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Description
High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the

High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete combustion resulting from open fires for cooking and heating. Monitoring air quality is an essential step to identifying these and other factors that affect air quality, and thereafter informing engineering and policy decisions to improve the quality of air. This study seeks to measure changes in air quality across spatial and temporal domains, with a specific focus on microclimates within an urban area. A prototype, low-cost air quality monitoring device has been developed to measure the concentrations of particulate matter, ozone, and carbon monoxide multiple times per minute. The device communicates data wirelessly via cell towers, and can run off-grid using a solar PV-battery system. The device can be replicated and deployed across urban regions for high-fidelity emissions monitoring to explore the effect of anthropogenic and environmental factors on intra-hour air quality. Hardware and software used in the device is described, and the wireless data communication protocols and capabilities are discussed.
ContributorsReilly, Kyle (Co-author) / Birner, Michael (Co-author) / Johnson, Nathan (Thesis director) / Gary, Kevin (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
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Description
The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the

The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the variance source when compared to the ANOVA method. Although the variance estimators deteriorate when varying degrees of non-normality is introduced through simulation; however, the POV method is shown to be a more stable measure of variance in the aggregate. The POV method also provides non-negative, stable estimates for interaction when compared to the ANOVA method. The POV method is shown to be more stable, particularly in low sample size situations. Based on these findings, it is suggested that the POV is not a replacement for more complex analysis methods, but rather, a supplement to them. POV is ideal for preliminary analysis due to the ease of implementation, the simplicity of interpretation, and the lack of dependency on statistical analysis packages or statistical knowledge.
ContributorsLittle, David John (Author) / Borror, Connie (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015