The urban heat island (UHI) refers to higher air temperatures (Tair) in urban areas than in surrounding rural environments, while the surface urban heat island (SUHI) describes analogous differences in land surface temperature (LST). This study presents a long-term assessment of UHI and SUHI in Modena, Italy, combining meteorological Tair observations with Landsat-derived LST from 188 daytime and 19 nighttime summer scenes (1985–2023). Four indicators—magnitude and range 1 of overall thermal variability and magnitude and range 2 of urban–rural thermal excess—were applied in parallel to LST and Tair to characterize the intensity and spatial variability of thermal conditions within a consistent daytime/nighttime framework. Results indicate significant long-term increases in summer LST, with daytime warming rates of 0.26 °C yr−1 (urban) and 0.27 °C yr−1 (rural). Daytime urban–rural LST differences ranged from 4 to 6 °C; nighttime differences were smaller (1–3 °C). Daytime Tair urban–rural differences were weak and not statistically significant, whereas nighttime Tair showed a clearer urban warming signal. Nighttime LST correlated more closely with Tair (r = 0.49–0.52 across indicators) than daytime LST, and nighttime LST showed strong correlations with Tair in both urban and rural areas (r = 0.96–0.98). Daytime imagery better captures SUHI spatial intensity, whereas nighttime observations provide a more consistent surface-to-atmosphere thermal link, highlighting the value of integrating satellite LST with in situ Tair for integrated UHI and SUHI assessment in medium-sized cities.

Urban green areas contribute to healthier cities by improving air quality, promoting physical activity and social cohesion, and mitigating the urban heat island effect. However, assessing exposure to green spaces remains a key methodological challenge in epidemiologic research. In this study, we compared traditional green space indices and developed a composite Green Exposure Index (GEI) integrating vegetation cover, density and accessibility to improve exposure assessment. We applied this new index in a population based amyotrophic lateral sclerosis (ALS) case-control dataset from a Northern Italy community. The GEI consists of three components: NDVI, the Green Coverage Ratio and an accessibility index defined for this application. We computed these components for all residential locations across an 8400 km2 domain from 1985 to 2020. Seasonal NDVI better captured vegetation patterns than annual values, and spatial aggregation restricted to vegetated areas reduced the overestimation associated with circular buffers. The GEI was evaluated under three illustrative weighting scenarios, which produced substantial differences in exposure classification and confirmed that metric choice strongly influences results. In our case study, the equally weighted GEI3 placed 79.7% of the population in the intermediate Mildly Exposed and Exposed categories, resulting in a balanced distribution better suited for epidemiologic analysis. Analysis of GEI time series revealed green space exposure changes from 1985 to 2020, identifying areas characterized by urbanization or green redevelopment. Findings from this case study show the added value of composite indices like the GEI for characterizing green space exposure and capturing long-term dynamics in vegetation and land use, with applications in epidemiology and urban planning.

Early-onset dementia (EOD) involves cognitive decline in individuals under 65, influenced by a complex interplay of genetic and environmental stressors. Applying a One Health framework, this study investigates the association between urban environmental factors—traffic-related air pollution, greenness, and artificial light at night (LAN)—and EOD risk. We conducted a population-based case-control study in Modena, Northern Italy (327 cases; 1949 age/sex-matched controls). Residential exposures were georeferenced using benzene as a traffic proxy, the Normalized Difference Vegetation Index (NDVI) for greenness, and VIIRS satellite data for LAN. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated via conditional logistic regression with mutual adjustment for all environmental variables. High benzene exposure (≥1 µg/m3), was associated with increased EOD risk. NDVI exhibited a non-linear relationship, with peak risk observed at intermediate greenness levels. Conversely, LAN exposure showed an inverse, approximately linear association with EOD risk. Sex-stratified analyses revealed a more pronounced association between benzene and EOD in males, while NDVI showed a linear risk reduction primarily in females. No sex-specific differences were observed for LAN. Urban environmental factors—specifically motorized traffic emissions, vegetation density, and nocturnal light—significantly correlate with EOD risk. These associations are characterized by non-linearity and sex-specific susceptibility, highlighting the need for multi-sectoral public health strategies within urban planning.

