An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

El proyecto EMECAS: protocolo del estudio multicéntrico en España de los efectos a corto plazo de la contaminación atmosférica sobre la salud.

The EMECAM Project demonstrated the short-term effect of air pollution on the death rate in 14 cities in Spain throughout the 1990-1995 period. The Spanish Multicentre Study on Health Effects of Air Pollution (EMECAS) is broadening these objectives by incorporating more recent data, information on hospital disease admissions and totaling 16 Spanish cities. This is an ecological time series study in which the response variables are the daily deaths and the emergency hospitalizations due to circulatory system diseases and respiratory diseases among the residents in each city. Pollutants analyses: suspended particles, SO2, NO2, CO and O3. Control variables: meteorological, calendar, seasonality and influenza trend and incidence. Statistical analysis: estimate of the association in each city by means of the construction of generalized additive Poisson regression models and metanalysis for obtaining combined estimators. The EMECAS Project began with the creation of three working groups (Exposure, Epidemiology and Analysis Methodology) which defined the protocol. The average levels of pollutants were below those established under the current regulations for sulfur dioxide, carbon monoxide and ozone. The NO2 and PM10 values were around those established under the regulations (40 mg/m3). This is the first study of the relationship between air pollution and disease rate among one group of Spanish cities. The pollution levels studied are moderate for some pollutants, although for others, especially NO2 and particles, these levels could entail a problem with regard to complying with the regulations in force.

Building energy saving techniques and indoor air quality : a dilemma

In European countries and North America, people spend 80 to 90% of time inside buildings and thus breathe indoor air. In Switzerland, special attention has been devoted to the 16 stations of the national network of observation of atmospheric pollutants (NABEL). The results indicate a reduction in outdoor pollution over the last ten years. With such a decrease in pollution over these ten years the question becomes: how can we explain an increase of diseases? Indoor pollution can be the cause. Indoor contaminants that may create indoor air quality (IAQ) problems come from a variety of sources. These can include inadequate ventilation, temperature and humidity dysfunction, and volatile organic compounds (VOCs). The health effects from these contaminants are varied and can range from discomfort, irritation and respiratory diseases to cancer. Among such contaminants, environmental tobacco smoke (ETS) could be considered the most important in terms of both health effects and engineering controls of ventilation. To perform indoor pollution monitoring, several selected ETS tracers can be used including carbon monoxide (CO), carbon dioxide (CO2), respirable particles (RSP), condensate, nicotine, polycyclic aromatic hydrocarbons (PAHs), nitrosamines, etc. In this paper, some examples are presented of IAQ problems that have occurred following the renewal of buildings and energy saving concerns. Using industrial hygiene sampling techniques and focussing on selected priority pollutants used as tracers, various problems have been identified and solutions proposed. [Author]

Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine.

Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelationbetween variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Biodiversity of Collembola in urban soils and their use as bioindicators for pollution

The objective of this work was to evaluate the effects of pollutants on the abundance and diversity of Collembola in urban soils. The research was carried out in three parks (Cişmigiu, Izvor and Unirea) in downtown Bucharest, where the intense car traffic accounts for 70% of the local air pollution. One site in particular (Cişmigiu park) was highly contaminated with Pb, Cd, Zn and Cu at about ten times the background levels of Pb. Collembola were sampled in 2006 (July, September, November) using the transect method: 2,475 individuals from 34 species of Collembola were collected from 210 samples of soil and litter. Numerical densities differed significantly between the studied sites.The influence of air pollutants on the springtail fauna was visible at the species richness diversity and soil pollution levels. Species richness was lowest in the most contaminated site (Cismigiu, 11 species), which presented an increase in springtails abundances, though. Some species may become resistant to pollution and occur in high numbers of individuals in polluted sites, which makes them a good bioindicator of pollutants.

Effects of particulate matters on inflammatory markers in the general adult population

