Fine particulate matter measurements in Swiss restaurants, cafés and bars: what is the effect of spatial separation between smoking and non-smoking areas?

We performed 124 measurements of particulate matter (PM(2.5)) in 95 hospitality venues such as restaurants, bars, cafés, and a disco, which had differing smoking regulations. We evaluated the impact of spatial separation between smoking and non-smoking areas on mean PM(2.5) concentration, taking relevant characteristics of the venue, such as the type of ventilation or the presence of additional PM(2.5) sources, into account. We differentiated five smoking environments: (i) completely smoke-free location, (ii) non-smoking room spatially separated from a smoking room, (iii) non-smoking area with a smoking area located in the same room, (iv) smoking area with a non-smoking area located in the same room, and (v) smoking location which could be either a room where smoking was allowed that was spatially separated from non-smoking room or a hospitality venue without smoking restriction. In these five groups, the geometric mean PM(2.5) levels were (i) 20.4, (ii) 43.9, (iii) 71.9, (iv) 110.4, and (v) 110.3 microg/m(3), respectively. This study showed that even if non-smoking and smoking areas were spatially separated into two rooms, geometric mean PM(2.5) levels in non-smoking rooms were considerably higher than in completely smoke-free hospitality venues. PRACTICAL IMPLICATIONS: PM(2.5) levels are considerably increased in the non-smoking area if smoking is allowed anywhere in the same location. Even locating the smoking area in another room resulted in a more than doubling of the PM(2.5) levels in the non-smoking room compared with venues where smoking was not allowed at all. In practice, spatial separation of rooms where smoking is allowed does not prevent exposure to environmental tobacco smoke in nearby non-smoking areas.

Nanotoxicology: a perspective and discussion of whether or not in vitro testing is a valid alternative

Despite the many proposed advantages related to nanotechnology, there are increasing concerns as to the potential adverse human health and environmental effects that the production of, and subsequent exposure to nanoparticles (NPs) might pose. In regard to human health, these concerns are founded upon the plethora of knowledge gained from research relating to the effects observed following exposure to environmental air pollution. It is known that increased exposure to environmental air pollution can cause reduced respiratory health, as well as exacerbate pre-existing conditions such as cardiovascular disease and chronic obstructive pulmonary disease. Such disease states have also been associated with exposure to the NP component contained within environmental air pollution, raising concerns as to the effects of NP exposure. It is not only exposure to accidentally produced NPs however, which should be approached with caution. Over the past decades, NPs have been specifically engineered for a wide range of consumer, industrial and technological applications. Due to the inevitable exposure of NPs to humans, owing to their use in such applications, it is therefore imperative that an understanding of how NPs interact with the human body is gained. In vivo research poses a beneficial model for gaining immediate and direct knowledge of human exposure to such xenobiotics. This research outlook however, has numerous limitations. Increased research using in vitro models has therefore been performed, as these models provide an inexpensive and high-throughput alternative to in vivo research strategies. Despite such advantages, there are also various restrictions in regard to in vitro research. Therefore, the aim of this review, in addition to providing a short perspective upon the field of nanotoxicology, is to discuss (1) the advantages and disadvantages of in vitro research and (2) how in vitro research may provide essential information pertaining to the human health risks posed by NP exposure.

Validation of a modified AVHRR aerosol optical depth retrieval algorithm over Central Europe

The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the Meteorological Operational Satellite (MetOp) polar orbiting satellites is the only instrument offering more than 25 years of satellite data to analyse aerosols on a daily basis. The present study assessed a modified AVHRR aerosol optical depth τa retrieval over land for Europe. The algorithm might also be applied to other parts of the world with similar surface characteristics like Europe, only the aerosol properties would have to be adapted to a new region. The initial approach used a relationship between Sun photometer measurements from the Aerosol Robotic Network (AERONET) and the satellite data to post-process the retrieved τa. Herein a quasi-stand-alone procedure, which is more suitable for the pre-AERONET era, is presented. In addition, the estimation of surface reflectance, the aerosol model, and other processing steps have been adapted. The method's cross-platform applicability was tested by validating τa from NOAA-17 and NOAA-18 AVHRR at 15 AERONET sites in Central Europe (40.5° N–50° N, 0° E–17° E) from August 2005 to December 2007. Furthermore, the accuracy of the AVHRR retrieval was related to products from two newer instruments, the Medium Resolution Imaging Spectrometer (MERIS) on board the Environmental Satellite (ENVISAT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua/Terra. Considering the linear correlation coefficient R, the AVHRR results were similar to those of MERIS with even lower root mean square error RMSE. Not surprisingly, MODIS, with its high spectral coverage, gave the highest R and lowest RMSE. Regarding monthly averaged τa, the results were ambiguous. Focusing on small-scale structures, R was reduced for all sensors, whereas the RMSE solely for MERIS substantially increased. Regarding larger areas like Central Europe, the error statistics were similar to the individual match-ups. This was mainly explained with sampling issues. With the successful validation of AVHRR we are now able to concentrate on our large data archive dating back to 1985. This is a unique opportunity for both climate and air pollution studies over land surfaces.

