Chemische und physikalische Charakterisierung von Aerosolpartikeln aus besonderen anthropogenen Emissionsquellen : ein integrativer Ansatz mittels online (HR-ToF-AMS)- und komplementärer offline (ATR-FTIR-Spektroskopie)-Analytik

Die Gesundheitseffekte von Aerosolpartikeln werden stark von ihren chemischen und physikalischen Eigenschaften und somit den jeweiligen Bildungsprozessen und Quellencharakteristika beeinflusst. Während die Hauptquellen der anthropogenen Partikelemissionen gut untersucht sind, stellen die spezifischen Emissionsmuster zahlreicher kleiner Aerosolquellen, welche lokal und temporär zu einer signifikanten Verschlechterung der Luftqualität beitragen können, ein Forschungsdesiderat dar.rnIn der vorliegenden Arbeit werden in kombinierten Labor- und Feldmessungen durch ein integratives Analysekonzept mittels online (HR-ToF-AMS ) und filterbasierter offline (ATR-FTIR-Spektroskopie ) Messverfahren die weitgehend unbekannten physikalischen und chemischen Eigenschaften der Emissionen besonderer anthropogener Aerosolquellen untersucht. Neben einem Fußballstadion als komplexe Mischung verschiedener Aerosolquellen wie Frittieren und Grillen, Zigarettenrauchen und Pyrotechnik werden die Emissionen durch Feuerwerkskörper, landwirtschaftliche Intensivtierhaltung (Legehennen), Tief- und Straßenbauarbeiten sowie abwasserbürtige Aerosolpartikel in die Studie mit eingebunden. Die primären Partikelemissionen der untersuchten Quellen sind vorrangig durch kleine Partikelgrößen (dp < 1 µm) und somit eine hohe Lungengängigkeit gekennzeichnet. Dagegen zeigen die Aerosolpartikel im Stall der landwirtschaftlichen Intensivtierhaltung sowie die Emissionen durch die Tiefbauarbeiten einen hohen Masseanteil von Partikeln dp > 1 µm. Der Fokus der Untersuchung liegt auf der chemischen Charakterisierung der organischen Partikelbestandteile, welche für viele Quellen die NR-PM1-Emissionen dominieren. Dabei zeigen sich wichtige quellenspezifische Unterschiede in der Zusammensetzung der organischen Aerosolfraktion. Die beim Abbrand von pyrotechnischen Gegenständen freigesetzten sowie die abwasserbürtigen Aerosolpartikel enthalten dagegen hohe relative Gehalte anorganischer Substanzen. Auch können in einigen spezifischen Emissionen Metallverbindungen in den AMS-Massenspektren nachgewiesen werden. Über die Charakterisierung der Emissionsmuster und -dynamiken hinaus werden für einige verschiedenfarbige Rauchpatronen sowie die Emissionen im Stall der Intensivtierhaltung Emissionsfaktoren bestimmt, die zur quantitativen Bilanzierung herangezogen werden können. In einem weiteren Schritt werden anhand der empirischen Daten die analytischen Limitierungen der Aerosolmassenspektrometrie wie die Interferenz organischer Fragmentionen durch (Hydrogen-)Carbonate und mögliche Auswertestrategien zur Überwindung dieser Grenzen vorgestellt und diskutiert.rnEine umfangreiche Methodenentwicklung zur Verbesserung der analytischen Aussagekraft von organischen AMS-Massenspektren zeigt, dass für bestimmte Partikeltypen einzelne Fragmentionen in den AMS-Massenspektren signifikant mit ausgewählten funktionellen Molekülgruppen der FTIR-Absorptionsspektren korrelieren. Bedingt durch ihre fehlende Spezifität ist eine allgemeingültige Interpretation von AMS-Fragmentionen als Marker für verschiedene funktionelle Gruppen nicht zulässig und häufig nur durch die Ergebnisse der komplementären FTIR-Spektroskopie möglich. Des Weiteren wurde die Verdampfung und Ionisation ausgewählter Metallverbindungen im AMS analysiert. Die Arbeit verdeutlicht, dass eine qualitative und quantitative Auswertung dieser Substanzen nicht ohne Weiteres möglich ist. Die Gründe hierfür liegen in einer fehlenden Reproduzierbarkeit des Verdampfungs- und Ionisationsprozesses aufgrund von Matrixeffekten sowie der in Abhängigkeit vorangegangener Analysen (Verdampferhistorie) in der Ionisationskammer und auf dem Verdampfer statt-findenden chemischen Reaktionen.rnDie Erkenntnisse der Arbeit erlauben eine Priorisierung der untersuchten anthropogenen Quellen nach bestimmten Messparametern und stellen für deren Partikelemissionen den Ausgangpunkt einer Risikobewertung von atmosphärischen Folgeprozessen sowie potentiell negativen Auswirkungen auf die menschliche Gesundheit dar. rn

