Category: Team news

‘Taps Aff!’: The Unexpected Challenges of Water Scarcity in Scotland. Article by Dr Laura Major, Dr Elizabeth Lawson, and Dr Jen Roberts

This short article, co-authored by Dr Laura Major, Dr Elizabeth Lawson, and Dr Jen Roberts, was published in The Geographer magazine (Royal Scottish Geographical Society). It explores how climate change is creating new water stress challenges for Scotland, a country often considered water-rich. It highlights unexpected impacts, from rural supply issues to water quality risks,

Dr. Justin Sperling was an invited speaker, program committee member, and session chair for Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications at the 2025 SPIE Photonics West Conference in San Francisco (US)

As part of the 2025 program committee, Dr. Justin Sperling worked with the conference chairs and other committee members to build the speaker + poster program schedule for the Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications conference during SPIE BiOS 2025.       Dr. Sperling was also an invited speaker

Invited talk to the Environmental Engineering Seminar Series at Newcastle University: Retrofitting proposal for rural septic tanks towards carbon neutrality. By Dr Tania Gomez Borraz

Dr Tania Gomez Borraz  gave an invited talk for 45 minutes, followed by a 15 minute Q&A session. The talk generated a lot of interest by the academic community, both researchers (PIs from Newcastle University) and students from different levels and backgrounds (civil engineers, social engineers, undergraduate and postgraduate). Opportunities for future work and collaborations

IAEA Circular Economy Technical Meeting

Dr Siming You was invited to attend the circular economy technical meeting held by the International Atomic Energy Agency (IAEA) in the United Nations Office at Vienna, Austria, between 11th Nov and 15th Nov 2024. Dr You shared his research on the environmental impact assessment of waste treatment and the machine learning-based process modelling of