Conference Agenda

Il progetto SCINTILLA, promosso dalla Regione Campania in collaborazione con il MASE, mira a sviluppare strumenti innovativi per il monitoraggio e la previsione della qualità dell’aria, con un focus sul particolato atmosferico e i suoi impatti sulla salute umana. Le attività hanno integrato modelli numerici (CAMx) su scala nazionale e regionale, alimentati da inventari emissivi aggiornati (ISPRA, EMEP, HERMESv3) e forzati con simulazioni meteorologiche WRF, validando le simulazioni tramite confronti con dati osservativi ARPAC per il 2017. I risultati evidenziano una buona capacità dei modelli di riprodurre i pattern stagionali e diurni di NO2, PM10 e PM2.5, seppure con una sottostima dei picchi invernali.

Per superare le limitazioni dei modelli deterministici, sono state adottate tecniche di Intelligenza Artificiale basate su reti neurali (AFNO), in grado di correggere il bias delle simulazioni e migliorare la previsione temporale delle concentrazioni di inquinanti. Sul fronte sperimentale, sono state condotte campagne di campionamento del particolato in due siti urbani (Napoli e Avellino), integrando misure in-situ, sensori a basso costo e dati meteorologici ad alta frequenza, al fine di una caratterizzazione chimico-fisica dettagliata e dell’analisi sugli effetti sanitari.

Una componente innovativa riguarda l’uso integrato di osservazioni satellitari (MAIAC MODIS AOD550) e modelli numerici per estendere la copertura del monitoraggio e calibrare stime spaziali di PM10 laddove scarseggiano le stazioni di misura. I risultati preliminari supportano la validità dell’approccio di data fusion per generare mappe continue di esposizione, più rappresentative per la valutazione del rischio sanitario e ambientale. Il progetto si pone così come riferimento per strategie regionali di tutela ambientale, offrendo metodologie trasferibili in altri contesti territoriali.

The latest generation of high-resolution, convection-permitting reanalyses, capable of representing atmospheric processes at small spatial scales (≤4 km), is crucial for studying the temporal and spatial evolution of phenomena such as convective storms and orographic precipitation. Leveraging long (>35 years) and continuous datasets over Italy, this study investigates the occurrence and characteristics of hourly precipitation extremes (HPE) and quantifies their potential increase over time. Previous studies have validated convection-permitting reanalyses against observations from climatological to daily scales, demonstrating their ability to capture fine-scale precipitation events, although spatial mismatches sometimes occur.

The work is based on the MERIDA HRES convection-permitting reanalysis (1986–2022). Spatially coherent hourly precipitation structures (~160,000 per year) are identified from hourly reanalysis fields through clustering techniques and percentile-based thresholds. Each of them is characterized by maximum spatial extent, timing, peak value, mean intensity. The resulting dataset allows calculation of seasonal climatological averages of their distribution, intensity, and spatial extent. HPE are then extracted using local annual maxima in hourly precipitation (RX1hour).

Results reveal a marked increase in HPE occurrences over Alpine and Prealpine regions during summer, and along some southern and insular coastlines in autumn. These spatial and seasonal patterns correspond to regions where convective processes dominate intense, localized precipitation, potentially amplified by climate change. This study provides detailed insights into hourly precipitation patterns over Italy and guidance for stakeholders to leverage reanalysis data for enhancing infrastructure resilience to extreme precipitation.

Rapid urbanization, deteriorating air quality, and climate change are increasingly interacting in ways that amplify risks for urban populations. Cities are both major sources of greenhouse gas emissions and hotspots of vulnerability, where dense populations face the compounded effects of air pollution and climate extremes. In the context of more frequent and severe weather events, urban areas must urgently design and implement adaptation strategies informed by emerging scientific evidence.

This study introduces a novel multiscale modeling framework that couples the ADMS-Urban dispersion model with the Weather Research and Forecasting (WRF) mesoscale model to simulate the combined effects of extreme heat events on thermal comfort and pollutant dispersion. Developed within the PRIN2022 project “Urban hEat and pollution iSlands inTerAction in Rome and possible mitigation strategies” (RESTART), the approach assimilates high-resolution WRF meteorological fields (up to 500 m) into the ADMS-Urban system to capture interactions between climate and air quality at the city scale.

Using the July 2022 heatwave in Rome (Italy) as a case study, we demonstrate the capability of this modeling chain to assess how extreme heat conditions influence both thermal comfort and pollutant concentrations in densely populated urban environments. Following sensitivity analyses to identify the most robust model configuration and validation against ground-based observations of meteorological and air quality variables (13 weather stations and 16 air quality stations), the framework is applied to evaluate two greening scenarios and their potential to mitigate heat stress and improve air quality across the metropolitan area.

Results highlight the applicability of this integrated modeling chain as a decision-support tool for assessing urban planning and climate adaptation strategies, with direct implications for enhancing resilience in cities facing growing environmental pressures.

Nella conservazione dei beni culturali rivestono particolare importanza il monitoraggio e la valutazione del microclima relativo all’opera stessa. In questo lavoro consideriamo le condizioni microclimatiche della Chiesa di San Panfilo di Tornimparte (AQ) dove è stata condotta una campagna di misura microclimatica (Ferrarese et al., 2023). La chiesa (XII-XIII secolo) è di grande interesse storico ed artistico in quanto ospita nel presbiterio un ciclo di affreschi del pittore rinascimentale Saturnino Gatti (1494).

