Search

A source apportionment scheme based on gas and aerosol phase reactive tracers has been implemented in the chemical-transport model FARM, to efficiently estimate contributions of different sources to ambient concentrations. The on-line scheme deals with all the main processes that the chemical species undergo in the model, to enhance consistency with the calculation of bulk concentrations. The fate of precursors through gas-phase chemical reactions is followed by an efficient solver that determines their incremental reactivity, while the contributions to the secondary particulate species from their gaseous precursors is determined by assuming the thermodynamic equilibrium between the two phases. The paper details the new employed methodologies and illustrates the application of the apportionment scheme (based on 6 source sectors) to PM10 and O3, simulated on three domains of different dimensions in Italy, all sharing the same horizontal resolution and a common region (Lombardy). Spatial patterns of results show, on average, a relevant contribution of heating on PM10 concentration in January, with local hotspots dominated by road traffic. Contributions appear consistent in the three simulated domains, apart from the boundary conditions, influenced by the dimension of the domain. Hourly series of contributions to O3 concentrations in July at three selected sites show the dominance of boundary conditions, underlining the large scale of O3 formation. Finally, for PM10 components, the resulting sectorial contributions are compared with the impacts computed via the brute force method, showing that results are similar for elemental carbon and sulfate, while they are different for nitrate and ammonium, due to a different allocation of contributions and impacts between the methods. Each approach responds in principle to a different purpose, and their combined use provides possibly a wide set of information useful for addressing the different air quality management needs. [ABSTRACT FROM AUTHOR]

The indoor climate of non-climatized churches is usually subject to cyclical fluctuations of temperature and relative humidity induced by external climate conditions which might be dampened by the high thermal capacity of their envelope. However, several phenomena affect their indoor climate (e.g., internal gains due to people and artificial lighting, air infiltration, etc.), which lead to environmental variations that might jeopardize the artworks contained within. In particular, one of the most influential parameters that may affect non-climatized churches is the massive and intermittent presence of people who constantly visit their spaces. In such regard, long-term monitoring allows the collection of environmental data with different building operation conditions and visitor fluxes. This paper analyses the indoor climate of the Milan Cathedral (Duomo di Milano) in Italy for three continuous years (including the lockdown period that occurred in 2020 caused by the COVID-19 pandemic), with a focus on visitors' effects on the indoor environment and the conservation of the main artworks contained within. The results of the analysis have shown that spaces with huge volume are most influenced by the opening of the doors rather than the hygrothermal contribution of the intermittent presence of massive crowds. Moreover, the absence of visitors for a prolonged period correlates with an improvement in the indoor conservation conditions for artworks, especially those made of hygroscopic materials, due to the reduction in short, rapid climate fluctuations. [ABSTRACT FROM AUTHOR]

The green transformation of the built environment is aimed at improving sustainability and can be supported by digitalization, which has become a significant tool to support the supply, integration, and management of information throughout the construction life cycle. In addition, climate change highly affects human comfort in the built environment and different strategies should be evaluated for adapting cities. This paper developed a digital workflow by integrating existing tools (i.e., Grasshopper, Ladybug, Honeybee, and Dragonfly) to evaluate how different green infrastructure strategies affected the thermal comfort by reducing the UTCI. The workflow was applied to a typical historical urban context (Catania, South of Italy), consisting of a square surrounded by three-floor buildings. Three basic scenarios were created that depended on the pavement material used in the built environment: a black stone pavement (reference material from Mount Etna), a permeable pavement, and grass. These three scenarios were combined with different green infrastructure strategies: tree pattern on the square, green walls and green roofs on the surrounding buildings, and the integrations of all these above-mentioned strategies. The results demonstrated that the integration of different green strategies (a grass square instead of pavement, with trees, and green walls and green roofs) increased the thermal comfort by reducing the UTCI by more than 8 °C compared to the existing urban context (black stone pavement and building envelope). However, this temperature reduction was highly affected by the location of the human body into the urban context and by the evaporation rates from vegetation. The workflow developed will be useful for designers to evaluate the effectiveness of different green strategies during the early-design stage in mitigating and adapting cities to climate change. [ABSTRACT FROM AUTHOR]

