Quantifying stream bank erosion and deposition rates in a central U.S. urban watershed

Title from PDF of title page (University of Missouri--Columbia, viewed on September 12, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Jason A. HubbartIncludes bibliographical references.M.S. University of Missouri--Columbia 2012."May 2012"Stream bank stability is gaining increasing attention in part because stream bank erosion can contribute as much as 80% of suspended sediment to streams, particularly in urbanizing watersheds. Stream bank erosion study sites were located in a lower reach of the Hinkson Creek Watershed located in Boone County, Missouri, USA. Streambank erosion and deposition rates were quantified using the erosion pin method comparing a remnant Bottomland Hardwood Forest (BHF) streambank to an Agricultural (Ag) streambank (922 m apart). Ten erosion pin plots (n = 342 pins) were installed that spanned the range of bank geometric and vegetation cover variability. Results indicated that during a drier (762 mm) than average (10yr avg=1077 mm) rainfall year 15.6 and 177.7 tonnes of soil erosion occurred on the right stream bank alone of the BHF and Ag sites respectively (Water Year 2011). Average erosion depth of the BHF and Ag was 18 and 112 mm/yr respectively. The greatest average depth of erosion occurred during the winter season (44.7 mm), followed by summer (13.1 mm) and spring (6.3 mm), fall had the lowest average erosion rate (1.1 mm). The stream bank erosion contributed approximately 67% of channel suspended sediment loading over WY 2011.Results hold important implications for land-use and land managers wishing to improve land-use practices, water quality and natural resource sustainability in dynamic urbanizing watersheds

ODOUR AND GAS EMISSIONS, ODOUR IMPACT CRITERIA, AND DISPERSION MODELLING FOR DAIRY AND POULTRY BARNS

Very limited research has been conducted to study the concentrations and emissions of odour, toxic gases (e.g., ammonia [NH3], hydrogen sulfide [H2S]), dust, and greenhouse gases (GHGs) from dairy and poultry barns in Canada. The major goals of this dissertation work were to study both the indoor and outdoor air pollution of a dairy, cage-layer, and broiler barn under the Canadian Prairies climate condition. The five odour properties, including odour concentration (OC), odour intensity (OI), hedonic tone (HT), persistence, and character descriptor, were studied for all three barns. The broiler barn presented the highest OC, strongest OI and most unpleasantness (HT) followed by the layer barn and then the dairy barn. It was found that OC, OI, and HT were significantly correlated with each other (P<0.01); increased OC was associated with increased OI but decreased HT. Then, new odour impact criteria were developed based on the derived relationships among OC, OI, and HT, with odour concentration limits being determined under both OI and HT limits. Seasonal concentration and emission profiles of odour, NH3 and H2S, GHG (carbon dioxide [CO2], methane [CH4], and nitrous oxide [N2O]), and respirable dust were characterized for the dairy, broiler, and layer barns by long-term monitoring over a year, and diurnal profiles of odour and gas concentrations and emissions were identified by continuous measurements for two days in mild, warm, and cold seasons, respectively. With NH3, H2S, and respirable dust concentrations, the indoor air quality of the three barns in different seasons were evaluated by not only considering the occupational health effect (respiratory irritation) of these individual air pollutants, but also their additive health effect. The worst indoor air quality was observed for the broiler barn followed by the dairy barn and then the layer barn. Also, the emission factors of odour, gases, and respirable dust were acquired. The highest annual average odour and NH3 emissions were from the layer barn (140 OU s-1 AU-1 and 1.10 mg s-1 AU-1), followed by the broiler barn (127 OU s-1 AU-1 and 1.06 mg s-1 AU-1) and then the dairy barn (45.9 OU s-1 AU-1 and 0.53 mg s-1 AU-1). The annual average CO2 and CH4 emissions were 116 and 3.1 mg s-1 AU-1 for the dairy barn, 437 and 0.06 mg s-1 AU-1 for the broiler barn, and 435 and 0.21 mg s-1 AU-1 for the layer barn. The impact of environmental parameters (T, RH, and VR) on concentrations and emissions of odour and gases were investigated, and then prediction models for odour emission were developed depending on the environmental parameters. To validate the performance of AERMOD for predicting odour dispersion, field odour plume measurements were conducted around the broiler barn. In consistent with previous studies, the modelled results were all greatly below the field measured results. Thus, scaling factors were generated to improve the comparison. One scaling factor was 286 by plotting all data and the other was 154 by only using the geometric mean of each odour plume. Both scaling factors achieved good agreements between model predictions and field measurements; however, the scaling factor of 154 was suggested to use due to its better performance over short distances (100-200 m). With the variable emission rates of odour, NH3, H2S, and respirable dust, dispersion modelling of the four air pollutants were conducted by the AERMOD dispersion model for all three barns to study their outdoor impact. Using both the recommended odour impact criteria by the Government of Saskatchewan (2012) and the developed odour impact criteria for the three barns in this study, directional setback distances were determined with the ambient threshold limits of NH3, H2S, and respirable dust being complied with. Additionally, odour impact criteria were found to be stricter than that of gases and respirable dust as the former always required greater setback distances than the latter

Electroencephalography (EEG)-based brain computer interfaces for rehabilitation

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand\u27s movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand\u27s motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy

Development of an Electroencephalography-Based Brain-Computer Interface Supporting Two-Dimensional Cursor Control

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at the average accuracy of 85.5±4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training

Stability results for a hierarchical size-structured population model with distributed delay

In this paper we investigate a structured population model with distributed delay. Our model incorporates two different types of nonlinearities. Specifically we assume that individual growth and mortality are affected by scramble competition, while fertility is affected by contest competition. In particular, we assume that there is a hierarchical structure in the population, which affects mating success. The dynamical behavior of the model is analysed via linearisation by means of semigroup and spectral methods. In particular, we introduce a reproduction function and use it to derive linear stability criteria for our model. Further we present numerical simulations to underpin the stability results we obtained