Odour impact assessment can be a very complex issue for plants characterized by variable emissions associated to different operating conditions occurring with unpredictable frequencies. An example of this type of plants are foundries which, in general, are equipped to treat, transform and process a variety of raw materials in order to reach the required prerequisites of their final products (i.e., alloys). Thus, depending on the raw materials entering the plant, it could be necessary to change very often, even several times a day, the operating conditions of the plant, leading to a continuous variation of their emissions. In this context, the present work aims to describe a methodology to characterize such complex and variable emission scenario from an olfactory point of view. This case-study focuses on the evaluation of the odour impact assessment of a non-ferrous metal smelting and alloying plant whose odorous emissions demonstrated to strongly differ from each other depending on the processing phase and type of raw materials used. Firstly, several olfactometric campaigns were carried out in different seasons and considering different operating conditions. By analysing the odour concentration values by means of a one-way ANOVA test, two “macro-classes” of operating conditions characterized by statistically different odour emissions were identified: “Low” odour condition and “High” odour condition. Then, a randomized dispersion model was developed to simulate the variability of emissions of the plant through a statistical approach implementing a specific MATLAB® function to randomly distribute the occurrence of the higher-emission conditions over the year. Finally, 6 different scenarios have been simulated by CALPUFF and the results obtained have been compared and evaluated, proving the importance of investigating the model sensitivity towards the choice of the hours in which the higher-emissions conditions occur.