Effect of free ammonia and free nitrous acid concentration on the anabolic and catabolic processes of an enriched Nitrosomonas culture

The effects of free ammonia (FA; NH3) and free nitrous acid (FNA; HNO2) concentrations on the metabolisms of an enriched ammonia oxidizing bacteria (AOB) culture were investigated using a method allowing the decoupling of growth and energy generation processes. A lab-scale sequencing batch reactor (SBR) was operated for the enrichment of an AOB culture. Fluorescent in-situ hybridization (FISH) analysis showed that 82% of the bacterial population in the SBR bound to the NEU probe specifically designed for Nitrosomonas europaea. Batch tests were carried out to measure the oxygen and ammonium consumption rates by the culture at various FA and FNA levels, in the presence or absence of inorganic carbon (CO2, HCO3, and CO32-). It was revealed that FA of up to 16.0 mgNH(3)-N (.) L-1, which was the highest concentration used in this study, did not have any inhibitory effect on either the catabolic or anabolic processes of the Nitrosomonas culture. In contrast, FNA inhibited both the growth and energy production capabilities of the Nitrosomonas culture. The inhibition on growth initiated at approximately 0.10 mgHNO(2)-(NL-1)-L-., and the data suggested that the biosynthesis was completely stopped at an FNA concentration of 0.40 mgHNO(2)-N (.) L-1. The inhibition on energy generation initiated at a slightly lower level but the Nitrosomonas culture was still oxidizing ammonia at half of the maximum rate at an FNA concentration of 0.50-0.63 mgHNO(2)-N (.) L-1. The affinity constant of the Nitrosomonas culture with respect to ammonia was determined to be 0.36 mgNH3-N (.) L-1, independent of the presence or absence of inorganic carbon. (c) 2006 Wiley Periodicals, Inc.

Sviluppo e Applicazione di Metodologie per l'Ottimizzazione di Sistemi Energetici