Species specific nitrogen isotope analysis by NanoSIMS

Der atmosphärische Kreislauf reaktiver Stickstoffverbindungen beschäftigt sowohl die Naturwissenschaftler als auch die Politik. Dies ist insbesondere darauf zurückzuführen, dass reaktive Stickoxide die Bildung von bodennahem Ozon kontrollieren. Reaktive Stickstoffverbindungen spielen darüber hinaus als gasförmige Vorläufer von Feinstaubpartikeln eine wichtige Rolle und der Transport von reaktivem Stickstoff über lange Distanzen verändert den biogeochemischen Kohlenstoffkreislauf des Planeten, indem er entlegene Ökosysteme mit Stickstoff düngt. Die Messungen von stabilen Stickstoffisotopenverhältnissen (15N/14N) bietet ein Hilfsmittel, welches es erlaubt, die Quellen von reaktiven Stickstoffverbindungen zu identifizieren und die am Stickstoffkeislauf beteiligten Reaktionen mithilfe ihrer reaktionsspezifischen Isotopenfraktionierung genauer zu untersuchen. rnIn dieser Doktorarbeit demonstriere ich, dass es möglich ist, mit Hilfe von Nano-Sekundärionenmassenspektrometrie (NanoSIMS) verschiedene stickstoffhaltige Verbindungen, die üblicherweise in atmosphärischen Feinstaubpartikeln vorkommen, mit einer räumlichen Auflösung von weniger als einem Mikrometer zu analysieren und zu identifizieren. Die Unterscheidung verschiedener stickstoffhaltiger Verbindungen erfolgt anhand der relativen Signalintensitäten der positiven und negativen Sekundärionensignale, die beobachtet werden, wenn die Feinstaubproben mit einem Cs+ oder O- Primärionenstrahl beschossen werden. Die Feinstaubproben können direkt auf dem Probenahmesubstrat in das Massenspektrometer eingeführt werden, ohne chemisch oder physikalisch aufbereited zu werden. Die Methode wurde Mithilfe von Nitrat, Nitrit, Ammoniumsulfat, Harnstoff, Aminosären, biologischen Feinstaubproben (Pilzsporen) und Imidazol getestet. Ich habe gezeigt, dass NO2 Sekundärionen nur beim Beschuss von Nitrat und Nitrit (Salzen) mit positiven Primärionen entstehen, während NH4+ Sekundärionen nur beim Beschuss von Aminosäuren, Harnstoff und Ammoniumsalzen mit positiven Primärionen freigesetzt werden, nicht aber beim Beschuss biologischer Proben wie z.B. Pilzsporen. CN- Sekundärionen werden beim Beschuss aller stickstoffhaltigen Verbindungen mit positiven Primärionen beobachtet, da fast alle Proben oberflächennah mit Kohlenstoffspuren kontaminiert sind. Die relative Signalintensität der CN- Sekundärionen ist bei kohlenstoffhaltigen organischen Stickstoffverbindungen am höchsten.rnDarüber hinaus habe ich gezeigt, dass an reinen Nitratsalzproben (NaNO3 und KNO3), welche auf Goldfolien aufgebracht wurden speziesspezifische stabile Stickstoffisotopenverhältnisse mithilfe des 15N16O2- / 14N16O2- - Sekundärionenverhältnisses genau und richtig gemessen werden können. Die Messgenauigkeit auf Feldern mit einer Rastergröße von 5×5 µm2 wurde anhand von Langzeitmessungen an einem hausinternen NaNO3 Standard als ± 0.6 ‰ bestimmt. Die Differenz der matrixspezifischen instrumentellen Massenfraktionierung zwischen NaNO3 und KNO3 betrug 7.1 ± 0.9 ‰. 23Na12C2- Sekundärionen können eine ernst zu nehmende Interferenz darstellen wenn 15N16O2- Sekundärionen zur Messung des nitratspezifischen schweren Stickstoffs eingesetzt werden sollen und Natrium und Kohlenstoff im selben Feinstaubpartikel als interne Mischung vorliegt oder die natriumhaltige Probe auf einem kohlenstoffhaltigen Substrat abgelegt wurde. Selbst wenn, wie im Fall von KNO3, keine derartige Interferenz vorliegt, führt eine interne Mischung mit Kohlenstoff im selben Feinstaubpartikel zu einer matrixspezifischen instrumentellen Massenfraktionierung die mit der folgenden Gleichung beschrieben werden kann: 15Nbias = (101 ± 4) ∙ f − (101 ± 3) ‰, mit f = 14N16O2- / (14N16O2- + 12C14N-). rnWird das 12C15N- / 12C14N- Sekundärionenverhältnis zur Messung der stabilen Stickstoffisotopenzusammensetzung verwendet, beeinflusst die Probematrix die Messungsergebnisse nicht, auch wenn Stickstoff und Kohlenstoff in den Feinstaubpartikeln in variablen N/C–Verhältnissen vorliegen. Auch Interferenzen spielen keine Rolle. Um sicherzustellen, dass die Messung weiterhin spezifisch auf Nitratspezies eingeschränkt bleibt, kann eine 14N16O2- Maske bei der Datenauswertung verwendet werden. Werden die Proben auf einem kohlenstoffhaltigen, stickstofffreien Probennahmesubstrat gesammelt, erhöht dies die Signalintensität für reine Nitrat-Feinstaubpartikel.

