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.

Material wealth is a key factor shaping human development and well-being. Every year, hundreds of studies in social science and policy fields assess material wealth in low- and middle-income countries assuming that there is a single dimension by which households can move from poverty to prosperity. However, a one-dimensional model may miss important kinds of prosperity, particularly in countries where traditional subsistence-based livelihoods coexist with modern cash economies. Using multiple correspondence analysis to analyze representative household data from six countries—Nepal, Bangladesh, Ethiopia, Kenya, Tanzania, and Guatemala—across three world regions, we identify a number of independent dimension of wealth, each with a clear link to locally relevant pathways to success in cash and agricultural economies. In all cases, the first dimension identified by this approach replicates standard one-dimensional estimates and captures success in cash economies. The novel dimensions we identify reflect success in different agricultural sectors and are independently associated with key benchmarks of food security and human growth, such as adult body mass index and child height. The multidimensional models of wealth we describe here provide new opportunities for examining the causes and consequences of wealth inequality that go beyond success in cash economies, for tracing the emergence of hybrid pathways to prosperity, and for assessing how these different pathways to economic success carry different health risks and social opportunities.

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.

Human populations differ reliably in the degree to which people favor family, friends, and community members over strangers and outsiders. In the last decade, researchers have begun to propose several economic and evolutionary hypotheses for these cross-population differences in parochialism. In this paper, we outline major current theories and review recent attempts to test them. We also discuss the key methodological challenges in assessing these diverse economic and evolutionary theories for cross-population differences in parochialism.

Combustion-derived aerosols in the marine boundary layer have been poorly studied, especially in remote environments such as the open Atlantic Ocean. The tropical Atlantic has the potential to contain a high concentration of aerosols, such as black carbon, due to the African emission plume of biomass and agricultural burning products. Atmospheric particulate matter samples across the tropical Atlantic boundary layer were collected in the summer of 2010 during the southern hemispheric dry season when open fire events were frequent in Africa and South America. The highest black carbon concentrations were detected in the Caribbean Sea and within the African plume, with a regional average of 0.6 μg m^-3 for both. The lowest average concentrations were measured off the coast of South America at 0.2 to 0.3 μg m^-3. Samples were quantified for black carbon using multiple methods to provide insights into the form and stability of the carbonaceous aerosols (i.e., thermally unstable organic carbon, soot like, and charcoal like). Soot-like aerosols composed up to 45% of the carbonaceous aerosols in the Caribbean Sea to as little as 4% within the African plume. Charcoal-like aerosols composed up to 29% of the carbonaceous aerosols over the oligotrophic Sargasso Sea, suggesting that non-soot-like particles could be present in significant concentrations in remote environments. To better apportion concentrations and forms of black carbon, multiple detection methods should be used, particularly in regions impacted by biomass burning emissions.

The notable increase in biofuel usage by the road transportation sector in Brazil during recent years has significantly altered the vehicular fuel composition. Consequently, many uncertainties are currently found in particulate matter vehicular emission profiles. In an effort to better characterise the emitted particulate matter, measurements of aerosol physical and chemical properties were undertaken inside two tunnels located in the São Paulo Metropolitan Area (SPMA). The tunnels show very distinct fleet profiles: in the Jânio Quadros (JQ) tunnel, the vast majority of the circulating fleet are light duty vehicles (LDVs), fuelled on average with the same amount of ethanol as gasoline. In the Rodoanel (RA) tunnel, the particulate emission is dominated by heavy duty vehicles (HDVs) fuelled with diesel (5% biodiesel). In the JQ tunnel, PM_2.5 concentration was on average 52 μg m^-3, with the largest contribution of organic mass (OM, 42%), followed by elemental carbon (EC, 17%) and crustal elements (13%). Sulphate accounted for 7% of PM_2.5 and the sum of other trace elements was 10%. In the RA tunnel, PM_2.5 was on average 233 μg m^-3, mostly composed of EC (52%) and OM (39%). Sulphate, crustal and the trace elements showed a minor contribution with 5%, 1%, and 1%, respectively. The average OC : EC ratio in the JQ tunnel was 1.59 ± 0.09, indicating an important contribution of EC despite the high ethanol fraction in the fuel composition. In the RA tunnel, the OC : EC ratio was 0.49 ± 0.12, consistent with previous measurements of diesel-fuelled HDVs. Besides bulk carbonaceous aerosol measurement, polycyclic aromatic hydrocarbons (PAHs) were quantified. The sum of the PAHs concentration was 56 ± 5 ng m^-3 and 45 ± 9 ng m^-3 in the RA and JQ tunnel, respectively. In the JQ tunnel, benzo(a)pyrene (BaP) ranged from 0.9 to 6.7 ng m^-3 (0.02–0.1‰ of PM_2.5)] whereas in the RA tunnel BaP ranged from 0.9 to 4.9 ng m^-3 (0.004–0. 02‰ of PM_2.5), indicating an important relative contribution of LDVs emission to atmospheric BaP.

About 2.5 × 10⁶ snapshots on microcrystals of photoactive yellow protein (PYP) from a recent serial femtosecond crystallographic (SFX) experiment were reanalyzed to maximum resolution. The resolution is pushed to 1.46 Å, and a PYP structural model is refined at that resolution. The result is compared to other PYP models determined at atomic resolution around 1 Å and better at the synchrotron. By comparing subtleties such as individual isotropic temperature factors and hydrogen bond lengths, we were able to assess the quality of the SFX data at that resolution. We also show that the determination of anisotropic temperature factor ellipsoids starts to become feasible with the SFX data at resolutions better than 1.5 Å.

Formally zerovalent (κ³-phosphine)Fe(η⁴-COT) complexes supported by either Triphos (PhP(CH₂CH₂PPh₂)₂) or Triphos* (H₃CC(CH₂PPh₂)₃) have been prepared following chelate addition to (COT)₂Fe (COT = 1,3,5,7-cyclooctatetraene) and by reduction of the respective dibromide complexes in the presence of excess COT. The solid-state structure of each complex was determined by single-crystal X-ray diffraction, and close inspection of the metrical parameters revealed significant COT ligand reduction, independent of the coordination geometry about iron. While the neutral and dianionic forms of the redox-active COT ligand have historically received a great deal of attention, a dearth of information regarding the often-evoked radical monoanion form of this ligand prompted the full electronic structure investigation of these complexes using a range of techniques. Comparison of the Mössbauer spectroscopic data collected for both (Triphos)Fe(η⁴-COT) complexes with data obtained for two appropriate reference compounds indicated that they possess a low-spin Fe(I) center that is antiferromagnetically coupled to a COT radical monoanion. Further evidence for this electronic structure determination by EPR spectroscopy and cyclic voltammetry is presented. A comparison of the solid-state metrical parameters determined in this study to those of related first-row transition-metal complexes has provided insight into the electronic structure analysis of related organometallic complexes.