Biogeochemical dynamics and greenhouse gas emissions from groundwater-fed springs in a tropical highland system, Taita Hills, East Africa

Groundwater-fed springs are crucial for human well-being in sub-Saharan Africa but are unsustainably managed, with a 2.3% annual population growth rate and 50% of the population relying on groundwater. Over-abstraction and contamination from land-use changes persist, impacting biogeochemical processes and greenhouse gas (GHG) emissions. However, the extent and effects of this contamination are understudied and not included in GHG budgets for tropical systems. Analyzing spring water quality provides valuable insights into subsurface processes and their effect on GHG emissions. This study, investigated the impact of land use changes on GHG emissions from 10 springs in diverse land uses (Agricultural and Mixed Forest-Agricultural) within a tropical highland ecosystem in Taita Hills, Kenya, from April 2023 to February 2024. Gas samples (CO₂, CH₄, and N₂O) were collected using the headspace equilibrium technique. Gas concentrations, fluxes, and transfer velocities were calculated and compared between the land uses. Additionally, in-situ measurements (pH, Electrical Conductivity, Dissolved Oxygen, stream velocity, and discharge) and laboratory analyses (NO₃-N, NH₄-N, DOC, TDN) determined the underlying biogeochemical conditions relevant to GHG emissions. The results indicated significantly higher average CO₂ flux from the agricultural-impacted springs compared to the mixed forest-agricultural springs (mean ± SE = 2722.7 ± 248.1 mg CO₂-C m^-2 h^-1 and mean ± SE = 2098.5 ± 75 mg CO₂-C m^-2 h^-1 respectively; p < 0.05). This was due to the significantly higher negative correlation with DO, pH and DOC, and positive correlation with stream velocity which may be attributed to microbial respiration and decomposition in the system. CH₄ was significantly higher in the mixed forest-agricultural springs compared to the agricultural springs (mean ± SE = 8.9 ± 1.2 mg CH₄-C m^-2 h^-1 and mean ± SE = 4.7 ± 0.8 mg CH₄-C m^-2 h^-1 respectively; p < 0.05). This was mainly due to the higher negative correlation with DO and positive correlation with DOC and NH₄-N which was only evident in the mixed forest-agricultural attributed to methanogenesis in these springs. N₂O was significantly higher in the agricultural springs with one order of magnitude higher than mixed forest-agricultural (mean ± SE = 3.6 ± 0.5 mg N₂O-N m^-2 h^-1 and mean ± SE = 0.3 ± 0.05 mg N₂O-N m^-2 h^-1 respectively; p < 0.05). N₂O was mainly driven by high positive correlations with NO₃-N, DO and stream velocity. Correlations inferred  nitrification was the main controlling process in the mixed forest-agricultural springs while denitrification was the major process in the agricultural springs due to the negative correlation with DO. The results indicate that increased fertilizer use increased NO₃-N thus an increase in GHG emissions. Understanding the various controlling processes at different land use points of the springs is crucial for better management. Our study suggests that water quality significantly influences biogeochemical processes and GHG emissions, such as high NO₃-N leading to N₂O-N. We also recommend that practicing mixed forest-agricultural could help manage CO₂ and N₂O emissions. Further analysis incorporating seasonal variations is underway to better understand the GHG hot moments.