Species-Specific Contribution to Atmospheric Carbon and Pollutant Removal: Case Studies in Two Italian Municipalities

Abstract

In order to maximize ecosystem services (ES), a proper planning of urban green areas is needed. In this study, the urban greenery of two Italian cities (Milan and Bologna) exposed to high levels of atmospheric pollutants was examined. Vegetation maps were developed through a supervised classification algorithm, trained over remote sensing images, integrated by local trees inventory, and used as input for the AIRTREE multi-layer canopy model. In both cities, a large presence of deciduous broadleaves was found, which showed a higher capacity to sequestrate CO2 (3,953,280 g m2 y−1), O3 (5677.76 g m2 y−1), and NO2 (2358.30 g m2 y−1) when compared to evergreen needle leaves that, on the other hand, showed higher performances in particulate matter removal (14,711.29 g m2 y−1 and 1964.91 g m2 y−1 for PM10 and PM2,5, respectively). We identified tree species with the highest carbon uptake capacity with values up to 1025.47 g CO2 m2 y−1 for Celtis australis, Platanus x acerifolia, Ulmus pumila, and Quercus rubra. In light of forthcoming and unprecedent policy measures to plant millions of trees in the urban areas, our study highlights the importance of developing an integrated approach that combines modelling and satellite data to link air quality and the functionality of green plants as key elements in improving the delivery of ES in cities.

Keywords

• AIRTREE model
• ecosystem services
• urban forest
• street trees
• remote sensing