The ARCADE Raman lidar has been tested and validated at L'Aquila before the deployment at CTA (Cherenkov Telescope Array) North site. Some modifications have been done on the original ARCADE system to improve its performances, and an extensive programme of measurements has been performed. The Raman lidar (RL) technique is discussed with specific care to the technical constrains of RL systems, and the signal analysis, i.e., the estimation of aerosol optical depth, volume backscatter coefficient, and water vapour profiles and their significance (errors and resolution). Some final comments and conclusions are outlined.

<p>Many cities worldwide extend upon alluvial aquifers which have a great potential for low temperature geothermal installations. Typically, the geothermal potential describes the ability to exchange heat with the subsurface and the relative sustainability.</p> <p>To estimate the geothermal potential of shallow aquifers many techniques have been adopted such as analytical solutions and numerical methods considering aquifer thermal parameters (e.g. porosity, thermal diffusivity) and the system configuration (e.g. diameter of pipes, borehole thermal resistance). Analytical methods are typically fast and easy to implement in a GIS environment but commonly neglect the effects of groundwater advection on heat transfer mechanisms. On the other hand, physically based numerical methods can handle conductive and advective transport and complex 3D geometries but have the limitation of domain size/resolution that makes modeling unfeasible at scales greater than city districts or cities.</p> <p>Hence, a new solution based on a surrogate model is presented to estimate the geothermal potential of aquifers at large scale covering a great variability of Darcy flow velocity. The model is based on the response of a synthetic transient-state 3D FEM model reproducing the infinite line source (ILS) configuration. The simulated thermal perturbation over a representative volume at different time stages was then used to calculate the thermal resistance of the aquifer and the corresponding (energy replenishment) potential combining the most relevant variables that affect the heat transport in porous media: thermal conductivity, specific heat capacity, saturation, porosity and flow velocity.</p> <p>Then, a machine learning regression-based surrogate model was generated by fitting the calculated response (thermal potential) for all possible combinations of input variables. The proposed model well replicates the ASHRAE analytical solution which is based on the ILS method for no groundwater flow, and goes beyond including the effects of thermal transport by groundwater.</p> <p>Finally, the model response was implemented in a GIS to obtain large scale geothermal potential maps in areas with highly variable groundwater flow velocity (between 10<sup>-5</sup> to 10 m/d) highlighting an expected increase of the geothermal potential due to the advective transport. Field experiments are necessary to verify the numerical findings aiming to reconsider the commonly adopted temperature delta thresholds in areas where the energy replenishment potential is high due to groundwater advection.</p>

<p>Low enthalpy geothermal applications such as ground coupled heat pumps (GCHP) and groundwater heat pumps (GWHP) are an attractive low-carbon solution for heating and cooling of buildings. Their efficiency depends on the subsurface thermal properties (e.g. thermal conductivity, heat capacity) and the hydrogeological/thermal regimes (e.g. groundwater flow, depth of the water table, temperature profile). The geothermal potential is an indicator used to quantify and compare the ability to exchange heat with the subsurface/groundwater according to specific technologies. Even though it has no unique definition, it is often obtained as a combination of the subsurface hydrogeological/thermal properties and the thermal regime and, by means of GIS techniques it can be spatialized to obtain geothermal potential maps.</p><p>The subsurface thermal properties vary in space according to the geological setting, while the hydrogeological and thermal regimes can vary both in space and time according to the fluid and heat budgets of the aquifers. However, despite few studies consider the variability of the geothermal potential in time due to possible variations of the hydrogeological and thermal regimes, it is essential to evaluate the efficiency of geothermal systems in a changing environment such as subsurface urban heat islands. The hydrogeological/thermal regimes are not stationary, especially beneath big cities where land and subsurface uses control the elevation of the water table and the shallow subsurface thermal regime. Moreover, the heating and cooling demand of buildings may vary due to climate change effects such as global warming and atmosphere urban heat island.</p><p>The potential to exchange heat with the subsurface in the Milan metropolitan area was estimated from hydrogeological and thermal regimes simulated by a fluid-flow/heat-transport city-scale numerical model, calibrated on the current state. Several scenarios were generated changing the boundary conditions according to projected changes of (I) the air temperature (based on RCP 2.6, 4.5 and 8.5 scenarios), (II) the groundwater head, (III) the land use/city size and (IV) the geothermal uses (based on the increment of installations and changes of the thermal demand), to estimate the changes and the seasonal variability of the subsurface temperature at different depths in different zones of the city. Finally, the future variations of the thermal potential were estimated for heating and cooling seasons combining the scenarios-projected subsurface temperatures with the hydrogeological and thermal properties, also considering the variation of heating and cooling thermal loads.</p>