Natural attenuation process occurring at hydrocarbon-impacted sites are driven by biogeochemical interactions between the subsurface, microbial communities, and environmental conditions. Beyond direct volatile organic compounds (VOCs) volatilization from the contamination source, aerobic biodegradation pathways lead to the consumption of hydrocarbons and the production of gaseous emission from the subsurface, including CO₂. Current monitoring campaigns for evaluating gas fluxes are generally conducted periodically, relying on either soil-gas sampling and subsequent laboratory analysis or the use of high-cost instrumentation for rapid and expedited concentration measurements. These methods, while providing representative results of average values over specific and narrow time intervals, do not allow for an accurate description of the temporal dynamics of VOC biodegradation and the consequent CO₂ emissions, which are known to exhibit significant fluctuations on both daily and seasonal scales. To overcome this limitation, there has been growing interest in recent years in developing low-cost systems that allow for continuous monitoring of gas emissions.This work, conducted as part of a research project funded by INAIL (BRiC ID21-2022), presents the development and application of a self-designed automated static flux chamber for real-time and continuous monitoring of biodegradation-related gas emissions from the subsurface. The system integrates low-cost Non-Dispersive Infrared (NDIR) sensors for CO₂ measurement, together with a Photoionization Detector sensor (PID) for VOC concentration measurements, and is additionally equipped with sensors for environmental parameters (i.e. temperature, relative humidity and atmospheric pressure). The chamber is equipped with two air pumps dedicated to periodic automatic air exchange, ensuring operational continuity and allowing the acquisition of one flux measurement every 20 minutes. The electronic hardware is managed by an ESP32 microcontroller and is completed with an SD card for raw data storage and with a LoRaWAN transmission module for real-time data visualization and management in remote IoT clouds. Furthermore, the system is externally powered by an AGM lead-acid battery, connected to a photovoltaic panel, enabling energy self-sufficiency during field deployments.The system was calibrated with a commercial multi-gas analyzer through a series of laboratory tests, with results comparable to those of commercially available instruments. Furthermore, experimental tests were conducted using the developed flux chamber prototype to investigate the biodegradation dynamics of soils artificially contaminated with two different fuel types. Continuous monitoring over a two-month period enabled the observation of biodegradation-related processes and the associated emissions of VOCs and CO2. Subsequently, the automated chamber was employed in a two-week monitoring campaign at a contaminated site, to evaluate its efficiency in real contamination scenarios.The system developed in this work represents a promising step toward an economical and scalable solution for a deeper understanding of soil biodegradation processes and the resulting gas emissions at contaminated sites, accounting for correlations with environmental parameters as temperature, humidity and atmospheric pressure. Furthermore, the integration with IoT environments, together with full system automation and energy self-sufficiency, provides a significant contribution to the digitalization and automation of subsurface monitoring techniques.

Urban green areas contribute to healthier cities by improving air quality, promoting physical activity and social cohesion, and mitigating the urban heat island effect. Despite this, exposure to green areas is often estimated using metrics that focus on different dimensions of greenery, leading to heterogeneous exposure estimates. In this study, we compared traditional green space indices and developed a composite Green Exposure Index (GEI) that integrates vegetation cover, density, and accessibility within a single quantitative framework to improve exposure assessment. We applied these indices to a population-based amyotrophic lateral sclerosis (ALS) case-control dataset from a Northern Italy community. We computed the index values for all residential locations across an 8400 km² urban-peri-urban domain from 1985 to 2020, using high-resolution remote sensing and land cover data. Comparisons between traditional indices showed high agreement between NDVI and Tasseled Cap Greenness (r ≥ 0.94), and exposure estimates derived from 100 m and 200 m buffers also remained strongly correlated (r = 0.94 - 0.96). Seasonal NDVI better captured vegetation patterns than annual values (r = 0.77 - 0.99), and spatial aggregation restricted to vegetated areas reduced the overestimation observed with circular buffers, improving classification accuracy while maintaining strong correlations (r > 0.80). The GEI consists of three components: seasonal NDVI, the Green Coverage Ratio (GCR), and an accessibility index defined for this application. Accessibility was calculated by assigning a value to each green area based on its type, with values decreasing with a logarithmic function as distance from the green area increased, reaching zero for distances beyond 1200 m. This threshold corresponds to the average distance traveled within a 15-minute walk, in line with the 15-minute city planning approach. The GEI was evaluated under three weighting scenarios, which produced substantial differences in exposure classification and confirmed that metric choice strongly influences results. The GCR alone classified 61.7% of the population as Not Exposed, whereas accessibility alone classified 86.1% as Exposed or Highly Exposed. The equally weighted GEI3 placed 79.7% of the population in the intermediate Mildly Exposed and Exposed categories, resulting in a balanced distribution. Analysis of the GEI time series revealed green space changes over the 36-year study period, reliably identifying areas affected by urbanization or green redevelopment. Findings from this case study demonstrate the added value of composite indices such as the GEI for characterizing green space exposure, enabling more comprehensive and robust assessments of the benefits and effects of green infrastructure, with applications in public health policy and urban planning.

