Anthropogenic radionuclides in atmospheric air over Switzerland during the last few decades.

The atmospheric nuclear testing in the 1950s and early 1960s and the burn-up of the SNAP-9A satellite led to large injections of radionuclides into the stratosphere. It is generally accepted that current levels of plutonium and caesium radionuclides in the stratosphere are negligible. Here we show that those radionuclides are present in the stratosphere at higher levels than in the troposphere. The lower content in the troposphere reveals that dry and wet deposition efficiently removes radionuclides within a period of a few weeks to months. Since the stratosphere is thermally stratified and separated from the troposphere by the tropopause, radioactive aerosols remain longer. We estimate a mean residence time for plutonium and caesium radionuclides in the stratosphere of 2.5-5 years. Our results also reveal that strong volcanic eruptions like Eyjafjallajökull in 2010 have an important role in redistributing anthropogenic radionuclides from the stratosphere to the troposphere.

Influence de la topographie et des accumulations d'air froid sur les températures moyennes mensuelles et annuelles en Suisse

Les températures moyennes mensuelles et annuelles mesurées à 177 endroits de la Suisse ont été ramenées à une même altitude à l'aide d'une formule empirique afin de détecter les influences de la topographie locale sur ce paramètre. Ces calculs ont montré que des accumulations d'air froid influencent fortement les températures moyennes en saison froide au fond de certaines vallées alpines larges et dépressions du Jura sur une épaisseur d'environ 100 mètres. Le déficit thermique dans ces vallées peut atteindre 2 à 3°C à l'échelle annuelle et 5 à 7°C en hiver comparativement aux versants et sommets environnants. Ces accumulations d'air froid n'influencent que peu les températures moyennes dans les vallées étroites et bien ventilées.

Extraction and concentration of vapors from fire debris for forensic purposes: evaluation of the use of Radiello Passive Air Sampler

The Radiello Passive Air Sampler is one of the latest innovations developed for the sampling of pollutants in the air by passive headspace. It has been reported that its properties allow an enhanced sensitivity, reproducibility and adsorption capacity. It therefore appears to be of interest in the extraction of potential residues of ignitable liquids present in fire debris when arson is suspected. A theoretical approach and several laboratory tests have made it possible to precisely characterize in a forensic perspective the potential of the device in extracting and concentrating the vapors of ignitable liquids found in fire debris. Despite some advantages, the Radiello device appears to be less efficient than traditional axial symmetry samplers.

First day of an oil spill on the open sea: early mass transfers of hydrocarbons to air and water.

During the first hours after release of petroleum at sea, crude oil hydrocarbons partition rapidly into air and water. However, limited information is available about very early evaporation and dissolution processes. We report on the composition of the oil slick during the first day after a permitted, unrestrained 4.3 m(3) oil release conducted on the North Sea. Rapid mass transfers of volatile and soluble hydrocarbons were observed, with >50% of ≤C17 hydrocarbons disappearing within 25 h from this oil slick of <10 km(2) area and <10 μm thickness. For oil sheen, >50% losses of ≤C16 hydrocarbons were observed after 1 h. We developed a mass transfer model to describe the evolution of oil slick chemical composition and water column hydrocarbon concentrations. The model was parametrized based on environmental conditions and hydrocarbon partitioning properties estimated from comprehensive two-dimensional gas chromatography (GC×GC) retention data. The model correctly predicted the observed fractionation of petroleum hydrocarbons in the oil slick resulting from evaporation and dissolution. This is the first report on the broad-spectrum compositional changes in oil during the first day of a spill at the sea surface. Expected outcomes under other environmental conditions are discussed, as well as comparisons to other models.

aIr-1, a newly found locus on mouse chromosome 16 encoding a trans-acting activator factor for MHC class II gene expression.

RJ 2.2.5 is a human B cell line that has lost the capacity to express MHC class II genes. The human class II-positive phenotype is restored in somatic cell hybrids between RJ 2.2.5 and mouse spleen cells. By karyotype and molecular studies of an informative family of hybrids we have now shown that the reexpression of human class II gene products, as well as the maintenance of the mouse class II-positive phenotype, correlates with the presence of mouse chromosome 16. Thus, the existence on this mouse chromosome of a newly found locus, designated by us aIr-1, that determines a trans-acting activator function for class II gene expression, is established. Possible implications of this finding are discussed.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Surface-to-air signals.

Powerful volatile regulators of gene expression, pheromones and other airborne signals are of great interest in biology. Plants are masters of volatile production and release, not just from flowers and fruits, but also from vegetative tissues. The controlled release of bouquets of volatiles from leaves during attack by herbivores helps plants to deter herbivores or attract their predators, but volatiles have other roles in development and in the control of defence gene expression. Some of these roles may include long-distance signalling within and perhaps between plants.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

A simple gas chromatography method for the analysis of monoethanolamine in air

A simple method determining airborne monoethanolamine has been developed. Monoethanolamine determination has traditionally been difficult due to analytical separation problems. Even in recent sophisticated methods, this difficulty remains as the major issue often resulting in time-consuming sample preparations. Impregnated glass fiber filters were used for sampling. Desorption of monoethanolamine was followed by capillary GC analysis and nitrogen phosphorous selective detection. Separation was achieved using a specific column for monoethanolamines (35% diphenyl and 65% dimethyl polysiloxane). The internal standard was quinoline. Derivatization steps were not needed. The calibration range was 0.5-80 μg/mL with a good correlation (R(2) = 0.996). Averaged overall precisions and accuracies were 4.8% and -7.8% for intraday (n = 30), and 10.5% and -5.9% for interday (n = 72). Mean recovery from spiked filters was 92.8% for the intraday variation, and 94.1% for the interday variation. Monoethanolamine on stored spiked filters was stable for at least 4 weeks at 5°C. This newly developed method was used among professional cleaners and air concentrations (n = 4) were 0.42 and 0.17 mg/m(3) for personal and 0.23 and 0.43 mg/m(3) for stationary measurements. The monoethanolamine air concentration method described here was simple, sensitive, and convenient both in terms of sampling and analytical analysis.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.