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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]

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach. [ABSTRACT FROM AUTHOR]

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. [ABSTRACT FROM AUTHOR]