The interest in radiation protection in industrial sectors involving Naturally Occurring Radioactive Materials (NORM) is increasingly growing. This is due also to the recent implementation of the European Council Directive 59/2013/Euratom which in Italy and in the other European Union Member States extends the field of application to industrial sectors never involved before. This paper reports main results of a research project on radiation protection in industries involving NORM carried out in Italy aimed to provide useful tools for stakeholders to comply new legal obligations. The project activities were mainly focused on different aspects relevant to the NORM involving industries, accounting for the positive list reported in the Italian law. Firstly, the inventory of the industries currently operating in Italy in order to identify the industrial sectors with an important radiological impact on population and workers was updated. Based on this information, a general methodology was elaborated taking into account a graded approach. The first phase consists in the identification and characterization of the most critical exposure scenarios and of the radiological content of NORMs involved in the different phases of the industrial processes. In the second phase calculation methods were developed for dose estimation for workers and members of public. These tools require the use of existing and well tested calculation codes, and the development of a dedicated user-friendly software. [ABSTRACT FROM AUTHOR]

This paper aims to present simple regressive equations to estimate the parameters of the three-parameter depth–duration–frequency (DDF) curve (3p-DDF), which accurately expresses, for a preassigned return period, the relationship between the rainfall depth and the rainfall duration over large duration ranges, from below 1 h (i.e., tens of minutes) to above 1 h (up to 24 h). These equations are developed to relate their parameters to those of the two-parameter DDF curve (2p-DDF), which can be estimated more easily being based on more readily available data related to rainfall durations above 1 h. In the applications, the regressive equations are first calibrated using recent pluviographic data in northern Italy, Germany, and Sweden. Two validation steps are then carried out to test the equations in terms of estimated rainfall depths using the same data as those used in the calibration step and data of stations from other geographic areas, i.e., Sicily in southern Italy, and from the past century, respectively. The results obtained prove this methodology capable of providing reliable estimation of short-duration rainfalls with various return periods in the absence of measurements with fine temporal resolution. [ABSTRACT FROM AUTHOR]

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, Δ H , and the corresponding values of the water storage, δ , computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes. [ABSTRACT FROM AUTHOR]

Particular Matter (PM) data are the most used for the assessment of air quality, but it is also useful to monitor VOC and CO. The health impact of PM increases with decreasing aerodynamic dimensions, therefore most of the monitoring is aimed at PM10 (fraction of PM with aerodynamic dimensions smaller than 10 µm) and PM2.5 (fraction with aerodynamic dimensions lower than 2.5 µm). Generally, anthropogenic emissions contribute mainly to PM2.5 levels, whereas natural sources can largely affect PM10 concentrations. PM2.5/PM10 ratio can be used as a proxy of the origin (anthropogenic vs natural) of the PM, providing a useful indication about the main sources of PM that characterizes a specific geographical or urban setting. This paper presents the results of the analysis of continuous measurements of PM10 and PM2.5 concentrations at eight stations of the regional air quality monitoring network in Abruzzo (Central Italy), in the period 2017–2018. The application of models based on machine learning technique shows that PM2.5/PM10 ratio can be used to classify PM emissions and to know the nature of the emission source (natural and anthropogenic), under determinate conditions, and properly taking into account the meteorological parameters. [ABSTRACT FROM AUTHOR]

Humans are mostly exposed to ionizing radiation through radon and its decay products. The results of indoor radon measurements in 39 kindergartens and primary schools in the Campania region of southern Italy are presented in this paper. The survey was carried out with CR-39 solid-state nuclear track detectors (SSNTDs). Radon concentrations were measured and ranged from 11 to 1416 Bq/m3, with a geometric mean of 77 Bq/m3 and a geometric standard deviation of 2. The findings revealed that 70% of the measures were below the WHO recommended level of 100 Bq/m3 and that 97 percent of the measurements were below the 300 Bq/m3 level set by Italian law. [ABSTRACT FROM AUTHOR]

