Indoor monitoring of biological contamination is essential for protecting cultural heritage and public health. However, conventional culture-based methods limit timely intervention. This study presents an affordable modular multisensor system for indirectly detecting airborne fungal contamination using Penicillium chrysogenum as a representative model organism and its environmental signatures. The proposed prototype integrates PMSA003I, BME688 and AMG8833 sensors and was evaluated under controlled environmental conditions. Biological ground truth was established using a volumetric inertial-impaction sampling protocol (SAS sampler), validating four contamination levels (~6 to 165, CFU/m3). A total of 1989 observations were analyzed. Non-parametric statistical tests (Kruskal–Wallis and Mann–Whitney U) confirmed significant differences between all the exposure conditions (p<0.001). Supervised machine learning (ML) models showed strong performance across all the classification tasks, with accuracy and AUC values near 100%. In most cases, pressure alone was sufficient. The statistical and ML analyses consistently identified pressure, particulate-related variables, gas resistance and humidity as the most informative features. Overall, the results indicate that the proposed approach can reliably capture indirect environmental signatures associated with airborne fungal presence under controlled conditions. The study supports the feasibility of low-cost multisensor systems for continuous indoor bioaerosol monitoring while highlighting the need for further optimization and validation in real-world environments.
Barbosa et al. (Tue,) studied this question.