AIMC Topic: Air Microbiology

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Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international Feb 4, 2025
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...
Fungi Spores, Fungal Air Pollution, Indoor Cyclonic Storms Air Microbiology Floods Housing Humans Machine Learning Algorithms Models, Theoretical
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Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.

Environmental pollution (Barking, Essex : 1987) Jan 16, 2025
Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilities. The risk of chronic exposure can be significantly reduced by accurately predicting airborne funga...
Air Microbiology Public Facilities Fungi Environmental Monitoring Air Pollutants Air Pollution, Indoor Particulate Matter Machine Learning
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Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Science advances Jan 8, 2025
Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep ...
Deep Learning Single-Cell Analysis Humans Algorithms Spectrum Analysis, Raman Bacteria Air Microbiology
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Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning.

Environmental pollution (Barking, Essex : 1987) Oct 28, 2024
Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originat...
Cladosporium Air Microbiology Fungi Air Pollutants Machine Learning Environmental Monitoring Gas Chromatography-Mass Spectrometry Volatile Organic Compounds Air Pollution, Indoor Solid Phase Microextraction
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Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).

International journal of environmental health research Apr 27, 2019
Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statis...
Neural Networks, Computer Fungi China Housing Air Microbiology Machine Learning Environmental Monitoring
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Dew point temperature affects ascospore release of allergenic genus Leptosphaeria.

International journal of biometeorology Jan 27, 2018
The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded ...
Allergens Spores, Fungal Cities Air Pollutants Environmental Monitoring Models, Theoretical Ascomycota Microclimate Forecasting Neural Networks, Computer Europe Temperature Air Microbiology
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