Emissions of Fungal Volatile Organic Compounds in Residential Environments and Temporal Emission Patterns: Implications for Sampling Methods

Currently, little is known about the occurrences of fungi-derived microbial volatile organic compounds (mVOCs) in various indoor materials and their detection in residential environments, despite mVOCs being linked to several acute health effects. We identified various mVOCs emitted from fungi grown on PVC wallpaper, silicone rubber, and malt extract agar. We also investigated mVOCs temporal emission and whether fungi-derived VOCs concentration can be used to estimate fungal concentration in the air using active and passive air sampling methods. Among the three fungal growth media included in this study, silicone rubber produced the most variety of mVOCs: 106 compounds (from Aspergillus niger), 35 compounds (from Alternaria alternata), and 85 compounds (from Penicillium chrysogenum). We also found the emission patterns of eight chemical classes (i.e., aromatics, ethers, aliphatics, alcohols, ketones, aldehydes, chlorides, and nitrides) from the three different fungi. From the results of our field experiments in 11 residential environments, passive air samplers led to higher correlations coefficients (0.08 to 0.86) between mVOCs' air concentrations and airborne fungal concentrations, compared with active air samplers, which showed negative correlation coefficients (-0.99 to -0.02) for most compounds. This study elucidated the occurrence and temporal emission patterns of fungal VOCs in residential environments.

Phenotypic variation from waterlogging in multiple perennial ryegrass varieties under climate change conditions

Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four common varieties (one diploid and three tetraploid) grown in atmospherically controlled growth chambers during two months of peak growth. The climate treatments compare ambient climatological conditions in North-western Europe to the RCP8.5 climate change scenario in 2050 (+2°C and 550 ppm CO2). At the end of each month multiple phenotypic plant measurements were made, the plants were harvested and then allowed to grow back. Using image analysis and principal component analysis (PCA) methodologies, we assessed how multiple predictors (phenotypic, environmental, genotypic, and temporal) influenced overall plant performance, productivity and phenotypic responses. Long-term waterlogging was found to reduce leaf-color intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than the diploid one. The climate change treatment was found to reduce color intensities further. Flooding was found to reduce plant productivity via reductions in color pigments and root proliferation. These effects will have negative consequences for global food security brought on by increased frequency of extreme weather events and flooding. Our imaging analysis approach to estimate effects of waterlogging can be incorporated into plant health diagnostics tools via remote sensing and drone-technology.

Data mining approaches to understanding the formation of secondary organic aerosol

This research used data mining approaches to better understand factors affecting the formation of secondary organic aerosol (SOA). Although numerous laboratory and computational studies have been completed on SOA formation, it is still challenging to determine factors that most influence SOA formation. Experimental data were based on previous work described by Offenberg et al. (2017), where volume concentrations of SOA were measured in 139 laboratory experiments involving the oxidation of single hydrocarbons under different operating conditions. Three different data mining methods were used, including nearest neighbor, decision tree, and pattern mining. Both decision tree and pattern mining approaches identified similar chemical and experimental conditions that were important to SOA formation. Among these important factors included the number of methyl groups for the SOA precursor, the number of rings for the SOA precursor, and the presence of dinitrogen pentoxide (N₂O₅).

Influence of pulmonary ventilation rate and breathing cycle period on the risk of cross-infection

This study examined the characteristics of the exhaled airflow pattern and breathing cycle period of human subjects and evaluated the influence of pulmonary ventilation rate and breathing cycle period on the risk of cross-infection. Measurements with five human subjects and a breathing thermal manikin were performed, and the peak exhaled airflow velocity from the mouth and the breathing cycle period were measured. Experiments on cross-infection between two breathing thermal manikins were then conducted in a full-scale test room, in which the pulmonary ventilation rate and breathing cycle period were varied systematically. Both peak flow velocity and breathing cycle length varied considerably between different subjects. The breathing cycle period in a standing posture was 18.9% lower than in a sitting posture. The influence of pulmonary ventilation rate and breathing cycle period extended up to a separation distance of 1.0 m between the two manikins. Increasing the pulmonary ventilation rate of the exposed person greatly increased the risk of cross-infection. Decreasing the breathing cycle period from the widely used "6 second" value led to a considerable increase in the risk of cross-infection. Standing posture resulted in a higher risk of cross-infection than sitting posture.