Assessing Emissions of Biogenic Volatile Organic Compounds and Their Correlation with Abiotic Factors in an Atlantic Forest Reserve Using Supervised Learning Methods

This study investigates the emissions of biogenic volatile organic compounds (BVOCs) in an Atlantic Forest fragment and examines their correlation with abiotic factors. Despite its rich biodiversity, the Atlantic Forest remains under threat and lacks extensive research on BVOC emissions. Using supervised learning methods (including traditional Partial Least Squares Discriminant Analysis (PLS-DA), PLS-DA with bootstrap resampling, and Support Vector Machine Ensemble (SVM ensemble) 10 BVOCs were analyzed and correlated with environmental variables. The results reveal emission patterns linked to abiotic conditions, with the models achieving high classification accuracy. The variables α-pinene, linalool, and isobornyl acetate contribute more significantly to evening/morning samples, whereas temperature and wind gusts contribute more to afternoon samples. The PLS-DA and SVM ensemble models demonstrated effective sample classification, with minimal misclassification errors. The PLS-DA bootstrap model enhanced the robustness and reliability of sample classifications, confirming the consistent and distinct differences between sample classes. To the best of our knowledge, this is the first study to apply the SVM ensemble method to BVOCs in air samples and their correlation with abiotic factors, highlighting its potential to enhance the understanding of atmospheric dynamics and inform conservation strategies for the Caparaó region and other similarly threatened biomes.