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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

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Integrated PM-MOX-Thermal Sensing for Monitoring Bioaerosol Dynamics in Controlled Indoor Environments.

Maria Inês Barbosa¹, Hugo Roxo¹, Pedro Ribeiro¹

¹CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.

Sensors (Basel, Switzerland)

|June 12, 2026

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This study introduces an affordable multisensor system for detecting airborne fungal contamination by analyzing environmental signatures. Machine learning models achieved near 100% accuracy, demonstrating the system

Area of Science:

Environmental Science
Microbiology
Sensor Technology

Background:

Indoor biological contamination poses risks to cultural heritage and public health.
Traditional culture-based methods for detecting bioaerosols are slow, hindering timely interventions.

Purpose of the Study:

To develop and evaluate an affordable, modular multisensor system for indirect detection of airborne fungal contamination.
To assess the system's ability to identify environmental signatures associated with fungal presence.

Main Methods:

Integrated PMSA003I, BME688, and AMG8833 sensors into a prototype system.
Validated the prototype against established fungal contamination levels (CFU/m³) using a SAS sampler.
Employed non-parametric statistical tests and supervised machine learning (ML) for data analysis.

Keywords:

MOX Penicillium chrysogenum bioaerosols indoor contamination monitoring mVOCs multisensor systems particulate matter sensing thermographic sensing

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Main Results:

Significant differences in environmental conditions were confirmed across all exposure levels (p<0.001).
ML models demonstrated high accuracy (near 100%) in classifying fungal presence.
Pressure, particulate matter, gas resistance, and humidity were identified as key predictive features.

Conclusions:

The proposed multisensor system reliably captures indirect environmental signatures of airborne fungi under controlled conditions.
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Further real-world validation and optimization are recommended for practical application.