Water and environmental resources which provide opportunities for households are threatened by human activities that leads to pollution. The research objectives were to understand the contribution water makes to the livelihoods of urban and peri-urban households; the factors influencing perceptions, attitudes and behaviour in relation to surface water and environmental quality, and measures for promoting community participation in water and environmental management. Ten communities were selected in Accra and its surrounding communities to reflect different levels of infrastructure provision for the study. Four focus group discussions were held in each community, with a mixed group, men, women, and young adults. Issues which emerged were investigated further in a structured household questionnaire survey involving 443 respondents. Key informant interviews were held with the most important government and non government regulatory, research and service provision departments and organizations in water, sanitation, and the environment sectors. Water samples from some of the selected communities were analyzed in the laboratory to compare with respondents’ perceptions. Among the households surveyed, 59.14% were engaged in a water dependent occupation which contributed over 80% of household income in some cases. The study also found that perceptions of water and environment are influenced by the existing social and cultural setting. There were common concepts which helped groups to interpret and make meaning from their environment. The prospects for successful water and environmental interventions can be enhanced through an understanding of this local knowledge and perceptions. There was no clear relationship between attitudes and environmental behaviour or between attitudes and socioeconomic status. Actual behaviour was influenced by ability to pay for services, their availability and the influence of shared community norms. Although citizen participation in water and environmental management decision making is very limited at present, community collective action holds good prospects for future interventions in water and environmental management.

Crowdsourcing is an approach to replace the work traditionally done by a single person with the collective action of a group of people via the Internet. It has established itself in the mainstream of research methodology in recent years using a variety of approaches to engage humans in solving problems that computers, as yet, cannot solve. Several common approaches to crowdsourcing have been successful, including peer production (in which the participants are inherently interested in contributing), microworking (in which participants are paid small amounts of money per task) and games or gamification (in which the participants are entertained as they complete the tasks). An alternative approach to crowdsourcing using social networks is proposed here. Social networks offer access to large user communities through integrated software applications and, as they mature, are utilised in different ways, with decentralised and unevenly-distributed organisation of content. This research investigates whether collective intelligence systems are facilitated better on social networks and how the contributed human effort can be optimised. These questions are investigated using two case studies of problem solving: anaphoric coreference in text documents and classifying images in the marine biology domain. Social networks themselves can be considered inherent, self-organised problem solving systems, an approach defined here as ‘groupsourcing’, sharing common features with other crowdsourcing approaches; however, the benefits are tempered with the many challenges this approach presents. In comparison to other methods of crowdsourcing, harnessing collective intelligence on social networks offers a high-accuracy, data-driven and low-cost approach.

A large body of data is analysed of the flow of concentrated sewage sludge through straight pipes. Mathematical models are obtained of the laminar and turbulent flow of each main category of sewage sludge. The sludges are modelled as time-independent, non-Newtonian relations between shear stress, rate of shearing strain, and solids concentration. Due to the inhomogeneity of sewage sludge, error analysis becomes pivotal to the data analysis, and options are examined for reducing the error of each model with one or more user-fitted parameters. Parameter estimation is discussed for viscous, time-independent, non-Newtonian, laminar and turbulent flow models. Due to extensive requirements of the data analysis, the parameter estimation methods are robust, and generally suitable for any shear flow relation. The difficulties of estimating parameters of shear flow models from pipe flow data are addressed. Numerical algorithms are presented for modelling the flow of time-independent, non-Newtonian, viscous fluids through a straight pipe. Laminar, critical and turbulent flow algorithms are developed to offer predictions such as pressure gradient, mean cross-sectional velocity, and the velocity distribution. To handle the requirements of the data analysis, the algorithms impose few restrictions on the type of shear flow relation, the flow velocity, and the pipe diameter. Suitable pipe flow equations are chosen, and are manipulated mathematically into forms that would yield robust and efficient schemes. The appropriate use of numerical methods for the algorithms is investigated. Mathematical models of sludge are for use by the sewage industry to give an idea of the flow behaviour of sludges for any relevant application. The parameter estimation techniques and pipe flow algorithms are not constrained to any particular pipe, fluid or flow conditions, so they would be useful for any relevant application.

