In the United States, approximately 3% of infants are diagnosed with a major birth defect each year. Whereas prevention efforts have led to decreases in some birth defects, the cause of approximately 70% of birth defects remains unknown. More recently, there has been increasing concern regarding exposures to environmental agents, such as air pollution during pregnancy, and the risk of birth defects. Over the past decade, there has been an increase in research studies examining the association between air pollution and birth defects. The results have been inconsistent, with some studies reporting that higher levels of exposure from the same pollutant increase the risk of birth defects, whereas others report no risk or even a decrease in risk. These inconsistencies may arise from differences in exposure assessment methods across epidemiological studies. A comprehensive series of sensitivity analyses were conducted to evaluate the impact of different exposure estimation decisions on the adjusted measures of associations between ambient air pollution exposure and birth defects. For each of six decisions – spatial scale, spatial limit, temporal scale, temporal limit, data aggregation sequence, and weighting scheme – two alternative choices were considered, and maternal exposure to PM2.5 and benzene were estimated for each ‘sensitivity case' (unique combination of choices across all decisions). For each sensitivity case, adjusted prevalence ratios (aPRs) and 95% confidence intervals (CI) were estimated from multivariable regression models comparing each exposure quartile to the first quartile; each measure of association represented the associations between either benzene or PM2.5 and one of the birth defects under study. To then evaluate the impact of selecting alternative choices for each exposure assessment decision, three metrics were used: 1) the percent difference in the aPR point estimates between alternative choices for a specific decision (calculated for each sensitivity case pairing); 2) the percent of sensitivity cases in which the aPR 95% CIs for alternative choices for a specific decision overlapped (i.e., were not statistically significantly different); and 3) the percent of sensitivity cases in which there was agreement in the substantive conclusion of the association between exposure (pollutant) and outcome (defect), the conclusion being either statistically significant increased risk, statistically significant decreased risk, or no statistically significant difference in risk. Second, a retrospective cohort study was conducted to investigate the association between maternal exposure to ambient benzene and PM2.5 and the risk of musculoskeletal birth defects in offspring. For both analyses, data on singleton infants born from 2000 to 2013 were obtained from the Florida Birth Defects Registry and air pollution data were obtained from the Environmental Protection Agency Air Quality System database. Exposure estimates were calculated for all birth defect cases and non-affected births during etiologically relevant time windows and multivariable regression models were used to obtain aPRs and 99% CIs comparing each quartile of exposure to the first. For PM2.5, across all exposure-outcome pairs under study, the alternative choices for the decision of spatial scale resulted in the largest median percent difference in aPRs (e.g., between county and block group) when results were aggregated for all quartiles. This was followed by the temporal scale and spatial limit decisions. For benzene, spatial limit resulted in largest median percent difference in aPRs, followed by spatial scale and temporal scale. However, for both pollutants, when evaluating the agreement in aPRs between alternative choices on the direction and significance of the association (i.e., statistically significant increase, decrease, or no risk), the decision of spatial limit resulted in the lowest percent agreement (biggest impact). Temporal limit had the second lowest percent agreement for PM2.5, whereas spatial scale had the second lowest for benzene. Finally, the metric assessing the level of overlap in 95% CIs for the measures of association was inconsequential, suggesting little impact of any exposure assessment decision, and doing little to differentiate between the relative impacts of each decision. That is because, for all exposure-outcome pairs, when comparing alternative choices for each decision, there was 100% overlapping intervals (i.e., no aPRs were statistically significantly different from each other). Lastly, exposure assessment decisions impacted the analytic sample sizes, with some decisions resulting in a three-fold difference in the sample size alone. Mothers of singleton infants exposed to higher levels of ambient PM2.5 were more likely than mothers with lower exposure levels to give birth to an infant with isolated anomalies of the skull and face bones, any rib and sternum anomalies, any skull and face anomalies, any spine anomaly, and other congenital anomalies of lower limb including pelvic girdle. Higher PM2.5 exposure was also associated with an increased risk of non-isolated anomalies of skull and face bones, any skull and face anomalies, and reduction deformities of the upper limb. Exposure to higher levels of benzene was associated with an increased risk of isolated congenital hip dislocation and congenital valgus deformities of feet as well as multiple inverse associations. The findings presented here indicate that measures of association between maternal pollutant exposures (PM2.5 and benzene) and selected birth defect outcomes in offspring are sensitive to exposure assessment decisions, with some decisions more impactful than others. The findings can be used, not only to explain the lack of consistency in results across existing epidemiological studies, but to guide decision-making in future studies. This study also adds to the growing body of epidemiological studies suggesting an association between specific air pollutants and birth defects. In the current political climate, it is important that researchers continue to provide evidence of the detrimental health effects of air pollution in order to circumvent change in current policies established to regulate and reduce pollution emissions.

When inhaled, bioaerosols exacerbate respiratory symptoms and diseases. Mitigating the negative health impacts of bioaerosols requires a robust understanding of the temporal and spatial dynamics of bioaerosols in the atmosphere as a function of their type (e.g., bacteria, fungal spores, plant pollens) and particle size, which determines their penetration into the respiratory tract. While it is known that bioaerosol concentrations vary by location, season and meteorological conditions, major gaps remain in understanding the co-occurrence of bioaerosols with one another, their size in the atmosphere, and their mass contributions to PM. Overall, research presented in this thesis advances the current knowledge about bioaerosols (including fungal spores, pollens, and bacteria) in following ways: 1) defining background and urban levels of bioaerosol concentrations in the Midwestern US across four seasons, 2) characterizing ambient bioaerosol and co-pollutant mixtures, 3) determining the influence of meteorology on their concentrations and size distributions, and 4) estimating bioaerosol contributions to PM mass. The spatial analysis of respirable particulate matter (PM10) across urban and background sites in Iowa demonstrated that urban areas are a source of fungal glucans, bacterial endotoxins and total proteins, which gives rise to significantly enhanced bioaerosols in urban locations compared to background sites. Similar urban enhancements in calcium—a crustal element—and its correlation with endotoxins suggested that wind-blown soil is likely the origin. Seasonally, fungal spores peaked in summer with temperature, while bacterial endotoxins peaked in autumn during the row crop harvesting season. Fungal spores, bacterial endotoxins, plant and animal detritus all peaked during the growing season, such that maximum exposures to multiple bioaerosol types concurrently. Under the influence of rain chemical tracers of pollens peaked and decreased in size from coarse (2.5-10 µm) to fine particles (< 2.5 µm), likely due to the osmotic rupture of pollen grains upon wetting. While fine-sized fungal spores also increased during rain events, maximum spore levels were observed in coarse-sized particles post-rain. The comparison of spring to late summer measurements demonstrated these influences of precipitation on bioaerosols also occur during late summer, when fungal spore levels are high and ragweed is the dominant pollen source. The ability to apportion PM mass to bioaerosols was advanced through the development of chemical profiles of pollens and their integration with chemical mass balance (CMB) source apportionment modeling, for the first time. In late-April to early-May in 2013, pollens were estimated to contribute 0.2 - 38% of PM₁₀ (0.04 – 0.8 µg m⁻³) while fungal spores contributed 0.7 – 17% of PM₁₀ (0.1 – 1.5 µg m⁻³). Collectively, this thesis provides insight into spatial, seasonal and daily variations of bioaerosols, and shows elevated outdoor exposures to bioaerosols among urban populations, with maximum levels occurring during growing seasons, periods of high temperature, and during/immediately following rainfall.

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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