Synergistic Integration of Multi-Sensor Satellite Data and Gradient Boosting Machine Learning for High-Resolution PM₂.₅ Estimation during Tropical Peatland Fires - Dessy Gusnita; Iis Sofiati; Fadhlullah Ramadhani; Angga Yolanda Putra; Risyanto; Waluyo Eko Cahyono; Tatik Kartika; Muhammad Priyatna; Estiningtyas Kusumastuti (Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya, 2026)
Tropical peatland and forest fires are critical contributors to regional haze and public health crises, yet ground-based monitoring in these regions remains sparse. This study develops a robust framework for estimating surface PM₂.₅ concentrations during extreme fire events (2021–2025) by integrating multi-sensor satellite observations with in-situ data through a Hist Gradient Boosting Regressor (HGBR) approach. To enhance predictive accuracy, we implemented advanced feature engineering, including 1–3 days of exogenous lags, rolling statistics (3 and 7-day windows), and aerosol–meteorological interaction variables. Our analysis of multiple pollutants (PM₂.₅, NO₂, SO₂, CO, HC, and O₃) reveals that during active fire periods, the Air Quality Index (AQI) frequently escalated to