Summary: Particulate air pollution is associated with increased cardiovascular risk. The induction of systemic inflammation following particle inhalation represents a plausible mechanistic pathway. The purpose of this study was to assess the associations of short-term exposure to ambient particulate matters of aerodynamic diameter less than 10 μm (PM10) with circulating inflammatory markers in 6183 adults in Lausanne, Switzerland. The results show that short-term exposure to PM10 was associated with higher levels of circulating IL-6 and TNF-α. The positive association of PM10 with markers of systemic inflammation materializes the link between air pollution and cardiovascular risk. Background: Variations in short-term exposure to particulate matters (PM) have been repeatedly associated with daily all-cause mortality. Particle-induced inflammation has been postulated to be one of the important mechanisms for increased cardiovascular risk. Experimental in-vitro, in-vivo and controlled human studies suggest that interleukin 6 (IL-6) and tumor-necrosis-factor alpha (TNF-α) could represent key mediators of the inflammatory response to PM. The associations of short-term exposure to ambient PM with circulating inflammatory markers have been inconsistent in studies including specific subgroups so far. The epidemiological evidence linking short-term exposure to ambient PM and systemic inflammation in the general population is scarce. So far, large-scale population-based studies have not explored important inflammatory markers such as IL-6, IL-1β or TNF-α. We therefore analyzed the associations between short-term exposure to ambient PM10 and circulating levels of high-sensitive CRP (hs-CRP), IL-6, IL-1β and TNF-α in the population-based CoLaus study. Objectives: To assess the associations of short-term exposure to ambient particulate matters of aerodynamic diameter less than 10 μm (PM10) with circulating inflammatory markers, including hs-CRP, IL-6, IL-1β and TNF-α, in adults aged 35 to 75 years from the general population. Methodology: All study subjects were participants to the CoLaus study (www.colaus.ch) and the baseline examination was carried out from 2003 to 2006. Overall, 6184 participants were included. For the present analysis, 6183 participants had data on at least one of the four measured circulating inflammatory markers. The monitoring data was obtained from the website of Swiss National Air Pollution Monitoring Network (NABEL). We analyzed data on PM10 as well as outside air temperature, pressure and humidity. Hourly concentrations of PM10 were collected from 1 January 2003 to 31 December 2006. Robust linear regression (PROC ROBUSTREG) was used to evaluate the relationship between cytokine inflammatory and PM10. We adjusted all analyses for age, sex, body mass index, smoking status, alcohol consumption, diabetes status, hypertension status, education levels, zip code, and statin intake. All data were adjusted for the effects of weather by including temperature, barometric pressure, and season as covariates in the adjusted models. We performed simple and multiple logistic regression analyses. Descriptive statistical analysis used the Wilcoxon rank sum test (for medians). All data analyses were performed using SAS software (version 9.2; SAS Institute Inc., Cary, NC, USA), and a two-sided significance level of 5% was used. Results: PM10 levels averaged over 24 hours were significantly and positively associated with continuous IL-6 and TNF-α levels, in the whole study population both in unadjusted and adjusted analyses. For each cytokine, there was a similar seasonal pattern, with wider confidence intervals in summer than during the other seasons, which might partly be due to the smaller number of participants examined in summer. The associations of PM10 with IL-6 and TNF-α were also found after having dichotomized these cytokines into high versus low levels, which suggests that the associations of PM10 with the continuous cytokine levels are very robust to any distributional assumption and to potential outlier values. In contrast with what we observed for continuous IL-1β levels, high PM10 levels were significantly associated with high IL-1β. PM10 was significantly associated with IL-6 and TNF-α in men, but with TNF-α only in women. However, there was no significant statistical interaction between PM10 and sex. For IL-6 and TNF-α, the associations tended to be stronger in younger people, with a significant interaction between PM10 and age groups for IL-6. PM10 was significantly associated with IL-6 and TNF-α in the healthy group and also in the "non-healthy" group, although the statistical interaction between healthy status and PM10 was not significant. Conclusion: In summary, we found significant independent positive associations of short-term exposure to PM10 with circulating levels of IL-6 and TNF-α in the adult population of Lausanne. Our findings strongly support the idea that short-term exposure to PM10 is sufficient to induce systemic inflammation on a broad scale in the general population. From a public health perspective, the reported association of elevated inflammatory cytokines with short-term exposure to PM10 in a city with relatively clean air such as Lausanne supports the importance of limiting urban air pollution levels.

Short-term effects of particulate matters on pulse pressure in two general population studies.

OBJECTIVES: To explore the association of short-term exposure to particulate matter with aerodynamic diameters less than 10 μm (PM10) with pulse pressure, SBP, and DBP taking outdoor temperature into account in two large population-based studies in Switzerland. METHODS: We used data from the Bus Santé study including 5605 adults in Geneva and the CoLaus study including 6183 adults in Lausanne. PM10 and meteorological data were measured from fixed monitoring stations. We analyzed the association of short-term exposure to PM10 (on the day of examination visit and up to 7 days before) with pulse pressure, SBP, and DBP by linear regression, controlling for potential confounders and effect modifiers. RESULTS: Average PM10 levels were 22.4 μg/m in Geneva and 31.7 μg/m in Lausanne. In adjusted models, for each 10 μg/m increase in 7-day PM10 average, pulse pressure and SBP increased by 0.583 (95% confidence interval, 0.296-0.870) mmHg and 0.490 (0.056-0.925) mmHg in Geneva, and 0.183 (0.017-0.348) mmHg and 0.036 (0.042-0.561) mmHg in Lausanne, respectively. Stronger associations of pulse pressure and SBP with PM10 were observed when outdoor temperature was above 5°C. CONCLUSION: Positive associations of pulse pressure and SBP with short-term exposure to PM10 were found and replicated in the Swiss adult population. Our results suggest that even low levels of air pollution may substantially impact cardiovascular risk in the general population.

