A critical review of 53 publications shows laboratory measurements of ionizable organic micropollutants often fail to predict field behavior in ...
A Bayesian data-driven method combining multiple physical constraints improved prediction, uncertainty, and mass balance in synthetic and laboratory groundwater contamination ...
Researchers have derived the first closed-form analytical solution combining a Neumann flux boundary, two-dimensional line sources, adsorption, and decay, revealing ...
University of Tennessee professor Jie Zhuang is one of nine scientists named 2026 Fellows of the Soil Science Society of ...
Researchers at Jilin University have developed a Kolmogorov-Arnold network-based inverse mapping model that identifies groundwater contamination sources within seconds, achieving ...
Researchers have combined Bayesian physics-informed neural networks with Stein variational gradient descent to deliver fast, probabilistic forecasts of contaminant transport ...
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