Texas, due to its geographical area, population, and economy is home to a variety of industrialized areas that have significant air quality problems. These urban areas are affected by elevated levels of fine particulate matter (PM2.5). The primary objective of this study was to identify and quantify local and regional sources of air pollution affecting the city of Houston, Texas. Positive Matrix Factorization (PMF) techniques were applied to observational datasets from two urban air quality monitoring sites in Houston from 2003 through 2008 in order to apportion sources of pollutants affecting the study region. Data from 68 species for Aldine and 91 for Deer Park were collected, evaluated, and revised to create concentration and uncertainty input files for the PMF2 and EPA PMF (PMF3) source apportionment models. A 11-sources solution for Aldine and 10-sources for Deer Park were identified as the optimal solutions with both models. The dominant contributors of fine particulate matter in these sites were found to be biomass burnings (2%-8.9%), secondary sulfates I (21.3%-7.6%) and II (38.8%-22.2%), crustal dust (8.9%-10.9%), industrial activities (10.9%-4.2%), traffic (23.1%-15.6%), secondary nitrates (4.4%-5.5%), fresh (1%-1.6%) and aged(5.1%-4.6%) sea salt and refineries (1.3%-0.6%), representing a strong case to confirm the high influence of local activities from the industrial area and the ship channel around the Houston channel. Additionally, potential source contribution function (PSCF) and conditional probability function (CPF) analyses were performed to identify local and regional source-rich areas affecting this urban airshed during the study period. Similarly, seasonal variations and patterns of the apportioned sources were also studied in detail.

Dallas is the third largest growing industrialized city in the state of Texas. the prevailing air quality here is highly influenced by the industrialization and particulate matter 2.5µm (PM2.5) has been found to be one of the main pollutants in this region. Exposure to PM2.5 in elevated levels could cause respiratory problems and other health issues, some of which could be fatal. the current study dealt with the quantification and analysis of the sources of emission of PM2.5 and an emission inventory for PM2.5 was assessed. 24-hour average samples of PM2.5 were collected at two monitoring sites under the Texas Commission on Environmental Quality (TCEQ) in Dallas, Dallas convention Centre (CAMS 312) and Dallas Hinton sites (CAMS 60). the data was collected from January 2003 to December 2009 and by using two positive matrix models PMF 2 and EPA PMF the PM2.5 source were identified. 9 sources were identified from CAMS 312 of which secondary sulfate (31% by PMF2 and 26% by EPA PMF) was found to be one of the major sources. Data from CAMS 60 enabled the identification of 8 sources by PMF2 and 9 by EPA PMF. These data also confirmed secondary sulfate (35% by PMF2 and 34% by EPA PMF) as the major source. to substantiate the sources identified, conditional probability function (CPF) was used. the influence of long range transport pollutants such as biomass burns from Mexico and Central America was found to be influencing the region of study and was assessed with the help of potential source contribution function (PSCF) analysis. Weekend/weekday and seasonal analyses were useful in understanding the behavioral pattern of pollutants. Also an inter comparison of the model results were performed and EPA PMF results was found to be more robust and accurate than PMF 2 results.

A major tropical storm will strike in the area of West Central Florida. In anticipation of this storm, this study seeks to predict the specific areas within the Baker Canal drainage basin that will be inundated as a result of this expected event. There are few references concerning extreme flooding in small drainage basins within existing literature. For the purposes of this study this event was considered to be a Probable Maximum Flood (PMF) as defined by Crippen and Bue (1977). The Hydrologic Engineering Centers' Geographic River Analysis System was used to develop water surface elevations and flow rates. Maps depicting this potential flooding at various flood stages were produced using the Environmental Survey Research Institute's geographic Information mapping program ArcView3.3. This investigation produced estimates of the surface area of a Probable Maximum Flood and the estimated flood inundated 23.7% of the study area. The estimated extent of Probable Maximum Flood indicates that the flood will affect one thousand and seventy six (1,076) homes and other structures. The study found that eight hundred and sixty three (863) acres or 27% of the land within the PMF flood zone is listed for future development by the County Planning Commission. When this projected development area is added to existing developed land area a total of 85% of all developed land within the estimated flood area will be submerged and subject to damage. An extreme flood study on a small drainage basin prior to the event can be a viable tool for mitigation planning if it is recognized that there are variables that can produce a relatively large range of error. The potential for this type of study is in its' comparison with an actual event affecting the same area. If the predicted study and the real event study agree within reasonable limits then, maximum flood investigations on small basins could be considered a useful tool in hazard reduction.

