PhD Researcher

Net Zero Polar Science DTP PhD in Mineral Weathering

This PhD is part of the Net Zero Polar Science DTP, which aims to make polar science possible in a net zero world. For further details visit

• Co-Supervisor:
  Prof. Michael Lim, Northumbria University
• Co-Supervisor:
  Dr Yuvaraj Dhandapani, University of Leeds
• External Partner:
  Geological Survey of Canada, Bedford Institute of Oceanography

The Arctic is warming nearly 4x faster than the global average, causing widespread thawing of frozen ground (permafrost). While the impact of decomposing organic matter within the permafrost receives significant attention, a critical CO₂ source remains unaccounted for: mineral weathering. When permafrost thaws, sulphide minerals (pyrite, FeS₂) oxidise to produce sulphuric acid, which dissolves carbonates and releases CO₂ into the atmosphere.

In Canada's Peel Plateau, this releases ~240,000 tonnes CO₂ annually – over 20% of Mackenzie Basin's total mineral weathering flux.

Sulphate concentration in Arctic rivers has increased 45% over the last 50 years, tracking with warming. Laboratory and field studies show CO₂ release can double with 10°C warming, yet climate models omit this feedback entirely. This project uses MASKE—a recently developed kinetic Monte Carlo (KMC) simulator proven for cement chemistry studies—to predict minerogenic CO₂ emissions from permafrost regions for the first time, filling a critical gap in Arctic carbon budgets.

This project addresses the Gt-scale gap in Arctic carbon budgets and investigates how computational methods can reduce the need for extensive field campaigns. Quantifying CO₂ emissions from mineral weathering across permafrost regions would require programmes extending across vast regions using regular, type-site, or random sampling, and relying on intensive logistics including helicopter transport, remote camps, and repeated sampling over decades—all of which are expensive and carbon intensive.

Working with the Geological Survey of Canada (GSC), these carbon, financial, environmental, and time costs will be quantified and compared to those associated with new modelling and targeted validation using existing datasets (GSC data and aerial imagery) and new low‑cost, low‑power logging sensors and climate chamber testing on existing samples. The Green algorithms framework will be used to calculate computational carbon footprints, considering factors such as processing time, number of cores, and memory usage and provide an effective, quantifiable, and scalable analysis of the carbon savings achieved.

We will also assess the carbon‑savings achieved using low‑carbon satellite data (permafrost temperature, active layer thickness, watershed boundaries) from missions like ESA's Envisat and MODIS. While satellite remote sensing provides a panoptic view of permafrost extent and surface conditions, sub‑surface mineral weathering processes and chemically competing pathways will be assessed by a combination of computational modelling and field data, thus providing a comprehensive picture of Arctic permafrost weathering.

The research addresses a Gt-scale gap in Arctic carbon budgets. Climate models assume mineral weathering consumes CO₂, yet sulphuric acid‑driven weathering releases it and is highly temperature dependent. With accelerating permafrost thaw, this unaccounted source could dramatically alter regional carbon balances.

Outcomes provide: (1) mechanistic rate laws for climate models, (2) emission estimates for national inventories, (3) risk maps prioritising monitoring investments, and (4) tools applicable beyond permafrost—for example transferrable applications in mine drainage and carbon mineralisation.