Aircraft noise, air pollution, and mortality from myocardial infarction

Objective: Myocardial infarction has been associated with both transportation noise and air pollution. We examined residential exposure to aircraft noise and mortality from myocardial infarction, taking air pollution into account. Methods: We analyzed the Swiss National Cohort, which includes geocoded information on residence. Exposure to aircraft noise and air pollution was determined based on geospatial noise and air-pollution (PM10) models and distance to major roads. We used Cox proportional hazard models, with age as the timescale. We compared the risk of death across categories of A-weighted sound pressure levels (dB(A)) and by duration of living in exposed corridors, adjusting for PM10 levels, distance to major roads, sex, education, and socioeconomic position of the municipality. Results: We analyzed 4.6 million persons older than 30 years who were followed from near the end of 2000 through December 2005, including 15,532 deaths from myocardial infarction (ICD-10 codes I 21, I 22). Mortality increased with increasing level and duration of aircraft noise. The adjusted hazard ratio comparing ≥60 dB(A) with <45 dB(A) was 1.3 (95% confidence interval = 0.96-1.7) overall, and 1.5 (1.0-2.2) in persons who had lived at the same place for at least 15 years. None of the other endpoints (mortality from all causes, all circulatory disease, cerebrovascular disease, stroke, and lung cancer) was associated with aircraft noise. Conclusion: Aircraft noise was associated with mortality from myocardial infarction, with a dose-response relationship for level and duration of exposure. The association does not appear to be explained by exposure to particulate matter air pollution, education, or socioeconomic status of the municipality.

A Decision-Tree-Based Approach to Smoke Spike Detection in a Heavy-Duty Diesel Engine

Smoke spikes occurring during transient engine operation have detrimental health effects and increase fuel consumption by requiring more frequent regeneration of the diesel particulate filter. This paper proposes a decision tree approach to real-time detection of smoke spikes for control and on-board diagnostics purposes. A contemporary, electronically controlled heavy-duty diesel engine was used to investigate the deficiencies of smoke control based on the fuel-to-oxygen-ratio limit. With the aid of transient and steady state data analysis and empirical as well as dimensional modeling, it was shown that the fuel-to-oxygen ratio was not estimated correctly during the turbocharger lag period. This inaccuracy was attributed to the large manifold pressure ratios and low exhaust gas recirculation flows recorded during the turbocharger lag period, which meant that engine control module correlations for the exhaust gas recirculation flow and the volumetric efficiency had to be extrapolated. The engine control module correlations were based on steady state data and it was shown that, unless the turbocharger efficiency is artificially reduced, the large manifold pressure ratios observed during the turbocharger lag period cannot be achieved at steady state. Additionally, the cylinder-to-cylinder variation during this period were shown to be sufficiently significant to make the average fuel-to-oxygen ratio a poor predictor of the transient smoke emissions. The steady state data also showed higher smoke emissions with higher exhaust gas recirculation fractions at constant fuel-to-oxygen-ratio levels. This suggests that, even if the fuel-to-oxygen ratios were to be estimated accurately for each cylinder, they would still be ineffective as smoke limiters. A decision tree trained on snap throttle data and pruned with engineering knowledge was able to use the inaccurate engine control module estimates of the fuel-to-oxygen ratio together with information on the engine control module estimate of the exhaust gas recirculation fraction, the engine speed, and the manifold pressure ratio to predict 94% of all spikes occurring over the Federal Test Procedure cycle. The advantages of this non-parametric approach over other commonly used parametric empirical methods such as regression were described. An application of accurate smoke spike detection in which the injection pressure is increased at points with a high opacity to reduce the cumulative particulate matter emissions substantially with a minimum increase in the cumulative nitrogrn oxide emissions was illustrated with dimensional and empirical modeling.

