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.

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.

The urban heat island (UHI) is an increasingly widespread phenomenon of concern to the wellbeing and the health of populations living in urban environments. The SUHI (Surface UHI) is directly related to UHI and influences its extension and intensity. Satellite images in the thermal infrared spectral region can be used to identify and study the SUHI. In this work, Landsat 8 TIR images were acquired to study the SUHI of a medium-sized municipality of the Po valley in the northern part of Italy. An additional Worldview 3 satellite image was used to classify the study area and retrieve the surface albedo of building roofs. Using the Local Climate Zone approach, existing roof materials were virtually replaced by solar reflective materials, and the mitigation potential of the SUHI and the UHI was quantified. This virtual scenario shows a decrease in the overheating of building roofs with respect to the ambient temperature of up to 33% compared to the current situation in the industrial areas. Focusing on UHI intensity, the air temperature decrease could be up to 0.5 °C.

Background: A few studies have suggested that light at night (LAN) exposure, i.e. lighting during night hours, may increase dementia risk. We evaluated such association in a cohort of subjects diagnosed with mild cognitive impairment (MCI). Methods: We recruited study participants between 2008 and 2014 at the Cognitive Neurology Clinic of Modena Hospital, Northern Italy and followed them for conversion to dementia up to 2021. We collected their residential history and we assessed outdoor artificial LAN exposure at subjects’ residences using satellite imagery data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2014–2022. We assessed the relation between LAN exposure and cerebrospinal fluid biomarkers. We used a Cox-proportional hazards model to compute the hazard ratio (HR) of dementia with 95% confidence interval (CI) according to increasing LAN exposure through linear, categorical, and non-linear restricted-cubic spline models, adjusting by relevant confounders. Results: Out of 53 recruited subjects, 34 converted to dementia of any type and 26 converted to Alzheimer’s dementia. Higher levels of LAN were positively associated with biomarkers of tau pathology, as well as with lower concentrations of amyloid β₁₋₄₂ assessed at baseline. LAN exposure was positively associated with dementia conversion using linear regression model (HR 1.04, 95% CI 1.01–1.07 for 1-unit increase). Using as reference the lowest tertile, subjects at both intermediate and highest tertiles of LAN exposure showed increased risk of dementia conversion (HRs 2.53, 95% CI 0.99–6.50, and 3.61, 95% CI 1.34–9.74). In spline regression analysis, the risk linearly increased for conversion to both any dementia and Alzheimer’s dementia above 30 nW/cm²/sr of LAN exposure. Adding potential confounders including traffic-related particulate matter, smoking status, chronic diseases, and apolipoprotein E status to the multivariable model, or removing cases with dementia onset within the first year of follow-up did not substantially alter the results. Conclusion: Our findings suggest that outdoor artificial LAN may increase dementia conversion, especially above 30 nW/cm²/sr, although the limited sample size suggests caution in the interpretation of the results, to be confirmed in larger investigations.