The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM2.5 concentration. Four distinct PM2.5 exposure categories were established using the quartile method. To assess the association between PM2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM2.5 annual levels exceeding the 5 µg/m3 limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering. [ABSTRACT FROM AUTHOR]

Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH4) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016–2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger dataset. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to COVID-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropogenic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions. [ABSTRACT FROM AUTHOR]

Indoor radon surveying and remediation were implemented in a single-family home affected by high levels of indoor radon in the Celleno municipality (central Italy) with the aim of identifying the contribution of radon sources, evaluating the factors affecting radon entry into the building, and reducing radon risk. Average radon levels were relatively low at the ground floor (286 ± 202 Bq m−3) and first floor (167 ± 84 Bq m−3) in autumn when the temperature was still warm and the windows were open, but increased up to 2776 ± 1768 Bq m−3 and 970 ± 202 Bq m−3 in the first half of December, when the heating system was on and the windows were closed. The inner walls of the pilot room at the ground floor, semi buried on one side, were then treated with a waterproof product (a silane terminated polymer) and the average radon was halved (1475 ± 1092 Bq m−3) in the following month, which was still characterised by winter conditions. Radon entry in the room was identified and sealed with the same product, and a radon accumulation space behind a NE-SW oriented wall was naturally ventilated, reducing radon below the reference level in April with northerly winds conditions. [ABSTRACT FROM AUTHOR]

Air pollution poses a significant threat to human health and ecosystems. Forecasting the concentration of key pollutants like particulate matter can help support air quality planning and prevention measures. Deep learning methods are becoming increasingly popular for predicting air pollution and particulate matter concentration. Architectures like Convolutional Neural Networks can effectively account for the geographical features of the study domain. This work tests a Feed-Forward, a Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN) on a polluted geographical domain in northern Italy. The best convolutional architecture was then implemented in two other quite different regions. The results show that the same CNN architecture provides remarkably accurate forecasts in all applications and that a network trained on PM10 data can accurately forecast PM2.5 concentrations up to 10 days ahead. These results suggest that the proposed CNN has high generalization capabilities and can thus be reliably used as a forecasting model for different areas. [ABSTRACT FROM AUTHOR]

The Campus of the Sapienza University of Rome, frequented daily by several thousands of students and workers, collects allergenic airborne pollen from many sources. Here, we report the results of detailed pollen monitoring of 49 pollen types within the University Campus, allowing us to trace the main local and regional sources of airborne pollen throughout the year. The amount of allergenic pollen has been calculated for each daily record to evaluate the risk of exposure for students and workers on Campus in relation to academic activities and to suggest possible mitigation measures. Our results show that the maximum pollen concentrations are recorded from March to May, and the highest floristic richness occurs in April–June. We distinguish massive local pollen producers from pollen of regional origin and local ornamental and invasive taxa. Pollen with extreme allergenicity is dominant from mid-January to mid-March and in May with Cupressaceae/Taxaceae, Corylus and Olea; high allergenicity from late March to late April with Platanus, Ostrya, Ginkgo and Moraceae; and medium allergenicity from late April to the beginning of May with Quercus and Pinus. In August–December, pollen concentration is relatively low. Diversified mitigation actions are suggested in relation to the provenance, allergenicity and emission period of pollen. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the impact of the COVID-19 pandemic on the carbon footprint (CF) of two research projects. These projects were initiated prior to the onset of the pandemic and subsequently concluded afterward, serving as the Base Case (BC) for analysis. Furthermore, the study seeks to explore the potential applicability of measures implemented during the period of lockdown for future mitigation of CF. The applied methodology, which adheres to the guidelines provided by the GHG Protocol and the Department for Environment Food & Rural Affairs (DEFRA) emission factors, is utilized to examine the CF of the projects under two different scenarios. The first scenario assumes that the projects were implemented without the pandemic, while the second scenario considers that the projects were conducted entirely during the pandemic. Among the two projects under review, one emphasizes innovation and entails a collaboration between academia and business. This project is supported by a limited number of employees, exclusively from domestic partners. The other project is more oriented toward policy-making and involves a larger group of partners from Greece and Italy. Its main priority is dissemination. Carbon dioxide (CO2) emissions associated with project activities mainly stem from electricity use, material consumption, project-hosted events, project participation in events, employees commuting, and equipment. Results show that in the first scenario, the projects exhibit a more than 40% increase in CO2 emissions compared to the BC, while in the second scenario, the implementation of measures such as teleworking, virtual participation in events, and digitization of bureaucratic processes lead to a reduction in emissions by at least 20%. The study suggests that adopting such measures after the COVID-19 pandemic could significantly decrease greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous event are analysed and go back to the presence of tropical cyclone Danielle in the eastern Atlantic. The performance of the weather research and forecasting (WRF) model using lightning data assimilation (LDA) is studied in this case by comparing the forecast with the control forecast without lightning data assimilation. The forecast performance is evaluated for precipitation and lightning. The case was characterised by four intense 3-h (3 h) periods. The forecasts of these four 3-h phases are analysed in a very short-term forecast (VSF) approach, in which a 3 h data assimilation phase is followed by a 3 h forecast. A homemade 3D-Var is used for lightning data assimilation with two different configurations: ANL, in which the lightning is assimilated until the start of the forecasting period, and ANL-1H, which assimilates lightning until 1 h before the 3 h forecasting period. A sensitivity test for the number of analyses used is also discussed. Results show that LDA has a significant and positive impact on the precipitation and lightning forecast for this case. [ABSTRACT FROM AUTHOR]

