Large-sample hydrogeochemical datasets are increasingly available, enabling new insights into spatial and temporal variability in solute chemistry across diverse natural and human-influenced environments. Although integrating heterogeneous datasets from multiple monitoring programs remains challenging, such resources allow researchers to move beyond individual site studies and conduct comparative analyses across catchments and regions. Large-sample approaches can reveal emergent hydrogeochemical patterns and help identify controlling factors such as climate, lithology, soils, land use, hydrology, and human activities. These datasets also support the development of robust statistical models, the application of machine learning techniques, and the transferability of insights across environmental systems.

This session invites contributions that compile, synthesize, and analyze large hydrogeochemical datasets to identify natural and anthropogenic drivers of solute patterns. We welcome studies on major ions, nutrients, trace metals, and emerging contaminants in inland waters (streams, rivers, lakes, groundwater) and soils.

• B - Biogeosciences
• EP - Earth and Planetary Surface Processes
• GC - Global Environmental Change
• IN - Informatics