Physics-informed spatiotemporal analysis of methane concentrations in an oil sands region

作者：Yang Xu, Hao Wang, Jude Dzevela Kong · 发表于：Journal of Environmental Management · 年份：2026 · DOI：10.1016/j.jenvman.2026.129618 · 被引用次数：2 · 研究领域：Groundwater flow and contamination studies、Geophysical and Geoelectrical Methods、Enhanced Oil Recovery Techniques

Methane (CH 4 ) emissions from complex industrial regions, especially oil sands ponds, exhibit strong spatial heterogeneity and episodic extremes, posing persistent challenges for reliable regional-scale concentration estimation. While recent data-driven models have improved predictive accuracy, their physical consistency, robustness to observation sparsity, and behaviour under extreme conditions remain insufficiently examined. Here, we develop a multi-source, physics-informed, and spatially structured prediction framework that integrates ground-based monitoring stations with satellite-derived background information to estimate regional CH 4 concentrations. The proposed framework explicitly enforces transport-consistent structure and spatial regularity while remaining robust to data gaps and sensor failures. Across independent validation experiments, the model achieves coefficients of determination exceeding 0.80 and demonstrates stable performance under simulated station dropout rates of up to 50%. Uncertainty calibration shows near-nominal predictive interval coverage (0.916 for a nominal 95% interval) with a mean interval width of ±215 ppb, indicating reliable probabilistic characterization. Spatial diagnostics further confirm physically realistic smoothness, with a roughness ratio of 0.64 relative to baseline interpolation methods, and strong alignment with wind-resolved transport patterns (Spearman ρ = 0.817). Using a minimal post-hoc monotonic correction as a diagnostic...