This study investigated the source attribution of ship emissions to atmospheric particulate matter with a median aerodynamic diameter less than, or equal to 2.5 micron (PM2.5) in the port city of Halifax, Nova Scotia, Canada. The USEPA PMF model successfully determined the following sources with the average mass (percentage) contribution: Sea salt 0.147 µg m-3 (5.3%), Surface dust 0.23 µg m-3 (8.3%), LRT Secondary (ammonium sulfate) 0.085 µg m-3 (3.1%), LRT Secondary (nitrate and sulfate) 0.107 µg m-3 (3.9%), Ship emissions 0.182 µg m-3 (6.6%), and Vehicles and re-suspended gypsum 2.015 µg m-3 (72.8%). A good correlation was achieved between PM2.5 total mass predicted and observed with R2 = 0.83, bias = -0.23, and RMSE = 0.09 µg m-3. In addition, a 2.5 times (60%) reduction in sulfate was estimated, when compared to 2006-2008 Government data in Halifax.

Thesis (Ph. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vi, 98 p. : ill. Includes abstract. Includes bibliographical references (p. 95-98).

Thesis (M.A.)--Trinity College, Hartford, Conn., 2007. / Title from PDF cover (viewed Aug. 27, 2007). Includes bibliographical references (leaves 75-78).

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.

Over the last decades, it has become clear that the size and chemical composition of atmospheric aerosol (AA) has a major impact on both human health and a number of processes in the atmosphere. Although there are increasing efforts to describe the behavior of AA, many phenomena are still not sufficiently understood to be able to predict aerosol behavior and associated phenomena to a satisfactory degree. This PhD thesis describes aerosol behavior at high temporal resolution within three main topics. The first topic is the description of the chemical and size composition of the non-refractory PM1 (NR-PM1) fraction at the Prague - Suchdol suburban station and the study of the influence of meteorological phenomena on the behavior of this aerosol. In order to identify seasonal effects, measurements were carried out in summer and winter. Positive Matrix Factorization (PMF) analysis was performed in connection with the aerosol description at the station, which identified chemically resolved mass profiles of aerosol sources and their temporal evolution. The second topic is the penetration of aerosol particles from the outdoor to the indoor environment. The influence of particle size and chemical composition on the penetration of particles from the outdoor to the indoor environment was investigated by...

The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.

We describe a method to estimate pollution profiles and contribution levels for distinct prominent pollution sources in a region based on daily pollutant concentration measurements from multiple measurement stations over a period of time. In an extension of existing work, we will estimate common source profiles but distinct contribution levels based on measurements from each station. In addition, we will explore the possibility of extending existing work to allow adjustments for synoptic regimes—large scale weather patterns which may effect the amount of pollution measured from individual sources as well as for particular pollutants. For both extensions we propose Bayesian methods to estimate pollution source profiles and contributions.

No description available.

京都大学 / 新制・論文博士 / 博士(工学) / 乙第13616号 / 論工博第4213号 / 新制||工||2002(附属図書館) / (主査)教授 高木 郁二, 教授 佐々木 隆之, 教授 米田 稔 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第25518号 / 地環博第261号 / 新制||地環||54(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)准教授 真常 仁志, 教授 舟川 晋也, 教授 西前 出 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DGAM