BACKGROUND:The contribution of environmental determinants in the etiology of amyotrophic lateral sclerosis (ALS) is still unclear. Among the various environmental factors, exposure to green spaces, also known as greenness, is attracting considerable interest as many studies have reported its beneficial associations to health outcomes, particularly to neurodegenerative diseases. METHODS:To investigate the relation between greenness and ALS risk, we conducted a population-based case-control study in a Northern Italy population (from Modena, Reggio Emilia and Parma provinces), including 499 cases of ALS newly-diagnosed from 1998 to 2011 and 1,935 sex-, age-, and province-matched controls randomly selected from study provinces residents. We evaluated the association between greenness in the proximity of residence and ALS risk, assessing exposure through multiple satellite-based and land-use derived indices, both conventional and novel devised, for a total of six indices, each providing specific information, including annual and seasonal Normalized Difference Vegetation Index (NDVI), NDVI-weighted to green areas, green cover ratio, accessibility index, and their combined Green Exposure Index (GEI). We used conditional logistic regression models to evaluate disease risk for increasing exposure through both fixed-categories and non-linear restricted cubic splines. RESULTS:We observed a non-linear U-shaped association between greenness and ALS risk with increased odds ratios at both low and high levels. Results were more defined when using NDVI-based indices, while the associations were smoother when considering GEI. The higher risk at low levels may be related to lower accessibility to green spaces with lower physical activity and higher exposure to outdoor air pollutants, whilst elevated greenness may reflect higher exposure to neurotoxic pesticides. These results were confirmed also after adjustment for potential confounders, namely magnetic fields and light at night. Sex stratified analysis yielded similar results, except for more distinct associations in females for GEI. CONCLUSIONS:Despite the limitations due to possible unmeasured confounding and exposure misclassification related to the use of residential data, our results provide evidence of an inverse association between intermediate residential greenness and ALS risk, and may have public health implications including disease prevention and urban planning.

Exposure to ultraviolet (UV) radiation significantly impacts human health. Consequently, comprehensive UV climatological databases are of great interest. UV exposure is evaluated by weighting UV spectra with spectral functions that describe physiological responses at each wavelength. The most widely used function is the erythemal weighting function, which is used to compute the UV Index (UVI) to assess the health risk associated with UV overexposure. The ERA5 datasets, produced by the Copernicus Climate Change Service (CDS), offer hourly ground-level UV radiation ([Formula: see text]), but do not include UVI. This study proposes a model to compute hourly UVI using exclusively ERA5 data, enabling direct access through the CDS to derive UVI statistics for locations of interest and potentially supporting the integration of a dedicated UVI product into ERA5. The model was developed using UV spectra simulated under clear-sky conditions with the uvspec radiative transfer model, accounting for atmosphere type, solar zenith angle, visibility, altitude, albedo, total ozone, and aerosol type. For these parameters, representative values typical of tropical, mid-latitude, and subarctic regions were used, effectively excluding Arctic conditions, and considering UVI values ≤ 12. The resulting formulation expresses UVI as a function of [Formula: see text], sun elevation, and total ozone. The model was validated using ground-based UVI measurements from six stations (over 17000 cases) and, in addition, compared with UVI derived from Copernicus Atmospheric Monitoring Service (CAMS) products (over 6000 cases) taken as a reference. Performance was assessed through the statistics of the differences between measured/modelled values and CAMS data under three scenarios: clear-sky conditions, varying cloud cover, and all-sky conditions. Under clear-sky conditions, the model uncertainties showed a small positive bias (≤ 0.5), with the absolute difference (AD) < 1 in 73% of the cases for ground measurements and in 86% of the cases for CAMS. The root mean squared difference (RMS) and the mean absolute deviation (MAD) were 0.9 and 0.7, respectively, for ground measurements, and 0.7 and 0.6 for CAMS. Under cloudy-sky conditions, model performance worsens significantly for CC > 0.4, with RMS and MAD reaching values of about 1.5. However, when considering relative uncertainties (percentage ratio between RMS and reference values of UVI), up to CC < 0.7 the RMS% remains below 15% for Very High-to-Extreme WHO/ICNIRP exposure categories and below 20% for Moderate-to-Extreme categories. A comparison between CAMS UVI and ground measurements was also performed, yielding results consistent with those described above. As an example, Appendix A illustrates how the model can be applied to generate daily and monthly UVI statistics over large geographical areas using only ERA5 data accessed through the CDS web portal.