The novelty of this research is that it investigates an on-site solution for the treatment of restaurant waste using a microwave generated plasma for pyrolysis and gasification. The developed system has been used to treat waste from a city centre fast food restaurant. The system was designed with the aim of reducing the amount of waste being sent to landfill by approximately 94%. The waste is mostly food based but also includes paper waste such as napkins. It was separated into three categories: mixed food, paper and fries. Samples of the mixed food and paper waste were analysed for chemical composition and calorific value. A 2.45GHz magnetron was used to supply 1kW of microwave power to a plasma cavity that had an argon flow rate of 1.5 litre per minute. The design of the microwave plasma cavity was performed using the simulation software, COMSOL. The cavity consists of a tapered waveguide section that is shorted at one end to produce a stationary wave with a large electric field at the gas nozzle. The field is strong enough to produce a self-striking argon plasma when the power is applied. Nitrogen was used to keep the plasma cavity clear of smoke, vapours and other hot gas. The best nitrogen flow rates were found to be around 2 litres/minute, although 5 litres/minute was used in the test to avoid the CO sensor saturating. The combination of the argon and nitrogen was used to purge the gasifier of oxygen. The pressure inside the gasifier was held at 200mbar during the experiments. The resulting plasma jet was used to produce syngas from the waste samples inside a thermally insulated, steel-walled reactor. Temperature profiles were recorded to find the best gas flow rates. 10g samples of the three waste categories were tested in triplicate and the results are presented. Syngas production was recorded using a Quintox gas analyser that measured CO, CO2 and O2. The data was captured every 10s during testing using a PC running a custom-built LabVIEW program. This program was also used to set the microwave output power and record the reflected power and temperatures using National Instruments cDAQ modules with analogue to digital converters. The CO and H2 in syngas can be used as a fuel to offset the cost of running the plasma jet. The results reveal that it is possible to generate the syngas using waste food materials. This study has included an investigation of some of the parameters, including power and flow rates of argon and nitrogen, on the plasma created. Others effects were taken into consideration throughout the research such as the study of the sample moisture levels and the final reduction of mass after the experiment. The ashes produced by the tests were investigated using SEM/EDX analysis. These results are also presented and analysed.

