Application of Atmospheric Dispersion Modelling for Optimal Siting of a Waste-to-Energy Plant in Complex Terrain: A Case Study in the Province of Trento

The Province of Trento is currently assessing the feasibility of a WtE facility to enhance the efficiency of local waste management. However, the region is characterized by deep Alpine valleys and complex meteorological conditions prone to poor atmospheric dispersion. Consequently, identifying a site that promotes optimal dispersion and minimizes pollutant accumulation is a critical environmental priority. This thesis presents a modelling procedure designed to support the decision-making process for the siting of such facilities. The meteorological field was first reconstructed using the CALMET diagnostic model, generating 3D wind fields utilized to drive the CALPUFF modelling system. This non-steady-state Lagrangian puff model was chosen for its ability to accurately simulate pollutant transport over complex terrain. The dispersion analysis focused on Particulate Matter (PM10) and Nitrogen Dioxide (NO2) comparing simulations across three distinct geographic domains within the Adige Valley, each with a grid resolution of 125 m. Concentration and deposition maps were produced using CALPOST and visualized in QGIS. Building upon the dispersion results, a localized Health Impact Assessment (HIA) was conducted to evaluate the exposure of the resident population. By applying epidemiological risk coefficients and baseline health data, the spatial distribution of excess risk for general mortality and myocardial infarction was quantified for each domain. The results revealed clear differences in how the complex terrain traps pollution and how these dispersion patterns directly intersect with local settlements. By comparing the calculated health risks and environmental footprints across the domains, the most suitable site was identified. Ultimately, this study demonstrates that combining advanced atmospheric modelling with spatial health impact assessments is essential for the safe and strategic planning of industrial infrastructure.