Methane (CH4) is one of the most potent greenhouse gases and its increasing concentration in the Earth’s atmosphere is linked to today’s global warming. The types of land and land-use have an impact on net CH4 fluxes, e.g. wetlands are generally net CH4 emitters while upland forest soils are a sink for CH4. This project aimed to elucidate the effect of afforestation and reforestation on net CH4 fluxes and to determine the control of the CH4-oxidising bacteria (methanotrophs) on net CH4 flux rate. This was investigated using a combination of molecular (T-RFLP, cloning/sequencing, microarray) and activity-specific (PLFA-SIP) approaches. Several sites were selected to analyse soil methanotrophs under shrubs regenerating after a fire compared to a native mature forest (in New Zealand), and under bog, grass, heath, pine and birch vegetation (in Scotland). Furthermore, a simple bottom-up approach was applied to seasonal measurements of local net CH4 fluxes in Scotland. These were upscaled to annual values in order to estimate the contribution to the national CH4 budget for each habitat investigated. The effect on CH4 mitigation of the conversion of different types of non-forested habitat to forests was then estimated. Afforestation/reforestation was always found to induce net CH4 oxidation at rates much faster than previously estimated. This preliminary analysis suggests that heathland conversion to birch forest was beneficial in term of CH4 sinks but it also induced large and permanent losses of soil C. However, bog afforestation with pine trees can potentially neutralise the national CH4 emissions from non-forested areas, while preserving soil C stocks. This project also revealed that changes in net CH4 flux due to land-use changes were closely related to shifts in the structure of the methanotrophic community. The relative abundance of members of the USCα cluster (high-affinity methanotrophs) was a strong predictor of net CH4 fluxes. Finally, the sole presence of trees suggested a niche-specific adaptation of the methanotrophs, which may have been correlated to some of the soil characteristics.

Airborne particulate matter (PM) has important harmful effects on human health, as well as a number of other important atmospheric effects. Although progress has been made in understanding the sources and effects of PM, there remains considerable uncertainty on a number of issues, including the nature of a lot of the carbonaceous material, which comprises 30{50% on average of PM mass. This project aims to compare different methods of PM measurement, and contribute understanding to the nature and origin of the carbonaceous fraction of PM. Daily samples of PM10 were collected from three sites in the Edinburgh area using Partisol-Plus 2025 Sequential Air Samplers: 1) Urban Background (20 August 2008 until 21 April 2010); 2) Rural (25 February 2009 until 21 April 2009); and 3) Roadside (10 September 2009 until 21 April 2010). These localities provided PM that was, respectively, representative of: 1) city-wide background air; 2) air at a location distanced from population centres, roads and industrial areas; and 3) air influenced by the emissions associated with traffic. Gravimetric PM10 concentration (µgm-3) was determined for each daily filter sample, after a blank correction to compensate for the relative humidity (RH)-influenced change in filter mass over time. The correction was successful, with good agreement attained between the Partisol and a PM10 Tapered Element Oscillating Microbalance Filter Dynamics Measurement System (TEOM-FDMS) co-located at the Urban Background site. The general levels of PM10 measured in this monitoring campaign indicate that the air in Edinburgh was relatively clean. The main factor causing exceedance of the daily European Union (EU) limit value was shown to be transport of PM10 from areas of mainland Europe. High PM10 concentrations were also strongly associated with calm weather conditions in Edinburgh, which allowed the build-up of particulate pollution over time. Aethalometer-equivalent daily concentrations of black carbon (BC) were determined by measuring the optical reflectance of the PM10 filters from the Partisol samplers. The conversion of reflectance values to BC concentrations relied on a number of correction factors, which may have impacted on the accuracy of the results with time and location. The concentration of BC in Edinburgh was shown to be relatively low, with the daily variation being controlled by local emissions and