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USGS2025

Risks of per- and polyfluoroalkyl substance exposure through marine fish consumption

Global food trade expansion has enriched diets worldwide but also heightened concerns about contaminant spread. Per- and polyfluoroalkyl substances (PFAS) can persist in the environment for decades, yet their risks through food trade remain unclear. The global median estimated daily intake (EDI) of C8-PFAS (perfluorooctanoic acid and perfluorooctane sulfonate) (0.023 nanograms per kilograms per day) was mapped from 212 marine fish species, which indicated higher EDIs in North America, Oceania, and Europe. Furthermore, European countries play a pivotal role in C8-PFAS trade flows, markedly reshaping exposure pathways and driving increased exposure in many nations. These dynamics highlight the importance of establishing food-safety regulations and international trade standards. Although perfluorooctane sulfonate hazard index decreased by 72% after its 2009 regulations, unregulated long-chain PFAS continue to pose elevated risks.

Science↗

USGS2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

Nature Reviews Earth & Environment↗