Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 169-176). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2003. / Includes bibliographical references (leaves 157-169).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

In this thesis, we have investigated several computational methods on data assimilation for air quality prediction, especially on the characteristic of sparse matrix and the underlying information of gradient in the concentration of pollutant species. In the first part, we have studied the ensemble Kalman filter (EnKF) for chemical species simulation in air quality forecast data assimilation. The main contribution of this paper is to study the sparse data observations and make use of the matrix structure of the Kalman filter updated equations to design an algorithm to compute the analysis of chemical species in the air quality forecast system efficiently. The proposed method can also handle the combined observations from multiple species together. We have applied the proposed method and tested its performance for real air quality data assimilation. Numerical examples have demonstrated the efficiency of the proposed computational method for Kalman filter update, and the effectiveness of the proposed method for NO2, NO, CO, SO2, O3, PM2.5 and PM10 in air quality data assimilation. Within the third part, we have set up an automatic workflow to connect the management system of the chemical transport model - CMAQ with our proposed data assimilation methods. The setup has successfully integrated the data assimilation into the management system and shown that the accuracy of the prediction has risen to a new level. This technique has transformed the system into a real-time and high-precision system. When the new observations are available, the predictions can then be estimated almost instantaneously. Then the agencies are able to make the decisions and respond to the situations immediately. In this way, citizens are able to protect themselves effectively. Meanwhile, it allows the mathematical algorithm to be industrialized implying that the improvements on data assimilation have directly positive effects on the developments of the environment, the human health and the society. Therefore, this has become an inspiring indication to encourage us to study, achieve and even devote more research into this promising method.

Biomass energy is the oldest form of energy harnessed by humans. Currently, processed biofuels, which are derived from biomass, are being pursued as a possi- ble route to decarbonize the transport sector - a particularly difficult task for both technological and sociological reasons. In this thesis I explore the impacts that large scale biofuel use could have on atmospheric chemistry. I review the current state of biofuels politically and technologically, focusing on ethanol and biodiesel. I discuss the salient features of tropospheric chemistry and in particular the oxidation of isoprene, an important biogenic volatile organic compound. I examine the impact that including isoprene oxidation has in a new chemistry-climate computer model, UKCA; the response of ozone turns out to depend on local chemical conditions. To evaluate the global model, I use data from the OP3 field campaign in Malaysia and compare it with output from the model chemical mechanism. The mechanism is able to reproduce NOx and ozone measurements well, though is more sensitive to representations of physical rather than chemical processes. I also perform a simple sensitivity study which examines crop changes in the region of Southeast Asia. In the final two chapters, I turn to two distinct phases of the biofuel life cycle. I characterize a potential future atmosphere through an ozone attribution study, then examine the impact of future cropland expansion (phase I of a biofuel life cycle) on tropospheric chemistry. I find that land use change has a large impact on ozone, and that it is more acute than another perturbation (CO2 suppression) to isoprene emissions. I then move to phase III of the life cycle - combustion - and look at the sensitivity of the model chemistry to surface transport emissions from biofuels as a replacement for conventional fuels. I find that biodiesel reduces surface ozone, while ethanol increases it, and that the response has both a linear and nonlinear component.

Texas is the second largest state in U.S.A. based on geographical area, population and the economy. It is home to several large coastal urban areas with major industries and infrastructure supporting the fossil-fuel based energy sector. Most of the major cities on the state have been impacted by significant air pollution events over the past decade. Studies conducted in the southern coastal region of TX have identified long range transport as a major contributor of particulate matter (PM) pollution along with local emissions. Biomass burns, secondary sulfates and diesel emissions sources are comprise as the dominant mass of PM2.5 have been noted to be formed by the long range transport biomass from Central America. Thus, the primary objective of this study was to identify and quantify local as well as regional sources contributing to the PM pollution in the coastal area of Brownsville located along the Gulf of Mexico. Source apportionment techniques such as principal component analysis (PCA) and positive matrix factorization (PMF) were employed on the air quality monitoring data to identify and quantify local and regional sources affecting this coastal region. As a supplement to the PMF and PCA, conditional probability function (CPF) analysis and potential source contribution function (PSCF) analysis were employed to characterize the meteorological influences for PM events. PCA identified an optimal solution of 6 sources affecting the coastal area of Brownsville, while PMF resolved 8 sources for the same area. Biomass comingled with sea salt was identified to be the dominant contributor from the PCA analysis with 30.2% of the apportioned PM mass in Brownsville, meanwhile PMF account secondary sulfates I & II with 27.6%. the other common sources identified included, biomass burning, crustal dust, secondary sulfate, oil combustion, mobile sources and miscellaneous traffic sources.

A system designed to control industrial emissions while protecting the environment has evolved from the Clean Air Act Amendments. The system's primary components are the pollution sources, government, economy, environment, and public influence. The functional relationship between all of the system's components manifests itself in the form of requirements for pollution permits. The pollution permits limit the environmental impact of the pollution sources while in general do not impose undue economic burdens on the sources. The environmental impact is determined by analyzing the source's pollution concentration distributions against the systems functional criteria which establish threshold limits for pollution concentrations. A computer model, a detail design component of the system, predicts ambient air concentration distributions around a proposed facility based on Gaussian Dispersion principles. <p>A case study of four sources in Giles County, Virginia served to illustrate the functional relationships of the system's components. A computer program, the Integrated Gaussian Model (IGM), predicted ambient air concentrations of pollutants resulting from the emissions by the four sources in the case study. The model application provided an opportunity to evaluate actual data produced by one of the system's primary detailed design components. Analysis of the results indicated that at least two of the Giles County sources in the region exceed the limits imposed by the system's criteria and thus have adverse impact on the environment. / Master of Science

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