meteorology. BC as a proportion of PM10 increased with sampling location in the order: Rural < Urban Background < Roadside. Predominantly traffic-related BC concentrations increased during periods of low wind speed and were not greatly influenced by long-range transport of aerosol. Daily water-soluble organic matter (WSOM) concentrations were obtained by aqueous extraction of the filter samples and measurement of the dissolved organic carbon (DOC). About 11% on average of the Edinburgh PM10 was WSOM. The majority of this WSOM seemed to have originated from air masses outside of the city, although there was a minor contribution from urban traffic sources. A solid phase extraction (SPE) procedure was used to isolate about one-third of the WSOM as hydrophobic compounds and this revealed a relative increase in the amount of less oxygenated material from traffic sources. Higher than average WSOM concentrations were strongly associated with calm weather conditions that allowed the transient build-up of particle concentrations. Some of the peaks in WSOM concentration were related to the transport of air masses from areas of mainland Europe where biogenic secondary organic aerosol (SOA) and biomass burning were likely sources. Analysis of the WSOM samples by UV-Vis absorption spectroscopy showed clear seasonal trends in the composition of hydrophobic watersoluble organic matter (HWSOM), interpreted as predominance of lower molecular weight aliphatic compounds in summer but predominance of larger aromatic and polyconjugated compounds in winter. Raman spectra were obtained for different carbonaceous reference materials. The results of curve fitting for these spectra gave D1 band full width at half maximum (FWHM) values that distinguished between diesel exhaust particles from a local bus and a humic acid sample. Analysis of Edinburgh PM10 samples using Raman microspectroscopy (RM) showed a variation in the structural order of the carbon compounds present between that of soot and HUmic-LIke Substances (HULIS), with a tendency towards more soot-like material being present. There was no strong relationship between carbonaceous order and BC concentration, showing that coloured organic compounds have the potential to influence reflectance measurements. The combination of these measurement approaches has yielded insights into the nature and variation in carbonaceous PM material with time and sampling location.

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This research examined the impact which onshore windfarms sited on peat-based soils have on streamwater carbon (C), phosphorus (P) and nitrogen (N) dynamics. Significant disturbance to peatland arises through the excavation of borrow pits, construction of access tracks, insertion of turbine bases and associated deforestation during windfarm development – potentially increasing the transfer of C, P and N from terrestrial stores to the stream network. To identify which impacts occur, streamwater samples from nine catchments draining the Whitelee windfarm, Scotland, Europe’s largest onshore windfarm, were collected approximately bi-monthly from October 2007 to September 2010 during and after windfarm construction, building on pre-disturbance data from Waldron et al. (2009). The samples were analysed for dissolved organic carbon (DOC), particulate organic carbon (POC), total organic carbon (TOC), soluble reactive phosphorus (SRP), total phosphorus (TP) and nitrate (NO3-). Time series were constructed and annual streamwater exports were calculated so that inter-catchment and annual differences could be detected. DOC concentrations ranged from 2.9 mg L-1 to 57.1 mg L-1 and DOC exports ranged from 9.0 g m-2 yr-1 to 42.3 g m-2 yr-1. POC concentrations ranged from 0.09 mg L-1 to 23.4 mg L-1 with POC exports ranging from 0.80 g m-2 yr-1 to 3.93 g m-2 yr-1. DOC exhibited seasonality with maximum concentrations and exports towards the end of each summer. Harmonic regression analysis of the TOC data indicated a slight increase during the maximum phase of the seasonal cycle, coincident with windfarm-related disturbance observed in one catchment only. Regardless of windfarm development, streamwater DOC exports for four peatland-dominated catchments were observed to exceed typical values of C sequestration rate for Scottish peatlands over the four year time series. SRP concentrations ranged from 1 ug L-1 to 289 ug L-1 and exports from 12 mg m-2 yr-1 to 104 mg m-2 yr-1 with TP concentrations and exports ranging from 2 ug L-1 to 