Spaceborne and airborne remote sensing data serve as powerful tools for the analysis andmonitoring of both urban and agricultural territories, with diverse applications contingent upon spatialresolution. In recent years, remote sensing imagery has been utilized for the recognition of protectedagriculture landcovers, such as greenhouses and mulch. Various studies in the scientific literature havefocused on satellite sensors like Sentinel-2 and WorldView-3, mapping the presence of protectedagriculture surfaces and implementing specific indices for recognition.A recurrent limitation in these studies lies in the often insufficient spatial resolution of the sensors,particularly for identifying smaller-sized greenhouses. Additionally, spectral resolution is crucial. Whilesome laboratory studies analyse the spectral characteristics of plastic surfaces typical of protectedagriculture, they often neglect the issue of mixed pixels inherent in satellite or aerial detection.The aim of this study is to analyze images from the AVIRIS airborne sensor over the agricultural area ofSalerno in southern Italy. AVIRIS, a hyperspectral sensor with over 400 bands covering the visible (VIS) tothe shortwave infrared (SWIR) region, provided images with a spatial resolution of 1m and 3m. Wescrutinize these images to discern the spectral signatures of different types of greenhouses in the studyarea, subsequently comparing them with other land cover classes. For this, we employ supportive tools,including specific spectral indices and transformations such as Tasselled Cap and Principal ComponentsAnalysis (PCA). We implement the Region of Interest (ROI) separability technique to identify distinctivespectral features in the signatures of protected agriculture coverings that differentiate them from othersurfaces. Finally, the spectral signatures obtained from AVIRIS offer the opportunity to simulate spectralresponses of other satellite sensors with lower spatial and/or spectral resolutions, assessing the suitabilityof currently available data for recognizing this specific type of surface.

Urban Heat Island (UHI) is a global warming phenomenon that affects a large number of people around the world. Studying this phenomenon requires accurate measurements of climatological variables, particularly air temperature at 2 meters (T2m) on the urban scale. To improve spatial resolution of T2m ERA5 data from 0.1° to 0.05°, we implemented a deep learning approach using a Super Resolution Deep Residual Neural Network (SRDRN) and compared its performance with the traditional LOcalized Constructed Analog (LOCA) downscaling method and bilinear interpolation. Our results demonstrate that SRDRN achieved greater performance metrics (RMSE = 0.9°C, R2 = 0.95) than LOCA (RMSE = 3.8°C, R2 = 0.27) and bilinear (RMSE = 1.9°C, R2 = 0.61) approaches, validating the benefit of deep learning for temperature downscaling applications and subsequent urban analysis. Future work will focus on achieving finer spatial resolution (0.01°) by incorporating additional auxiliary variables to improve model performance.