'Herbal' but potentially hazardous: an analysis of the constituents and smoke emissions of tobacco-free waterpipe products and the air quality in the cafes where they are served

BACKGROUND: There are limited data on the composition and smoke emissions of 'herbal' shisha products and the air quality of establishments where they are smoked. METHODS: Three studies of 'herbal' shisha were conducted: (1) samples of 'herbal' shisha products were chemically analysed; (2) 'herbal' and tobacco shisha were burned in a waterpipe smoking machine and main and sidestream smoke analysed by standard methods and (3) the air quality of six waterpipe cafes was assessed by measurement of CO, particulate and nicotine vapour content. RESULTS: We found considerable variation in heavy metal content between the three products sampled, one being particularly high in lead, chromium, nickel and arsenic. A similar pattern emerged for polycyclic aromatic hydrocarbons. Smoke emission analyses indicated that toxic byproducts produced by the combustion of 'herbal' shisha were equivalent or greater than those produced by tobacco shisha. The results of our air quality assessment demonstrated that mean PM2.5 levels and CO content were significantly higher in waterpipe establishments compared to a casino where cigarette smoking was permitted. Nicotine vapour was detected in one of the waterpipe cafes. CONCLUSIONS: 'Herbal' shisha products tested contained toxic trace metals and PAHs levels equivalent to, or in excess of, that found in cigarettes. Their mainstream and sidestream smoke emissions contained carcinogens equivalent to, or in excess of, those of tobacco products. The content of the air in the waterpipe cafes tested was potentially hazardous. These data, in aggregate, suggest that smoking 'herbal' shisha may well be dangerous to health.

Capillary thermal desorption unit for near real-time analysis of VOCs at sub-trace levels. Application to the analysis of environmental air contamination and breath samples

A capillary microtrap thermal desorption module is developed for near real-time analysis of volatile organic compounds (VOCs) at sub-ppbv levels in air samples. The device allows the direct injection of the thermally desorbed VOCs into a chromatographic column. It does not use a second cryotrap to focalize the adsorbed compounds before entering the separation column so reducing the formation of artifacts. The connection of the microtrap to a GC–MS allows the quantitative determination of VOCs in less than 40 min with detection limits of between 5 and 10 pptv (25 °C and 760 mmHg), which correspond to 19–43 ng m−3, using sampling volumes of 775 cm3. The microtrap is applied to the analysis of environmental air contamination in different laboratories of our faculty. The results obtained indicate that most volatile compounds are easily diffused through the air and that they also may contaminate the surrounding areas when the habitual safety precautions (e.g., working under fume hoods) are used during the manipulation of solvents. The application of the microtrap to the analysis of VOCs in breath samples suggest that 2,5-dimethylfuran may be a strong indicator of a person's smoking status

Pollution trends using bark of morus alba in the cities of buenos aires and mendoza (Argentina)

A comparative study of elements deposited on tree bark was carried out for urban and periurban areas of two of the most important cities in Argentina. The content of Fe, Mg, Al, Mn, Zn, Pb, Ba, Cr, Hg, Cu, Ni, Cd and Sb was determined by inductively coupled plasma atomic emission spectrometry (ICP-OES) in Morus alba tree bark collected in the cities of Buenos Aires and Mendoza. The main air pollutants detected in the Buenos Aires urban area were Ba, Cr, Cu and Ni and indicate significative difference from the Mendoza urban and periurban areas. Significantly, higher concentrations of Zn, Ba, Cr and Cu were recorded in the periurban area of the city of Buenos Aires than in Mendoza. Bark samples were strongly influenced by dust and show Al, Fe, Mg and other element accumulations that indicate that soil particles were carried out by wind. Elements like Ba and Zn, commonly linked to traffic emissions, showed the highest concentrations in the Buenos Aires metropolitan area, possibly due to more intensive vehicular traffic. Our results indicated that intensity of vehicular traffic and not city structure is responsible for air pollution.