328 ug L-1 and exports from 25 mg m-2 yr-1 to 206 mg m-2 yr-1 respectively. In two catchments where windfarm-related clear-felling and extensive brash mulching were carried out, as much as a tenfold increase was observed from June 2007 in P concentration, coincident with the timings of windfarm-related forestry operations. The water quality status of these two catchments declined from “good” to “moderate” in terms of the Water Framework Directive (2000) UK Technical Advisory Group Environmental Standards for SRP in Rivers, and had still not shown a full recovery after a further two years. NO3- concentrations ranged from 0.001 mg L-1 to 2.44 mg L-1 and exports from 0.07 g m-2 yr-1 to 1.64 g m-2 yr-1. Harmonic regression analysis indicated a potential impact on streamwater NO3- concentration through a change in the seasonal pattern observed in three catchments during 2007-2008, coinciding with windfarm-related disturbance. To determine the most likely controls of C, P and N, a geographic information system (GIS) analysis was employed to describe the physiography of each catchment and to quantify the extent of windfarm-related disturbance. Multiple linear regression analysis was performed using median concentration and export for a low-disturbance phase from June 2006 to August 2007, and a maximum-disturbance phase from September 2007 until May 2009, with the catchment characteristics from the GIS analysis to identify potential impacts of windfarm-related disturbance. The percentage of the catchment which was HOST class 15 and 29, both peat-based soil types, was observed to influence streamwater C, with the proportion of the catchment which was pasture observed to influence streamwater N. Windfarm-related disturbances were also found to control streamwater dynamics. The extent of deforestation was observed to greatly increase streamwater P concentration and that there was a smaller increase in C, with consequent impacts on SRP and POC export. The source of this additional C and P resulted most likely from forestry operations, namely, clear-felling large areas of catchment and extensive brash mulching associated with the windfarm habitat restoration, with new organic material available for decomposition and the potential for erosion of the newly-exposed soil surface. Residual fertiliser used to establish the conifer trees, the reduced vegetation uptake of soil P and P release from the roots of the felled trees are three further sources of P in streamwater. The distance to the nearest disturbance was also found to influence streamwater dynamics. This research has shown that windfarm-related infrastructure can also influence streamwater chemistry, although exerting a less obvious impact: increasing access track length was correlated with decreasing POC and P concentration causing subsequent decreases in export. This is likely to be caused by the effective use of settlement ponds, flocculation blocks and ditch blocking in order to reduce the amount of particulate matter reaching the stream network and potentially through the adsorption of P by the access track construction material. Windfarm-related disturbance did not influence N dynamics; NO3- concentration was strongly correlated with HOST class 24 and the proportion of the catchment which was pasture, and NO3- exports were influenced by the flow length per catchment area, the percentage of coniferous forest cover and the extent of pasture. Two recommendations for best practice at future windfarm developments on peatland are made: firstly, monitor C, P and N in streamwater in all catchments before, during and after windfarm construction on C rich soils as part of EIA process in order to ensure that water quality is maintained in rivers, to determine whether discharges from windfarm developments should require to be licensed and to improve the accuracy of C payback time estimates; and secondly, minimise brash-mulching to reduce the impact on streamwater P. Furthermore, some suggestions for future research were proposed, including: plot studies to determine the best practice mitigation measures for P release in peatland catchments, longitudinal catchment studies, investigation of the relationship between access track construction material and streamwater dynamics, installation of continuous monitoring equipment along with a sampling regime which targets high flow events, examination of the changing stiochiometry, quantification of inorganic C and gaseous forms of C, and measuring catchment C sequestration rates.