Accurate urban water demand forecasting plays a key role in the planning and design of municipal water supply infrastructure. The reliable prediction of water demand is challenging for water companies, specifically when considering the implications of climate change (Zubaidi et al., 2018). Several studies have documented that weather variables drive water consumption in the short-term, and it enhances the accuracy of the prediction model when it is combined with socio-economic factors. However, the impact of climate change on the municipal water demand has yet to be challenged. To surmount this challenge, more research work is needed to accurately estimate the required quantity of water with increasing water demands. Recently, Artificial Neural Networks (ANNs) have been found to be an innovative approach to predict water demand. This PhD study aims to develop a novel methodology to forecast the impact of climate change on municipal water demands for a long-term time series based on the baseline period 1980-2010. It should be highlighted that, based on our knowledge, this is the first study of substantial duration, based on data collected from 1980-2010, which focuses on the associations between monthly climate change and municipal water consumption. A new approach is therefore proposed to quantifying municipal water demands through the assessment of climatic factors, using a combination of a Singular Spectrum Analysis (SSA) technique, three hybrid computational intelligence algorithms and an ANN model. These hybrid algorithms include a Lightning Search Algorithm (LSA-ANN), a Gravitational Search Algorithm (GSA-ANN) and Particle Swarm Optimisation (PSO-ANN). The SSA technique is adopted to decompose the time series of water consumption and climate variables to detect the stochastic signal for each time series. In the same context, the hybrid algorithms are used to find the best value of learning rate coefficient and the number of neurons in both hidden layers of the ANN model. Based on the performance of each hybrid algorithm, the most accurate and reliable water demand forecast model will be selected and used for estimating future water consumption. The considered environments of this study are applied in Australia and the United States from America for mitigating the uncertainty associated with the geographic location (the data of the United States of America was used to support the reliability of developing the municipal water demands prediction model). Furthermore, the Long Ashton Research Station Weather Generator (LARS-WG) model is utilised to simulate future climate factors over three periods (2011-2030, 2046-2065 and 2080-2099) based on the B1, A1B and A2 emission scenarios and seven General Circulation Models (GCMs). The future projection of these climate factors is applied directly to the impact model of water consumption to obtain the projected municipal water demand for different future periods and different greenhouse emission scenarios. The principal findings of this research are the following: from the model perspective, 1) the SSA is a powerful technique when used to remove the effect of socio-economic factors and noise, and detect the stochastic signal time series for water consumption. 2) The ANN model has better performance in term of optimising the correlation between observed and predicted water consumption when using the (LSA-ANN) algorithm. 3) The evaluation of the ANN model (using a validation data set) for Melbourne and Columbia Cities gives a correlation coefficient of 0.96 and 0.95, and the root mean square errors are 0.025 and 0.016 respectively. These findings indicate the capability of the proposed model to predict water demands with high accuracy in different continents. 4) The high performance of LARS-WG model results are found to be appropriate for the simulation of future climate variables. 5) The harmonisation between future monthly water demand (for the periods 2011-2030, 2046-2065 and 2080-2099) and stochastic signals of climate variables, relative to baseline period 1980-2010, emphasises the reliability of the present methodology. However, from the water demand perspective, the water percentage demand (WPD) are likely to rise in winter, drop in summer and fluctuate in both spring and autumn seasons for all periods and under all greenhouse emission scenarios. The results of WPD distribute between -3.5% and 3% for all periods and emission scenarios. The A2 scenario shows the highest and lowest values of WPDs compared to the A1B and B1 scenarios, in particular, in the 3rd period. The mean of seasonal WPD values shows that there is no dominant scenario as the best or the worst case of water demand over all future periods. The highest amount of seasonal demand happens in winter (A2 scenario, 3rd period), and the lowest amount of seasonal demand occurs in autumn (A1B scenario, 3rd period). In conclusion, this study facilitates the conception of the impact of climate change on municipal water demand from the baseline period 1980-2010.

Due to increasing awareness of potential Cr toxicity, there is a pressing need to establish sensitive and robust Cr fractionation and speciation methodologies that will be enable separation of the two redox Cr species (CrIII and CrVI) from different environmental phases and their quantification. The intention of this work was to assess the behaviour of Cr species, especially CrVI, in soils and the factors controlling Cr solubility, fractionation, redox transformation rates and uptake by plants. The analysis methods relied on alkaline extraction in TMAH, liquid chromatography (LC) to separate the chromium species and inductively couple plasma mass spectrometry (ICP-MS) for quantification of chromium. The interference of 40Ar12C+ background peak at mass 52 was reduced by using the CCT-KED facility of the ICP-MS. A solution of 50 mM TRIS buffer, 40 mM NH4NO3, 10-5 M ammonium-EDTA at pH 7.0 was used as the chromatographic eluent. The method developed is suitable for determining CrVI in soil, following alkaline extraction in TMAH, but not for CrIII due to poor recovery, redox transformation and strong binding of CrIII with humic acid despite attempts to preserve the trivalent species using EDTA and heating. The extraction method was applied to assessing Cr speciation and fractionation in a wide range of soil ecosystems collected from urban sites in Wolverhampton, Nottingham, London and a historical sewage sludge disposal farm in Nottinghamshire. To predict soil CrVI content the use of TMAH-extractable Cr (CrTMAH) was better (R=0.911) compared to total soil Cr content (Crtotal; R=0.554). The same analytical approaches were also applied to the development of a method to determine isotopically exchangeable CrVI in soils. This employed isotopically enriched 50CrVI as a ‘spike’ isotope added to soils suspended in varying concentrations of TMAH in an attempt to resolve a consistent fraction of isotopically exchangeable, or ‘labile’, CrVIO42- in soil. It was apparent that, because of the slow exchange kinetics of CrVI in soils, it was difficult to determine a consistent isotopically exchangeable fraction. Nevertheless, the investigation did suggest a refinement of the simple TMAH extraction protocol could enable direct determination of labile soil CrVI. The kinetics of CrVI interaction with a geocolloid (humic acid) was assessed and humic acid was found capable of both reducing CrVI and binding with the resulting CrIII species. Finally, Cr uptake by maize grown on a historical sewage sludge disposal farm was assessed with several approaches to finding a correlation between Cr in soil and Cr uptake by plants. The concentration of CrVI in soil, and its solubility, could be reasonably well predicted from Crtotal or CrTMAH and soil properties. However, restricted uptake of CrVI by the maize plants, and probably reduction of CrVI to CrIII in the root system, made it impossible to predict Cr transfer to shoots or the speciation of the Cr in maize shoots. Overall, due mainly to the apparent ability of the maize plants to control uptake and speciation of CrVI, the produce was considered safe to be consumed by ruminants as regards CrVI content.

