In nuclear facilities, managing atmospheric contamination risks from the release of radioactive aerosols is critical for maintaining safety and radiation protection. Continuous Air Monitors (CAMs) assess such risks by continuously sampling ambient aerosol and measuring air activity concentrations. Despite their utility, CAMs face challenges in environments containing both radon progeny aerosols and larger non-radioactive particles, particularly in dismantling sites. These challenges lead to the degradation of alpha spectra and potential false alarms. This study introduces a simulation-based model to replicate and understand the behaviour of CAMs in such environments, aiming to refine their performance under atypical conditions. This simulation-based model combines a Diffusion-Limited Aggregation (DLA) algorithm to simulate aerosol deposition and a Monte Carlo (MC) algorithm to model alpha particles transport through these deposits, thereby replicating the detector response in such environments. Preliminary results demonstrate the model's promising capabilities, with the impact of individual aerosols accurately represented, validating the performance of the MC approach. Ongoing efforts aim to deepen the representation of aerosol deposition processes, thereby leading to improve the model’s results.