
Type
article
Volume
718
ISSN
0048-9697
Journal
Abstract
Air pollution in the urban environment is widely recognized as one of the most harmful threats for human health. International organizations such as the United Nations and the European Commission are highlighting the potential role of nature in mitigating air pollution and are now funding the implementation of Nature-Based Solutions, especially at the city level. Over the past few decades, the attention of the scientific community has grown around the role of urban forest in air pollution mitigation. Nevertheless, the understanding on Particulate Matter (PM) retention mechanisms by tree leaves is still limited. In this study, twelve tree species were sampled within an urban park of an industrial city. Two techniques were used for leaf analysis: Vacuum/Filtration and Scanning Electron Microscopy coupled with Energy Dispersive X-ray spectroscopy, in order to obtain a quali-quantitative analysis of the different PM size fractions. Results showed that deposited PM loads vary significantly among species. Different leaf traits, including micro and macromorphological characteristics, were observed, measured and ranked, with the final aim to relate them with PM load. Even if no significant correlation between each single leaf characteristic and PM deposition was observed (p > 0.05), multivariate analysis revealed relationships between clusters of leaf traits and deposited PM. Thus, by assigning a score to each trait, an Accumulation index (Ai) was calculated, which was significantly related to the leaf deposited PM load (p ≤ 0.05).
File(s)![Thumbnail Image]()
![Thumbnail Image]()
Name
SgrignaSTOTEN2020_PostPrint.pdf
Description
Versione accettata
Size
6.3 MB
Format
Adobe PDF
Checksum (MD5)
ca676dcaf8fd0814d589fac8cbe35844
Name
SgrignaSTOTEN2020.pdf
Description
Versione pubblicata
Size
2.67 MB
Format
Adobe PDF
Checksum (MD5)
c63ca4f701eb6ca7587aee998ed3d541