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.

archives@tulane.edu / Gestational diabetes mellitus (GDM) increases the lifetime risk of developing type 2 diabetes mellitus (T2DM) in the mother; however, biological mechanisms remain relatively unknown, and known risk factors have shown to be incomplete. Both epidemiological and experimental research suggest that environmental exposure to particulate matter (PM2.5) may initiate and further progress chronic diseases such as T2DM. This study investigates the association between PM2.5 exposure and the risk of T2DM among women with a history of GDM. Associations between prevalent and incident T2DM with PM2.5 utilized two PM2.5 metrics: 1) annual average PM2.5 concentration and 2) annual average modeled PM2.5 exposure, calculated from daily PM2.5 concentration levels provided by the USRA/NASA Marshal Space Flight Center. Data from the Southern Community Cohort Study, who at recruitment reported a previous diagnosis of GDM, for whom T2DM, risk factor, and follow-up information were available, was provided. In total, 2403 participants were included in the analysis of prevalent T2DM, and 1036 participants were included in the analysis of incident T2DM. Associations between proximity to roadways and race with PM2.5 metrics were also conducted. Participants that live close to roadways were exposed to higher annual average PM2.5 concentrations and annual average modeled PM2.5 exposures. When stratified by race, non-Black participants were exposed to higher averages. After adjustment, a significant association was observed between annual average PM2.5 concentration and incident T2DM (hazards ratio (HR)= 1.022, 95% confidence interval (CI): 1.003, 1.040). No association was observed between annual average PM2.5 concentrations and prevalent T2DM. Annual average modeled PM2.5 exposure was not associated with either prevalent or incident T2DM. Results were partly consistent with previous literature. Additional studies with a greater range of air pollution exposures, including higher levels, additional pollutants, and more tailored exposure models, are warranted to investigate hypothesized associations. / 0 / Ashley Bell

The fine and ultra fine size of diesel particulate mater (DPM) are of great health concern and significantly contribute to the overall cancer risk. In addition, diesel particles may contribute a warming effect on the planet's climate. The composition of these particles is composed principally of elemental carbon (EC) with adsorbed organic compounds, sulfate, nitrate, ammonia, metals, and other trace elements. The purpose of this study was to depict the seasonality and modeling of particulate matter in the Southeastern US produced by the diesel fueled sources (DFSs). The modeling results came from four one-month cases including March, June, September, and December to represent different seasons in 2003 by linking Models-3/CMAQ and SMOKE. The 1999 National Emissions Inventory Version 3 (NEI99) was used in this analysis for point, area, and non-road sources, whereas the National Mobile Inventory Model (NMIM) was used to create the on-road emissions. Three urban areas, Atlanta, Birmingham, and Nashville were selected to analyze the DPM emissions and concentrations. Even though the model performance was not very strong, it could be considered satisfactory to conduct seasonal distribution analysis for DPM. Important hourly DPM seasonality was observed in each city, of which higher values occurred at the morning traffic rush hours. The EC contributions of primary DPM were similar for all three sites (~ 74%). The results showed that there is no significant daily seasonality of DPM contribution to PM2.5 for any of these three cities in 2003. The annual DPM contribution to total PM2.5 for Atlanta, Nashville, and Birmingham were 3.7%, 2.5%, and 2.2%, respectively.

Indiana University-Purdue University Indianapolis (IUPUI) / This study looked at the time period of June through mid-October, 2013, based on the results of earlier studies that the strongest correlation between the PM2.5 and AOD data sets occurs during the summer and fall. Terra satellite data was used in this study due to availability of images for the geographic area of the state of Indiana during the time period of the study. PM2.5 measurements from 12 IDEM continuous monitoring sites, which were collected at noon local time, were compared with MODIS AOD data. Despite the limitations of useful data and smaller data sets, this study shows encouraging results, and illustrates that there is a relationship between remotely sensed MODIS AOD data and fine particulate matter (PM2.5) data collected from ground sensors within the geographic region of the state of Indiana. It is believed that this topic should be studied further and expanded upon.

The discharge of sediment-laden rivers into the Mississippi Sound increases the turbidity of coastal waters. The concentration of suspended particulates is an important parameter in the analysis of coastal water quality factors. The spatiotemporal resolution associated with satellite sensors makes remote sensing an ideal tool to monitor suspended particulate concentrations. Accordingly, the presented research evaluated the validity of published algorithms that relate remote sensing reflectance (Rrs) with suspended particulate matter for the Mississippi Sound. Additionally, regression analysis was used to correlate in situ SPM concentrations with coincident observations of visible and near-infrared band reflectance collected by the MODIS Aqua sensor in order to develop a predictive model for SPM. The most robust algorithm yielded an RMSE of 15.53% (n = 86) in the determination of SPM concentrations. The application of this algorithm allows for the rapid assessment of water quality issues related to elevated SPM concentrations in the Mississippi Sound.

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京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22060号 / 工博第4641号 / 新制||工||1724(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 高野 裕久, 教授 米田 稔, 准教授 上田 佳代 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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