Characterization of Halyomorpha Halys (Brown Marmorated Stink Bug) Biogenic Volatile Organic Compound Emissions and Their Role in Secondary Organic Aerosol Formation

The formation of aerosols is a key component in understanding cloud formation in the context of radiative forcings and global climate modeling. Biogenic volatile organic compounds (BVOCs) are a significant source of aerosols, yet there is still much to be learned about their structures, sources, and interactions. The aims of this project were to identify the BVOCs found in the defense chemicals of the brown marmorated stink bug Halymorpha halys and quantify them using gas chromatography-mass spectrometry (GC/MS) and test whether oxidation of these compounds by ozone-promoted aerosol and cloud seed formation. The bugs were tested under two conditions: agitation by asphyxiation and direct glandular exposure. Tridecane, 2(5H)-furanone 5-ethyl, and (E)-2-decenal were identified as the three most abundant compounds. H. halys were also tested in the agitated condition in a smog chamber. It was found that in the presence of 100-180 ppm ozone, secondary aerosols do form. A scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) were used to characterize the secondary aerosols that formed. This reaction resulted in 0.23 mu g/bug of particulate mass. It was also found that these secondary organic aerosol particles could act as cloud condensation nuclei. At a supersaturation of 1%, we found a kappa value of 0.09. Once regional populations of these stink bugs stablilize and the populations estimates can be made, the additional impacts of their contribution to regional air quality can be calculated. Implications: Halymorpha halys (brown marmorated stink bugs) are a relatively new invasive species introduced in the United States near Allentown, Pennsylvania. The authors chemically speciated the bugs' defense pheromones and found that tridecane, 5-ethyl-2(5H)-furanone, and (E)-2-decenal dominated their emissions. Their defense emissions were reacted with atmospherically relevant concentrations of ozone and resulted in 0.23 g of particulate matter per emission per bug. Due to the large population of these bugs in some regions, these emissions could contribute appreciably to a region's PM2.5 (particulate matter with an aerodynamic diameter 2.5 m) levels.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.

A prospective study of the impact of air pollution on respiratory symptoms and infections in infants

Rationale: There is increasing evidence that short-term exposure to air pollution has a detrimental effect on respiratory health, but data from healthy populations, particularly infants, are scarce. Objectives: To assess the association of air pollution with frequency and severity of respiratory symptoms and infections measured weekly in healthy infants. Methods: In a prospective birth cohort of 366 infants of unselected mothers, respiratory health was assessed weekly by telephone interviews during the first year of life (19,106 total observations). Daily mean levels of particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3) were obtained from local monitoring stations. We determined the association of the preceding week's pollutant levels with symptom scores and respiratory tract infections using a generalized additive mixed model with an autoregressive component. In addition, we assessed whether neonatal lung function influences this association and whether duration of infectious episodes differed between weeks with normal PM10 and weeks with elevated levels. Measurements and Main Results: We found a significant association between air pollution and respiratory symptoms, particularly in the week after respiratory tract infections (risk ratio, 1.13 [1.02-1.24] per 10 μg/m(3) PM10 levels) and in infants with premorbid lung function. During times of elevated PM10 (>33.3 μg/m(3)), duration of respiratory tract infections increased by 20% (95% confidence interval, 2-42%). Conclusions: Exposure to even moderate levels of air pollution was associated with increased respiratory symptoms in healthy infants. Particularly in infants with premorbid lung function and inflammation, air pollution contributed to longer duration of infectious episodes with a potentially large socioeconomic impact.