La riduzione dei consumi di combustibili fossili e lo sviluppo di tecnologie per il risparmio energetico sono una questione di centrale importanza sia per l’industria che per la ricerca, a causa dei drastici effetti che le emissioni di inquinanti antropogenici stanno avendo sull’ambiente. Mentre un crescente numero di normative e regolamenti vengono emessi per far fronte a questi problemi, la necessità di sviluppare tecnologie a basse emissioni sta guidando la ricerca in numerosi settori industriali. Nonostante la realizzazione di fonti energetiche rinnovabili sia vista come la soluzione più promettente nel lungo periodo, un’efficace e completa integrazione di tali tecnologie risulta ad oggi impraticabile, a causa sia di vincoli tecnici che della vastità della quota di energia prodotta, attualmente soddisfatta da fonti fossili, che le tecnologie alternative dovrebbero andare a coprire. L’ottimizzazione della produzione e della gestione energetica d’altra parte, associata allo sviluppo di tecnologie per la riduzione dei consumi energetici, rappresenta una soluzione adeguata al problema, che può al contempo essere integrata all’interno di orizzonti temporali più brevi. L’obiettivo della presente tesi è quello di investigare, sviluppare ed applicare un insieme di strumenti numerici per ottimizzare la progettazione e la gestione di processi energetici che possa essere usato per ottenere una riduzione dei consumi di combustibile ed un’ottimizzazione dell’efficienza energetica. La metodologia sviluppata si appoggia su un approccio basato sulla modellazione numerica dei sistemi, che sfrutta le capacità predittive, derivanti da una rappresentazione matematica dei processi, per sviluppare delle strategie di ottimizzazione degli stessi, a fronte di condizioni di impiego realistiche. Nello sviluppo di queste procedure, particolare enfasi viene data alla necessità di derivare delle corrette strategie di gestione, che tengano conto delle dinamiche degli impianti analizzati, per poter ottenere le migliori prestazioni durante l’effettiva fase operativa. Durante lo sviluppo della tesi il problema dell’ottimizzazione energetica è stato affrontato in riferimento a tre diverse applicazioni tecnologiche. Nella prima di queste è stato considerato un impianto multi-fonte per la soddisfazione della domanda energetica di un edificio ad uso commerciale. Poiché tale sistema utilizza una serie di molteplici tecnologie per la produzione dell’energia termica ed elettrica richiesta dalle utenze, è necessario identificare la corretta strategia di ripartizione dei carichi, in grado di garantire la massima efficienza energetica dell’impianto. Basandosi su un modello semplificato dell’impianto, il problema è stato risolto applicando un algoritmo di Programmazione Dinamica deterministico, e i risultati ottenuti sono stati comparati con quelli derivanti dall’adozione di una più semplice strategia a regole, provando in tal modo i vantaggi connessi all’adozione di una strategia di controllo ottimale. Nella seconda applicazione è stata investigata la progettazione di una soluzione ibrida per il recupero energetico da uno scavatore idraulico. Poiché diversi layout tecnologici per implementare questa soluzione possono essere concepiti e l’introduzione di componenti aggiuntivi necessita di un corretto dimensionamento, è necessario lo sviluppo di una metodologia che permetta di valutare le massime prestazioni ottenibili da ognuna di tali soluzioni alternative. Il confronto fra i diversi layout è stato perciò condotto sulla base delle prestazioni energetiche del macchinario durante un ciclo di scavo standardizzato, stimate grazie all’ausilio di un dettagliato modello dell’impianto. Poiché l’aggiunta di dispositivi per il recupero energetico introduce gradi di libertà addizionali nel sistema, è stato inoltre necessario determinare la strategia di controllo ottimale dei medesimi, al fine di poter valutare le massime prestazioni ottenibili da ciascun layout. Tale problema è stato di nuovo risolto grazie all’ausilio di un algoritmo di Programmazione Dinamica, che sfrutta un modello semplificato del sistema, ideato per lo scopo. Una volta che le prestazioni ottimali per ogni soluzione progettuale sono state determinate, è stato possibile effettuare un equo confronto fra le diverse alternative. Nella terza ed ultima applicazione è stato analizzato un impianto a ciclo Rankine organico (ORC) per il recupero di cascami termici dai gas di scarico di autovetture. Nonostante gli impianti ORC siano potenzialmente in grado di produrre rilevanti incrementi nel risparmio di combustibile di un veicolo, è necessario per il loro corretto funzionamento lo sviluppo di complesse strategie di controllo, che siano in grado di far fronte alla variabilità della fonte di calore per il processo; inoltre, contemporaneamente alla massimizzazione dei risparmi di combustibile, il sistema deve essere mantenuto in condizioni di funzionamento sicure. Per far fronte al problema, un robusto ed efficace modello dell’impianto è stato realizzato, basandosi sulla Moving Boundary Methodology, per la simulazione delle dinamiche di cambio di fase del fluido organico e la stima delle prestazioni dell’impianto. Tale modello è stato in seguito utilizzato per progettare un controllore predittivo (MPC) in grado di stimare i parametri di controllo ottimali per la gestione del sistema durante il funzionamento transitorio. Per la soluzione del corrispondente problema di ottimizzazione dinamica non lineare, un algoritmo basato sulla Particle Swarm Optimization è stato sviluppato. I risultati ottenuti con l’adozione di tale controllore sono stati confrontati con quelli ottenibili da un classico controllore proporzionale integrale (PI), mostrando nuovamente i vantaggi, da un punto di vista energetico, derivanti dall’adozione di una strategia di controllo ottima.

