ERG attends Goldschmidt 2025 in Prague

The 2025 Goldschmidt Conference is held in Prague on July 6-11, 2025. Goldschmidt is the largest international conference dedicated to geochemistry, organized by the Geochemical Society and the European Association of Geochemistry.  

ERG members Philippe Van Cappellen and Xiaochuang Bu, together with colleagues from China and the USA convene a session on “Global change signatures in catchment biogeochemistry”.  

Ecohydrology Research Group Presentations this year include: 

• Jovana Radosavljevic, Ali Reza Shahvaran, Elodie Passeport, Stephanie Slowinski, Fereidoun Rezanezhad, Yamin Deng and Philippe Van Cappellen: Land use and water chemistry as key controls on global carbon greenhouse gas emissions from ponds 

• Stephanie Slowinski, Della Zhou, Jovana Radosavljevic, Cynthia Bova, Hannah Weatherson, Mir Amir Mohammad Reshadi, Md Abdus Sabur, Fereidoun Rezanezhad and Philippe Van Cappellen: The climate change mitigation potential of urban stormwater ponds: contextualizing their greenhouse gas emissions 

• Fereidoun Rezanezhad and Philippe Van Cappellen: Winter Climate Warming and Carbon Loss in Northern Permafrost Peatlands 

• Hao Tian, Philippe Van Cappellen, Yao Du and Yamin Deng: Seasonal Dynamics of Groundwater Methane Fluxes to an Oxbow Lake in the Yangtze Plain: Hydrological and Biogeochemical Controls 

• Noelle Starling, Chris Parsons and Philippe Van Cappellen: Impacts of large dam reservoirs on phosphorus and nitrogen concentrations and loads in Lake Winnipeg’s watershed (Canada-USA) 

• Xiaochuang Bu, Chris Parsons and Philippe Van Cappellen: Patterns and Trajectories of Organic Carbon Loads in Arctic Rivers from 2000 to 2024 

• Kunfu Pi, Xianjun Xie, Philippe Van Cappellen and Yanxin Wang: Arsenic redox disequilibrium and biogeochemical cycling in geogenic contaminated groundwater 

• Mehdi Gharasoo, Silvio Janetz, Marcus Fahle, Tamara Van Staden, Philippe Van Cappellen and Christoph Neukum: Advancing Geochemical Understanding through Machine Learning Classification of Large Spatio-Temporal Soil and Groundwater Data