This thesis describes the development of a system to assist the design of long-term remediation strategies for radioactively contaminated land. Existing radiological models, that estimate the uptake of radionuclides by plants and the doses arising from exposure to external radiation, were combined with a spatially implemented food-chain model, to allow the temporal and spatial variation of radionuclide transport through the terrestrial environment, and the resulting doses of exposed human populations, to be estimated. Doses are estimated using a novel method for the simulation of human populations, which includes the generation of sub-populations by Monte-Carlo sampling and consideration of the geographical origins of consumed food products. Various simulated radiological countermeasures have been incorporated into the system (e.g. clean-feeding of livestock), allowing the effects of potential remediation strategies to be assessed. Furthermore, a method has been developed which efficiently identifies the optimum set of countermeasures for a given scenario according to a defined merit function using cost benefit analysis, which can be extended to include terms that account for the preference for averting high levels of individual dose, and the social costs of a number of countermeasure side-effects (e.g. disruption of normal daily life). To assess the applicability of the system, it was used to evaluate potential remediation strategies for hypothetical, large-scale nuclear accidents within two contrasting case study sites (Cumbria, UK and Zaragoza, Spain). In both case studies the system successfully identified optimal remediation strategies which were, according to the defined merit function, significant improvements upon simple food and dose rate restriction strategies.

Carbon dioxide capture and storage by mineralisation (CCSM) is considered to be an alternative solution for reducing anthropogenic C0₂in some regions, where geological storage is not possible or considered uneconomically viable. However, low efficiency of mineral dissolution and use of unrecyclable additives are two key barriers for the development of CCSM. A novel CCSM process with recyclable ammonium salts is proposed to overcome these barriers in this study. This process integrates mineral carbonation with C0₂capture by employing NH₃, NH₄HSO₄and C0₂containing ammonium salts in the capture step, mineral dissolution and carbonation steps, respectively. The NH₄HSO₄ and NH₃can then be regenerated by thermal decomposition of (NH₄)₂SO₄, which is the by-product from the process. The use of C0₂ containing ammonium salts as the source of C0₂can avoid desorption and compression of C0₂, which account for 70 % of the total energy consumption in the whole CCS chain. In this work, a CCSM process route at low solid to liquid ratio (50 g/I) was experimentally investigated to validate the process concept. It was found that the dissolution efficiency of magnesium (Mg) can achieve 100 % by using NH₄HSO₄and the carbonation efficiency can reach 96.5 % by using CO₂containing ammonium salts from the capture step and addition of aqueous NH₃. Three products, including Si rich residue, Fe rich residue and pure hydromagnesite were obtained from the process. The TGA studies reported that the regeneration efficiency of NH₄HSO₄ and NH₃ in this process was 95 %. Both dissolution and carbonation efficiencies achieved in this work are higher than the values reported in previous work. In order to reduce the water usage, a CCSM process at high solid to liquid ratio (200-300 g/I) was developed. It was found that the dissolution efficiency of Mg was 64 and 72 % at 200 and 300 g/l, respectively. The increase of dissolution efficiency with a solid to liquid ratio could be explained by the removal of passive product layer caused by particle-particle interaction. At a solid to liquid ratio of 300 g/l, the highest carbonation efficiency achieved was 65.4 %. Magnesite instead of hydromagnesite was found after carbonation due to the CO₂ pressure caused by the decomposition of ammonium salts above 70 °C. Additionally, the carbonation efficiency was doubled by using (NH₄)₂CO₃compared to that using NH₄HCO₃. A preliminary evaluation was conducted to estimate the OPEX, including energy consumption, chemical costs and feedstock cost, based on the experimental results from the two process routes developed. In order to get low OPEX, the optimization process conditions, such as solid to liquid ratio and reaction time, were determined. Then, experiments at these optimized conditions were conducted. The dissolution efficiency of Mg from serpentine with particle size 75-150 pm using 2.8 M NH₄HSO₄at 100 g/l solid to liquid ratio for 1h was around 80 %. The carbonation efficiency was 96 % when the molar ratio of Mg: CO₂ containing NH⁴+ salts: NH₃was 1: 1.5: 2. Thus, the mass balance of the process showed that 3.0 t' of serpentine, 0.2 t of NH₄HSO₄and 0.1 t of NH₃ were required to sequester 1t of CO₂and produce 1.9 t of magnesite. Moreover, 1.7 t of high Si content (46.9 wt. %) and 0.3 t of high Fe content (60 wt. %) were produced. Finally, a cost evaluation study including CAPEX and OPEX was made using Aspen plus software to simulate the optimized CCSM process with recyclable ammonium salts for a 100 MW coal-fired power plant. For the input of 60 t/h CO₂, 93 % of them can be sequestered by the process with 29.5 % energy consumption and the total carbon capture and storage costs was 71.8 US$/t CO₂sequestered, excluding the product sale.

