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Temporal Variations And Sources Of Organic Pollutants In Two Urban Atmopsheres: Ankara And OttawaOguz Kuntasal, Oznur 01 May 2005 (has links) (PDF)
This study aimed at providing a thorough understanding of temporal and spatial variations of VOCs and underlying factors in different microenvironments in two different urban atmospheres, with different degrees of regulatory enforcement. The VOC data were collected in field campaigns conducted in Ankara, Turkey, and Ottawa, Canada over the years 2000-2004. Insight into the sources of VOCs in different urban atmospheres was sought by using three commonly used receptor models namely / Positive Matrix Factorization (PMF), Chemical Mass Balance (CMB) Model and Conventional Factor Analysis (CFA). Motor vehicle related source profiles were developed to use in receptor modeling. Motor vehicles are the most abundant VOC sources with about 60% and 95% contributions to ambient levels in Ankara and Ottawa, respectively. Residential heating (31%) during winter season, biogenic (9%) and architectural coating (12%) emissions during summer season and solvent use (about 12%) emissions are the next abundant VOC sources in Ankara.
In addition, a new method to estimate the contribution of sources from wind sectors in urban atmosphere was developed and implemented in this study. The comparison of the results of these two cities demonstrated the influence of control measures on ambient levels and sources of VOCs observed in different urban atmospheres. VOC levels in Ankara exceed EU levels and they are about factor of two higher than that are measured in Ottawa owing to lack of implementation of emission control regulations for VOCs in Ankara compared to well adopted regulations in Ottawa.
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Time-lapse Analysis of Borehole and Surface Seismic Data, and Reservoir Characterization of the Ketzin CO2 Storage Site, GermanyYang, Can January 2012 (has links)
The CO2SINK (and CO2MAN) project is the first onshore CO2 storage project in Europe. The research site is located near the town of Ketzin, close to Potsdam in Germany. Injection started in June 2008, with a planned injection target of 100,000 tonnes of CO2. In February 2011, around 45, 000 tons of CO2 had been injected into the saline aquifer at an approximate depth of 630 m. This thesis focuses on time-lapse analysis of borehole seismic data, surface seismic data and reservoir characterization at the Ketzin site. Baseline Moving Source Profiling (MSP) data were acquired in the borehole Ketzin 202/2007 (OW2), along seven lines in 2007. The zero-offset Vertical Seismic Profile (VSP) data were acquired in the same borehole. The main objective of the VSP and MSP survey was to generate high-resolution seismic images around the borehole. After modeling and data processing, the sandy layers within the Stuttgart Formation can potentially be imaged in the VSP and MSP data whereas reflections from these layers are not as clearly observed in the 3D surface seismic data. 2D and pseudo-3D time-lapse seismic surveys were conducted at the Ketzin site. Interpretation of 2D baseline and repeat stacks shows that no CO2 leakage related time lapse signature is observable where the 2D lines allow monitoring of the reservoir. This is consistent with the time-lapse results of the 3D surveys showing an increase in reflection amplitude just centered around the injection well. The results from the pseudo-3D surveys are also consistent with the 3D seismic time-lapse studies and show that the sparse pseudo-3D geometry can be used to qualitatively map the CO2 in the reservoir with significantly less effect than the full 3D surveying. The 2nd pseudo-3D repeat survey indicates preferential migration of the CO2 to the west. There are no indications of migration into the caprock on either of the repeat surveys. Amplitude Versus Offset (AVO) analysis was performed on both 2D and 3D repeat surveys. A Class 3 AVO anomaly is clearly observed on the 3D repeat data and matches the synthetic modeling well. No AVO anomaly was observed on the 2D repeat data, which was anticipated, but the result shows signs of a pressure response at the reservoir level in the data. Reflection coefficients were calculated using surface seismic data (3D and pseudo-3D) at the site. Pre-injection calculations agree well with calculations from logging data. Post-injection calculations are in general agreement with the seismic modeling, but generally show higher amplitudes than those expected. The full 3D data show a better image of the reflection coefficients before and after injection than the pseudo-3D data and can potentially be used to make quantitative calculations of CO2 volumes. The pseudo-3D data only provide qualitative information.
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Temporally Correlated Dirichlet Processes in Pollution Receptor ModelingHeaton, Matthew J. 31 May 2007 (has links) (PDF)
Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.
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