The large scale understanding of natural organic matter:processes and application

Adams, Jess (2017) The large scale understanding of natural organic matter:processes and application. PhD thesis, UNSPECIFIED.

[img]
Microsoft Excel (2017AdamsPhD_Appendix 6.1 - site locations, concentrations, pH, conductivity1)
2017AdamsPhD_Appendix_6.1_site_locations_concentrations_pH_conductivity1.xlsx - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (25kB)
[img]
Microsoft Excel (2017AdamsPhD_Appendix 6.3 - model variable parameters1)
2017AdamsPhD_Appendix_6.3_model_variable_parameters1.xlsx - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (27kB)
[img]
Preview
PDF (2017AdamsPhD)
2017AdamsPhD.pdf - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (2MB)
[img]
Preview
PDF (2017AdamsPhD_Appendix 4.1 - UK soil samples)
2017AdamsPhD_Appendix_4.1_UK_soil_samples.pdf - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (132kB)
[img]
Microsoft Excel (2017AdamsPhD_Appendix 5.1 - Global riverine DO14C data and rejected data)
2017AdamsPhD_Appendix_5.1_Global_riverine_DO14C_data_and_rejected_data.xlsx - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (90kB)
[img]
Microsoft Excel (2017AdamsPhD_Appendix 4.3 - raw PO14C data)
2017AdamsPhD_Appendix_4.3_raw_PO14C_data.xlsx - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (17kB)
[img]
Preview
PDF (2017AdamsPhD_Appendix 4.2 - soil data against depth)
2017AdamsPhD_Appendix_4.2_soil_data_against_depth.pdf - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (425kB)
[img]
Microsoft Excel (2017AdamsPhD_Appendix 6.2 - mesocosm regressions)
2017AdamsPhD_Appendix_6.2_mesocosm_regressions.xlsx - Published Version
Available under License Creative Commons Attribution-NoDerivs.

Download (19kB)

Abstract

Natural biogeochemical cycles of the macronutrient elements carbon (C), nitrogen (N) and phosphorus (P) have been transformed by food and fuel production, through atmospheric pollution and climate change. Further, land disturbance has led to considerable losses of nutrients from terrestrial ecosystems. This investigation aims to explore and address several barriers to understanding natural organic matter cycling across terrestrial and aquatic ecosystems. Soil organic matter (SOM) turnover models are often constrained by C and N, while data on organic P is lacking. Twenty UK soils were used to provide the first investigation of organic P in density fractionated SOM pools. Organic matter in the mineral fraction was considerably more enriched in oP. Stoichiometric ratios agreed with a new classification model, which provides important constraints for models of nutrient cycles. Radiocarbon (14C) measurements of aquatic OM indicates sources and turnover on different timescales. Here, the first analysis of particulate O14C in UK rivers suggested topsoil was the major source. Significantly depleted material was found in a catchment with historical mining activity. Global, temporal analysis of dissolved O14C enabled quantification of different OM sources, and highlighted the importance of assessing the data against the changing atmospheric 14C signal. New dissolved O14C data for rural, arable and urban catchments were more depleted than the global averages. In industry, there is a growing need to manage aquatic nutrient enrichment through rapid and reliable monitoring. A model of UV absorbance was tested against freshwaters that were biased towards eutrophic conditions. The results demonstrated the weak absorbing components of algal DOM, and new variable model parameters were introduced, which quantified the contribution of algal DOM. This could have implications on model predictions of DOC concentration, and a generally applicable spectroscopic model is questionable. This investigation considerably expands the dataset available for modelling large scale biogeochemical cycles, highlights the importance of an integrated approach, and considers the implications involved with applied modelled predictions of aquatic DOC.

Item Type:
Thesis (PhD)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2300
Subjects:
ID Code:
88450
Deposited By:
Deposited On:
01 Nov 2017 11:18
Refereed?:
No
Published?:
Published
Last Modified:
29 May 2020 06:00