Sugar factories are a significant source of water pollution, particularly in developing countries such as Pakistan, where the sugar industry is the second largest after tanneries. The wastewater is disposed of untreated to the environment, since traditional wastewater treatment processes are capital-intensive, energy-demanding and complex in operation. The common approach is to use waste stabilization ponds or lagoons mostly operated on complete retention basis. This work is an attempt to highlight the possibility of effectively applying wastewater lagooning process utilizing the inherent organic contents of sugar factory wastewater with the aid of an algae-bacterial consortium (ABC) to investigate its capacity to utilize this resource to produce renewable fuel while de-polluting wastewater rather than it being a liability to be disposed of. A lagoon photo tank (LPT) resembling a prototype raceway lagoon was designed and used to carry out mass cell cultivation on a sugar-oriented medium for the assessment and inter-optimization of the process conditions such as temperature, incident light (IL), pH, dissolved oxygen (DO). The evaluation of the process performance was observed via the analyses of parameters such as chemical oxygen demand (COD), total organic carbon and cell mass growth. The first part of this study was related with the baseline assessment of LPT process conditions using distilled water as well as sucrose solution or sugar water to establish basis for a priori analysis of simulated sugar factory wastewater (SSFW) carried out in the second part. The suitability of the dosing of copolymer Polyacrylate polyalcohol was optimised through a series of trial runs to aid in the immobilization of mono or mixotrophic cultures of green algae Chlorella Vulgaris and bacteria Pseudomonas Putida at the surface of LPT in order to influence reduction in the organic concentration of SSFW. This research study has contributed to the knowledge base of the concerned area of study with respect to hitherto unknown application of copolymer Polyacrylate polyalcohol, which showed viable characteristics in the cultivation medium in terms of cell immobilization at the surface of LPT resulting in the formation of growth-conducive copolymer-algae matrices leading to the rapid growth of the cell mass with increased process efficiency. This process optimisation resulted in SSFW depollution by around 89% along with energetic biomass growth with a calorific value of 27 kJ g1 and at an optimum growth rate of 1.2 d1 suggesting towards the potential of copolymer addition in the system to enhance the efficiency of the organisms inducing optimum substrate utilization.

The problem of water pollution has been an environmental concern for many years. Numerous researchers are looking for an effective method to solve this issue. Heterogeneous photocatalysis, using a semiconductor as a catalyst, is a promising method for the destruction of water polluting pesticides. This method has been called the Advanced Oxidation Process (AOP) which is one of the techniques for water treatment. Titanium dioxide (TiO2) is the most widely accepted photocatalyst because it is non-toxic, stable to photocorrosion, low cost and can potentially work using sunlight rather than artificial sources of light. When titanium dioxide is illuminated by UV radiation, the absorption of photons of energy is then equal to or greater than its band gap width. This artefact leads to the formation of conduction-band electrons and valence-band holes on the surface of TiO2, which yield hydroxyl radicals, the primary oxidising species needed for the photocatalytic degradation of pollutants. Supercritical water hydrothermal synthesis (ScWHS) is one of novel approaches for nanoparticle manufacture which involves the mixing of an aqueous metal salt stream with a supercritical water stream to produce nano-sized metal oxide particles. The engineering design for the mixing of these two fluids is critical and a novel nozzle reactor has been developed at the University of Nottingham that can produce high quality particles with an excellent control over particle size and particle size distribution. By application of this technique, titanium dioxide (TiO2), in nanoparticle form, was produced and used for the photocatalytic treatment of wastewater. In this thesis, the photocatalytic degradation of the three pesticides - isoproturon, simazine and propazine - was measured using 3 different types of reactors: thin film fixed bed reactor (TFFBR), a stirred reactor, and a fluidised bed photoreactor. Various conditions were used: for example without UVC and UVA illumination, with commercial TiO2, with the synthesised TiO2, without any photocatalyst, low concentration, high concentration, and at different TiO2 concentrations. The optimum TiO2 concentration for the treatment of the three chosen pesticides was equal to 5 g litre-1. The efficiency in decreasing the pesticides concentration of the synthesised TiO2 (from ScWHS technique) with all three types of reactors was lower than that of the commercial titanium dioxide (P-25 TiO2). The fluidised bed reactor appeared to give the highest performance amongst three reactors.

