SCIENTIFIC PUBLICATIONS

• Winter warm spells over Italy: Spatial–temporal variation and large‐scale atmospheric circulation. Di Bernardino, A., Iannarelli, A. M., Casadio, S., Siani, A. M. (2024). International Journal of Climatology, 44(4), 1262-1275. 

DOI: 10.1002/joc.8388

ABSTRACT: This study analyses winter warm spells (WWS) in the central Mediterranean from 1993 to 2022 using daily maximum temperature data from eight Italian airport stations. WWS are defined as sequences of at least six consecutive days in which maximum temperatures exceed the calendar-day 90th percentile, calculated over a 5-day centred window for the reference period. The analysis identifies both regional and nationwide events and examines their synoptic drivers. December is the most affected month, with average durations of 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Only one WWS influenced the entire peninsula, associated with persistent high-pressure systems. The study also highlights that the 5-day threshold may underestimate moderate WWS in complex terrain, suggesting the need for a shorter duration criterion.

• Temperature trends and influence of the base period selection on climate indices in the Mediterranean region over the period 1961-2020. Di Bernardino, A., Casadio, S., Iannarelli, A. M., & Siani, A. M. (2024). International Journal of Climatology, 44(16), 5969-5985.

DOI: 10.1002/joc.8678

ABSTRACT: This study analyses daily maximum and minimum temperatures from 18 Mediterranean coastal stations over 1961–2020 to assess temperature trends and compute 10 climate indices related to temperature extremes. Using the Seasonal-Kendall test, significant positive trends emerge across the region, with stronger warming in minimum than in maximum temperatures, especially in the western Mediterranean after 1990. Climate indices are calculated using two base periods (1961–1990 and 1991–2020), showing that shifting to a later, warmer baseline raises percentile-based thresholds, resulting in fewer identified warm extremes and more cold extremes. Trends in extreme events are most sensitive to the base-period choice in the western Mediterranean and the Adriatic Sea. The study demonstrates that the selected baseline strongly influences the detection of extremes and that using a recent period may partially obscure ongoing warming.

• A Machine Learning Algorithm to Convert Geostationary Satellite LST to Air Temperature Using In Situ Measurements: A Case Study in Rome and High-Resolution Spatio-Temporal UHI Analysis. 

Cecilia, A., Casasanta, G., Petenko, I., & Argentini, S. (2025). Remote Sensing, 17(3), 468.

DOI: 10.3390/rs17030468

ABSTRACT: This study develops a gradient-boosting machine learning model to estimate air temperature from geostationary satellite land surface temperature (LST) data and uses these estimates to analyse urban heat island (UHI) dynamics. Using Rome, Italy, as a case study, the model is trained with air temperature observations from 15 weather stations, incorporating multi-temporal LST inputs along with additional predictors. The resulting temperature fields, produced at 3 km spatial and hourly temporal resolution, allow for a detailed assessment of UHI intensity during the summers of 2019–2020, offering far greater spatiotemporal detail than analyses based solely on in situ measurements. Results also indicate slightly higher nocturnal UHI intensities compared to earlier studies, a difference attributed to the improved representation of rural, low-imperviousness areas now included in the full-domain temperature mapping.

• Assessment of the urban pollution island intensity in Rome (Italy) from in-situ PM measurements. Di Bernardino, A., Erriu, M., Argentini, S., Campanelli, M., Casasanta, G., Cecilia, A., Falasca, S., and Siani, A. M. (2024). Bulletin of Atmospheric Science and Technology, 5(1), 10.

DOI: 10.1007/s42865-024-00071-0

ABSTRACT: This study evaluates the Urban Pollution Island Intensity (UPII) in Rome, Italy, using daily PM10 and PM2.5 concentrations from 2018 to 2023. UPII quantifies the pollution difference between urban areas and surrounding rural zones, but its assessment strongly depends on the selection of reference stations, influenced by factors such as orography, station location, and street orientation. Three methods for calculating UPII are tested, varying the subset of urban stations based on environmental classification.

Results show minor differences between “urban traffic” and “urban background” stations, indicating that proximity to emission sources moderately affects concentrations, likely due to limited ventilation in street canyons. PM10 is particularly sensitive to station selection, with winter differences between methods reaching 100%. The study suggests that including a larger number of urban stations improves UPII estimation and underscores that careful station selection is essential for accurately assessing urban pollution and designing effective air quality management strategies.

