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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 Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the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 Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete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
Measuring and estimating solar resource availability is critical for assessing new sites for solar energy generation. This includes beam radiation, diffuse radiation, and total incident radiation. Total incident radiation is pertinent to solar photovoltaic (PV) output and low-temperature solar thermal applications whereas beam radiation is used for concentrating solar power

Measuring and estimating solar resource availability is critical for assessing new sites for solar energy generation. This includes beam radiation, diffuse radiation, and total incident radiation. Total incident radiation is pertinent to solar photovoltaic (PV) output and low-temperature solar thermal applications whereas beam radiation is used for concentrating solar power (CSP). Global horizontal insolation (GHI) data are most commonly available of any solar radiation measurement, yet these data cannot be directly applied to solar power generator estimation because solar PV panels and solar CSP collectors are not parallel to the earth’s surface. In absence of additional measured data, GHI data may be broken down into its constituent parts—diffuse radiation and beam radiation—using statistical techniques that incorporate explanatory variables such as the clearness index. This study provides a suite of methods and regression models to estimate diffuse radiation as a function of various explanatory variables using both piecewise and continuous fits. Regression analyses using the clearness index are completed for seven locations in the United States and four locations in other regions of the world. The multi-site analysis indicates that models developed using training data for a single location perform best in that location, yet general models can be created that perform reasonably well across any locality and then applied to estimate solar resource availability in new locations around the world. Results from the global and site-specific models perform better than the existing models in literature and indicate that models perform different in different sky conditions e.g. clear or cloudy sky. Results also show that continuous models perform equivalent or better than the piecewise models. Newly generated piecewise models showed improvement over some intervals in the clearness index. A combination of fits from this study and existing literature was used to improve overall performance of modeling techniques used in diffuse radiation estimation. Germany was selected for more detailed studies of a single case study using the clearness index, ambient temperature, relative humidity, and absolute humidity as explanatory variables. Clearness index is the most important variable for diffuse radiation calculation whereas the relative humidity and the temperature are the secondary variable for improving calculation. Absolute humidity plays similar role as temperature in improving the calculation on the other hand relative humidity improves it very slightly over the absolute humidity and temperature.
ContributorsSingh, Uday P (Author) / Johnson, Nathan (Thesis advisor) / Rogers, Bradley (Committee member) / Tamizhmani, Govindasamy (Committee member) / Arizona State University (Publisher)
Created2016