Le condizioni microclimatiche sono state misurate per circa un anno in diversi punti all'interno della chiesa e in due siti all’esterno: un primo in prossimità dell'edificio e un secondo presso la più vicina stazione meteorologica. Il presente lavoro si propone di descrivere la campagna di monitoraggio e le misure effettuate durante tutto l’anno e quindi di analizzare le condizioni microclimatiche interne ed esterne. Il clima storico all’interno della chiesa è stato identificato applicando la normativa corrente e la discussione dei risultati ha permesso di identificare eventi potenzialmente pericolosi per la conservazione degli affreschi.

Le condizioni interne ed esterne sono state confrontate utilizzando alcuni indici statistici: PI (Performance Index), IME (Index of Microclimatic Excursion), IMV (Index of Microclimatic Variability), NDR (Normalized Diurnal Range), RHratio (ratio in Relative Humidity) e il raggio minimo dei micropori vuoti (Racca et al., 2024).

I risultati mostrano che tutti gli indici sono in grado di distinguere tra condizioni interne ed esterne, mentre IME, IMV e NDR sono anche sensibili alle diverse condizioni all'interno della chiesa. Tra gli indici, l'IMV sembra descrivere meglio le condizioni microclimatiche, poiché è definito utilizzando sia la temperatura che l'umidità relativa e non dipende da soglie basate sugli standard o sull'esperienza dei curatori. Gli indici si sono dimostrati uno strumento utile per confrontare diverse condizioni microclimatiche e potrebbero essere inclusi nelle pratiche per la valutazione del microclima.

Soiling deposition is a widespread issue affecting photovoltaic (PV) systems of all types, with varying characteristics depending on the geographical location, the season, the prevailing weather conditions, and the geometry of the system (e.g., tilt angle, panel type, surface treatments, etc). There are instruments capable of estimating the presence of soiling on PV panels, such as optical measurement sensors that analyze the type of deposited particles, or comparative techniques like the daily manual cleaning of radiometers. However, these methods are typically expensive and complex, making them unsuitable and economically unfeasible for Commercial & Industrial (C&I) systems. The objective of this study is to estimate the degree of soiling through a comparison between ground-based irradiance measurements using radiometers and irradiance data derived from MSG (Meteosat Second Generation) meteorological satellite observations. By analyzing these two data sources—one affected by soiling and the other independent from it—it was possible to develop a Performance Ratio Index capable of assessing the degree of soiling on the ground-based radiometer, and consequently, the level of soiling on the photovoltaic system itself. Post-processing techniques (such as filtering out low irradiance values, applying temporal moving windows of variable size, etc.) were required to isolate the information related to soiling while eliminating other sources of noise and interference present in the measured data. A comparison with dry and wet deposition events of atmospheric pollutants (dust and PM10), using data from the CAMS (Copernicus Atmosphere Monitoring Service) project, enabled the evaluation of the system’s ability to correctly identify major soiling events, contributing to its calibration and optimization. This automated system for detecting the soiling level in PV installations is particularly suitable for operational use in C&I systems, which can benefit from targeted cleaning interventions that offer a positive economic return when significant soiling is detected.

Wildfires have become increasingly frequent in southern Europe, particularly in Spain, Portugal, Italy, and Greece. Although fire is a natural component of Mediterranean ecosystems, the expansion of recreational use of natural and forest areas has increased the number of human-caused fires. Climate change further exacerbates this situation, intensifying extreme temperatures and droughts and altering two of the three primary drivers of wildfires: fuel and weather.
The Canadian Forest Fire Weather Index (CFWI) is a meteorologically based index widely adopted to estimate fire danger. It requires only temperature, wind speed, relative humidity, and precipitation as input, and consists of six components: three fuel-moisture codes and three fire behavior indices. Although originally developed for Canadian boreal conditions, the CFWI has been applied in Mediterranean regions due to its simplicity and reliance on standard meteorological data. Studies have shown its ability in capturing fire danger in Mediterranean environments, though further evaluation has been recommended, especially in drier landscapes. However, the CFWI is a fire weather index that does not explicitly resolve the moisture content in the basic fuel moisture classes (1h, 10h, 100h, 1000h) required for the initialization of the fuel moisture in modeling systems based on the Rothermel model, such as WRF-SFIRE. This work investigates the integration of fuel moisture into wildfire simulations of Italian case studies using the coupled atmosphere-fire model WRF-SFIRE. The model includes a predictive time lag dead fuel moisture model to resolve spatial and temporal variability in the fuel moisture content, and the influence of fuel flammability on fire spread. This study examines various methods for treating the fuel moisture in the model. We compare the results of simulations performed under three approaches: (i) uniform fuel moisture initialization kept constant during the simulation, (ii) dynamic fuel moisture modeling using a time-lag model initialized from the equilibrium moisture content, and (iii) dynamic fuel moisture modeling with initialization based on fuel moisture values derived from FWI moisture codes. The objectives of this study are multiple. First, to evaluate the skill of WRF-SFIRE in reproducing Italian wildfire events. Second, to quantify the impact of spatial fuel moisture variability in the fuel-moisture initialization on fire spread simulations. A further objective is to assess whether FWI codes can reliably be mapped to dead-fuel moisture classes for initializing WRF-SFIRE. Finally, the study aims to evaluate the capability of WRF to reproduce CFWI time series and thresholds, and to examine the potential of WRF-derived FWI as a diagnostic tool for fire danger in Mediterranean regions.

Research project implemented under the National Recovery and Resilience Plan (NRRP), Project title “National Biodiversity Future Center -NBFC”. CUP J33C22001190001