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Hospitals represent a busy environment with the majority of the medical operations relying on electricity, for example lighting and medical equipment. Hence, it is important to conserve it to ensure high quality of services, improve patients' wellbeing as well as to the reduction of the hospitals' carbon footprint and the impacts on the environment. The studies carried out in hospitals considered the use of renewable energy or the use of power efficient equipment to tackle the energy problem. The challenge remains open in how to tackle the energy problem in a hospital through behavioural change. This study represents a step into reducing electricity costs of Medway NHS Foundation Trust (MNFT) in the UK. The proposed idea is to use technology to persuade MNFT staff to monitor their behaviour and with the right motive, from selected and appointed energy delegates, sustain a pro-environmental behaviour. This paper describes the methodology and system proposed to reduce electricity costs in MNFT by inducing pro-environmental behaviour with the aid of technology. This involves a smart electricity metering system to collect and communicate energy data to a centralised server that pushes the data onto a dedicated web interface. Furthermore, addressing the psychological factors by appointing energy delegates to monitor the consumption, in selected areas, as well as motivate the staff members. In addition, monitoring human dynamics and analysing it against energy data to identify a relation between occupancy and electricity consumption trends in hospital wards.

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The characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. This paper presents an algorithm for the prediction of NOx (NO and NO 2 ) emissions in flue gas through flame radical imaging, flame temperature monitoring and application of Neural Network techniques. Radiation images of flame radicals OH*, CN*, CH* and C 2 * are captured using an intensified multi-wavelength imaging system. Flame temperature is determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values of the flame radicals are extracted. These characteristic values, together with the flame temperature, are then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig have shown the effectiveness of the proposed method for the prediction of NOx emissions.