The atmosphere is an important pathway for microplastic (MP) transport; however, observations are limited, as traditional sampling methods are generally labor-intensive. Biological monitors (biomonitors) have been widely used as a simple alternative to determine the abundance or presence of anthropogenic pollutants. Here, we compared the effectiveness of co-located lichen and moss species as biomonitors of the atmospheric deposition of microplastics. Samples of the epiphytic lichen Evernia prunastri and the epigeic moss Pseudoscleropodium purum were collected from five remote areas of central Italy. A total of 154 MPs were found across all samples, 93.5% of which were fibers and 6.5% were fragments. The accumulation of MPs for lichens (range of 8–12 MP/g) was significantly lower than for mosses (12–17 MP/g), which might be related to their structural characteristics or habitat positions (epiphytic versus epigeic). Nonetheless, higher accumulation facilitates analytical determination and provides greater separation from the limit of detection, suggesting that mosses are preferred over lichens for studying the deposition of airborne MPs. This study further suggests that biomonitoring may be an effective tool to assess the spatial distribution of atmospheric microplastics, which is a key requirement for the development of waste mitigation policies. [ABSTRACT FROM AUTHOR]

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window. [ABSTRACT FROM AUTHOR]

The concentrations of trace elements in atmospheric bulk depositions (wet plus dry) were investigated from two highly industrialised areas of Sicily (southern Italy) from June 2018 to July 2019, in order to recognise the main natural and anthropogenic sources. A side objective of this study was to improve the common sampling procedures and analytical methods used for monitoring trace elements in atmospheric deposition. The trace element VWM (Volume-Weighted Mean) concentrations ranged from less than 0.01 μg L−1 for trace elements such as Cs, Tl, and U, up to 24 μg L−1 for minor elements (Al, Zn, Sr), in the filtered aliquot, while they reached concentrations up to 144 μg L−1 for the same elements, in the unfiltered aliquot. Therefore, significant differences in concentrations between these two aliquots were found, particularly for Al, Fe, Ti, Zn, Cr, Pb, Se, Cs, and U. This implies that filtering operations may produce a consistent underestimation of concentrations of certain 'constituents' of the atmospheric deposition. Natural (marine spray, local and regional geogenic input, volcanic emanations) and anthropogenic sources (industrial emissions, auto vehicular traffic, and diffuse background pollution) which influence rainwater chemistry were identified. Enrichment factors (EFs), with respect to the upper crust composition, provided clear evidence of the different sources above mentioned: Ti, Fe, Al, Cs, Cr, Rb, and Co have low EFs (<1), and are referable to the (local and/or regional) geogenic input, while Se, Sb, Zn, B, Cd, Cu, Mo, Sr, As, with high EFs (>10), highlight the influence of marine and/or industrial sources. The study produced a novel dataset on the atmospheric deposition rate of several trace elements, which had never been studied in the investigated areas. Finally, a comparison of trace element deposition rates in the studied areas with the atmospheric deposition reported for 53 different sites, belonging to 20 different European nations, was made. The comparison showed that some elements, such as Al, V, Zn, and Mo had higher median deposition fluxes in the Sicilian sites than in European monitoring sites. [ABSTRACT FROM AUTHOR]