Seasonal variation and sources of low molecular weight organic acids in precipitation in the rural area of Anshun

Low mol. wt. (LMW) org. acids are important and ubiquitous chem. constituents in the atm. A comprehensive study of the chem. compn. of pptn. was carried out from June 2007 to June 2008 at a rural site in Anshun, in the west of Guizhou Province, China. During this period, 118 rainwater samples were collected and the main LMW carboxylic acids were detd. using ion chromatog. The av. pH of rainwater was 4.89 which is a typical acidic value. The most abundant carboxylic acids were formic acid (vol. wt. mean concn.: 8.77 μmol L-1) and acetic acid (6.90 μmol L-1), followed by oxalic acid (2.05 μmol L-1). The seasonal variation of concns. and wet deposition fluxes of org. acids indicated that direct vegetation emissions were the main sources of the org. acids. Highest concns. were obsd. in winter and were ascribed to the low winter rainfall and the contribution of other air pollution sources northeast of the study area. The ratio of formic and acetic acids in the pptn. ([F/A]T) was proposed as an indicator of pollution source. This suggested that the pollution resulted from direct emissions from natural or anthropogenic sources. Comparison with acid pptn. in other urban and rural areas in Guizhou showed that there was a decreasing contribution of LMW org. acids to free acidity and all anions in rainwater from urban to remote rural areas. Consequently, it is necessary to control emissions of org. acids to reduce the frequency of acid rain, esp. in rural and remote areas. [on SciFinder(R)]

Association between reported exposure to road traffic and respiratory symptoms in children: evidence of bias

BACKGROUND: Many studies showing effects of traffic-related air pollution on health rely on self-reported exposure, which may be inaccurate. We estimated the association between self-reported exposure to road traffic and respiratory symptoms in preschool children, and investigated whether the effect could have been caused by reporting bias. METHODS: In a random sample of 8700 preschool children in Leicestershire, UK, exposure to road traffic and respiratory symptoms were assessed by a postal questionnaire (response rate 80%). The association between traffic exposure and respiratory outcomes was assessed using unconditional logistic regression and conditional regression models (matching by postcode). RESULTS: Prevalence odds ratios (95% confidence intervals) for self-reported road traffic exposure, comparing the categories 'moderate' and 'dense', respectively, with 'little or no' were for current wheezing: 1.26 (1.13-1.42) and 1.30 (1.09-1.55); chronic rhinitis: 1.18 (1.05-1.31) and 1.31 (1.11-1.56); night cough: 1.17 (1.04-1.32) and 1.36 (1.14-1.62); and bronchodilator use: 1.20 (1.04-1.38) and 1.18 (0.95-1.46). Matched analysis only comparing symptomatic and asymptomatic children living at the same postcode (thus exposed to similar road traffic) showed similar ORs, suggesting that parents of children with respiratory symptoms reported more road traffic than parents of asymptomatic children. CONCLUSIONS: Our study suggests that reporting bias could explain some or even all the association between reported exposure to road traffic and disease. Over-reporting of exposure by only 10% of parents of symptomatic children would be sufficient to produce the effect sizes shown in this study. Future research should be based only on objective measurements of traffic exposure.

Translocation and potential neurological effects of fine and ultrafine particles a critical update

ABSTRACT: Particulate air pollution has been associated with respiratory and cardiovascular disease. Evidence for cardiovascular and neurodegenerative effects of ambient particles was reviewed as part of a workshop. The purpose of this critical update is to summarize the evidence presented for the mechanisms involved in the translocation of particles from the lung to other organs and to highlight the potential of particles to cause neurodegenerative effects.Fine and ultrafine particles, after deposition on the surfactant film at the air-liquid interface, are displaced by surface forces exerted on them by surfactant film and may then interact with primary target cells upon this displacement. Ultrafine and fine particles can then penetrate through the different tissue compartments of the lungs and eventually reach the capillaries and circulating cells or constituents, e.g. erythrocytes. These particles are then translocated by the circulation to other organs including the liver, the spleen, the kidneys, the heart and the brain, where they may be deposited. It remains to be shown by which mechanisms ultrafine particles penetrate through pulmonary tissue and enter capillaries. In addition to translocation of ultrafine particles through the tissue, fine and coarse particles may be phagocytized by macrophages and dendritic cells which may carry the particles to lymph nodes in the lung or to those closely associated with the lungs. There is the potential for neurodegenerative consequence of particle entry to the brain. Histological evidence of neurodegeneration has been reported in both canine and human brains exposed to high ambient PM levels, suggesting the potential for neurotoxic consequences of PM-CNS entry. PM mediated damage may be caused by the oxidative stress pathway. Thus, oxidative stress due to nutrition, age, genetics among others may increase the susceptibility for neurodegenerative diseases. The relationship between PM exposure and CNS degeneration can also be detected under controlled experimental conditions. Transgenic mice (Apo E -/-), known to have high base line levels of oxidative stress, were exposed by inhalation to well characterized, concentrated ambient air pollution. Morphometric analysis of the CNS indicated unequivocally that the brain is a critical target for PM exposure and implicated oxidative stress as a predisposing factor that links PM exposure and susceptibility to neurodegeneration.Together, these data present evidence for potential translocation of ambient particles on organs distant from the lung and the neurodegenerative consequences of exposure to air pollutants.

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.

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.

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.