Climate change is affecting planet Earth. The main cause is anthropogenic emissions of greenhouse gases, the principal one being carbon dioxide, released in the atmosphere as a by-product of the combustion of hydrocarbons for the generation of energy. Carbon capture and storage (CCS) is a technology that would prevent carbon dioxide from being emitted into the atmosphere by safely sequestering it underground. For so doing, CO2 must be captured at large emission points and transported at high pressure to underground reservoirs, where the gas can be injected and stored for thousands of years to come. During surface transportation, leakages from high pressure facilities would pose a risk to the general public, for carbon dioxide is toxic at high concentrations. In this study, atmospheric dispersion of carbon dioxide is studied by the usage of software that solves mathematical equations and algorithms simulating the pollutant dispersion. Dispersion models are used to estimate or predict downwind distances covered by toxic concentrations of the pollutant, emitted from sources such as high-pressure transportation facilities within CCS projects. Two modelling tools from two different classes (Gaussian ALOHA 5.4 and Computational Fluid Dynamics PANACHE 3.4.1) have been evaluated against release field experiments using the statistical model evaluation method proposed by Hanna et al. (1993,2004) and Hanna and Chang (2001), and applied for the consideration of the dense gas CO2, released in large amounts due to leakages. Predictions from the two models have been compared and the limitations of both examined, when dealing with a gas that presents the distinctive physical characteristics of carbon dioxide. The models have been used and compared in simulating representative failure cases within CCS transportation with release parameters taken from the literature. The Computational Fluid Dynamics (CFD) model showed a much higher precision when describing the release of the gas from a HP facility, mainly when dealing with the jet release caused by leakages of any dimensions. When dealing with the transportation of toxic gases, the magnitude of hazards posed by potential failure events within the transportation system is proportional to the extent of the area covered by toxic concentrations of the gas, when modelling representative leakages. Results of this investigation depict a lowering of the Risk involved in the transportation of CO2 by up to an order of magnitude, when modelling the same releases with CFD tools, instead of the more common Gaussian models. The European Union recognizes that deployment of CCS for hydrocarbon power generation, in parallel with the production of renewable energies, is the only way to meet the target for temperature stabilization. For its Impact Assessment on CCS, the EU used results from a risk assessment compiled after the utilization of a Gaussian model. In this thesis, a criticism of this choice is put forward, considering that, when introducing the technology to the general public and regional scale administrators, a Risk Assessment derived using results from Gaussian models can over-estimate the risk in a way not favourable to the purpose.

The key aims of this project were: (i) investigation of atmospheric nitrogen deposition, focused on discrimination between bulk, wet and dry deposition, and between particulate matter and gas phase, (ii) accurate quantification of the contributions of dissolved organic and inorganic nitrogen to each type of deposition, and (iii) exploration of the origin and potential sources of atmospheric water soluble organic nitrogen (WSON). This project was particularly focused on the WSON fraction because, despite it being a potentially important source of bioavailable reactive nitrogen, a number of questions regarding its deposition mechanism (wet vs. dry), composition and origin (natural, anthropogenic or mixed) remain unanswered. There are two major difficulties in WSON determination: (i) the diversity of organic nitrogen compounds compared with the inorganic forms, and (ii) the lack of a direct determination method: the derivation of organic nitrogen concentrations in an aqueous sample involves the determination of the dissolved inorganic nitrogen (DIN) species concentration, comprising nitrate and ammonium, the determination of the total dissolved nitrogen (TDN) concentration, and the subtraction of the DIN concentrations from the TDN concentration. TDN determination requires a preparatory digestion step: high-temperature catalytic oxidation was the method used in this study. Ion chromatography (IC) was the method of choice for nitrate determination, whilst ammonium determination was by IC for rain samples, and by flow injection analysis for air samples collected into aqueous media. This thesis is structured in 3 main parts: the first part examines weekly rain data over a period of 22 months from June 2005 to March 2007 collected in 2 types of rain collector (bulk deposition and “dry+wet” deposition) located in a semi-rural area 15 km southwest of Edinburgh, UK (N55°51′44″, W3°12′19″). Bulk deposition collectors were the standard rain gauges used in the UK national network for monitoring precipitation composition. “Dry+wet” deposition collectors were flushing rain gauges equipped with a rain detector, a spray nozzle, a 2-way valve and two independent bottles to collect funnel washings (dry deposition) and true wet deposition. A key objective in this part of the work was the accurate quantification of inorganic and organic water-soluble nitrogen species contribution to each type of deposition. On average, for the 27 weekly samples with 3 valid replicates for the 2 types of collectors, DON represented 23% of the TDN in bulk deposition. Dry deposition of particles and gas on the funnel surface, rather than rain, contributed over half of all