We apply extreme value theory (EVT) to study the daily precipitation and temperature extremes in the Calabria region (southern Italy) mainly considering a long-term observational dataset (1990–2020) and also investigating the possible use of the ERA5 (ECMWF Reanalysis v5) fields. The efficiency of the EVT applied on the available observational dataset is first assessed—both through a punctual statistical analysis and return-level maps. Two different EVT methods are adopted, namely the peak-over-threshold (POT) approach for the precipitation and the block-maxima (BM) approach for the temperature. The proposed methodologies appear to be suitable for describing daily extremes both in quantitative terms, considering the punctual analysis in specific points, and in terms of the most affected areas by extreme values, considering the return-level maps. Conversely, the analysis conducted using the reanalysis fields for the same time period highlights the limitations of using these fields for a correct quantitative reconstruction of the extremes while showing a certain consistency regarding the areas most affected by extreme events. By applying the methodology on the observed dataset but focusing on return periods of 50 and 100 years, an increasing trend of daily extreme rainfall and temperature over the whole region emerges, with specific areas more affected by these events; in particular, rainfall values up to 500 mm/day are predicted in the southeastern part of Calabria for the 50-year-return period, and maximum daily temperatures up to 40 °C are expected in the next 100 years, mainly in the western and southern parts of the region. These results offer a useful perspective for evaluating the exacerbation of future extreme weather events possibly linked to climate change effects. [ABSTRACT FROM AUTHOR]

An updated climatology of EF1+ (Enhanced Fujita category 1 or stronger) tornadoes in Italy (445 events in the period of 1990–2021) confirms that the central Tyrrhenian (CT) and the southeastern (SE) regions of the Italian peninsula represent two of the areas most affected by tornadoes in the Mediterranean. We performed a comparative analysis between these two hotspots, examining the large-scale atmospheric features associated to the tornado occurrences, throughout the analysis of radiosounding observations and reanalysis (ERA5) fields. The conditions in which the tornadoes develop are investigated using metrics regarding atmospheric instability and/or horizontal/vertical wind shear. Common synoptic characteristics are found for both regions, i.e., a prevalent occurrence of tornadoes on the eastern flank of approaching troughs, with moderate shear/CAPE mean values. A large number of events develop in a high-shear/low-CAPE (HSLC) environment, in the presence of positive sea surface and 850 hPa temperature anomalies. Conversely, the upper-level winds suggest different characteristics of the tornado-spawning cells in the two areas, i.e., multi-cells/linear systems for CT and isolated supercells for SE. The maximum values for some typical atmospheric fields/instability parameters in the areas around the tornado hotspots permit the obtention of thresholds that identify the most favorable environments for tornado formation in these Italian areas. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most important environmental problems for rural, urban and industrial areas. This study assesses the concentrations, the possible interaction with the vegetation conditions and the sources of trace elements in atmospheric aerosol particles. To this aim, a novel holistic approach integrating biomonitoring techniques, satellite observations and multivariate statistical analysis was carried out in a semi-rural area before an on-shore reservoir (crude oil and gas) and an oil/gas pre-treatment plant identified as "Tempa Rossa" (High Sauro Valley—Southern Italy) were fully operative. The atmospheric trace element concentrations (i.e., Al, Ca, Cd, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, S, Ti and Zn) were assessed by "lichen-bag" monitoring. Satellite-derived normalized difference vegetation index (NDVI') estimates were used to support the identification of environmental imbalances affecting vegetation conditions and linked to possible anthropogenic drivers. Principal component analysis (PCA) allowed identifying both natural and anthropogenic trace element sources, such as crustal resuspension, soil and road dust, traffic, biomass burning and agriculture practices. Empirical evidence highlighted an interaction between NDVI' and S, Ni, Pb and Zn. The health risk impact of atmospheric trace elements on the exposed population, both adults and children, considering inhalation, ingestion and the dermal contact pathway, highlighted a possible non-carcinogenic risk concerning Ni and a not-negligible carcinogenic risk related to Cr(VI) for the adult population in the study area. [ABSTRACT FROM AUTHOR]