The diploma thesis deals with the determination of sources of polycyclic aromatic hydrocarbons (PAH) and the changes in the PAH concentrations during the last 11 years, from 2006 to 2016. The data were sampled at National Atmospheric Observatory in Košetice, a representative station for the Central European Region. Multidimensional statistical methods as Positive Matrix Factorization (PMF), conditional bivariate probability function (CBPF), and Potential Source Contribution Function (PSCF) were used for data analyses. In the first part, the changes in concentrations of the four specific PAH, Benzo(a)pyrene (BaP), Fluoranthene (FLA), the sums of all the PAH (SUMA), and the Toxical Equivalent (TEQ) was studied. The highest concentration of all four specific PAH was detected at the beginning of the studied period. The immission limit for BaP was not exceeded. Statistically significant decreasing trend for BaP, TEQ, and SUMA PAH was identified. Second part of the thesis studied the PAH source apportionment. The study proved that the station was strongly influenced by the local domestic heating on one hand, and long-distance transportation from the west, and specifically from the northwest on the other hand. The influence of long-distance transport has an increasing trend over time, vice-versa for the...

This thesis examines the relationship between the Popular Mobilization Forces and the state in post-conflict Iraq. It critically assesses their link as mutually exploitative and derives back their agency to both actors. The concept of hybridity, to characterise a behaviour that is simultaneously cooperative and competitive, is applied to both terms of the dyad. Overcoming the Western conception of the state, the research offers to consider the Iraq as a post- Weberian system where hybrid state and non-state actors collaborate to offer an alternative political order.

A fatoração PMF - Positive Matrix Factorization - é um método de resolução de problemas em que variáveis observadas conjuntamente são modeladas como a combinação linear de fatores potenciais, representada pela multiplicação de duas matrizes. Este método tem sido utilizado principalmente em áreas de estudo em que as variáveis observadas são sempre não negativas, e quando é aplicada uma modelagem fatorial ao problema. Assume-se a premissa de que são resultantes da multiplicação de duas matrizes com entradas não negativas, ou seja, os fatores potenciais, e os poderadores da combinação linear são desconhecidos, e têm valores não negativos. Neste método além da possibilidade de restringir a busca dos valores das matrizes da fatoração a valores não negativos, também é possível incluir a medida de incerteza relacionada a cada observação no processo de obtenção da fatoração como um modo de reduzir o efeito indesejado que valores outliers podem causar no resultado. Neste trabalho é feito um estudo de sensibilidade da fatoração PMF em relação a algumas medidas de incertezas presentes na literatura, utilizando o software EPA PMF 5.0 com ME-2. Para realizar este estudo foi desenvolvida uma metodologia de simulação de base de dados a partir de perfis de fontes emissoras conhecidas incluindo valores outliers, e aplicação sequencial da fatoração PMF com o software ME-2 nas bases de dados simuladas. / The PMF factorization - Positive Matrix Factorization - is a problem solving method in which jointly observed variables are modeled as a linear combination of potential factors, represented by the multiplication of two matrices. This method has been used primarily in study areas in which the observed variables are always non negative, and when it is applied a factor modeling in the problem. It is made the assumption that the variables in study come from the two matrices multiplication both having non negative components, i.e., the potential factors, and the linear combination values are unknown, and all of them have non negative values. In this method, besides the possibility of constraining the search of the matrix factorization values on non negative values, it is also possible to include the uncertainty measure related to each observation on factorization process as a way to reduce the undesired effect which outliers can cause to the outcome. This paper presents a study of the sensitivity of the factorization PMF over some uncertainties measures present in literature, using EMP PMF 5.0 with ME-2 software. To carry out this study was developed a methodology of database simulation from known emitting sources profiles including outliers values, and a sequential application of PMF factorization with ME-2 software in simulated databases.

