Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.

DISTRIBUTED REPRODUCIBLE RESEARCH USING CACHED COMPUTATIONS

The ability to make scientific findings reproducible is increasingly important in areas where substantive results are the product of complex statistical computations. Reproducibility can allow others to verify the published findings and conduct alternate analyses of the same data. A question that arises naturally is how can one conduct and distribute reproducible research? This question is relevant from the point of view of both the authors who want to make their research reproducible and readers who want to reproduce relevant findings reported in the scientific literature. We present a framework in which reproducible research can be conducted and distributed via cached computations and describe specific tools for both authors and readers. As a prototype implementation we introduce three software packages written in the R language. The cacheSweave and stashR packages together provide tools for caching computational results in a key-value style database which can be published to a public repository for readers to download. The SRPM package provides tools for generating and interacting with "shared reproducibility packages" (SRPs) which can facilitate the distribution of the data and code. As a case study we demonstrate the use of the toolkit on a national study of air pollution exposure and mortality.

A Method for Visualizing Multivariate Time Series Data

Visualization and exploratory analysis is an important part of any data analysis and is made more challenging when the data are voluminous and high-dimensional. One such example is environmental monitoring data, which are often collected over time and at multiple locations, resulting in a geographically indexed multivariate time series. Financial data, although not necessarily containing a geographic component, present another source of high-volume multivariate time series data. We present the mvtsplot function which provides a method for visualizing multivariate time series data. We outline the basic design concepts and provide some examples of its usage by applying it to a database of ambient air pollution measurements in the United States and to a hypothetical portfolio of stocks.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

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.

Considering sustainability factors in the development project life-cycle : a framework for increasing successful adoption of improved stoves

Indoor air pollution from combustion of solid fuels is the fifth leading contributor to disease burden in low-income countries. This, and potential to reduce environmental impacts, has resulted in emphasis on use of improved stoves. However, many efforts have failed to meet expectations and effective coverage remains limited. A disconnect exists between technologies, delivery methods, and long-term adoption. The purpose of this research is to develop a framework to increase long-term success of improved stove projects. The framework integrates sustainability factors into the project life-cycle. It is represented as a matrix and checklist which encourages consideration of social, economic, and environmental issues in projects. A case study was conducted in which an improved stove project in Honduras was evaluated using the framework. Results indicated areas of strength and weakness in project execution and highlighted potential improvements for future projects. The framework is also useful as a guide during project planning.

Translocation and cellular entering mechanisms of nanoparticles in the respiratory tract

Anthropogenic nano-sized particles (NSP), ie, particles with a diameter of less than 100 nm, are generated with or without purpose as chemically and physically well-defined materials or as a consequence of combustion processes respectively. Inhalation of NSP occurs on a regular basis due to air pollution and is associated with an increase in respiratory and cardiovascular morbidity and mortality. Manufactured NSP may intentionally be inhaled as pharmaceuticals or unintentionally during production at the workplace. Hence the interactions of NSP with the respiratory tract are currently under intensive investigation. Due to special physicochemical features of NSP, its biological behaviour may differ from that of larger sized particles. Here we review two important themes of current research into the effects of NSP on the lungs: 1) The potential of NSP to cross the blood-air barrier of the lungs, thus gaining access to the circulation and extrapulmonary organs. It is currently accepted that a small fraction of inhaled NSP may translocate to the circulation. The significance of this translocation requires further research. 2) The entering mechanisms of NSP into different cell types. There is evidence that NSP are taken up by cells via well-known pathways of endocytosis but also via different mechanisms not well understood so far. Knowledge of the quantitative relationship between the different entering mechanisms and cellular responses is not yet available but is urgently needed in order to understand the effects of intentionally or unintentionally inhaled NSP on the respiratory tract.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.

Lifestyle and stroke risk: a review

PURPOSE OF REVIEW: In recent years, many epidemiological studies have given new insights into old and new lifestyle factors that influence the risk of cerebrovascular events. In this review, we refer to the most important articles to highlight recent advances, especially those important for stroke prevention. RECENT FINDINGS: This review focuses on the most recent studies that show the association of environmental factors, nutrition, alcohol, tobacco, education, lifestyle and behavior with the risk of vascular disease, including ischemic stroke and cerebral hemorrhage. The link between air pollution and stroke risk has become evident. Low education levels and depression are established as risk factors. This is also true for heavy alcohol consumption, although moderate drinking may be protective. Active and passive smoking are independent risk factors, and a smoking ban in public places has already reduced cardiovascular events in the short term. Physical activity reduces stroke risk; overweight increases it. However, clinical trials to assess the effect of weight reduction on stroke risk are still lacking. Fruits, vegetables, fish, fibers, low-fat dairy products, potassium and low sodium consumption are known and recommended to reduce cardiovascular risk. Data on omega 3 fatty acid, folic acid and B vitamins are inconsistent, and antioxidants are not recommended. SUMMARY: Stroke can be substantially reduced by an active lifestyle, cessation of smoking and a healthy diet. Both public and professional education should promote the awareness that a healthy lifestyle and nutrition have the potential to reduce the burden of stroke.

