Environmental data collected by European and national agencies are at the centre of environmental policies and often require change of support, data calibration and data fusion. Appropriate statistical tools are necessary to harmonise data with different spatial and temporal resolutions from heterogeneous sources to compute various aggregation statistics and measure uncertainty. This necessity is particularly evident within the ongoing Italian extended partnership “Growing Resilient INclusive and Sustainability” (GRINS), funded under the National Recovery and Resilience Plan (NRRP) by the European Union - NextGenerationEU. The GRINS project aims at developing a national platform called “AMELIA” where hundreds of variables will be harmonised to the same resolution, i.e. municipality and daily. In this study, we propose a geostatistical methodology. In particular, we will compare two well-known spatio-temporal models, Fixed Rank Kriging (FRK) and Hidden Dynamic Geostatistical Model (HDGM) declined toward data harmonisation purposes. Our study will show a real-world application to the Italian air quality, highlighting the peculiarities of the two models.