The rapid depletion of fossil fuels has raised increasing attention worldwide, and initiated intensive research for sustainable alternatives for energy production. Among these, biodiesel from microalgae has appeared as one of the most promising candidate due to their ability to accumulate large amount of lipids. Indeed, microalgae can achieve a productivity up to 25 higher than other crop sources without need of cultivatable soil, therefore without competing with food production. In the meantime, microalgae have also shown promising results for the treatment of various kind of wastewaters. However, the cultivation of microalgae for energy production suffers from the large costs of harvesting and dewatering of biomass, prior to lipid extraction and biofuel production, which accounts for up to 50% of operating costs. Therefore, the search for cost-effective methods of harvesting and dewatering of microalgae biomass has become necessary to optimize their usage. This study investigates forward osmosis (FO) for the dewatering of microalgae biomass and its implementation within a photobioreactor used for wastewater treatment. FO is a cost-effective filtration process based on the differences of osmotic pressure across a semi-permeable membrane. The use of FO for microalgae dewatering is of high interest, given the high fouling ability of microalgae biomass and the low fouling promises of FO. First, the feasibility of using FO for microalgae dewatering was assessed, focusing on better understanding the fouling mechanisms involved. The filtration performances have been investigated under various operating parameters. It has been found that when Ca2+-containing draw solutions were used, microalgae responded to the back diffusion of calcium ions by an extensive excretion of carbohydrates, accelerating the formation of algal flocs, thus enhancing the rate and extent of flux decline and reducing the algae dewatering efficiency. However, most of the fouling was reversible by simple hydraulic flushing. In addition, substantial adsorption of algal biomass was observed on the feed spacer. Also, Scenedesmus obliquus and Chlamydomonas reinhardtii, with fructose and abundant glucose and mannose in its cell wall, showed strong response to the back diffusion of calcium ions which encouraged S. obliquus to produce more extracellular carbohydrates and formed a stable gel network between algal biomass and extracellular carbohydrates, leading to algae aggregation and severe loss in both water flux and algae biomass during FO dewatering with Ca2+-containing draw solution. Among the species investigated, Chlorella vulgaris without fructose was the most suitable microalgae species to be dewatered by FO with a high algae recovery and negligible flux decline regardless of which draw solution was applied. These findings improve mechanical understanding of FO membrane fouling by microalgae; have significant implications for the algae species selection; and are critical for the development and optimization of FO dewatering processes. Finally, the implementation of FO dewatering with continuous microalgae biomass production and synthetic wastewater treatment was investigated. Two systems (External FO ; Immersed FO) have been studied and compared in order to provide insights on the advantages and disadvantages of each system. Constant parameters have been set identical for both systems: operation time; photobioreactor; hydraulic retention time; biomass production; FO permeate volume. The results reveals that the wastewater treatment efficiency (nutrients removal), as well as the production of biomass were greater with the immersed system due to a greater microalgae growth. However, these may not be sustainable in a long term operation of the immersed system. The external FO system was found better in terms of salinity build-up and FO dewatering performances. Overall, an external FO dewatering is recommended due to its better flexibility and sustainability.