Richness of Lichen Species, Especially of Threatened Ones, Is Promoted by Management Methods Furthering Stand Continuity

Lichens are a key component of forest biodiversity. However, a comprehensive study analyzing lichen species richness in relation to several management types, extending over different regions and forest stages and including information on site conditions is missing for temperate European forests. In three German regions (Schwäbische Alb, Hainich-Dün, Schorfheide-Chorin), the so-called Biodiversity Exploratories, we studied lichen species richness in 631 forest plots of 400 m2 comprising different management types (unmanaged, selection cutting, deciduous and coniferous age-class forests resulting from clear cutting or shelterwood logging), various stand ages, and site conditions, typical for large parts of temperate Europe. We analyzed how lichen species richness responds to management and habitat variables (standing biomass, cover of deadwood, cover of rocks). We found strong regional differences with highest lichen species richness in the Schwäbische Alb, probably driven by regional differences in former air pollution, and in precipitation and habitat variables. Overall, unmanaged forests harbored 22% more threatened lichen species than managed age-class forests. In general, total, corticolous, and threatened lichen species richness did not differ among management types of deciduous forests. However, in the Schwäbische-Alb region, deciduous forests had 61% more lichen species than coniferous forests and they had 279% more threatened and 76% more corticolous lichen species. Old deciduous age classes were richer in corticolous lichen species than young ones, while old coniferous age-classes were poorer than young ones. Overall, our findings highlight the importance of stand continuity for conservation. To increase total and threatened lichen species richness we suggest (1) conserving unmanaged forests, (2) promoting silvicultural methods assuring stand continuity, (3) conserving old trees in managed forests, (4) promoting stands of native deciduous tree species instead of coniferous plantations, and (5) increasing the amount of deadwood in forests.

Comparison of the toxicity of diesel exhaust produced by bio- and fossil diesel combustion in human lung cells in vitro

Alternative fuels are increasingly combusted in diesel- and gasoline engines and the contribution of such exhausts to the overall air pollution is on the rise. Recent findings on the possible adverse effects of biodiesel exhaust are contradictive, at least partly resulting from the various fuel qualities, engine types and different operation conditions that were tested. However, most of the studies are biased by undesired interactions between the exhaust samples and biological culture media. We here report how complete, freshly produced exhausts from fossil diesel (B0), from a blend of 20% rapeseed-methyl ester (RME) and 80% fossil diesel (B20) and from pure rapeseed methyl ester (B100) affect a complex 3D cellular model of the human airway epithelium in vitro by exposing the cells at the air–liquid interface. The induction of pro-apoptotic and necrotic cell death, cellular morphology, oxidative stress, and pro-inflammatory responses were assessed. Compared to B0 exhaust, B20 exhaust decreased oxidative stress and pro-inflammatory responses, whereas B100 exhaust, depending on exposure duration, decreased oxidative stress but increased pro-inflammatory responses. The effects are only very weak and given the compared to fossil diesel higher ecological sustainability of biodiesel, it appears that – at least RME – can be considered a valuable alternative to pure fossil diesel.

Biokinetics of nanoparticles and susceptibility to particulate exposure in a murine model of cystic fibrosis.

