## 2 Materials and Methods
### i-Tree Canopy  
The land cover assessment of the MCN area was performed using i-Tree Canopy tool v.7, using a classification method based on automatically retrieved aerial imagery. The tool is part of the i-Tree Tools software suite that provides urban and rural forestry analysis and benefits assessment tools. The i-Tree Canopy tool estimates tree coverage, as  
Figure 1: The study area of the Metropolitan City of Naples. The red borders highlight the 91 municipalities of the former Province of Naples (in red) and the City of Naples (in green). well as other user-decided cover classes, by generating random points within a specified area to be manually classified [41]. In addition, it allows the estimation of the coverage area by providing the most recent aerial Google Maps/Google Earth imagery data (with a resolution of about 0.3 m) in order to perform a statistical analysis of the classification of randomly generated points [48]. The i-Tree Canopy analysis is performed following three steps:  
1. Drawing boundaries or importing a file with the boundaries of the investigated area. Boundaries for the MCN were retrieved from a dedicated repository provided by the administration ([[http://sit.citametropolitan.na.it/](http://sit.citametropolitan.na.it/)]([http://sit.citametropolitan.na.it/](http://sit.citametropolitan.na.it/)), accessed on 27 August 2019);
2. Naming the cover classes to be assessed. In this work, the cover classes are defined encompassing all the different land cover types present in the studied area (Table 1). 3. Classifying points into the defined cover classes. In Table 1, the current tree coverage is represented by TI and TP, and the potential tree coverage is represented by IP (e.g., sidewalks, parking lots, or plazas) and PP (e.g., bare earth). The accuracy of the final assessment will depend upon the ability of the analyst to correctly classify each randomly generated point into one of the chosen cover classes. The precision of the estimate increases with the increase in assessed points, as the standard error (SE) will decrease. Being \"\(n\)\" the number of points assessed in each class and \"\(N\)\" the total number of assessed points, \(SE\) for each class is calculated as in (1):  
\[SE=\sqrt{\left(\frac{pq}{N}\right)}, \tag{1}\]  
where \(p=n/N\) and \(q=1-p\). If \(n<10\), the \(SE\) is calculated as (2):  
\[SE=\left(\sqrt{n}\right)/N, \tag{2}\]  
[PERSON] et al. (2017) [48] state that 500 random points are adequate to survey megacities. In this study, to produce more accurate results, 803 points were assessed and classified according to the user-defined cover classes reported in Table 1.  
The benefits investigated with the i-Tree Canopy tool are based on the percentage of tree coverage and climatic conditions. Table 2 reports the conversion factors for uptaken mass of different polluting flows and for volume of avoided runoff per unit area of trees per year, and the related economic values. The removal rates and monetary values of the considered environmental functions are derived from analyses conducted in the United States using i-Tree Eco, a component tool of the i-Tree software suite, within urban and rural areas and then aggregated at the national level [56]; data are then provided as specific sets related to chosen locations, within the report of tool results. Monetary values for pollutant removal are estimated as the incidence of adverse health effects resulting from changes