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Strong sesquiterpene emissions from Amazonian soils
E Bourtsoukidis1, T Behrendt2, A M Yañez-Serrano3,4,5
1Atmospheric Chemistry and Biogeochemistry Departments, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128, Mainz, Germany. e.bourtsoukidis@mpic.de.
Soil microbes in the Amazon rainforest emit significant amounts of sesquiterpenes (SQTs), previously unaccounted for volatile organic compounds. These microbial emissions rival canopy sources, highlighting a new understanding of atmospheric chemistry.
Area of Science:
- Atmospheric Chemistry
- Microbiology
- Ecology
Background:
- The Amazon rainforest is a major global source of volatile isoprenoids.
- Canopy emissions are traditionally assumed to be the primary source influencing atmospheric chemistry.
- Recent data suggests additional, unidentified volatile sources exist.
Purpose of the Study:
- To identify and quantify previously unreported volatile organic compound sources in the Amazon.
- To investigate the role of soil microorganisms in atmospheric volatile emissions.
- To develop a model for predicting soil-atmosphere sesquiterpene fluxes.
Main Methods:
- Laboratory analysis of soil samples to determine sesquiterpene emission rates.
- Investigating the influence of soil moisture, oxygen, and microbial activity (rRNA abundance) on emissions.
- Developing a predictive model for soil-atmosphere sesquiterpene fluxes.
Main Results:
- Soil microorganisms are a significant source of reactive sesquiterpenes (SQTs).
- SQT emissions from Terra Firme soil during the dry season were comparable to global canopy emission models.
- Emission rates were correlated with soil moisture, oxygen levels, and microbial transcript abundance.
Conclusions:
- Soil microorganisms represent a major, previously unaccounted source of atmospheric sesquiterpenes.
- This discovery establishes a critical link between soil microbial communities and atmospheric volatile organic compound dynamics.
- Understanding these soil-based emissions is crucial for accurate atmospheric chemistry modeling.
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