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Description
Future robotic and human missions to the Moon and Mars will need in situ capabilities to characterize the mineralogy of rocks and soils within a microtextural context. Such spatially-correlated information is considered crucial for correct petrogenetic interpretations and will be key observations for assessing the potential for past habitability on Mars. These data will also enable the selection of the highest value samples for further analysis and potential caching for return to Earth. The Multispectral Microscopic Imager (MMI), similar to a geologist's hand lens, advances the capabilities of current microimagers by providing multispectral, microscale reflectance images of geological samples, where each image pixel is comprised of a 21-band spectrum ranging from 463 to 1735 nm. To better understand the capabilities of the MMI in future surface missions to the Moon and Mars, geological samples comprising a range of Mars-relevant analog environments as well as 18 lunar rocks and four soils, from the Apollo collection were analyzed with the MMI. Results indicate that the MMI images resolve the fine-scale microtextural features of samples, and provide important information to help constrain mineral composition. Spectral end-member mapping revealed the distribution of Fe-bearing minerals (silicates and oxides), along with the presence of hydrated minerals. In the case of the lunar samples, the MMI observations also revealed the presence of opaques, glasses, and in some cases, the effects of space weathering in samples. MMI-based petrogenetic interpretations compare favorably with laboratory observations (including VNIR spectroscopy, XRD, and thin section petrography) and previously published analyses in the literature (for the lunar samples). The MMI was also deployed as part of the 2010 ILSO-ISRU field test on the slopes of Mauna Kea, Hawaii and inside the GeoLab as part of the 2011 Desert RATS field test at the Black Point Lava Flow in northern Arizona to better assess the performance of the MMI under realistic field conditions (including daylight illumination) and mission constraints to support human exploration. The MMI successfully imaged rocks and soils in outcrops and samples under field conditions and mission operation scenarios, revealing the value of the MMI to support future rover and astronaut exploration of planetary surfaces.
ContributorsNúñez Sánchez, Jorge Iván (Author) / Farmer, Jack D. (Thesis advisor) / Christensen, Philip R. (Committee member) / Garcia-Pichel, Ferran (Committee member) / Robinson, Mark S. (Committee member) / Sellar, R. Glenn (Committee member) / Williams, Lynda B. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018