WASOP, a qualitative methodology for waste minimization: systems thinking, HAZOP principles and nuclear waste

Purpose – The international nuclear community continues to face the challenge of managing both the legacy waste and the new wastes that emerge from ongoing energy production. The UK is in the early stages of proposing a new convention for its nuclear industry, that is: waste minimisation through closely managing the radioactive source which creates the waste. This paper proposes a new technique (called waste and source material operability study (WASOP)) to qualitatively analyse a complex, waste-producing system to minimise avoidable waste and thus increase the protection to the public and the environment. Design/methodology/approach – WASOP critically considers the systemic impact of up and downstream facilities on the minimisation of nuclear waste in a facility. Based on the principles of HAZOP, the technique structures managers' thinking on the impact of mal-operations in interlinking facilities in order to identify preventative actions to reduce the impact on waste production of those mal-operations.' Findings – WASOP was tested with a small group of experienced nuclear regulators and was found to support their qualitative examination of waste minimisation and help them to work towards developing a plan of action. Originality/value – Given the newness of this convention, the wider methodology in which WASOP sits is still in development. However, this paper communicates the latest thinking from nuclear regulators on decision-making methodology for supporting waste minimisation and is hoped to form part of future regulatory guidance. WASOP is believed to have widespread potential application to the minimisation of many other forms of waste, including that from other energy sectors and household/general waste.

The challenge of managing coexistent type 2 diabetes and obesity

The presence of obesity with type 2 diabetes increases morbidity and mortality from each condition. Excess adiposity accentuates insulin resistance and complicates the treatment of type 2 diabetes. Glucagon-like peptide 1 receptor agonists promote weight loss, whereas metformin, dipeptidyl peptidase 4 inhibitors, and a glucosidase inhibitors are typically weight neutral. The anabolic effects of increased insulin secretion and action restrict the benefits of treatment in obese patients. New treatments should ideally reduce hyperglycaemia and excess adiposity. Potential new treatments include analogues of intestinal and adipocyte hormones, inhibitors of renal glucose reabsorption and cellular glucocorticoid activation, and activators of cellular energy production.

Pryolytic and kinetic study of Chlorella Vulgaris under isothermal and non-isothermal conditions

Algae are a new potential biomass for energy production but there is limited information on their pyrolysis and kinetics. The main aim of this thesis is to investigate the pyrolytic behaviour and kinetics of Chlorella vulgaris, a green microalga. Under pyrolysis conditions, these microalgae show their comparable capabilities to terrestrial biomass for energy and chemicals production. Also, the evidence from a preliminary pyrolysis by the intermediate pilot-scale reactor supports the applicability of these microalgae in the existing pyrolysis reactor. Thermal decomposition of Chlorella vulgaris occurs in a wide range of temperature (200-550°C) with multi-step reactions. To evaluate the kinetic parameters of their pyrolysis process, two approaches which are isothermal and non-isothermal experiments are applied in this work. New developed Pyrolysis-Mass Spectrometry (Py-MS) technique has the potential for isothermal measurements with a short run time and small sample size requirement. The equipment and procedure are assessed by the kinetic evaluation of thermal decomposition of polyethylene and lignocellulosic derived materials (cellulose, hemicellulose, and lignin). In the case of non-isothermal experiment, Thermogravimetry- Mass Spectrometry (TG-MS) technique is used in this work. Evolved gas analysis provides the information on the evolution of volatiles and these data lead to a multi-component model. Triplet kinetic values (apparent activation energy, pre-exponential factor, and apparent reaction order) from isothermal experiment are 57 (kJ/mol), 5.32 (logA, min-1), 1.21-1.45; 9 (kJ/mol), 1.75 (logA, min-1), 1.45 and 40 (kJ/mol), 3.88 (logA, min-1), 1.45- 1.15 for low, middle and high temperature region, respectively. The kinetic parameters from non-isothermal experiment are varied depending on the different fractions in algal biomass when the range of apparent activation energies are 73-207 (kJ/mol); pre-exponential factor are 5-16 (logA, min-1); and apparent reaction orders are 1.32–2.00. The kinetic procedures reported in this thesis are able to be applied to other kinds of biomass and algae for future works.

