Making sense of low cost sensors. Air quality monitoring in Gothenburg Sweden

Over the recent decade, Emergence and commercialization of the new, low-cost, sensor technologies have created the possibility of major paradigm shifts in air quality monitoring. Their price of three orders of magnitude lower than standard/reference instruments provides the opportunity for new applications such as higher geographical and temporal resolutions of the measurements. There have been studies on the performance of a network of these sensors, however, their individual reliability is still questionable. This study aimed to evaluate the performance of one of the most common low-end sensors available on the market, SDS011, as well as a middle-end sensor, SDS019 under different circumstances such as temperature and humidity. The main research questions were: how reliable are these sensors and what are the causes of errors for these sensors and is it possible to find correction factors based on meteorological data? To address the research questions, a range of experiments in different environments, including field and laboratory, have been conducted under several humidity and temperatures. The results of the experiments illustrated a high linear correlation between the SDS011 and SDS019 sensors with the reference sensor(Optical Particle Sizer) at laboratory experiments. The data were fitted to the reference sensor using a linear regression model. additionally, a multiple linear regression was applied to include the temperature and relative humidity as additional input parameters to the regression model. The results of the multiple and normal regression were compared and discussed under different circumstances for both SDS011 and SDS019 sensors. The field experiments showed significant differences between the SDS011 and reference instruments and these could not be explained by humidity alone. They were not significantly reduced when applying laboratory correction factors either. A three week comparison of the SDS011 against the golden standard for PM in air quality monitoring (TEOM) showed periods with both decent and poor agreement, illustrating that the SDS011 sensors respond to PM but that they are rather unreliable when used as single devices. Further research work is needed to understand this. Nevertheless the sensors are suitable for operation in a network to obtain spatial air quality information , both as stationary and mobile

Probing Dynamics of Oligosaccharides by Interference Phenomena in NMR Relaxation

Oligosaccharides (carbohydrates) are a large class of biological molecules that are important as energy sources in the human body and have enormously varied biological functions. It is generally believed that biological activities of carbohydrates are related to their internal dynamics. The dynamic properties of some oligosaccharides in solution are studied in this thesis, by NMR relaxation. We have employed relaxation interference effects to investigate the conformational dynamics within oligosaccharides (in-tramolecular dynamics) and paramagnetic relaxation enhancement (PRE) as an experimental tool to study intermolecular dynamics. Most of the thesis concerns the dynamics of the methylene group in the two possibly mobile parts of the oligosaccharide: in the exocyclic hydroxymethyl moiety and in the glycosidic linkage position. To perform conformational dynamic studies, the more traditional auto-relaxation pa-rameters are combined with the relaxation interference terms or the cross-correlated relaxation rates (CCRRs). Some experimental schemes based on the initial-rate technique were developed for measuring CCRRs. The techniques are useful for labelled sugars as well as naturally abundant ones. Furthermore, various dynamical models ranging from the Lipari–Szabo approach to several more informative and complicated models such as the two-site jump model, restricted internal rotation and slowly relaxing local structure (SRLS), have been employed to interpret our experimental data. We have combined and com-pared different models; we have also developed a novel approach to existing models, by scaling dipolar coupling constants (DCC), to extract the dynamic behaviour and structural properties of the system. We found that the auto- and cross-correlated relaxation data analyses yield a consistent picture of the dynam-ics in all cases. Additionally, our investigations show that CCRRs are practically important for verifica-tion of certain dynamical and structural information that is difficult to be determined by other means. Moreover, the anisotropy of the carbon-13 chemical shielding tensor in the methylene group has been estimated, using the interference between dipole-dipole and chemical shift anisotropy. This thesis also discusses using the PRE to investigate sugar dynamics relative to a paramagnetic MRI contrast agent in solution, which might be important in medicine. We have studied the intramolecu-lar dynamics of the trisaccharide raffinose in the presence of a gadolinium complex. We also investigated the effect of translational diffusion instead of rotational diffusion, which is normally more important in NMR. The paramagnetically enhanced spin–lattice relaxation rates of aqueous protons over a wide range of magnetic fields and of carbon-13 and protons of the sugar at high fields have been measured. The nuclear magnetic relaxation dispersion of water protons and the PREs of proton and carbon in the sugar are interpreted in terms of the model recently developed in our laboratory, allowing both outer- and inner-sphere PREs for water protons, but allowing only the outer sphere PRE for nuclei in the sugar. We found that the relative diffusion has a stronger effect on the PRE than the electron spin relaxation

Renewable Energy Investment Planning and Policy Design

In this dissertation, we leverage predictive and prescriptive analytics to develop decision support systems to promote the use of renewable energy in society. Since electricity from renewable energy sources is still relatively expensive, there are variety of financial incentive programs available in different regions. Our research focuses on financial incentive programs and tackles two main problem: 1) how to optimally design and control hybrid renewable energy systems for residential and commercial buildings given the capacity based and performance based incentives, and 2) how to develop a model-based system for policy makers for designing optimal financial incentive programs to promote investment in net zero energy (NZE) buildings. In order to customize optimal investment and operational plans for buildings, we developed a mixed integer program (MIP). The optimization model considers the load profile and specifications of the buildings, local weather data, technology specifications and pricing, electricity tariff, and most importantly, the available financial incentives to assess the financial viability of investment in renewable energy. It is shown how the MIP model can be used in developing customized incentive policy designs and controls for renewable energy system

Renewable Energy Investment Planning and Policy Design

In this dissertation, we leverage predictive and prescriptive analytics to develop decision support systems to promote the use of renewable energy in society. Since electricity from renewable energy sources is still relatively expensive, there are variety of financial incentive programs available in different regions. Our research focuses on financial incentive programs and tackles two main problem: 1) how to optimally design and control hybrid renewable energy systems for residential and commercial buildings given the capacity based and performance based incentives, and 2) how to develop a model-based system for policy makers for designing optimal financial incentive programs to promote investment in net zero energy (NZE) buildings. In order to customize optimal investment and operational plans for buildings, we developed a mixed integer program (MIP). The optimization model considers the load profile and specifications of the buildings, local weather data, technology specifications and pricing, electricity tariff, and most importantly, the available financial incentives to assess the financial viability of investment in renewable energy. It is shown how the MIP model can be used in developing customized incentive policy designs and controls for renewable energy system

Distributed Adaptive Droop Control Method for Flexibility Enhancement of Islanded DC Microgrids Including Electric Springs

DC Microgrids (DC-MGs) are gaining attention as they pave the way for merging various means of energy resources with DC outputs. In the context of islanded DC-MGs, the intermittent nature of renewable energy resources and the uncertainty in demand profiles across various timescales pose prominent challenges for ensuring continuous power supply. To overcome this concern, a combination of generation units and energy storage systems are conventionally employed. However, in this paper, the storage system is replaced by the electric spring, which is a more effective demand-side management technique, aimed at enhancing flexibility in response to potential uncertainties and regulating the common bus voltage of standalone DC-MGs. To provide proper energy-sharing coordination between different resources, a distributed adaptive droop control strategy is employed. An adaptive droop method seems inevitable because voltage regulation or current-sharing accuracy can be significantly affected by line impedances, especially under high-load states. The effectiveness of the presented method has been verified through simulation scenarios using MATLAB®/Simulink