Large-scale complex systems have large numbers of variables, network structure of interconnected subsystems, nonlinearity, spatial distribution with several time scales in its dynamics, uncertainties and constrained. Decomposition of large-scale complex systems into smaller more manageable subsystems allowed for implementing distributed control and coordinations mechanisms. This thesis proposed the use of distributed softly switched robustly feasible model predictive controllers (DSSRFMPC) for the control of large-scale complex systems. Each DSSRFMPC is made up of reconfigurable robustly feasible model predictive controllers (RRFMPC) to adapt to different operational states or fault scenarios of the plant. RRFMPC reconfiguration to adapt to different operational states of the plant is achieved using the soft switching method between the RRFMPC controllers. The RRFMPC is designed by utilizing the off-line safety zones and the robustly feasible invariant sets in the state space which are established off-line using Karush Kuhn Tucker conditions. This is used to achieve robust feasibility and recursive feasibility for the RRFMPC under different operational states of the plant. The feasible adaptive cooperation among DSSRFMPC agents under different operational states are proposed. The proposed methodology is verified by applying it to a simulated benchmark drinking water distribution systems (DWDS) water quality control.

Concerns about climate change and our awareness on energy security have risen during the last decades leading the search for new forms of energy to reduce the world’s dependence on fossil fuels. Bioenergy has been proposed as one route to contribute significantly to meet global energy demand by using renewable sources of energy. The overall objective of this work was to study and optimise a hydrolysis treatment of lignocellulosic biomass but particularly of Miscanthus \(\chi\) giganteus under the biorefinery concept. A modified organosolv method using subcritical water, ethanol and carbon dioxide on a high-pressure batch reactor was proposed and tested for its efficacy on the hydrolysis and fractionation of Miscanthus into its lignocellulose main components, hemicellulose, cellulose and lignin. Temperature (80–200\(^0\)C), reaction time (5–60 min), ethanol concentration (0–70%), carbon dioxide initial pressure (10–55 bar) and load size (2.5–15 g) were the parameters studied and respective ranges. Optimisation models for solubilisation and delignification were obtained and validated using a central composite design based on a response surface methodology. According to both models temperature is the parameter that affects hydrolysis the most obtaining the highest hydrolysis solubilisation and delignification at 200\(^0\)C. On the other side CO2 initial pressure was not significant, what should be further investigated in the future at higher pressures. Reducing sugars quantification obtained a maximum concentration of 2g/10g Miscanthus by DNS assay with an optimal temperature to hydrolyse hemicellulose from 140 to 180\(^0\)C. FTIR analysis of each fraction confirmed a successful separation of the biomass main components with a reduction in the cellulose fibres crystallinity. Temperature was considered the most significant parameter to fractionate biomass with the highest temperature (200 \(^0\)C) being the one that produced a better quality fibres, supernatant and lignin in terms of contamination by the other fractions. However, results also showed that higher temperature tends to oxidise lignin. Fibres analysis by scanning electron microscopy showed that fibre structure was preserved but presented lignin-type globules on their surface indicating lignin reprecipitation.

This research has examined the combustion and emissions of a spray-guided direct-injection spark-ignition (DISI) engine using exhaust gas recirculation (EGR). The impact of EGR type, swirl and tumble intake airflows, compression ratio and fuel type were also investigated. EGR addition resulted in significant fuel consumption improvements and differing particulate matter (PM) behaviour depending on the knock limited maximum brake torque (KLMBT) spark advance achieved. When comparing EGR types, cooled EGR achieved the best fuel consumption and cooled EGR after three-way catalyst (TWC) achieved the best gaseous emissions (NOx and HC). Swirl and tumble intake airflows significantly increased fuel consumption. However, these increases could be minimized with EGR addition. Swirl significantly reduced the accumulation mode particulate emissions, providing a potential solution for PM reduction. EGR addition did not significantly affect PM for the swirl and tumble intake airflow conditions. 20%vol 1-butanol addition to gasoline fuel (Bu20) resulted in significant PM reductions at 8.5 bar IMEP. At 7.0 bar IMEP, EGR addition allowed the KLMBT spark timing to be advanced, as the compression ratio was increased. Fuel consumption was improved by 0.4% due to the spark advance and reduced pumping losses, and PM improved because the formation of primary particles was reduced.