Abstract:The Urban Heat Island (UHI) phenomenon, namely urban areas where the atmospheric temperature is significantly higher than in the surrounding rural areas, is currently a very well-known topic both in the scientific community and in public debates. Growing urbanization is one of the anthropic causes of UHI. The UHI phenomenon has a negative impact on the life quality of the local population (thermal discomfort, summer thermal shock, etc.), thus investigations and analyses on this topic are really useful and important for correct and sustainable urban planning; this study is included in this context. A multi-temporal analysis was performed in the municipality of Modena (Italy) to identify and estimate the Surface Urban Heat Island (SUHI, strictly correlated to the UHI phenomenon) from 2014 to 2017. For this purpose, Landsat-8 satellite images were processed with Quantum Geographic Information System (QGIS) to obtain the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). For every pixel, LST and NDVI values of three regions of interest (ROI, i.e., Countryside, Suburbs, and City Center) were extracted and their correlations were investigated. A maximum variation of 6.4 • C in the LST values between City Center and Countryside was highlighted, confirming the presence of the SUHI phenomenon even in a medium-sized municipality like Modena. The implemented procedure demonstrates that satellite data are suitable for SUHI identification and estimation, therefore it could be a useful tool for public administration for urban planning policies.Keywords: urban heat island; land surface temperature; remote sensing; Landsat-8; semi-automatic classification plugin; QGIS; global warming; urbanization BackgroundAbout half of the world population lives in urban areas [1]. The global urbanization rate is expected to increase by 70% compared to the current world population [2], both because of the continued emergence of new urban areas [3] and because of the constant population migration from rural to urban and suburban areas [4,5]. It is not therefore surprising that the negative impacts of urbanization are an ever-growing global concern [6-10]. Urbanization has a negative impact on the environment, mainly due to pollution, changes in the physical and chemical properties of the atmosphere, and in the type of cover of the soil surface [11]. These phenomena lead to so-called Urban Heat Islands (UHI), namely urban areas where the atmospheric temperature is significantly higher than that in the surrounding rural areas [12]. The presence of UHIs is an increasing phenomenon studied by the international scientific community because of its dangerous and significant effects. In fact, the temperature increase has effects on the environment (higher temperatures cause higher energy consumption, photochemical smog, and worsening of the air quality), on the climate and on human health [13][14][15][16].

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system.

In order to assess the impact of traffic on local air quality a microscale simulation of pollutant concentration fields was produced for two busy intersections, in Reggio Emilia and in Modena, Italy. The simulation was performed by the model suite Micro-Swift-Spray, a Lagrangian particle dispersion model accounting for buildings. Direct measurements of traffic flow were continuously collected in Reggio Emilia over the period January 13-24, 2014 by a two channel radar traffic counter and in Modena from October 28 to November 8, 2016 by four single channel radar traffic counters and used for the hourly modulation of vehicular emissions. Combining radar counts with vehicular fleet composition for each municipality, specific emission factors were obtained. For both cities, simulated concentration fields were compared to local air quality measurements at the nearest urban traffic and urban background sites. The simulated NO x showed large correlation with the observations, notwithstanding some underestimation. The results proved the reliability of the procedure and provided a fair estimate of the NO 2 mass fraction of total NO x (primary NO 2 ) due to vehicular emissions in the investigated traffic sites.