Waste engineering fluids, such as coolants and cutting fluids, are difficult to treat because they have variable physical natures, are particularly toxic and have a very high Chemical Oxygen Demand. The complex and unknown chemical content of the many different products available is also problematic. Current technologies, such as nanofiltration or chemical treatment, are quite effective at reducing the COD of the waste metalworking fluids before disposal. These technologies remove free or emulsified oil and high molecular weight components, but they have their limitations. In addition, the more stringent legislation on waste disposal and effluent discharge induces an economical stress on engineering industries. It can be anticipated that future legislations will introduce eco-toxicology measurements into industrial effluent discharge consents. A modular on-site treatment plant to treat semi-synthetic metalworking fluids was developed in this thesis. The approach was to combine different technologies and to inter-optimise their performances. The technologies used were membrane filtration, bioremediation and chemical treatment. The use of activated carbon was also studied. Membrane filtration included the study of ultrafiltration and nanofiltration. For the bioremediation process, a bio-consortia was developed and tested over 8 months. A final design of the whole process is given. The proposed treatment plant transforms the waste metalworking fluid into two products: very low chemical oxygen demand aqueous phase at 30 mg/l COD and a recovered oil showing a calorific value of 42 kJ/kg which could be a possible commodity. The whole treatment plant is scalled-up for thye treatment of 500 L of waste metalworking fluid per day.

This thesis is concerned with the optimal design of Water Distribution Networks (WDNs). The design involves finding an acceptable trade-off between cost minimisation and the maximisation of numerous system benefits. The primary design problem involves cost-effective specification of a pipe network layout and pipe sizes in order to satisfy expected consumer water demands within required pressure limits. The design of a WDN has many variable parameters such as position and size of the water sources, position and the size of the pipes and position of the treatment plants. However, the layout is constrained by the location of existing facilities such as streets and buildings and other geographic features. The total costs may consist of the cost of network materials such as pipes, construction works and system operation and maintenance. The problem may be extended to consider the design of additional components, such as reservoirs, tanks, pumps and valves. Practical designs must also cater for the uncertainty of demand, the requirement of surplus capacity for future growth, and the hydraulic reliability of the system under different demand and potential failure conditions. The thesis reviews the literature related to water distribution networks, their design and optimisation. It then presents a Genetic Algorithm (GA) formulation to assist in developing the design of a water distribution network. The main aim of this research is to investigate the possibility of combining GAs and GIS in the design optimisation. A decision mechanism is developed which enables the model to reach a meaningful solution and provide a practical design technique for WDNs. The aim is also to provide an experimental analysis of the combined GA and decision mechanism to solve the problem in hand and to assess the robustness of these techniques when applied to different instances. An initial prototype model is presented for the design of a WDN which is used to determine the necessary features of the 'final' model. These features include the world in which the model will be built, the design of the fitness function, chromosome representation, and GA operators. The research mainly concluded that the initial model prototype was useful to determine the necessary features and to produce the final model which enables a variety of necessary factors to be explicitly included in the design of WDNs. This initial model suggested that the final model should include the decision mechanism, which is a matter of policy management and hydraulics, and hydraulic principles which allowed to compare the behaviour of different parameters and to simulate the functioning of the network under different scenarios. Water allocation and distribution policies can be applied according to the importance of the demand area and the ability of the system to deliver sufficient water amounts. These policies link essential hydraulic and institutional relationships as well as water uses and users and allocation decision-making process. It was also found that the representation of the world layout is important. The world is described in GIS in terms of models that define the concepts and procedures needed to translate real-world features into data. The important aspects in the chromosome representation are the node positions, the links. In this case, a chromosome must contain the three-dimensional node coordinates, the connection between nodes, the head required to pump the water. The best model parameters were extracted to be used in real-life situations. The result of tests on an example world demonstrated that the model was successful, and the potential exists for the use of this formulation in more complex and real-world scenarios.

