61
the same average values applied by McPhearson et al. (2013) to the city of New York.
The same approach was used to assess air pollution removal, focusing on PM10
deposition on grass and woody vegetation (McPhearson et  al. 2013). Based on
Derkzen et  al. (2015), values for air pollution removal where doubled for green
areas located within a 50-m buffer from streets to account for the higher concentration of PM10 that increases the deposition flux. Cooling was assessed following the
approach described in Chap. 4. Finally, noise reduction was assessed by adopting
the values proposed by Derkzen et al. (2015). For each ES, the values were then
converted into dimensionless scores ranging from 0 to 10, where 10 corresponds to
the value of the best-performing land cover type.
Operationally, the first step to map the supply of ES consisted of the visual
inspection of an aerial image to identify the main land cover types in the four
sample areas, followed by screen digitising in a GIS. The land-cover information
was then combined with the standardized values from Table 6.2 to obtain supply
maps for each ES. To get an overall indicator of ES supply in each sample area, the
Fig. 6.1 The four selected sample areas in the city of Trento
Table 6.2 Average values for ES supply for different land cover types, in dimensional and
dimensionless form
Carbon storage
Air pollution
removal
Micro-climate
regulation
Noise reduction
Built-up and
sealed
–
0
–
0 –
0
–
0
Bare soil
8.2 kg/m
2
5.3 –
0 1.2 °C
3.3 –
0
Grass and
shrubs
8.4 Kg/m
2
5.4 1.12 g/m
2 /year 4 1.2 °C
3.3 0.375
Db(A)/100m
2
1.8
Trees and
woodland
15.5 kg/m
2
10 2.73 g/m
2 /year 10 3.6 °C
10
2 Db(A)/100m
2
10
6.3 Application to a Study Area in the City of Trento