A fatoração PMF - Positive Matrix Factorization - é um método de resolução de problemas em que variáveis observadas conjuntamente são modeladas como a combinação linear de fatores potenciais, representada pela multiplicação de duas matrizes. Este método tem sido utilizado principalmente em áreas de estudo em que as variáveis observadas são sempre não negativas, e quando é aplicada uma modelagem fatorial ao problema. Assume-se a premissa de que são resultantes da multiplicação de duas matrizes com entradas não negativas, ou seja, os fatores potenciais, e os poderadores da combinação linear são desconhecidos, e têm valores não negativos. Neste método além da possibilidade de restringir a busca dos valores das matrizes da fatoração a valores não negativos, também é possível incluir a medida de incerteza relacionada a cada observação no processo de obtenção da fatoração como um modo de reduzir o efeito indesejado que valores outliers podem causar no resultado. Neste trabalho é feito um estudo de sensibilidade da fatoração PMF em relação a algumas medidas de incertezas presentes na literatura, utilizando o software EPA PMF 5.0 com ME-2. Para realizar este estudo foi desenvolvida uma metodologia de simulação de base de dados a partir de perfis de fontes emissoras conhecidas incluindo valores outliers, e aplicação sequencial da fatoração PMF com o software ME-2 nas bases de dados simuladas. / The PMF factorization - Positive Matrix Factorization - is a problem solving method in which jointly observed variables are modeled as a linear combination of potential factors, represented by the multiplication of two matrices. This method has been used primarily in study areas in which the observed variables are always non negative, and when it is applied a factor modeling in the problem. It is made the assumption that the variables in study come from the two matrices multiplication both having non negative components, i.e., the potential factors, and the linear combination values are unknown, and all of them have non negative values. In this method, besides the possibility of constraining the search of the matrix factorization values on non negative values, it is also possible to include the uncertainty measure related to each observation on factorization process as a way to reduce the undesired effect which outliers can cause to the outcome. This paper presents a study of the sensitivity of the factorization PMF over some uncertainties measures present in literature, using EMP PMF 5.0 with ME-2 software. To carry out this study was developed a methodology of database simulation from known emitting sources profiles including outliers values, and a sequential application of PMF factorization with ME-2 software in simulated databases.

À ce jour, l'identification des sources de Composés Organiques Volatils (COV) a fait l'objet de nombreuses études, afin de déterminer leur contribution à la pollution atmosphérique. Néanmoins, le comportement dynamique de l'atmosphère, dans sa dimension micro-météorologique, n'a jamais été pris en compte dans une approche sources-récepteur. Celui-ci est pourtant essentiel dans l'interprétation des mesures physico-chimiques de la pollution de l'air en champ proche, dans une région urbaine sous influence industrielle. La complexité de ces zones d'études provient non seulement des différents modes d'émissions et de la variété des activités industrielles, mais aussi des phénomènes météorologiques à multi-échelles, qui influent la dispersion et le transport à petite échelle spatiale (typiquement quelques kilomètres). En nous appuyant sur la mesure de 85 COV (dont 23 COV oxygénés) sur une durée de plusieurs mois, nous avons développé une méthodologie novatrice, associant lors de l'analyse des résultats d'un modèle sources-récepteur PMF (Positive Matrix Factorization) des grandeurs météorologiques, pour identifier des sources et comprendre leur comportement dynamique. En introduisant la turbulence verticale, pour la première fois dans ce type d'analyse, la température et le rayonnement solaire, comme paramètre d'analyse des facteurs de comportements ont pu être différenciés, permettant de distinguer les modes d'émissions diffuses et canalisées. Ainsi, nous avons pu classer les sources des COV par nature et par mode d'émissions, et également mettre en évidence des masses d'air âgées contenant des espèces secondaires. / Recently, sources apportionment of Volatile Organic Compounds (VOC) has been the subject of a great numbers of studies, in order to determine their contributions to atmospheric pollution. However, the dynamical behavior of atmosphere, within its micro-meteorological scale, has never been taken into account in the sources-receptor approach, yet it is the main factor to interpret near-field physic-chemical measurements of air pollution, in an urban area under industrial influences. The complexity of such a study area results from not only the emission modes and the various industrial activities, but also the meteorological phenomenon in multi-scale, which influences the dispersion and transport in a small scale (tipically several kilometers). With measurements of 85 VOC (including 23 oxygenated VOC) during several months, we have developed an innovative methodology, associating the results of the PMF (Positive Matrix Factorization) modeling and the meteorological parameters, in order to identify the sources and to understand their dynamical behaviors. By introducing the vertical turbulence for the first time in this kind of analysis, the temperature and the solar radiation, as parameters of factor analyses, two behaviors have been distinguished, leading to identify the emissions near ground and in the upper part of surface layer. In this way, we have labeled the sources according to their nature as well as their emission mode, and we have highlighted the aged air mass containing secondary pollutants.