BACKGROUND Persons with cystic fibrosis (CF) are at-risk for health effects from ambient air pollution but little is known about the interaction of nanoparticles (NP) with CF lungs. Here we study the distribution of inhaled NP in a murine CF model and aim to reveal mechanisms contributing to adverse effects of inhaled particles in susceptible populations. METHODS Chloride channel defective CftrTgH (neoim) Hgu mice were used to analyze lung function, lung distribution and whole body biokinetics of inhaled NP, and inflammatory responses after intratracheal administration of NP. Distribution of 20-nm titanium dioxide NP in lungs was assessed on ultrathin sections immediately and 24 h after a one-hour NP inhalation. NP biokinetics was deduced from total and regional lung deposition and from whole body translocation of inhaled 30-nm iridium NP within 24 h after aerosol inhalation. Inflammatory responses were assessed within 7 days after carbon NP instillation. RESULTS Cftr mutant females had moderately reduced lung compliance and slightly increased airway resistance compared to wild type mice. We found no genotype dependent differences in total, regional and head deposition or in secondary-organ translocation of inhaled iridium NP. Titanium dioxide inhalation resulted in higher NP uptake by alveolar epithelial cells in Cftr mutants. Instillation of carbon NP induced a comparable acute and transient inflammatory response in both genotypes. The twofold increase of bronchoalveolar lavage (BAL) neutrophils in Cftr mutant compared to wild type mice at day 3 but not at days 1 and 7, indicated an impaired capacity in inflammation resolution in Cftr mutants. Concomitant to the delayed decline of neutrophils, BAL granulocyte-colony stimulating factor was augmented in Cftr mutant mice. Anti-inflammatory 15-hydroxyeicosatetraenoic acid was generally significantly lower in BAL of Cftr mutant than in wild type mice. CONCLUSIONS Despite lacking alterations in lung deposition and biokinetics of inhaled NP, and absence of significant differences in lung function, higher uptake of NP by alveolar epithelial cells and prolonged, acute inflammatory responses to NP exposure indicate a moderately increased susceptibility of lungs to adverse effects of inhaled NP in Cftr mutant mice and provides potential mechanisms for the increased susceptibility of CF patients to air pollution.

PM2.5-bound oxygenated PAHs, nitro-PAHs and parent-PAHs from the atmosphere of a Chinese megacity: seasonal variation, sources and cancer risk assessment

Polycyclic aromatic compounds (PACs) in air particulate matter contribute considerably to the health risk of air pollution. The objectives of this study were to assess the occurrence and variation in concentrations and sources of PM2.5-bound PACs [Oxygenated PAHs (OPAHs), nitro-PAHs and parent-PAHs] sampled from the atmosphere of a typical Chinese megacity (Xi'an), to study the influence of meteorological conditions on PACs and to estimate the lifetime excess cancer risk to the residents of Xi'an (from inhalation of PM2.5-bound PACs). To achieve these objectives, we sampled 24-h PM2.5 aerosols (once in every 6 days, from 5 July 2008 to 8 August 2009) from the atmosphere of Xi'an and measured the concentrations of PACs in them. The PM2.5-bound concentrations of Σcarbonyl-OPAHs, ∑ hydroxyl + carboxyl-OPAHs, Σnitro-PAHs and Σalkyl + parent-PAHs ranged between 5–22, 0.2–13, 0.3–7, and 7–387 ng m− 3, respectively, being markedly higher than in most western cities. This represented a range of 0.01–0.4% and 0.002–0.06% of the mass of organic C in PM2.5 and the total mass of PM2.5, respectively. The sums of the concentrations of each compound group had winter-to-summer ratios ranging from 3 to 8 and most individual OPAHs and nitro-PAHs had higher concentrations in winter than in summer, suggesting a dominant influence of emissions from household heating and winter meteorological conditions. Ambient temperature, air pressure, and wind speed explained a large part of the temporal variation in PACs concentrations. The lifetime excess cancer risk from inhalation (attributable to selected PAHs and nitro-PAHs) was six fold higher in winter (averaging 1450 persons per million residents of Xi'an) than in summer. Our results call for the development of emission control measures.

Ice-Core Based Assessment of Historical Anthropogenic Heavy Metal (Cd, Cu, Sb, Zn) Emissions in the Soviet Union

The development of strategies and policies aiming at the reduction of environmental exposure to air pollution requires the assessment of historical emissions. Although anthropogenic emissions from the extended territory of the Soviet Union (SU) considerably influenced concentrations of heavy metals in the Northern Hemisphere, Pb is the only metal with long-term historical emission estimates for this region available, whereas for selected other metals only single values exist. Here we present the first study assessing long-term Cd, Cu, Sb, and Zn emissions in the SU during the period 1935–1991 based on ice-core concentration records from Belukha glacier in the Siberian Altai and emission data from 12 regions in the SU for the year 1980. We show that Zn primarily emitted from the Zn production in Ust-Kamenogorsk (East Kazakhstan) dominated the SU heavy metal emission. Cd, Sb, Zn (Cu) emissions increased between 1935 and the 1970s (1980s) due to expanded non-ferrous metal production. Emissions of the four metals in the beginning of the 1990s were as low as in the 1950s, which we attribute to the economic downturn in industry, changes in technology for an increasing metal recovery from ores, the replacement of coal and oil by gas, and air pollution control.