The aim of the present work was to improve the predictive capabilities of current modelling methods used to assess the long-term biosphere impacts of underground repositories for radioactive wastes. A number of issues related to parameter and conceptual uncertainties associated with compartmental biosphere models that simulate transport and accumulation of radionuclides in soils were addressed. The structure of compartmental models used for radiological risk assessments has not evolved noticeably over the past few decades and most of these models rely on simple assumptions. For example, compartmental models used to predict activity concentrations of radionuclides released into soils over very long timescales (typical of the lifetime of underground disposal repositories) assume arbitrary model specifications such as soil layer thickness (the vertical discretisation of the soil column) and length of the time step. Moreover, the majority of the available models assume invariant sorption characteristics of radionuclides with soil depth and hence employ constant solid-liquid distribution coefficient (Kd) values regardless of soil characteristics known to affect radionuclide sorption (e.g. pH, redox potential, moisture content and organic matter). The empirical Kd has a profound effect on long-term predictions of radionuclide behaviour in soil since it determines the degree of radionuclide retardation due to interaction with the soil. It is associated with considerable uncertainty due to differences in experimental conditions and methods used for its measurement and the variation in soil characteristics. In this study, three soil types (arable, grassland and woodland) were incubated under anaerobic conditions and the behaviour of naturally occurring selenium, iodine, rhenium and uranium, expressed as Kd, was investigated. The results indicate that variation in soil characteristics (e.g. moisture content, pH, mineral and organic carbon content) is a significant source of K, variability. Soils relatively higher in organic matter content (e.g. top soils) have higher sorptive capacities for trace elements than mineral subsoils and hence higher Kds. Dynamic, complex behaviour of K, under flooded, anaerobic soil condtions was measured over a 3 week period in soil microcosms. This dynamic behaviour was driven by the shift in soil redox potential which was associated with solubilisation of soil organic and mineral (Fe oxide) phases. Overall, the maximum observed variation in K, over the entire incubation period did not exceed 2 orders of magnitude. Biosphere models were constructed which combined a physically-based water flow model and the compartmental approach and used to simulate the long-term vertical distribution of radionuclides in the soil as well as radionuclide dynamics under different environmental conditions. Investigating radionuclide dynamics on a short timescale could only be achieved using models with a daily time step since short-term variation was obscured by a longer (annual) time step. Simulation results give insights into some of the limitations of available biosphere modelling methods for radiological risk assessment that are often overlooked. For example, soil radionuclide activity concentrations calculated using compartmental models are sensitive to the vertical discretisation (i.e. thickness of soil layers into which the soil column is divided) and time step of the model, hence the structure of the model should not be set arbitrarily. The discretisation procedure proposed in the present study may provide a useful framework to select the appropriate structure of biosphere assessment models. With respect to the effect of uncertainty in K, on model calculations, the results show that equilibrium timescales and radionuclide activity concentrations in the soil at equilibrium increase as the K, increases. For example, the time to reach steady state radionuclide activity concentrations in the vegetated topsoil increased 14-fold and 7-fold, respectively, when K, was increased 28-fold, which is a small variation compared to the uncertainty of Kd commonly reported in the literature (e.g. a few orders of magnitude). The Kd also affects short and long-term radionuclide dynamics in soils; the activity concentration of a radionuclide with low Kd (weakly sorbing) is more responsive to seasonal fluctuations in climatic and hydrological conditions than a radionuclide with a large Kd (strongly sorbing). Radionuclide uptake by plant roots, especially those which access highly contaminated soil layers adjacent to the contaminated aquifer, could be an important mechanism that provides a direct pathway between shallow, contaminated aquifers and the soil surface where elavated contamination poses greater risks.

