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

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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.

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