• Exploring different methods to evaluate the Urban Pollution Island Intensity based on multi-year observations of aerosol and gases. Di Bernardino, A., Erriu, M., Falasca, S., and Siani, A. M. (2025). Atmospheric Pollution Research, 102677. 

DOI: 10.1016/j.apr.2025.102677

ABSTRACT: This study proposes methodologies to evaluate Urban Pollution Island Intensity (UPII) in Rome, Italy, using in-situ measurements of PM10, PM2.5, NO, NO2, and O3 from 2018 to 2023, varying the subset of urban stations according to their environmental classification. Urban traffic stations consistently recorded higher pollutant concentrations than urban background sites, and combining both types provides the most representative assessment of UPII and metropolitan pollution levels.

Seasonal variations are largest for PM10 and NO, while daily cycles show bimodal peaks corresponding to rush hours; NO2 peaks are delayed by one hour, and O3 exhibits positive UPII values during summer rush hours. Multi-pollutant indices indicate poor air quality, especially in colder months. Pollution is primarily driven by NO2, PM10, and O3 in central areas and by PM2.5 and O3 in rural surroundings. Variations reflect combined emission, meteorological, and photochemical influences, highlighting the need to consider multiple factors when designing targeted air quality management strategies.

• Dynamical and chemical impacts of urban green areas on air pollution in a city environment. Biagi, B., Brattich, E., Cintolesi, C., Barbano, F., Di Sabatino, S. (2025). Urban Climate, 60, 102343.

DOI: 10.1016/j.uclim.2025.102343

ABSTRACT: This study evaluates the impact of vegetation on urban air pollution in Bologna, Italy, using a methodology that incorporates both the aerodynamic and emission effects of trees. The aerodynamic effect is represented by modifying surface roughness through a morphometric approach, while tree emissions are modelled as point sources based on species characteristics and environmental conditions. The ADMS-Urban model simulates pollutant variability during summer 2023 in a densely vegetated neighbourhood, with results validated against measurements from an ad-hoc campaign. Simulations show that emissions from green areas can locally increase pollutant concentrations by up to 7.4% during the day, causing persistent accumulation in surrounding areas. The findings indicate that vegetation emissions significantly influence urban air quality, with effects comparable to building structures. This underscores the need to consider tree emissions in urban dispersion models to accurately assess pollution and optimize the design of green nature-based solutions.

• Influence of atmospheric parameters on the interaction between Urban Heat and Pollution Islands in a Mediterranean coastal city. Di Bernardino, A., Argentini, S., Brattich, E., Campanelli, M., Casasanta, G., Cecilia, A., Erriu, M., Falasca, S., Faggi, A., Siani, A.M. (2026). Atmospheric Research 332, 108702

DOI: 10.1016/j.uclim.2025.102343

ABSTRACT: Urban Heat Island (UHI) and Urban Pollution Island (UPI) processes shape urban climate and air quality, yet their interaction remains insufficiently quantified, particularly in Mediterranean coastal cities. Existing research often examines these phenomena separately or over short time spans, leaving uncertainties regarding the meteorological drivers governing the UHI-UPI co-evolution. This study provides a multi-year, observation-based assessment of the coupled dynamics between Urban Heat Island Intensity (UHII) and Urban Pollution Island Intensity (UPII) in Rome (Italy), focusing on the atmospheric conditions that modulate their relationship. Air temperature, humidity, and wind speed, together with major air pollutants (PM10, PM2.5, NO2, NO, and O3), were analysed using an integrated statistical framework. Lag-correlation analysis revealed that the strongest UHII-UPII relationship occurs when nocturnal UHII is shifted backward by one day, reflecting daytime pollutant accumulation and nighttime trapping. Regression results highlighted daily mean air temperature and wind speed as the primary drivers modulating the UHII-UPII association. Spearman correlations showed negative associations between UHII and NO (−0.60), PM10 (−0.45), NO2 (−0.35), and PM2.5 (−0.34), alongside positive correlations with O3 (0.54) and NO2/NO (0.42). These correlations intensified during heatwaves and calm wind conditions, suggesting enhanced interactions under extreme weather and stagnant atmospheric conditions. UHII peaks in summer, while UPII maximizes in winter for all pollutants except for O₃, which exhibits an opposite pattern. These findings reveal a complex interplay between urban warming and pollutant accumulation, highlighting the need for integrated urban planning to address joint UHII-UPII challenges under ongoing urbanization and intensifying severe heat episodes.