N-containing species (inorganic and organic). Some discrepancies were found between bulk and flushing rain gauges, for deposition of both TDN and DON, suggesting biological conversion and loss of inorganic N in the flushing samplers. The second stage of this project was the investigation of in situ atmospheric concentrations of WSN species, both organic and inorganic. Simultaneous daily measurements of ammonium, nitrate and WSON were made between July and November 2008 at the same semi-rural site in south-east Scotland. Discrimination between material from the gas and particle phases was achieved by means of Cofer scrubbers and PTFE membrane filters, respectively. Average concentrations of NH3 (determined as NH4+), NO2/HNO3 (determined as NO3-) and WSON in the gas phase were 82 ± 54 nmol N m-3, 2.6 ± 2.5 nmol N m-3 and 18 ± 12 nmol N m-3 respectively, and in the particle phase were 20 ± 24 nmol N m-3, 10 ± 9 nmol N m-3 and 8 ± 7 nmol N m-3 respectively (± represent standard deviation across all valid daily samples, not error of triplicate samples). Except for oxidised inorganic N, average concentrations in the gas phase were larger than in the particle phase. No evidence of solely agricultural or solely combustion sources of WSON was found, as no correlation in either phase between WSON and either NH4+ or NO3- could be established. The final stage of this project was to gain a broader picture of nitrogen deposition across Europe. Rain samples were analysed for TDN and DIN from a network of 21 different sampling locations, from the south of Portugal to the north of Finland. The average DON contribution across the 21 sampling sites was ~ 19% and, according to the data gathered in this study, DON species appeared largely unrelated specifically to agricultural or to combustion nitrogen sources, although the results varied widely from site to site. Overall, this work has shown that ON and its deposition comprises a significant component of total atmospheric reactive nitrogen.

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy.

In the savanna woodlands of Southern Africa, locally know as miombo, carbon cycling is poorly quantified and many of the key processes remain obscure. For example, seasonal constraints on productivity and leaf display are not well understood. Also, fire is known to be a key process, with around 50% of the annual global area burned occurring in Africa, but detailed understanding of its ecological effects is lacking. Land use change and woodland degradation are changing the structure and functioning of these tropical woodlands, which cover 2.7 million km2 of Southern Africa and provide ecosystem services which support the livelihoods of over 100 million people. In this thesis I quantify the major carbon stocks of the woodlands in Nhambita Regulado, Gorongosa District in Sofala Province, Mozambique. I also examine processes that affect these stocks, including fire and clearance for agriculture. Furthermore, I quantify the seasonal cycle of leaf display, and its relationship to climate. I conducted a series of experimental burns and found that fire intensity was strongly related to rates of top-kill and root stock mortality. Top-kill rates decreased as tree diameter increased up to 10 cm DBH. After this point increased size did not affect top-kill rates, possibly because of accumulated wounds and rottenness. I then extrapolated these results to long term predictions of tree populations and carbon stocks by modelling the interactions of fire, mortality and tree growth. The model was able to successfully predict woody vegetation structure at two sites with known fire regimes, including a 50-year fire experiment in Marondera, Zimbabwe. The results show that annual fires in miombo suppress all woody vegetation. Low intensity fires every 2.5 years are required to maintain observed stem biomass in Nhambita. High intensity fires lead to high top-kill rates (12%), even among large stems. Manipulating fire intensity rather than frequency seems to be the most practical approach to limiting degradation by fire in these ecosystems. Using a three year time series of hemispherical photographs of the tree canopy, combined with satellite data, I find that tree leaf phenology is not directly related to seasonal rainfall patterns, both in Nhambita and across Southern Africa. Pre-rain green-up is the dominant phenology, from the semi arid savannas of the south of the continent to the wet miombo of the Congo basin. Wet miombo woodlands have longer periods of green-up before rain onset (mean 60 days) compared with dry miombo (37 days). Green up-dates show little interannual variability but large spatial variability. The importance of pre-rain green-up in determining how these ecosystems will respond to changing rainfall patterns is unknown, but is an important area for future study. I quantified carbon stocks in the Nhambita woodlands in the soil (69% of total carbon stocks of 111 tC ha-1), tree stems (19%) and roots (8%) as well as other smaller pools. An allometric relationship between root and stem biomass and stem diameter was developed, and used to evaluate the uncertainties in stem carbon estimation at plot and landscape scale. We find that the uncertainty (95% confidence intervals) at plot scale can be quite large (60% of the mean) but this is reduced to around 25% at landscape scale. Strategies for effective inventories of miombo woodland are presented. Using a chronosequence of abandoned farmland, we estimate that stem biomass recovers from clearance after around 30 years of abandonment. Changes in soil carbon stocks are less well understood and need further work. This thesis concludes by outlining further work needed to model the carbon cycle of these woodlands, as well as discussing the implication of pre-rain green-up for satellite observations of land cover changes and biomass mapping.

