All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

The representation of spatial wind distribution is recognized as a serious difficulty when modeling the hydrodynamics of lakes surrounded by a complex topography. To address this issue, we propose to force a 3‐D lake model with the wind field simulated by a high‐resolution atmospheric model, considering as a case study a 61 km2 prealpine lake surrounded by mountain ranges that reach 1800 m above the lake's surface, where a comprehensive data set was available in the stratified season. The improved distributed description of the wind stress over the lake surface led to a significant enhancement in the representation of the main basin‐scale internal wave motions, and hence provided a reference solution to test the use of simplified approaches. Moreover, the analysis of the power exerted by the computed wind field enabled us to identify measuring stations that provide suitable wind data to be applied uniformly on the lake surface in long‐term simulations. Accordingly, the proposed methodology can contribute to reducing the uncertainties associated with the definition of wind forcing for modeling purposes and can provide a rational criterion for installing representative measurement locations in prealpine lakes.

Despite the progress made in recent years, reliable modeling of indoor air quality is still far from being obtained. This requires better chemical characterization of the pollutants and airflow physics included in forecasting tools, for which field observations conducted simultaneously indoors and outdoors are essential. The project “Integrated Evaluation of Indoor Particulate Exposure” (VIEPI) aimed at evaluating indoor air quality and exposure to particulate matter (PM) of humans in workplaces. VIEPI ran from February 2016 to December 2019 and included both numerical simulations and field campaigns carried out in universities and research environments located in urban and non-urban sites in the metropolitan area of Rome (Italy). VIEPI focused on the role played by micrometeorology and indoor airflow characteristics in determining indoor PM concentration. Short- and long-term study periods captured diurnal, weekly, and seasonal variability of airflow and PM concentration. Chemical characterization of PM10, including the determination of elements, ions, elemental carbon, organic carbon, and bioaerosol, was also carried out. Large differences in the composition of PM10 were detected between inside and outside as well as between different periods of the day and year. Indoor PM composition was related to the presence of people, to the season, and to the ventilation regime.

Ocean transport and dispersion processes are at the present time simulated using Lagrangian stochastic models coupled with Eulerian circulation models that are sup- plying analyses and forecasts of the ocean currents at unprecedented time and space resolution. Using the Lagrangian approach, each particle displacement is described by an average motion and a fluctuating part. The first one represents the advection associated with the Eulerian current field of the circulation models while the second one describes the sub-grid scale diffusion. The focus of this study is to quantify the sub-grid scale diffusion of the Lagrangian models written in terms of a horizontal eddy diffusivity. Using a large database of drifters released in different regions of the Mediterranean Sea, the Lagrangian sub-grid scale diffusion has been computed, by considering different regimes when averaging statistical quantities. In addition, the real drifters have been simulated using a trajectory model forced by OGCM currents, focusing on how the Lagrangian properties are reproduced by the simulated trajectories. © Springer-Verlag 2012

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