The application of solid adsorbents for gas separation in pre-combustion carbon capture from gasification processes has gained attention in recent times. This is due to the potential of the technology to reduce the overall energy penalty associated with the capture process. However, this requires the development of solid adsorbent materials with large selectivity, large adsorption capacity, fast adsorption kinetics for CO2 coupled with good mechanical strength and thermal stability. In this work, results on CO2 adsorption performance of three different types of adsorbents; a commercial activated carbon, phenolic resin activated carbons and zeolite templated carbons have been reported at atmospheric and high pressures conditions. The commercial activated carbon was obtained from Norit Carbons UK, the phenolic resin activated carbon was obtained from MAST Carbon Ltd., while the templated carbons were synthesized in the laboratory. A commercial activated carbon was used as bench mark for this study. Surface modification of these carbons was also undertaken and their CO2 uptake measurements at ambient and high pressure conditions were recorded. The commercial and templated carbons were modified by functionalising with amine group, while the phenolic resin carbon was modified by oxidation. The textural properties of the adsorbents was examined using the Micromeritics ASAP, while the CO2 adsorption capacities were conducted using the thermogravimetric analyser (TGA) and the High pressure volumetric analyser (HPVA). Textural properties of synthesized templated adsorbents were seen to depend on the textural characteristics of the parent material. The β-type zeolite produced the carbons with the best textural property. Increase in activation temperature and addition of furfuryl alcohol (FA) enhanced the surface area of most of the templated carbons. The textural property of all the adsorbents under study was seen to differently affect the CO2 uptake capacity at atmospheric (0.1 MPa) and high pressure conditions (up to 4 MPa). Micropore volume and surface area of the commercial activated carbons, phenolic resin activated carbons, and the templated carbons greatly influenced the adsorption trends recorded at ambient conditions. Total pore volumes positively influenced adsorption trend for templated carbons, but not the phenolic resin activated carbons at ambient and high pressure. This also positively influenced the adsorption trend for the commercial activated carbons, but at ambient conditions only. The surface area and the micropore volume have no effect on the adsorption trends for the templated carbons and the commercial activated carbons at high pressure conditions. However, these played a positive role in the adsorption capacities of the phenolic resin activated carbons at the same experimental conditions. Micropore volume and surface area of adsorbents play a major role on the adsorption trends recorded for the modified adsorbents at ambient conditions only. No trend was recorded for adsorption capacities at high pressure conditions. Only the oxidized phenolic resin activated carbon showed a positive adsorption trend with respect to total pore volume at high pressure condition. The amine modified commercial activated carbon showed no positive adsorption trend with respect to the total pore volume at both ambient and high pressure conditions, while the amine modified templated carbon showed no adsorption trend with respect to the textural properties at ambient and high pressure conditions. CO2 uptake measurements for the modified and unmodified templated carbon and phenolic resin carbon, were observed to be higher than those of the commercial activated carbon at ambient and high pressure conditions. Maximum CO2 uptake was recorded at 25 oC. At ambient pressure, the phenolic resin carbon (MC11) showed the highest CO2 uptake of approximately 3.3 mmol g-1, followed by the commercial activated carbon (2.4 mmol g-1), then, the templated carbon (2.4 mmol g-1). At high pressure, the templated carbons (β-AC7-2%) showed the highest CO2 uptake (21.3 mmol g-1), followed by phenolic resin carbon (MC4 - 12.2 mmol g-1), and the commercial activated carbon (6.6 mmol g-1). When samples were modified, the amine modified templated carbon and oxidized phenolic resin carbon showed the highest CO2 uptake of 2.9 mmol g-1 each at ambient pressure, followed by the commercial activated carbon (2.7 mmol g-1). At high pressure conditions, the oxidized phenolic resin carbon showed the highest (10.6 mmol g-1) uptake level, followed by the templated carbon (8.7 mmol g-1), and commercial activated carbon (6.5 mmol g-1).

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.

This thesis introduces three main elements to support decision-making in air quality-related transport planning. The first are novel automatic collection and processing algorithms for traffic flow and geospatial data for input to air pollution models of transport schemes under analysis. The second is a novel strategy to improve the modelling of air quality by the calibration of input background concentrations. The third is a novel 3D air pollution dispersion interface for the 3D visualisation of the air quality predictions in 3D digital city models. Four urban transport schemes were used for the initial development of, and for testing, the applicability and validation of future air quality predictions of the decision-support system based on the above three elements. The automation of the input data collection and processing reduced significantly the time and effort required to set up the air pollution model. The calibration of background concentrations significantly improved the accuracy of, not only the annual mean, but also the hourly, air quality predictions and effectively reduced the model runtime. The 3D air pollution dispersion interface provided an intuitive 3D visualisation of the air quality predictions at and above the ground surface in a single 3D virtual scene. The application of this decision-support system enabled the development of alternative future traffic scenarios so a proposed urban transport scheme might contribute to achieving certain air quality objectives. The validation of the future air quality predictions showed that the methods used for the future projection of air pollution input data slightly increase the error between the modelled and actual annual mean NO2 future concentrations. They also significantly increase the error between the modelled and actual hourly NO2 future concentrations