The impact of a wind farm on carbon emissions from peat
SUMMARY:
Onshore wind farms reduce carbon emissions from fossil fuels. When wind farms are built on peat soils, during construction of wind turbines, peat is dug up, stored and then returned to the site. Areas undergo peatland restoration, to increase water and carbon storage and improve biodiversity.
This project will investigate the carbon emissions from these areas – comparing areas where peat has been returned and/or restored with nearby more natural areas. We will investigate carbon emissions to the atmosphere and into water, to build a carbon budget of a wind farm restoration project.
INTRODUCTION:
Background: There are increasing demands on energy companies to provide electricity from renewable sources; so SSE and Viking Energy have built the largest onshore wind farm on mainland Shetland, to provide power to 475,000 homes. The Viking Wind Farm has predominantly been built on peat soils. When peat is in good condition, it stores enormous amounts of carbon; however, when peat is degraded, it is a source of carbon to the atmosphere. Peatland restoration is a nature-based solution to climate change, and the Viking wind farm plan to restore over 260 hectares of degraded peat. During construction of the wind farm, peat was moved and stored, and will be returned to sites during restoration phases. Traditional restoration techniques will be used, to retain water, prevent erosion and improve biodiversity, on areas of degraded peatland.
Environmental Context: Gaseous carbon emissions from degraded and damaged peat to the atmosphere are higher than those from natural areas (Leifeld and Menichetti 2018). Aquatic fluxes of carbon are also higher from degraded peatlands than natural areas (Wallage et al 2006). The composition and carbon content of aquatic fluxes changes as peatland areas change from damaged to restored (Moody 2024), and determines how it degrades, and how much of it is converted into greenhouse gases while in the water (Moody and Worrall 2017). Aquatic carbon is predominantly exported from catchments as dissolved or particulate organic matter, which is a complex mix of molecules. The most accurate way to investigate its molecular composition is using Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). This analysis can show the thousands of compounds that comprise organic matter (Bell et al 2020), revealing which compounds are unique to locations or restoration states, and which are likely to be easily degraded to greenhouse gases.
Project: This project will measure gaseous carbon emissions from natural, degraded and restored areas of peat on Shetland. It will also determine the impact of restoration on the aquatic carbon content and composition in water stored on site (in peat pools and lochans) and draining from these areas (in burns and streams). We will use FT-ICR MS to determine OM composition in water. These will show how the wind farm restoration has impacted carbon cycling in the peat and water, and the impact of building wind turbines on peat.
AIMS:
- Investigate gas emissions from restored, damaged and natural peatland areas on Shetland
- Investigate carbon concentrations and compositions in water (pools, lochans, burns and streams) in restored, damaged and natural peatland areas on Shetland
OUTLINE:
Year 1:
- Literature review to familiarise with restoration processes, greenhouse gas emissions from peat, aquatic fluxes of carbon
- Training and skills needs assessment
- Liaise with stakeholder to determine which peatland areas to investigate
- Site visits and collect initial water and gas samples
Year 2:
- On-going monitoring and analysis of gas and water from Shetland
- Preliminary data analysis
Year 3:
- On-going monitoring and analysis of gas and water from Shetland
- Data analysis
- Conference
Year 3.5:
- Data analysis
- Paper and thesis writing
- Report for stakeholders
TRAINING:
- Literature review methods and techniques
- Field sampling methods
- Lab analysis techniques
- Data and statistics analysis training
REQUIREMENTS:
- Working knowledge of R or Python
- Experience with mass spectrometry or chromatography datasets