Organic aerosol is a major constituent of atmospheric fine particulates (PM2.5), which adversely affect human health and change the Earth’s radiative energy balance. Primary organic aerosol is directly emitted from sources and secondary organic aerosol (SOA) is formed in the atmosphere following oxidation of volatile organic compounds (VOC) from anthropogenic and biogenic sources. Biogenic SOA is enhanced by anthropogenic pollutants such as sulfate and NOx that mainly come from fossil fuel combustion. However, the extent to which the anthropogenic pollutants enhance biogenic SOA in different environments is unknown. The central hypothesis of this thesis is that organosulfates, organic compounds containing a sulfate ester group, are useful as tracers for anthropogenically-influenced biogenic SOA. This research aims to provide a better understanding of the sources of PM2.5 organic carbon (OC), particularly secondary organic carbon (SOC), through the inclusion of organosulfates in an organic tracer-based source apportionment model. The specific objectives of this research include 1) development of a highly sensitive and accurate method to quantify highly polar organosulfates in atmospheric aerosols, 2) identification and quantification of major organosulfate species in the ambient air, and 3) determination of anthropogenic and biogenic sources and their contributions to PM2.5 OC using an organic tracer-based positive matrix factorization (PMF) model. A highly sensitive and accurate method was developed and validated for the quantification of highly polar organosulfates using hydrophilic interaction liquid chromatography (HILIC) and tandem mass spectrometry (MS/MS). The developed method shows excellent retention of carboxylic acid and hydroxyl containing organosulfates. The HILIC-MS/MS method was applied to PM2.5 samples collected in summer 2013 at a rural site in Centreville, AL. Quantified organosulfates accounted for approximately 0.3% of PM2.5 OC. Other major organosulfates, for which standards are not available, were monitored by their fragmentation to the bisulfate anion and/ or sulfate ion radical. The major organosulfates were determined to be 2-methyltetrol sulfate and other isoprene-derived organosulfates. Eight sources of the PM2.5 OC in Centreville, AL were identified using PMF model through the application of organosulfates and commonly used organic tracers measured in samples collected during the daytime and nighttime: vehicle emissions (8%), prescribed burning (11%), isoprene SOC formed under low-NOx (13%) and high-NOx conditions (11%), SOC formed by photochemical reactions (9%), oxidatively aged biogenic SOC (6%), sulfuric acid-influenced SOC (21%), and monoterpene SOC formed under high-NOx conditions (21%). The organosulfates enabled organic tracer-based PMF to resolve sulfuric acid-influenced SOC, while the daytime and nighttime measurements enabled organic tracer-based PMF to resolve SOC formation pathways with diurnal variations (e.g. SOC formed by photochemical reactions). The PM2.5 OC in Centreville was mainly secondary in origin (81%) and was influenced by NOx, ozone (a product of photochemical reactions of NOx and VOC), and sulfuric acid. Together, primary and secondary OC influenced by the fossil fuel use was 76%. Thus, the majority of the PM2.5 OC in Centreville during summer can be controlled by the reduction of fossil fuel use. The HILIC-MS/MS method was also applied to daily PM2.5 samples collected from an urban site in Atlanta, GA during August 2015. The major organosulfate species identified in Atlanta were dominated by 2-methyltetrol sulfate and other isoprene-derived organosulfates, similar to Centreville. They contributed 16% of PM2.5 OC and accounted for the majority of the isoprene-derived SOA that had not previously been identified at the molecular level. The concentrations of the major isoprene-derived organosulfates in Atlanta were two to six times higher than in Centreville. The greatest enhancement was obtained for 2-methylglyceric acid sulfate, a known isoprene SOA tracer formed under high-NOx conditions, reflecting the 15 times higher average NOx concentration in Atlanta during August 2015 compared to Centreville in summer 2013. These results indicate that NOx had a stronger influence on isoprene-derived organosulfate formation in urban Atlanta compared to rural Centreville. Overall, these results indicate that organosulfates are useful tracers for anthropogenically-influenced biogenic SOA. Thus, it is important to quantify them for use in organic tracer-based PMF modeling to determine the anthropogenically-influenced biogenic SOC in PM2.5 OC.

The presence of ion-pairs in electrolyte solutions affects the activity of dissolved species as well as the solubility of minerals. The extent of ion-pairing in a system is predicted by an association constant, K[subscript A], which for sparingly soluble salts are frequently determined experimentally in binary or ternary systems. This introduces complex activity coefficient calculations that often require unavailable parameters. Barite (BaSO₄) and celestite (SrSO₄) are sparingly soluble minerals with interest in the oil and mining industry, yet the values of K[subscript A] for the ion-pairs BaSO₄(aq.) and SrSO₄(aq.) are still uncertain. Molecular dynamics simulations are used to obtain the K[subscript A] values for these two salts through potential of mean force (PMF) calculations. The molecular mechanisms involved in the association reactions are also explored, in particular the role of the association intermediates in the overall reaction as described by the Eigen mechanism. Additionally, the kinetics of water exchange around the free and paired ions is examined and the residence time of a water coordinated to the free and paired cation is calculated.