Dairy cattle make a significant contribution to global methane emissions. Milking cows in the UK make up about a fifth of the total cattle population, with Holstein-Friesian cows being the most common breed. Investigating ways to minimise methane, a potent greenhouse gas (GHG) produced by dairy cows from enteric fermentation and manure, has gained importance in recent years due its role in climate change. Currently, GHG emissions from UK dairy farming are predicted using the Intergovernmental Panel on Climate Change (IPCC) Tier II methodology. The IPCC Tier II methodology and statistical prediction equations from the literature were evaluated for their ability to reliably model methane output using data from the Langhill Holstein-Friesian experimental herd. The Langhill dairy herd is on a long-term breeding and feeding systems experiment, and cows are on average 88% North American Holstein genes. The production systems within the herd represent a range of dairy systems that may be found commercially. Therefore, production values were assumed to be representative of those that could be found in the commercial Holstein-Friesian population, so factors affecting system methane emissions and appropriate mitigation options could be investigated. Prediction equations using dry matter (DM) intake and gross energy intake as input values were the most appropriate equations for reliably estimating daily enteric methane output. However, if DM intake values are not available, the IPCC Tier II method was found to provide a suitable prediction of methane emissions over a cow‘s lactation and lifetime. This study found that GHG emissions from enteric fermentation and manure, expressed as carbon dioxide equivalents (CO2-eq.), account for about 66% of dairy system CO2-eq. emissions, with enteric methane output being the main contributor (34% of system CO2-eq. emissions). Breeding for increased kilograms of milk fat plus protein production was shown to help reduce dairy system methane emissions. Cows of predominantly North American Holstein genes in this study produced more milk when fed a diet with a low proportion of forage and had lower GHG emissions and land requirement per kilogram energy corrected milk than similar cows fed a diet with a higher proportion of forage. Strategies to mitigate GHG emissions (including methane) and the environmental impact of dairy systems should seek to select animals that better utilise their feed intake to meet their genetic potential for milk production.

The aim of this thesis is to use computer simulation methods to consider adsorption of both water and carbon dioxide onto oxide surfaces. The materials chosen have direct relevance to current environmental concerns, alkaline earth metal oxides for carbon sequestration and uranium dioxide for the storage and stability of nuclear materials. Chapter one outlines both previous experimental and computational work relevant to these research areas. The computational methodologies used in this thesis are described in chapters two and three. Chapter two outlines how the forces between atoms in the simulation are modelled using both potential-based and electronic structure models. Chapter three details how these are then used to find lowest energy configurations. The main results of the alkaline earth metal oxides are discussed in chapters four and five. Chapter four uses multiconfigurational static lattice simulations of water and CO2 surface adsorptions to identify the most probable adsorption sites and to generate surface phase diagrams as a function of surface composition. Whereas the focus of chapter five is to model interactions in liquid water with both surface and nanoparticles. Chapters six and seven describe the results of simulations on uranium dioxide. Chapter six uses electronic structure methods to model defects and nonstoichiometry in bulk and thin film structures. Chapter seven then describes the calculations of the interactions of water with uranium dioxide surfaces, in terms of both gas phase adsorption and the mineral – water interface with results showing the favourability of surface hydroxylation on the {100} and {110} surfaces. Finally, a summary of the main findings and achievements of this thesis are given in chapter nine, along with a discussion of possible future work.