The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change.

The identification of parameters that can quantitatively describe the different characteristics of urban morphology is fundamental to studying urban ventilation and microclimate at the local level and developing parameterizations of the dynamic effect of an urban area in mesoscale models. This paper proposes a methodology to calculate four morphological parameters, namely mean height, aspect ratio, sky view factor, and plan area ratio, of five cities located in southern (Bari and Lecce), central (Naples and Rome), and northern (Milan) Italy. The calculation is performed using the Geographical Information System (GIS), starting from morphological and land use data collected and analyzed in shapefiles. The proposed methodology, which can be replicated in other cities, also presents in detail the procedure followed to properly build input data to calculate the sky view factor using the UMEP GIS tool. The results show a gradual increase in the plan area index, λ_p, and mean building height, $\overline{H}$, moving from the south to the north of Italy. Maximum values of λ_p and $\overline{H}$ are obtained in the regions of Milan, Rome, and Naples, where the highest spatially-averaged values are also found, i.e., λ_p = 0.22, $\overline{H}$ = 10.9 m in Milan; λ_p = 0.19, $\overline{H}$ = 12.7 m in Rome; λ_p = 0.20, $\overline{H}$ = 12 m in Naples. Furthermore, for all the cities investigated, areas characterized by the Corine Land Cover class as “continuous urban fabric” are those with medium sky view factor SVF values (around 0.6–0.7) and λ_p values (around 0.3) typical of intermediate/compact cities. The methodology employed here for calculating morphological parameters using GIS proves to be replicable in different urban contexts. This opens to a better classification of cities in local climate zones (LCZ), as shown for the Lecce region, useful for urban heat island (UHI) studies and to the development of parameterizations of the urban effects in global and regional climate models.

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM₁₀) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM₁₀ concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM₁₀ exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM₁₀ concentrations were 5.2 and 38.6 µg/m³, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM₁₀ levels, and compared with exposure <5 µg/m³, we found no excess ALS risk at 5–10 µg/m³ (OR 0.87, 95% CI 0.39–1.96), 10–20 µg/m³ (0.94, 95% CI 0.24–3.70), and ≥20 µg/m³ (0.87, 95% CI 0.05–15.01). Based on maximum PM₁₀ concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10–20 µg/m³ (OR 4.27, 95% CI 0.69–26.51) compared with those exposed <10 µg/m³. However, risk decreased at 20–50 µg/m³ (OR 1.49, 95% CI 0.39–5.75) and ≥50 µg/m³ (1.16, 95% CI 0.28–4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM₁₀ concentrations (OR 1.13, 95% CI 0.50–2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM₁₀ concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.

Based on epidemiologic and laboratory studies, exposure to air pollutants has been linked to many adverse health effects including a higher risk of dementia. In this study, we aimed to evaluate the effect of long-term exposure to outdoor air pollution on risk of conversion to dementia in a cohort of subjects with mild cognitive impairment (MCI). We recruited 53 Italian subjects newly-diagnosed with MCI. Within a geographical information system, we assessed recent outdoor air pollutant exposure, by modeling air levels of particulate matter with equivalent aerodynamic diameter ≤10 μm (PM₁₀) from motorized traffic at participants' residence. We investigated the relation of PM₁₀ concentrations to subsequent conversion from MCI to any type of dementia. Using a Cox-proportional hazards model combined with a restricted cubic spline model, we computed the hazard ratio (HR) of dementia with its 95% confidence interval (CI) according to increasing PM₁₀ exposure, adjusting for sex, age, and educational attainment. During a median follow up of 47.3 months, 34 participants developed dementia, in 26 cases diagnosed as Alzheimer's dementia. In non-linear restricted spline regression analysis, mean and maximum annual PM₁₀ levels positively correlated with cerebrospinal fluid total and phosphorylated tau proteins concentrations, while they were inversely associated with β-amyloid. Concerning the risk of dementia, we found a positive association starting from above 10 μg/m³ for mean PM₁₀ levels and above 35 μg/m³ for maximum PM₁₀ levels. Specific estimates for Alzheimer's dementia were substantially similar. Adding other potential confounders to the multivariable model or removing early cases of dementia onset during the follow-up had little effect on the estimates. Our findings suggest that exposure to outdoor air pollutants, PM₁₀ in particular, may non-linearly increase conversion from MCI to dementia above a certain ambient air concentration.

Air quality plans must be demonstrated to be economically sustainable and environmentally effective. This paper presents a full cost–benefit and environmental analysis of a large regional air quality plan involving several different actions covering a large spectrum of fields, from domestic heating to passenger and freight transport, from electricity generation to agriculture. The impact of each action is analyzed looking at the possible energy savings, greenhouse gases (GHG) emission reductions, the improvement in air quality, and the consequent decrease in external costs, namely the reduced impact on population health. The analysis is performed by applying a flexible and fast computer tool (RIAT+) that allows for a rapid simulation of different pollutant emission scenario, to assess different air quality indices (AQIs) over a regional scale domain. The results show that, in most cases, the economic savings exceed the implementation costs and thus that these actions can be introduced in air quality plans for the domain under study. The reduced health and climate costs, though relevant in absolute terms, are, in general, only a fraction of the economic benefits of energy savings. This is not true for the measures acting on improvements in electricity generation, since a reduction in power plant emissions (generally with high stacks, far from populated areas) does not significantly impact the air quality inside the region. A shift in energy production to renewable sources can instead provide noticeable effects on GHG emissions. This research raises some interesting and general questions about the adequacy of the methodologies applied to attribute costs (and benefits) to actions, improving a variety of sectors that are different from the one in which the measures are applied here.

LCZs refer to a classification system that exists out of 17 classes, 10 of which can be described as urban, proposed as the new standard for characterizing and comparing urban landscapes. This study evaluates the reliability of the LCZ level 0 map obtained from WUDAPT by comparing it with a more detailed LCZ map inferred from the morphological information of several Italian cities. Morphological data from Digital Elevation Models (DEMs) are used to obtain a detailed morphological characterization of each city. Preliminary results for the city of Lecce show that the WUDAPT L0 method misclassified some LCZs, especially at the core urban cells, whereas wider matching is observed at the boundary between urban and rural areas.