On 10 July 2019, a giant hail-bearing supercell hit the Adriatic coast of central Italy. Hailstones with a maximum diameter of 14 cm were reported in the city of Pescara between 10:00 and 11:00 UTC. In this work, the main synoptic and mesoscale features, responsible for the triggering and the development of the supercell, are analyzed using the WRF model. The intrusion of Bora wind over the northern and central Adriatic was relevant for two reasons: on the one side, the arrival of low-level cold air produced an uplift of the pre-existing warm air and favored the triggering of convection; on the other side, the strong vertical wind shear, also due to the presence of intense upper-level southwesterlies, created conditions favorable to the formation of the supercell. The predictability of the event is also discussed, comparing simulations starting at different initial times and forced with GFS and IFS forecasts. The model results show that the runs initialized at earlier times reproduced more accurately the track and the time evolution of the supercell. The HAILCAST module of WRF was also used to simulate hailstorm characteristics, such as the average hailstone diameter. WRF-HAILCAST simulations proved to be in fair agreement with the radar reflectivity retrievals and with local reports. [ABSTRACT FROM AUTHOR]

The adversities of climate change represent a serious risk factor on both food production, rural territories and landscapes. In light of these irreversible trends, the process of adaptation of the rural territory is a necessary step, in order to increase its climate resilience. In this study, the vulnerability assessment was the tool used to evaluate the specific phenomenon of "flooding and backwatering" in the case study of Emilia Romagna region (Italy). The approach was based on the IPCC's risk analysis methodology, populated by different layers regarding specifical proxies related to the adaptive capacity and the sensitivity of the territory to water stagnation, then we assessed with the normalization process of the different information levels. The aim of the research consisted in the realization of a regional map of the vulnerability to floods and backwaters, in order to rank the territory to this specific phenomenon. The final result could be useful both for the relationship with planning choices and for local-based actions in agricultural policies that in Italy are taken at regional level. [ABSTRACT FROM AUTHOR]

Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts. [ABSTRACT FROM AUTHOR]

The honeybee (Apis mellifera L.) has been used in several studies for monitoring the environmental health status in terms of pollution, due to its wide-ranging foraging flights. Based on this consideration, this study aimed to analyze heavy metal pollution in Molise Region (Italy), by investigating five sites characterized by different levels of contamination. Furthermore, the authors carried out a sampling activity for a long period, in order to obtain a complete dataset. In this way, detailed information about the status of the environments was able to be obtained. The main purpose of this work was to assess the health status of Molise Region and to confirm the suitability of honeybees as environmental bioindicators of heavy metal pollution, by analyzing their variability over time and space. Furthermore, the study compared the health status associated with contamination in terms of heavy metals with that in two different areas of Italy, using hierarchical cluster analysis and principal component analysis, to evaluate the correlation existing among the three different areas of Italy. Following the findings, the authors suggest the use of honeybees as a bioindicator for heavy metal pollution in air quality studies. [ABSTRACT FROM AUTHOR]

