ASU Scholarship Showcase

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

The effects of urbanization on ozone levels have been widely investigated over cities primarily located in temperate and/or humid regions. In this study, nested WRF-Chem simulations with a finest grid resolution of 1 km are conducted to investigate ozone concentrations O₃ due to urbanization within cities in arid/semi-arid environments. First, a method based on a shape preserving Monotonic Cubic Interpolation (MCI) is developed and used to downscale anthropogenic emissions from the 4 km resolution 2005 National Emissions Inventory (NEI05) to the finest model resolution of 1 km. Using the rapidly expanding Phoenix metropolitan region as the area of focus, we demonstrate the proposed MCI method achieves ozone simulation results with appreciably improved correspondence to observations relative to the default interpolation method of the WRF-Chem system. Next, two additional sets of experiments are conducted, with the recommended MCI approach, to examine impacts of urbanization on ozone production: (1) the urban land cover is included (i.e., urbanization experiments) and, (2) the urban land cover is replaced with the region's native shrubland. Impacts due to the presence of the built environment on O₃ are highly heterogeneous across the metropolitan area. Increased near surface O₃ due to urbanization of 10–20 ppb is predominantly a nighttime phenomenon while simulated impacts during daytime are negligible. Urbanization narrows the daily O₃ range (by virtue of increasing nighttime minima), an impact largely due to the region's urban heat island. Our results demonstrate the importance of the MCI method for accurate representation of the diurnal profile of ozone, and highlight its utility for high-resolution air quality simulations for urban areas.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

An interesting occurrence of a Rossby wave breaking event observed during the VORCORE experiment is presented and explained. Twenty-seven balloons were launched inside the Antarctic polar vortex. Almost all of these balloons evolved in the stratosphere around 500K within the vortex, except the one launched on 28 October 2005. In this case, the balloon was caught within a tongue of high potential vorticity (PV), and was ejected from the polar vortex. The evolution of this event is studied for the period between 19 and 25 November 2005. It is found that at the beginning of this period, the polar vortex experienced distortions due to the presence of Rossby waves. Then, these waves break and a tongue of high PV develops. On 25 November, the tongue became separated from the vortex and the balloon was ejected into the surf zone. Lagrangian simulations demonstrate that the air masses surrounding the balloon after its ejection were originating from the vortex edge. The wave breaking and the development of the tongue are confined within a region where a planetary Quasi-Stationary Wave 1 (QSW1) induces wind speeds with weaker values. The QSW1 causes asymmetry in the wind speed and the horizontal PV gradient along the edge of the polar vortex, resulting in a localized jet. Rossby waves with smaller scales propagating on top of this jet amplify as they enter the jet exit region and then break. The role of the QSW1 on the formation of the weak flow conditions that caused the non-linear wave breaking observed near the vortex edge is confirmed by three-dimensional numerical simulations using forcing with and without the contribution of the QSW1.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Background: A limitation of traditional outcome studies from behavioral interventions is the lack of attention given to evaluating the influence of moderating variables. This study examined possible moderation effect of baseline activity levels on physical activity change as a result of the Ready for Recess intervention.

Methods: Ready for Recess (August 2009-September 2010) was a controlled trial with twelve schools randomly assigned to one of four conditions: control group, staff supervision, equipment availability, and the combination of staff supervision and equipment availability. A total of 393 children (181 boys and 212 girls) from grades 3 through 6 (8–11 years old) were asked to wear an Actigraph monitor during school time on 4–5 days of the week. Assessments were conducted at baseline (before intervention) and post intervention (after intervention).

Results: Initial MVPA moderated the effect of Staff supervision (β = −0.47%; p < .05), but not Equipment alone and Staff + Equipment (p > .05). Participants in the Staff condition that were 1 standard deviation (SD) below the mean for baseline MVPA (classified as “low active”) had lower MVPA levels at post-intervention when compared with their low active peers in the control condition (Mean_diff = −10.8 ± 2.9%; p = .005). High active individuals (+1SD above the mean) in the Equipment treatment also had lower MVPA values at post-intervention when compared with their highly active peers in the control group (Mean_diff = −9.5 ± 2.9%; p = .009).

Conclusions: These results indicate that changes in MVPA levels at post-intervention were reduced in highly active participants when recess staff supervision was provided. In this study, initial MVPA moderated the effect of Staff supervision on children’s MVPA after 6 months of intervention. Staff training should include how to work with inactive youth but also how to assure that active children remain active.

Background: In the United States, approximately one in 110 pregnancies end in stillbirth affecting more than 26,000 women annually. Women experiencing stillbirth have a threefold greater risk of developing depressive symptoms compared to women experiencing live birth. Depression contributes negatively to health outcomes for both mothers and babies subsequent to stillbirth. Physical activity may improve depression in these women, however, little is known about acceptable physical activity interventions for women after stillbirth. This is the purpose of this descriptive exploratory study.

Methods: Eligible women were between ages 19 and 45, and experienced stillbirth within one year of the study. An online survey was used to ask questions related to 1) pregnancy and family information (i.e., time since stillbirth, weight gain during pregnancy, number of other children) 2) physical activity participation, 3) depressive symptomatology, and 4) demographics.

Results: One hundred seventy-five women participated in the study (M age = 31.26 ± 5.52). Women reported participating in regular physical activity (at least 150 minutes of moderate activity weekly) before (60%) and during (47%) their pregnancy, as well as after their stillbirth (61%). Only 37% were currently meeting physical activity recommendations. Approximately 88% reported depression (i.e., score of >10 on depression scale). When asked how women cope with depression, anxiety, or grief, 38% said physical activity. Of those that reported using physical activity to cope after stillbirth, they did so to help with depression (58%), weight loss (55%), and better overall physical health (52%). To cope with stillbirth, women used walking (67%), followed by jogging (35%), and yoga (23%). Women who participated in physical activity after stillbirth reported significantly lower depressive symptoms (M = 15.10, SD = 5.32) compared to women who did not participate in physical activity (M = 18.06, SD = 5.57; t = -3.45, p = .001).

Conclusions: Physical activity may serve as a unique opportunity to help women cope with the multiple mental sequelae after stillbirth. This study provides data to inform healthcare providers about the potential role of physical activity in bereavement and recovery for women who have experienced stillbirth. Additional research is necessary in this vulnerable population.

Background: The transition to parenthood is consistently associated with declines in physical activity. In particular, working parents are at risk for inactivity, but research exploring physical activity barriers and facilitators in this population has been scarce. The purpose of this study was to qualitatively examine perceptions of physical activity among working parents.

Methods: Working mothers (n = 13) and fathers (n = 12) were recruited to participate in one of four focus group sessions and discuss physical activity barriers and facilitators. Data were analyzed using immersion/crystallization in NVivo 10.

Results: Major themes for barriers included family responsibilities, guilt, lack of support, scheduling constraints, and work. Major themes for facilitators included being active with children or during children’s activities, being a role model for children, making time/prioritizing, benefits to health and family, and having support available. Several gender differences emerged within each theme, but overall both mothers and fathers reported their priorities had shifted to focus on family after becoming parents, and those who were fitting in physical activity had developed strategies that allowed them to balance their household and occupational responsibilities.

Conclusions: The results of this study suggest working mothers and fathers report similar physical activity barriers and facilitators and would benefit from interventions that teach strategies for overcoming barriers and prioritizing physical activity amidst the demands of parenthood. Future interventions might consider targeting mothers and fathers in tandem to create an optimally supportive environment in the home.