Adaptations of intestinal macrophages to an antigen-rich environment

Intestinal macrophages, preferentially located in the subepithelial lamina propria, represent in humans the largest pool of tissue macrophages. To comply with their main task, i.e. the efficient removal of microbes and particulate matter that might have gained access to the mucosa from the intestinal lumen while maintaining local tissue homeostasis, several phenotypic and functional adaptations evolved. Most notably, microbe-associated molecular pattern (MAMP) receptors, including the lipopolysaccharide receptors CD14 and TLR4, but also the Fc receptors for IgA and IgG are absent on most intestinal Mø. Here we review recent findings on the phenotypic and functional adaptations of intestinal Mø and their implications for the pathogenesis of inflammatory bowel diseases.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Spatio-temporal Associations Between GOES Aerosol Optical Depth Retrievals and Ground-Level PM2.5

We assess the strength of association between aerosol optical depth (AOD) retrievals from the GOES Aerosol/Smoke Product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform and therefore provides dense temporal coverage with half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging over time actually reduces correlations over space dramatically, but statistical calibration allows averaging over time that produces strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 40% of days in a month have a GASP AOD retrieval compared to 14% for MODIS and 4% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing the possibility of a long-term record that pre-dates the availability of most PM2.5 monitoring data and other satellite instruments.

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

Reduced Bayesian Hierarchical Models: Estimating Health Effects of Simultaneous Exposure to Multiple Pollutants

Quantifying the health effects associated with simultaneous exposure to many air pollutants is now a research priority of the US EPA. Bayesian hierarchical models (BHM) have been extensively used in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for potential confounding of other pollutants and other time-varying factors. However, when the scientific goal is to estimate the impacts of many pollutants jointly, a straightforward application of BHM is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters, which often do not have an easy interpretation. In this paper we introduce a new BHM formulation, which we call "reduced BHM", aimed at analyzing clustered data sets in the presence of a large number of random effects that are not of primary scientific interest. At the first stage of the reduced BHM, we calculate the integrated likelihood of the parameter of interest (e.g. excess number of deaths attributed to simultaneous exposure to high levels of many pollutants). At the second stage, we specify a flexible random-effect distribution directly on the parameter of interest. The reduced BHM overcomes many of the challenges in the specification and implementation of full BHM in the context of a large number of nuisance parameters. In simulation studies we show that the reduced BHM performs comparably to the full BHM in many scenarios, and even performs better in some cases. Methods are applied to estimate location-specific and overall relative risks of cardiovascular hospital admissions associated with simultaneous exposure to elevated levels of particulate matter and ozone in 51 US counties during the period 1999-2005.

Two dimensional Lattice Boltzmann simulation of fluid flow through an idealized micro-structure of disordered media

This technical report discusses the application of Lattice Boltzmann Method (LBM) in the fluid flow simulation through porous filter-wall of disordered media. The diesel particulate filter (DPF) is an example of disordered media. DPF is developed as a cutting edge technology to reduce harmful particulate matter in the engine exhaust. Porous filter-wall of DPF traps these soot particles in the after-treatment of the exhaust gas. To examine the phenomena inside the DPF, researchers are looking forward to use the Lattice Boltzmann Method as a promising alternative simulation tool. The lattice Boltzmann method is comparatively a newer numerical scheme and can be used to simulate fluid flow for single-component single-phase, single-component multi-phase. It is also an excellent method for modelling flow through disordered media. The current work focuses on a single-phase fluid flow simulation inside the porous micro-structure using LBM. Firstly, the theory concerning the development of LBM is discussed. LBM evolution is always related to Lattice gas Cellular Automata (LGCA), but it is also shown that this method is a special discretized form of the continuous Boltzmann equation. Since all the simulations are conducted in two-dimensions, the equations developed are in reference with D2Q9 (two-dimensional 9-velocity) model. The artificially created porous micro-structure is used in this study. The flow simulations are conducted by considering air and CO2 gas as fluids. The numerical model used in this study is explained with a flowchart and the coding steps. The numerical code is constructed in MATLAB. Different types of boundary conditions and their importance is discussed separately. Also the equations specific to boundary conditions are derived. The pressure and velocity contours over the porous domain are studied and recorded. The results are compared with the published work. The permeability values obtained in this study can be fitted to the relation proposed by Nabovati [8], and the results are in excellent agreement within porosity range of 0.4 to 0.8.