In this work, the results of a sensitivity study based on ICON-LAM simulations at 2.5 km of spatial resolution over the Italian area, driven by ECMWF IFS global data, are presented. The main aim is to provide a contribution to the selection of suitable parameterization schemes that result in more effective for a proper representation of the Italian climate features. Model evaluation was conducted in terms of the air temperature and precipitation for three subregions, comparing a set of 13 simulations against SCIA and E-OBS standard datasets. In addition, evaluation was also conducted against selected data stations scattered over the Italian area. We found that the ICON-LAM model was able to provide a good representation of the temperature over Italy, whereas non-negligible biases were observed for precipitation in certain regions. The model proved to be sensitive to changes in physical parameterization schemes. In particular, we found that the explicit treatment of deep convection and the "clouds as in turbulence" scheme for cloud cover allowed for a better representation of precipitation in the summer over the Alpine region. The single moment scheme is currently the best option for cloud microphysics. [ABSTRACT FROM AUTHOR]

The combined use of Lecce-University AERONET-photometer measurements and PM2.5, PM10, NO2, CO, and SO2 concentrations from different sites of Apulia-Region Air-Quality Agency represents the peculiarity of this study, which evaluates the impact of COVID-19 lockdown (LD) measures on aerosol and gaseous pollutants. Monthly-averaged columnar and surface parameters of the 2020-year were compared with corresponding monthly parameters of the ref-year obtained by averaging 2017, 2018, and 2019 measurements in order to evaluate LD measure impacts by Average Percent Departure (APD%). Photometer measurements showed that LD measures were likely responsible for the decrease in Aerosol Optical Depth (AOD). The APD% estimated between the 2020- and ref-year AOD (at 440 nm) was characterized by negative values from June to August, reaching the smallest mean value (−46%) in June. Moreover, the columnar aerosol load appeared less affected by continental urban/industrial particles than previous years in the summer of 2020. The PM-concentration-APD% calculated at ten sites was characterized by monthly trends similar to those of AOD-APD%. PM-APD% values varied from site to site and smaller values (up to −57% in June) were on average detected at urban/suburban sites than at background sites (up to −37%). The impact of LD measures on gaseous pollutants was observed from the onset of LD. [ABSTRACT FROM AUTHOR]

Minimetrò (MM) is a ropeway public mobility system that has been in operation in the city of Perugia for about ten years to integrate with urban mobility and lighten vehicular traffic in the historic city center. The purpose of this work was to evaluate the impact of MM as a source of pollutants in the urban context, and the exposure of people in the cabins and the platforms along the MM line. These topics have been investigated by means of intensive measurement and sampling campaigns performed in February and June 2015 on three specific sites of the MM line representative of different sources and levels of urban pollution. Stationary and dynamic measurements of particle size distribution, nanoparticle and black carbon aerosol number and mass concentrations measurements were performed by means of different bench and portable instruments. Aerosol sampling was carried out using low volume and high-volume aerosol samplers, and the samples nalysed by off-line methods. Results show that MM is a considerable source of atmospheric particulate matter having characteristics very similar to those of the common urban road dust in Perugia. In the lack of clear indications on road dust effect, the contribution of MM to the aerosol in Perugia cannot be neglected. [ABSTRACT FROM AUTHOR]

Indoor built environments' design and management require a holistic approach inspired by ergonomic principles and sustainability criteria. This is especially in case of renovation of existing buildings where any kind of intervention requires the direct feedback of occupants. This work deals with two aspects of these issues, often studied separately: the quality of interior spaces, in terms of Indoor Environmental Quality (IEQ), and the quality of the architecture in terms of orientation and wayfinding. A methodology focused on the subjective evaluation of the IEQ giving relevance to users and their fruition needs is also proposed. Main findings from a specific subjective investigation carried out at the Fisciano Campus of the University of Salerno (Italy) demonstrate that the subjective approach is a valuable tool to make more sustainable intervention strategies. In this way, all multidisciplinary skills can be synergically involved in improving the livability of a complex reality as University Campuses are. [ABSTRACT FROM AUTHOR]