ASU Scholarship Showcase

The United State generates the most waste among OECD countries, and there are adverse effects of the waste generation. One of the most serious adverse effects is greenhouse gas, especially CH4, which causes global warming. However, the amount of waste generation is not decreasing, and the United State recycling rate, which could reduce waste generation, is only 26%, which is lower than other OECD countries. Thus, waste generation and greenhouse gas emission should decrease, and in order for that to happen, identifying the causes should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling waste influences carbon dioxide emissions from the waste sector. The annual-based U.S. data from 1990 to 2012 were used. The data were collected from various data sources, and the Granger causality test was applied for identifying the causal relationships. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. This implies that the waste generation will not decrease even if GDP increases. And, if waste generation decreases or recycling rate increases, the greenhouse gas emission will decrease. Based on these results, it is expected that the waste generation and carbon dioxide emission from the waste sector can decrease more efficiently.

Construction waste management has become extremely important due to stricter disposal and landfill regulations, and a lesser number of available landfills. There are extensive works done on waste treatment and management of the construction industry. Concepts like deconstruction, recyclability, and Design for Disassembly (DfD) are examples of better construction waste management methods. Although some authors and organizations have published rich guides addressing the DfD's principles, there are only a few buildings already developed in this area. This study aims to find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation. Furthermore, it aims to provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.

Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

As the construction continue to be a leading industry in the number of injuries and fatalities annually, several organizations and agencies are working avidly to ensure the number of injuries and fatalities is minimized. The Occupational Safety and Health Administration (OSHA) is one such effort to assure safe and healthful working conditions for working men and women by setting and enforcing standards and by providing training, outreach, education and assistance. Given the large databases of OSHA historical events and reports, a manual analysis of the fatality and catastrophe investigations content is a time consuming and expensive process. This paper aims to evaluate the strength of unsupervised machine learning and Natural Language Processing (NLP) in supporting safety inspections and reorganizing accidents database on a state level. After collecting construction accident reports from the OSHA Arizona office, the methodology consists of preprocessing the accident reports and weighting terms in order to apply a data-driven unsupervised K-Means-based clustering approach. The proposed method classifies the collected reports in four clusters, each reporting a type of accident. The results show the construction accidents in the state of Arizona to be caused by falls (42.9%), struck by objects (34.3%), electrocutions (12.5%), and trenches collapse (10.3%). The findings of this research empower state and local agencies with a customized presentation of the accidents fitting their regulations and weather conditions. What is applicable to one climate might not be suitable for another; therefore, such rearrangement of the accidents database on a state based level is a necessary prerequisite to enhance the local safety applications and standards.

Urban land–atmosphere interactions can be captured by numerical modeling framework with coupled land surface and atmospheric processes, while the model performance depends largely on accurate input parameters. In this study, we use an advanced stochastic approach to quantify parameter uncertainty and model sensitivity of a coupled numerical framework for urban land–atmosphere interactions. It is found that the development of urban boundary layer is highly sensitive to surface characteristics of built terrains. Changes of both urban land use and geometry impose significant impact on the overlying urban boundary layer dynamics through modification on bottom boundary conditions, i.e., by altering surface energy partitioning and surface aerodynamic resistance, respectively. Hydrothermal properties of conventional and green roofs have different impacts on atmospheric dynamics due to different surface energy partitioning mechanisms. Urban geometry (represented by the canyon aspect ratio), however, has a significant nonlinear impact on boundary layer structure and temperature. Besides, managing rooftop roughness provides an alternative option to change the boundary layer thermal state through modification of the vertical turbulent transport. The sensitivity analysis deepens our insight into the fundamental physics of urban land–atmosphere interactions and provides useful guidance for urban planning under challenges of changing climate and continuous global urbanization.

The net storage heat flux (ΔQ[subscript S]) is important in the urban surface energy balance (SEB) but its determination remains a significant challenge. The hysteresis pattern of the diurnal relation between the ΔQ[subscript S] and net all-wave radiation (Q[superscript ∗]) has been captured in the Objective Hysteresis Model (OHM) parameterization of ΔQ[subscript S]. Although successfully used in urban areas, the limited availability of coefficients for OHM hampers its application. To facilitate use, and enhance physical interpretations of the OHM coefficients, an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the SEB is conducted, allowing development of AnOHM (Analytical Objective Hysteresis Model). A sensitivity test of AnOHM to surface properties and hydrometeorological forcing is presented using a stochastic approach (subset simulation). The sensitivity test suggests that the albedo, Bowen ratio and bulk transfer coefficient, solar radiation and wind speed are most critical. AnOHM, driven by local meteorological conditions at five sites with different land use, is shown to simulate the ΔQ[subscript S] flux well (RMSE values of ∼ 30 W m[superscript −2]). The intra-annual dynamics of OHM coefficients are explored. AnOHM offers significant potential to enhance modelling of the surface energy balance over a wider range of conditions and land covers.