Decision support for lifecycle planning and risk management of small-scale biomass combined heat and power (bCHP) projects in the UK

Biomass is projected to account for approximately half of the new energy production required to achieve the 2020 primary energy target in the UK. Combined heat and power (CHP) bioenergy systems are not only a highly efficient method of energy conversion, at smaller-scales a significant proportion of the heat produced can be effectively utilised for hot water, space heating or industrial heating purposes. However, there are many barriers to project development and this has greatly inhibited deployment in the UK. Project viability is highly subjective to changes in policy, regulation, the finance market and the low cost incumbent; a high carbon centralised energy system. Unidentified or unmitigated barriers occurring during the project lifecycle may not only negatively impact on the project but could ultimately lead to project failure. The research develops a decision support system (DSS) for small-scale (500 kWe to 10 MWe) biomass combustion CHP project development and risk management in the early stages of a potential project’s lifecycle. By supporting developers in the early stages of project development with financial, scheduling and risk management analysis, the research aims to reduce the barriers identified and streamline decision-making. A fuzzy methodology is also applied throughout the developed DSS to support developers in handling the uncertain or approximate information often held at the early stages of the project lifecycle. The DSS is applied to a case study of a recently failed (2011) small-scale biomass CHP project to demonstrate its applicability and benefits. The application highlights that the proposed development within the case study was not viable. Moreover, further analysis of the possible barriers with the DSS confirmed that some possible modifications to be project could have improved this, such as a possible change of feedstock to a waste or residue, addressing the unnecessary land lease cost or by increasing heat utilisation onsite. This analysis is further supported by a practitioner evaluation survey that confirms the research contribution and objectives are achieved.

Supply chain integration in the UK bioenergy industry:findings from a pilot study

Interest in bioenergy as a viable alternative to fossil fuels is increasing. This emergent sector is subject to a range of ambitious initiatives promoted by National Governments to generate energy from renewable sources. Transition to energy production from biomass still lacks a feasible infrastructure particularly from a supply chain and business perspective. Supply chain integration has not been studied widely providing a deficit in the literature and in practice. This paper presents results from a pilot study designed to identify attributes that helps optimise such supply chains. To consider this challenge it is important to identify those characteristics that integrate bioenergy supply chains and ascertain if they are distinct from those found in conventional energy models. In general terms the supply chain is defined by upstream at the point of origin of raw materials and downstream at the point of distribution to final customer. It remains to be seen if this is the case for bioenergy supply chains as there is an imbalance between knowledge and practice, even understanding the terminology. The initial pilot study results presented in the paper facilitates understanding the gap between general supply chain knowledge and what is practiced within bioenergy organisations. © 2014 Elsevier Ltd. All rights reserved.

The dual effect of normobaric hypoxia on heart rate variability and substrate partitioning following interval cycling

Recent studies have shown the importance of the beat-by-beat changes in heart rate influenced by the autonomic nervous system (ANS), or heart rate variability (HRV). The purpose of this study was to examine the lasting effects of hypoxic exercise on HRV, and its influences on substrate usage. Results from this study could lead an increased understanding on this topic. Eight active healthy males (age: 31±11 years; height: 180±7 cm; weight: 83±8 kg; VO₂max (maximal oxygen consumption): 4.4±0.6 L•min⁻¹) underwent normoxic and hypoxic (FᵢO₂= 0.15) conditions during high-intensity interval (HIIT) cycling (70%-high interval, 35%-rest interval). Cycling intensity was determined by a peak power output cycling test. Each experimental session consisted of a basal metabolic rate determination, up to 45-minutes of HIIT cycling, and three 30-minute post-exercise metabolic rate measurements (spanning 3 hours and 15 minutes after exercise). During exercise, RPE was higher (p<0.01) and LAC (lactate) increased (p=0.001) at each point of time in hypoxia, with no change in normoxia. After hypoxic exercise, the SNS/PNS ratio (overall ANS activity) was significantly higher (p<0.01) and significantly decreased through time in both conditions (p<0.01). In addition, a significant interaction between time and conditions (p<0.02) showed a decrease in LAC concentration through time post-hypoxic exercise. The findings showed that a single bout of hypoxic exercise alters ANS activity post-exercise along with shifting substrate partitioning from glycolytic to lipolytic energy production. The significant decrease in LAC concentration post-hypoxic exercise supports the notion that hypoxic HIIT induces a greater muscle glycogen depletion leading to increased fat oxidation to sustain glycogenesis and gluconeogenesis to maintain blood glucose level during recovery.

