Spatiotemporal estimation of ambient forest phytoncides: Unveiling patterns through geospatial-based machine learning approach
Year: 2025
Type: article
Abstract: This study investigated biogenic volatile organic compounds (BVOCs) emitted by tree species, with a specific focus on estimating their ambient air concentrations within the Xitou Nature Education Area, Taiwan. Employing geospatial-based machine learning approaches, which are rarely applied in this context, we aimed to estimate the ambient levels of key forest phytoncides which are representative compounds within the BVOC group. Data on phytoncide, including camphene and α-pinene, were directly collected from the study area. Geospatial data including meteorological factors, topography, land cover, and nearby landmarks were additionally collected and set as predictor variables influencing phytoncides. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were integrated with an explainable artificial intelligence tool to develop the model estimates. To evaluate model performance, we conducted overfitting tests, 10-fold cross-validation, and stratified analysis. The results showed that RF and XGB were the most effective algorithms, explaining approximately 83.3% and 98.4% of the spatiotemporal variability in camphene and α-pinene, respectively. The robustness of these models was confirmed through extensive validation. Spatial pattern analysis revealed that variations in these biogenic compound concentrations were linked to meteorological conditions and vegetation types. Finally, this study presented an innovative approach to accurately estimating and mapping the spatial distribution of forest phytoncides, providing valuable insights to support environmental management, urban planning, and public health.
Source: Ecological Indicators
Authors: Aji Kusumaning Asri, Hao-Ting Chang, Chia-Pin Yu, Wan-Yu Liu, Yinq-Rong Chern, Rui-Hao Xie, Shih-Chun Candice Lung, Kai Hsien Chi, Yu-Cheng Chen, Sen-Sung Cheng, Gary Adamkiewicz, John D. Spengler, Chih-Da Wu
Institutions: National Cheng Kung University, National Taiwan University, National Chung Hsing University, Industrial Technology Research Institute, Research Center for Environmental Changes, Academia Sinica, Academia Sinica, National Yang Ming Chiao Tung University, National Health Research Institutes, National United University, Kaohsiung Medical University, China Medical University, Harvard University
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,650
Awards: G1185127871, Phorestall - Planetary Health by Healing Forests as Nature Based Solution, An Integrated Analysis of Digital Twin and Multi-Dimensional Spatial Information Technologies on Air Pollution Assessment: 3d-Distribution of Highly Exposed Population and Their Exposures to Ultra Fine Particles in Taichung Area, An Integrated Analysis of Digital Twin and Multi-Dimensional Spatial Information Applied in Air Pollution Modelling:Development of a Ensemble Learning Model for 3d Air Pollution Variaiton Estimations Using Land-Use Regression,Uav-Based Systems,And Gee( II ), Adopting Innovative Sensor Technology and Data Science to Formulate Evidence-Based Adaptation Strategies for Tackling Environmental Health Challenges under Climate Change( II ), Satellite Science Interdisciplinary Proejct-Satellite Remote Sensing Technology for Sdgs Implementation( II )
OpenAlex ID: W4410485041