• A Machine Learning Algorithm to Convert Geostationary Satellite LST to Air Temperature Using In Situ Measurements: A Case Study in Rome and High-Resolution Spatio-Temporal UHI Analysis. Cecilia, A., Casasanta, G., Petenko, I., & Argentini, S. (2025). Remote Sensing, 17(3), 468.

doi:10.3390/rs17030468

ABSTRACT:  Air temperature (𝑇𝑎) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. This study aims to develop a machine learning-based model, namely gradient boosting, to estimate 𝑇𝑎 from geostationary satellite LST data and to apply these estimates to investigate UHI dynamics. Using Rome, Italy, as a case study, the model was trained with 𝑇𝑎 data from 15 weather stations, taking multi-temporal LST values (instantaneous and lagged up to 4 h) and additional predictors. The model achieved an overall RMSE of 0.9 °C. The resulting 𝑇𝑎 fields, with a 3 km spatial and hourly temporal resolution, enabled a detailed analysis of UHI intensity and dynamics during the summers of 2019–2020, significantly enhancing the spatial and temporal detail compared to previous studies based solely on in situ data. The results also revealed a slightly higher nocturnal UHI intensity than previously reported, attributed to the inclusion of rural areas with near-zero imperviousness, thanks to the complete mapping of 𝑇𝑎 across the domain now accessible.

• Temperature Differences Between Rooftop and Urban Canyon Sensors: Diurnal Dynamics, Drivers, and Implications. Marinelli, L., Cecilia, A., Casasanta, G., Conidi, A., Petenko, I., & Argentini, S. (2025). Sensors, 25(13), 4121.

doi:10.3390/s25134121

 ABSTRACT: nderstanding temperature variations within the complex urban canopy layer (UCL) is challenging due to limitations and discrepancies between temperature measurements taken in urban canyons and on rooftops. The key question is how much these measurements differ and what factors contribute to these differences. According to the guidance by the World Meteorological Organization (WMO), rooftop observations are not encouraged for urban monitoring, due to potentially anomalous microclimatic conditions, whereas measurements within urban canyons are recommended. This is particularly relevant given the increasing number of rooftop sensors deployed through citizen science, raising questions about the representativeness of such data. This study aimed to address this knowledge gap by comparing temperatures within the UCL using two sensors: one located on a rooftop, and the other positioned within the canyon. The temperature difference between these two nearby locations followed a clear diurnal cycle, peaking at over 1 °C between 12:00 and 16:00 local time, with the canyon warmer than the rooftop. This daytime warming was primarily driven by solar radiation and, to a lesser extent, by wind speed, but only under clear-sky conditions. During the rest of the day, the temperature difference remained negligible.

• The impact of tree canopy cover and imperviousness on air temperature during summer using low cost sensors on public transportation in Rome, Italy. Cecilia, A., Marinelli, L., Borghi, C., Casasanta, G., Chirici, G., Conidi, A., & Argentini, S. (2026).  

  Urban Climate, 66, 102832

doi:10.1016/j.uclim.2026.102832

ABSTRACT: This study investigates the relationship between Air Temperature (AT), tree canopy cover, and imperviousness in Rome, Italy, using a novel approach based on low-cost sensors mounted on public buses. The system operates autonomously, requiring no on-site personnel, and provides continuous measurements across the entire urban area and at all hours of the day. Data were collected over 53 clear-sky summer days under stable meteorological conditions and aggregated onto a 500 m grid after quality control and normalization.Results show a statistically significant cooling associated with canopy cover only during the early morning hours, before the onset of the sea-breeze circulation, highlighting the role of mesoscale ventilation in suppressing local daytime cooling in coastal cities. At night, AT exhibits a strong linear increase with imperviousness, with differences up to 3.6 °C between fully urbanized and non-urbanized areas. The diurnal cycle of Urban Heat Island (UHI) intensity, derived from the imperviousness-based method, shows negligible values during daytime and peaks of 3–4 °C at night.Unlike previous assessments in Rome based on fixed monitoring stations, the dense spatial coverage provided by mobile measurements allows a more robust reconstruction of the diurnal UHI cycle and reveals that previously reported daytime UHI signals were likely influenced by sea-breeze-induced thermal gradients.By leveraging automated, citywide measurements with low-cost sensors, this study provides new insights into the spatial and temporal variability of urban heat and supports the development of targeted adaptation strategies.