Identifying the Structure and Fate of Wastewater Derived Organic Micropollutants by High-resolution Mass Spectrometry

Human activities represent a significant burden on the global water cycle, with large and increasing demands placed on limited water resources by manufacturing, energy production and domestic water use. In addition to changing the quantity of available water resources, human activities lead to changes in water quality by introducing a large and often poorly-characterized array of chemical pollutants, which may negatively impact biodiversity in aquatic ecosystems, leading to impairment of valuable ecosystem functions and services. Domestic and industrial wastewaters represent a significant source of pollution to the aquatic environment due to inadequate or incomplete removal of chemicals introduced into waters by human activities. Currently, incomplete chemical characterization of treated wastewaters limits comprehensive risk assessment of this ubiquitous impact to water. In particular, a significant fraction of the organic chemical composition of treated industrial and domestic wastewaters remains uncharacterized at the molecular level. Efforts aimed at reducing the impacts of water pollution on aquatic ecosystems critically require knowledge of the composition of wastewaters to develop interventions capable of protecting our precious natural water resources.

The goal of this dissertation was to develop a robust, extensible and high-throughput framework for the comprehensive characterization of organic micropollutants in wastewaters by high-resolution accurate-mass mass spectrometry. High-resolution mass spectrometry provides the most powerful analytical technique available for assessing the occurrence and fate of organic pollutants in the water cycle. However, significant limitations in data processing, analysis and interpretation have limited this technique in achieving comprehensive characterization of organic pollutants occurring in natural and built environments. My work aimed to address these challenges by development of automated workflows for the structural characterization of organic pollutants in wastewater and wastewater impacted environments by high-resolution mass spectrometry, and to apply these methods in combination with novel data handling routines to conduct detailed fate studies of wastewater-derived organic micropollutants in the aquatic environment.

In Chapter 2, chemoinformatic tools were implemented along with novel non-targeted mass spectrometric analytical methods to characterize, map, and explore an environmentally-relevant “chemical space” in municipal wastewater. This was accomplished by characterizing the molecular composition of known wastewater-derived organic pollutants and substances that are prioritized as potential wastewater contaminants, using these databases to evaluate the pollutant-likeness of structures postulated for unknown organic compounds that I detected in wastewater extracts using high-resolution mass spectrometry approaches. Results showed that application of multiple computational mass spectrometric tools to structural elucidation of unknown organic pollutants arising in wastewaters improved the efficiency and veracity of screening approaches based on high-resolution mass spectrometry. Furthermore, structural similarity searching was essential for prioritizing substances sharing structural features with known organic pollutants or industrial and consumer chemicals that could enter the environment through use or disposal.

I then applied this comprehensive methodological and computational non-targeted analysis workflow to micropollutant fate analysis in domestic wastewaters (Chapter 3), surface waters impacted by water reuse activities (Chapter 4) and effluents of wastewater treatment facilities receiving wastewater from oil and gas extraction activities (Chapter 5). In Chapter 3, I showed that application of chemometric tools aided in the prioritization of non-targeted compounds arising at various stages of conventional wastewater treatment by partitioning high dimensional data into rational chemical categories based on knowledge of organic chemical fate processes, resulting in the classification of organic micropollutants based on their occurrence and/or removal during treatment. Similarly, in Chapter 4, high-resolution sampling and broad-spectrum targeted and non-targeted chemical analysis were applied to assess the occurrence and fate of organic micropollutants in a water reuse application, wherein reclaimed wastewater was applied for irrigation of turf grass. Results showed that organic micropollutant composition of surface waters receiving runoff from wastewater irrigated areas appeared to be minimally impacted by wastewater-derived organic micropollutants. Finally, Chapter 5 presents results of the comprehensive organic chemical composition of oil and gas wastewaters treated for surface water discharge. Concurrent analysis of effluent samples by complementary, broad-spectrum analytical techniques, revealed that low-levels of hydrophobic organic contaminants, but elevated concentrations of polymeric surfactants, which may effect the fate and analysis of contaminants of concern in oil and gas wastewaters.

