Estimating Canopy Isoprene Concentrations over a Deciduous Dipterocarp Forest in Northern Thailand Using a Calibrated Box Model and Physics-Informed Machine Learning: A Pilot Study

This is the authors' abstract. We don't add key points for this paper.

Deciduous dipterocarp forests (DDFs), widespread in mainland Southeast Asia, are potentially important yet poorly quantified sources of isoprene, a dominant biogenic VOC and ozone/aerosol precursor, and observation-based estimates are essentially absent for DDFs in northern Thailand. We present a pilot study estimating canopy isoprene over a DDF at the University of Phayao using a zero-dimensional (0-D) Guenther (G93) box model, benchmarked against tree-based machine learning (ML). Modeled concentrations were compared with 30 wet-season tower samples (parts per trillion; thermal-desorption GC–MS) at three heights (12, 27, and 42 m) over four days. An effective mixing height was calibrated per level and evaluated by strict leave-one-day-out cross-validation. The model reproduced the mean daytime level and diurnal accumulation shape (cross-validated R2 = 0.64–0.68; MAPE ≈ 11%); because the clear-sky forcing is near-invariant, this reflects the within-day signal rather than day-to-day skill. A sensitivity analysis showed that the emission factor, loss rate, and mixing height are confounded through the ratio Es/(kH). In this exploratory comparison, supplying the box-model output as an ML feature recovered skill otherwise lost (ΔR2 up to +1.16), but an hour-of-day feature did the same, so the box model matches a diurnal-climatology baseline and its advantage is interpretability. This exploratory workflow offers a simple, low-data approach for tropical BVOC estimation.