INTRODUCTION:Dementia with symptom onset before the age of 65 is referred to as early-onset dementia (EOD). Many gaps exist regarding EOD etiology, including the role of environmental factors. METHODS:We conducted a population-based case-control study in Modena province, Northern Italy, enrolling and geocoding 326 EOD cases and 1,941 sex- and age-matched controls, to investigate the association of traffic-related benzene, green spaces around the place of residence, and exposure to artificial outdoor light at night (LAN). We used nonlinear modeling to assess the relation between environmental variables and disease risk, overall, and separately for Alzheimer's dementia (AD) and non-AD. RESULTS:Green spaces generally showed an inverse association with EOD risk that was almost linear for AD and inverted U-shaped for non-AD. We observed a weak positive association between traffic-related benzene exposure and EOD risk that seemed limited to AD, with little change in risk for non-AD. Exposure to LAN showed an inverse linear association with small differences across the two disease subgroups. Analyses stratified by sex and age showed generally stronger (but statistically imprecise) associations in females and older individuals. CONCLUSION:Overall, these results are consistent with some environmental influences on EOD risk, particularly with a beneficial effect of green spaces and LAN, as well as a possible adverse role of air pollution, particularly for AD.

This study introduces an innovative, low-cost static flux chamber for real-time monitoring of volatile organic compound (VOC) emissions at contaminated sites. Compared to traditional static flux chambers, the developed system is fully automated, eliminating the need for continuous operator intervention in the field. The cylindrical stainless-steel chamber (6.28 L) is equipped with internal sensors for temperature, pressure, and humidity, and a low-cost PID sensor for VOC detection (0.001-40 ppm). VOC flux is determined over 10 min measurement cycles, with two micro diaphragm pumps purging the chamber to reset concentrations. An Arduino Uno microcontroller manages the system, enabling local data storage (SD card) and a LoRa module to send real-time data to the cloud using IoT systems. Powered by a 12 V battery, rechargeable via a photovoltaic panel, the system ensures continuous operation. The prototype costs less than 1.5 k€, significantly cheaper than commercial devices. Accuracy and repeatability were assessed through lab-scale emission tests under dynamic conditions using various aliphatic and aromatic VOCs. Results closely matched those from a commercial gas analyzer and a Comsol Multiphysics numerical model, confirming the system reliability. These findings support its potential as a cost-effective alternative for continuous VOC monitoring at contaminated sites.

Abstract 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.

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts.

Introduction: Long-term contamination of tap water and groundwater by perfluoroalkyl and polyfluoroalkyl substances (PFASs) has been documented in the Veneto region of northern Italy. This study aimed to assess the exposure of individuals residing in the contaminated area and to test several toxicokinetic (TK) models of varying complexities to identify an efficient method for predicting perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) concentrations in human serum using observed data.The ultimate goal is to provide public health officials with guidance on selecting the appropriate TK model for specific contexts, a reliable and rapid tool to support human bio-monitoring (HBM) studies. Methods: Two simpler empirical TK models and a more complex multi-compartment physiologically based toxicokinetic (PBTK) model were compared with individual and aggregate data from an HBM study. In addition, the PBPK model was modified by adjusting input parameters and introducing new terms into the equations within the original model code. These modifications aimed to optimize the results compared to the original model, with some versions incorporating adjustments to account for the influence of menstruation in women. All models were evaluated to understand their strengths and weaknesses, providing guidance on the appropriate model to use according to specific scenarios. Results: The results obtained from the tested models were quite similar, with significant improvements observed only in the modified models. Simpler models also provided satisfactory results in scenarios involving low PFOS serum concentrations and recent exposure cessation. In many cases, predictions demonstrated high accuracy, particularly at the aggregate level and for women. Conclusions: These findings suggest that environmental protection agencies and health authorities may benefit from employing the tested models at the aggregate level as an initial step in HBM studies, rather than conducting more invasive and expensive screening campaigns.