Taken together, my work represents significant progress in the characterization of polar organic chemical pollutants associated with wastewater-impacted environments by high-resolution mass spectrometry. Application of these comprehensive methods to examine micropollutant fate processes in wastewater treatment systems, water reuse environments, and water applications in oil/gas exploration yielded new insights into the factors that influence transport, transformation, and persistence of organic micropollutants in these systems across an unprecedented breadth of chemical space.

Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.

Experimental study on the wave measurements of wave buoys

Wave measurement is of vital importance for assessing the wave power resources and for developing wave energy devices, especially for the wave energy production and the survivability of the wave energy device. Wave buoys are one of the most popular measuring technologies developed and used for long-term wave measurements. In order to figure out whether the wave characteristics can be recorded by using the wave buoys accurately, an experimental study was carried out on the performance of three wave buoy models, viz two WaveScan buoys and one ODAS buoy, in a wave tank using the European FP7 MARINET facilities. This paper presents the test results in both time and frequency domains and the comparison between the wave buoys and wave gauge measurements. The analysis results reveal that for both regular and irregular waves, the WaveScan buoys have better performances than the ODAS buoy in terms of accuracy and the WaveScan buoys measurements have a very good correlation with those from the wave gauges.

Influence of Increased Human Presence in the Mills River Basin on Water Availability and Drought

Periods of drought and low streamflow can have profound impacts on both human and natural systems. People depend on a reliable source of water for numerous reasons including potable water supply and to produce economic value through agriculture or energy production. Aquatic ecosystems depend on water in addition to the economic benefits they provide to society through ecosystem services. Given that periods of low streamflow may become more extreme and frequent in the future, it is important to study the factors that control water availability during these times. In the absence of precipitation the slower hydrological response of groundwater systems will play an amplified role in water supply. Understanding the variability of the fraction of streamflow contribution from baseflow or groundwater during periods of drought provides insight into what future water availability may look like and how it can best be managed. The Mills River Basin in North Carolina is chosen as a case-study to test this understanding. First, obtaining a physically meaningful estimation of baseflow from USGS streamflow data via computerized hydrograph analysis techniques is carried out. Then applying a method of time series analysis including wavelet analysis can highlight signals of non-stationarity and evaluate the changes in variance required to better understand the natural variability of baseflow and low flows. In addition to natural variability, human influence must be taken into account in order to accurately assess how the combined system reacts to periods of low flow. Defining a combined demand that consists of both natural and human demand allows us to be more rigorous in assessing the level of sustainable use of a shared resource, in this case water. The analysis of baseflow variability can differ based on regional location and local hydrogeology, but it was found that baseflow varies from multiyear scales such as those associated with ENSO (3.5, 7 years) up to multi decadal time scales, but with most of the contributing variance coming from decadal or multiyear scales. It was also found that the behavior of baseflow and subsequently water availability depends a great deal on overall precipitation, the tracks of hurricanes or tropical storms and associated climate indices, as well as physiography and hydrogeology. Evaluating and utilizing the Duke Combined Hydrology Model (DCHM), reasonably accurate estimates of streamflow during periods of low flow were obtained in part due to the model’s ability to capture subsurface processes. Being able to accurately simulate streamflow levels and subsurface interactions during periods of drought can be very valuable to water suppliers, decision makers, and ultimately impact citizens. Knowledge of future droughts and periods of low flow in addition to tracking customer demand will allow for better management practices on the part of water suppliers such as knowing when they should withdraw more water during a surplus so that the level of stress on the system is minimized when there is not ample water supply.

