Monitoring Fluvial Organic Carbon Mineralisation in UK Blanket Bogs Using Custom-Made Sensors

  • Sarah Brown

Student thesis: Master of Philosophy


Peatlands are the largest terrestrial organic carbon store although their unique climate and ecology makes them vulnerable to the effects of climate change. Organic carbon lost from peatlands may enter streams and undergo processing and potential mineralisation to inorganic forms, including the greenhouse gas carbon dioxide. The rate of fluvial organic carbon mineralisation has large spatial and temporal variability and estimates of mineralisation rates are a source of uncertainty in carbon budgets. Previous studies have identified that mineralisation and evasion of CO2 is driven by multiple environmental parameters including temperature, light intensity, geomorphology, and organic carbon loads. These factors all experience their own natural variation, driving a mosaic of process outcomes across the landscape. Field monitoring is critical to characterising the impact of environmental parameters on fluvial carbon mineralisation rates. In recent decades, there has been a proliferation of microcontroller boards that can be used to design bespoke environmental sensors. Here we present a systematic review of Physical Geography literature that has used microcontroller technology, focused on why researchers chose that methodological approach. To illustrate this methodological approach , we present the fully replicable designs for a low-cost, microcontroller-based CO2 data logger. As a case study it demonstrates a high post-calibration accuracy and pilot field data confirms blanket bog streams as variable sources of CO2. The proliferation of open-source, user-made, and 'Do-It-Yourself' (DIY) sensors in physical sciences could allow researchers to answer questions previously unanswerable due to the limitations of existing proprietary equipment. The extensive monitoring required to capture the variability of mineralisation processes in blanket bog streams means a DIY approach may provide valuable scientific knowledge, as well as methodological innovation.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorMartin Evans (Supervisor), Claire Goulsbra (Supervisor) & Emma Shuttleworth (Supervisor)


  • Geography
  • Microcontroller
  • Peatland
  • Sensors
  • Carbon

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