Preprints
https://doi.org/10.5194/egusphere-2025-3779
https://doi.org/10.5194/egusphere-2025-3779
26 Aug 2025
 | 26 Aug 2025
Status: this preprint is open for discussion and under review for Biogeosciences (BG).

Multi-stress interaction effects on BVOC emission fingerprints from oak and beech: A cross-investigation using Machine Learning and Positive Matrix Factorization

Biplob Dey, Toke Due Sjøgren, Peeyush Khare, Georgios I. Gkatzelis, Yizhen Wu, Sindhu Vasireddy, Martin Schultz, Alexander Knohl, Riikka Rinnan, Thorsten Hohaus, and Eva Y. Pfannerstill

Abstract. Forest ecosystems are increasingly stressed through heatwaves, drought periods, and other factors such as ozone pollution or insect infestations. These stressors have a profound impact on the emissions of biogenic volatile organic compounds (BVOC) from trees, which in turn influence aerosol formation and atmospheric oxidation cycles and thus feedback on the atmospheric cleansing capacity and climate change itself. While previous studies have investigated the impacts of specific stressors on BVOC emissions, analyses of combined stress effects are rare, even though the stressors seldomly occur in isolation. This study investigates the impact of heat and ozone stress, both individually and in combination, on BVOC emissions from two ecologically significant temperate tree species: European beech (Fagus sylvatica L.) and English oak (Quercus robur L.). In a climate-controlled chamber, both tree species were subjected to heat stress (38 ± 3.3 °C) and ozone stress (~120 ppb), separately and in combination. BVOC emission fluxes were measured using proton transfer reaction time-of-flight mass spectrometry, and the results were compared across pre-stress, heat, ozone, and combined heat-ozone conditions.

Heat stress elicited the strongest emission increases of isoprene, monoterpene, and green leaf volatiles in both species, while ozone suppressed the emissions of most BVOCs. Combined stress led to non-additive responses different from those in single-stress scenarios. Both machine learning and positive matrix factorization analyses were performed to identify key VOC fingerprint markers that may be applied to identify stress-impacted emissions from field data, and both methods showed good agreement. The OH reactivity of the emissions, which serves as a measure for their atmospheric chemistry and ozone formation impacts, was consistently highest under heat stress for both species. However, ozone stress led to reduced OH reactivity of emissions (by 10–18 %).

Our results underscore that the study of realistic combinations of stressors is crucial to understand future BVOC emissions and indicate that BVOC emissions could alter atmospheric chemistry and feedback with air quality and climate as heatwaves and pollutant-induced stress become more frequent due to climate change.

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Biplob Dey, Toke Due Sjøgren, Peeyush Khare, Georgios I. Gkatzelis, Yizhen Wu, Sindhu Vasireddy, Martin Schultz, Alexander Knohl, Riikka Rinnan, Thorsten Hohaus, and Eva Y. Pfannerstill

Status: open (until 07 Oct 2025)

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Biplob Dey, Toke Due Sjøgren, Peeyush Khare, Georgios I. Gkatzelis, Yizhen Wu, Sindhu Vasireddy, Martin Schultz, Alexander Knohl, Riikka Rinnan, Thorsten Hohaus, and Eva Y. Pfannerstill

Data sets

Data set: BVOC fluxes from oak and beech under ozone and heat stress Biplob Dey et al. https://github.com/biplobforestry/Stress_BVOC_Fingerprints

Model code and software

Model code for random forest model and PMF validation Biplob Dey https://github.com/biplobforestry/Stress_BVOC_Fingerprints

Biplob Dey, Toke Due Sjøgren, Peeyush Khare, Georgios I. Gkatzelis, Yizhen Wu, Sindhu Vasireddy, Martin Schultz, Alexander Knohl, Riikka Rinnan, Thorsten Hohaus, and Eva Y. Pfannerstill

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Short summary
Trees release reactive gases that affect air quality and climate. We studied how these emissions from European beech and English oak change under realistic scenarios of combined and single heat and ozone stress. Heat increased emissions, while ozone reduced most of them. When stressors were combined, the effects were complex and varied by species. Machine learning identified key stress-related compounds. Our findings show that future tree stress may alter air quality and climate interactions.
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