Matching Items (84)
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Description

Attributing observed CO2 variations to human or natural cause is critical to deducing and tracking emissions from observations. We have used in situ CO2, CO, and planetary boundary layer height (PBLH) measurements recorded during the CalNex-LA (CARB et al., 2008) ground campaign of 15 May-15 June 2010, in Pasadena, CA,

Attributing observed CO2 variations to human or natural cause is critical to deducing and tracking emissions from observations. We have used in situ CO2, CO, and planetary boundary layer height (PBLH) measurements recorded during the CalNex-LA (CARB et al., 2008) ground campaign of 15 May-15 June 2010, in Pasadena, CA, to deduce the diurnally varying anthropogenic component of observed CO2 in the megacity of Los Angeles (LA). This affordable and simple technique, validated by carbon isotope observations and WRF-STILT (Weather Research and Forecasting model - Stochastic Time-Inverted Lagrangian Transport model) predictions, is shown to robustly attribute observed CO2 variation to anthropogenic or biogenic origin over the entire diurnal cycle. During CalNex-LA, local fossil fuel combustion contributed up to similar to 50% of the observed CO2 enhancement overnight, and similar to 100% of the enhancement near midday. This suggests that sufficiently accurate total column CO2 observations recorded near midday, such as those from the GOSAT or OCO-2 satellites, can potentially be used to track anthropogenic emissions from the LA megacity.

ContributorsNewman, S. (Author) / Jeong, S. (Author) / Fischer, M.L. (Author) / Xu, X. (Author) / Haman, C.L. (Author) / Lefer, B. (Author) / Alvarez, S. (Author) / Rappenglueck, B. (Author) / Kort, E.A. (Author) / Andrews, A. E. (Author) / Peischl, J. (Author) / Gurney, Kevin (Author) / Miller, C.E. (Author) / Yung, Y.L. (Author) / College of Liberal Arts and Sciences (Contributor)
Created2013-04-26
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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

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.
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
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Description

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

ContributorsDavis, Kenneth J. (Author) / Deng, Aijun (Author) / Lauvaux, Thomas (Author) / Miles, Natasha L. (Author) / Richardson, Scott J. (Author) / Sarmiento, Daniel P. (Author) / Gurney, Kevin (Author) / Hardesty, R. Michael (Author) / Bonin, Timothy A. (Author) / Brewer, W. Alan (Author) / Lamb, Brian K. (Author) / Shepson, Paul B. (Author) / Harvey, Rebecca M. (Author) / Cambaliza, Maria O. (Author) / Sweeney, Colm (Author) / Turnbull, Jocelyn C. (Author) / Whetstone, James (Author) / Karion, Anna (Author) / College of Liberal Arts and Sciences (Contributor)
Created2017-05-23
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Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05