Anticipating climate-induced changes to forest cover in Ontario and the implications for future bioenergy development

Climate change is expected to have marked impacts on forest ecosystems. In Ontario forests, this includes changes in tree growth, stand composition and disturbance regimes, with expected impacts on many forest-dependent communities, the bioeconomy, and other environmental considerations. In response to climate change, renewable energy systems, such as forest bioenergy, are emerging as critical tools for carbon emissions reductions and climate change mitigation. However, these systems may also need to adapt to changing forest conditions. Therefore, the aim of this research was to estimate changes in forest growth and forest cover in response to anticipated climatic changes in the year 2100 in Ontario forests, to ultimately explore the sustainability of bioenergy in the future. Using the Haliburton Forest and Wildlife Reserve in Ontario as a case study, this research used a spatial climate analog approach to match modeled Haliburton temperature and precipitation (via Fourth Canadian Regional Climate Model) to regions currently exhibiting similar climate (climate analogs). From there, current forest cover and growth rates of core species in Haliburton were compared to forests plots in analog regions from the US Forest Service Forest Inventory and Analysis (FIA). This comparison used two different emission scenarios, corresponding to a high and a mid-range emission future. This research then explored how these changes in forests may influence bioenergy feasibility in the future. It examined possible volume availability and composition of bioenergy feedstock under future conditions. This research points to a potential decline of softwoods in the Haliburton region with a simultaneous expansion of pre-established hardwoods such as northern red oak and red maple, as well as a potential loss in sugar maple cover. From a bioenergy perspective, hardwood residues may be the most feasible feedstock in the future with minimal change in biomass availability for energy production; under these possible conditions, small scale combined heat and power (CHP) and residential pellet use may be the most viable and ecologically sustainable options. Ultimately, understanding the way in which forests may change is important in informing meaningful policy and management, allowing for improved forest bioenergy systems, now and in the future.

The Quantum Mitochondrion and Optimal Health

A sufficiently complex set of molecules, if subject to perturbation, will self-organise and show emergent behaviour. If such a system can take on information it will become subject to natural selection. This could explain how self-replicating molecules evolved into life and how intelligence arose. A pivotal step in this evolutionary process was of course the emergence of the eukaryote and the advent of the mitochondrion, which both enhanced energy production per cell and increased the ability to process, store and utilise information. Recent research suggest that from its inception life embraced quantum effects such as “tunnelling” and “coherence” while competition and stressful conditions provided a constant driver for natural selection. We believe that the biphasic adaptive response to stress described by hormesis – a process that captures information to enable adaptability, is central to this whole process. Critically, hormesis could improve mitochondrial quantum efficiency, improving the ATP/ROS ratio, while inflammation, which is tightly associated with the aging process, might do the opposite. This all suggests that to achieve optimal health and healthy ageing, one has to sufficiently stress the system to ensure peak mitochondrial function, which itself could reflect selection of optimum efficiency at the quantum level.

Green Economy and Employment: employment potential of the ecological transition in Spain

The Green Economy offers real possibilities for productive innovation, economic growth and employment creation in Spain. These three factors are critical to facilitate the necessary change in the productive model to overcome the crisis. However, the measures taken by the current Conservative government have moved in the opposite direction: significant cutting in incentives for renewable, increasing tax burden on renewable energy production to self-consumption and privatizing public spaces of social and environmental interest. This hinders the achievement of the environmental objectives of the Europe 2020 strategy. A strategy that is born already in itself highly limited, unambitious and subordinated to the interests of energy oligopolies and the imperatives of the Stability and Growth Pact (Maastricht) and the Austerity policies imposed from EU institutions to overcome the 2008 financial crisis. So the Ecological Transition goes further, claiming a substantially change in Economic Policy away form the increasing commodification proposed by the Green Economy. Despite these limitations, young and unemployed people have much to gain from a comprehensive development of environmental industries. Therefore, innovative-sustainable plans, investment and training in green sectors are necessary to make easier the transition from a services low-valued economy to an innovative and sustainable model to make our country an environmental reference in Europe.