Abstract Background A few studies have suggested that exposure to lighting during night hours, i.e. light at night (LAN), may increase the risk of dementia. In this study, we aimed to evaluate the association between exposure to outdoor artificial LAN and risk of conversion to dementia in an Italian cohort of subjects with mild cognitive impairment (MCI). Methods We recruited subjects with a diagnosis of MCI at the Cognitive Neurology Clinic of Modena Hospital in the period 2008-2014, and we followed these subjects up to 2021 for conversion to dementia. We collected their residential history and we assessed LAN exposure at subjects’ residences using satellite imagery data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2014-2022. Using a Cox-proportional hazards model adjusted for relevant confounders, we computed 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. Results Out of 53 recruited subjects, 34 converted to dementia of any type including 26 Alzheimer’s dementia. In linear regression analysis, LAN exposure was positively associated with dementia conversion (HR 1.03, 95% CI 1.00-1.06 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 2.26, 95% CI 0.88-5.85, and 2.89, 95% CI 1.10-7.58). In spline regression analysis, the risk linearly increased up to a LAN exposure of 30 nW/cm2/sr, above which a plateau seemed to be reached. Results were almost confirmed when limited to conversion to Alzheimer’s dementia, except for an almost linear relation. Conclusions Our findings suggest that exposure to outdoor artificial LAN may increase conversion from MCI to any type of dementia, especially above 30 nW/cm2/sr, while such relation appears to be almost linear for Alzheimer’s dementia. Key messages • Light at night exposure above 30 nW/cm2/sr was associated with risk of conversion to dementia. • Light at night showed almost linear association with risk of conversion to Alzheimer’s dementia.

VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

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Global warming has become a critical environmental, social, and economic threat, with increasing frequency and intensity of extreme weather events. This study aims to analyse temperature trends and climate indices in the Po Valley, a significant economic and agricultural region in Italy, by examining data from two historical stations: the urban Modena Observatory and the rural Mount Cimone Observatory. The analysis extends previous studies to 2018, assessing the magnitude of climate changes since the 1950s and isolating the Urban Heat Island (UHI) effect in Modena. Significant warming trends were confirmed at both sites, with in maximum (TX) and minimum (TN) temperatures trends nearly doubling from 1981 to 2018 compared to 1951–2018. For example, TX trends reached 0.84°C·decade−1 in Modena and 0.62°C·decade−1 at Mount Cimone, while TN trends were 0.77 and 0.80°C·decade−1, respectively. Extreme climate indices showed a substantial increase in warm days and nights (TX90p and TN90p, respectively). Particularly we found TX90p of 27.5 days·decade−1 in Modena and 15 days·decade−1 at Mount Cimone while TN90p of 29.5 days·decade−1 in Modena, 22 days·decade−1 at Mount Cimone. The UHI effect significantly impacts Modena's temperature trends. Urbanization contributes up to 65% of the rise in warm nights. Specifically, frost days decreased by 1.88 days·decade−1 (37% of Urban Contribute, UC), tropical nights increased by 5.16 days·decade−1 (57% UC), warm nights increased by 12.7 days·decade−1 (65% UC), and cool nights decreased by 3.19 days·decade−1 (39% UC). Overall, the study underscores the importance of considering both global and local factors in regional climate trend analysis.

Understanding black carbon (BC) levels and its sources in urban environments is of paramount importance due to the far-reaching health, climate, and air quality implications. While several recent studies have assessed BC concentrations at specific fixed urban locations, there is a notable lack of knowledge in the existing literature on spatially resolved data alongside source estimation methods. This study aims to fill this gap by conducting a comprehensive investigation of BC levels and sources in Modena (Po Valley, Italy), which serves as a representative example of a medium-sized urban area in Europe. Using a combination of multi-wavelength micro-aethalometer measurements and a hybrid Eulerian-Lagrangian modelling system, we studied two consecutive winter seasons (February-March 2020 and December 2020-January 2021). Leveraging the multi-wavelength absorption analyser (MWAA) model, we differentiate sources (fossil fuel combustion, FF, and biomass burning, BB) and components (BC vs. brown carbon, BrC) from micro-aethalometer measurements. The analysis reveals consistent, minimal diurnal variability in BrC absorption, in contrast to FF-related sources that exhibit distinctive diurnal peaks during rush hours, while BB sources show less diurnal variation. The city itself contributes significantly to BC concentrations (52 +/- 16 %), with BB and FF playing a prominent role (35 +/- 15 % and 9 +/- 4 %, respectively). Long-distance transport also influences BC concentrations, especially in the case of BB and FF emissions, with 28 +/- 1 % and 15 +/- 2 %, respectively. When analysing the traffic-related concentrations, Euro 4 diesel passenger cars considerably contribute to the exhaust emissions. These results provide valuable insights for policy makers and urban planners to manage BC levels in medium-sized urban areas, taking into account local and long-distance sources.

BACKGROUND: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). METHODS: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. RESULTS: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. CONCLUSIONS: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.