This report describes the work carried out to create the new final map of contamination levels across Italy, as part of the collaboration between RSE and Terna, using a multidisciplinary methodology that includes both experimental approaches, with periodic measurements, and modeling approaches. This approach is one of the first applications to estimate pollution levels that electric line insulators are exposed to, incorporating an air quality assessment modeling system, a method developed exclusively within the RdS framework.

To achieve this goal, in previous years, 211 chains of glass insulators were installed, from which both soluble (saline) and insoluble deposits were collected and analyzed.

Using this data, in the previous three-year period, an algorithm was developed to reconstruct the maximum contamination levels on the insulators based on two measured indicators: the potential conductivity of the soluble fraction (ESDD) and the total mass of the insoluble deposit (NSDD). These values were then adjusted based on daily cumulative precipitation data from the nearest weather station to the insulator, to reconstruct the actual maximum value for the period.

Based on the ESDD and NSDD data, both reprocessed with the algorithm and derived directly from raw measurements, it was possible during this year’s activities to reconstruct the corresponding Equivalent Salinity value, an indicator that replaced the previous Site Pollution Severity in the final mapping. The Equivalent Salinity value, calculated for the entire Italian territory, showed an increasing gradient from north to south and from inland areas towards the coast. The results indicated that salinity is indeed the most suitable parameter for representing the contamination level of insulating surfaces in a peninsular region like Italy.

From this data, a preliminary grid-based map was created, derived solely from point observations using an interpolation algorithm. Specifically, the algorithm chosen uses the inverse distance as a weight in the interpolation process. However, the proposed interpolation algorithm did not provide adequate coverage for some areas due to the scarcity and low representativeness of available data. As a result, data fusion techniques were explored to integrate observed data with gridded fields of ESDD and NSDD produced by a model. The choice of technique involved an in-depth literature review of various available methods, which identified Optimal Interpolation (OI) as the best method for this work. This method was applied to the observed salinity field and the modeling data derived from dry deposition fields produced nationally by the SMOKE-WRF-CAMx air quality modeling system, operational at RSE, with a resolution of 4 km.

The final map, validated through cross-validation, showed that after the OI process, the original salinity field was reshaped to account for observed measurements. The final field closely resembled the modeled field in northern Italy, where the model results were already close to observations, while the influence of the measurements was more evident in central-southern regions and the major islands, where the model struggled to reproduce the variability of experimental data. However, the technique also showed limitations, as it tended to underestimate higher salinity values. Therefore, this spatialization technique will need further investigation in future activities.