Determining Pu-241 in environmental samples: case studies in alpine soils

A procedure was developed for determining Pu-241 activity in environmental samples. This beta emitter isotope of plutonium was measured by ultra low level liquid scintillation, after several separation and purification steps that involved the use of a highly selective extraction chromatographic resin (Eichrom-TEVA). Due to the lack of reference material for Pu-241, the method was nevertheless validated using four IAEA reference sediments with information values for Pu-241. Next, the method was used to determine the Pu-241 activity in alpine soils of Switzerland and France. The Pu-241/Pu-239,Pu-240 and Pu-238/Pu-239,Pu-240 activity ratios confirmed that Pu contamination in the tested alpine soils originated mainly from global fallout from nuclear weapon tests conducted in the fifties and sixties. Estimation of the date of the contamination, using the Pu-241/Am-241 age-dating method, further confirmed this origin. However, the Pu-241/Am-241 dating method was limited to samples where Pu-Am fractionation was insignificant. If any, the contribution of the Chernobyl accident is negligible.

The environmental roots of non-communicable diseases (NCDs) and the epigenetic impacts of globalization

BACKGROUND: Non-communicable diseases (NCDs) are increasing worldwide. We hypothesize that environmental factors (including social adversity, diet, lack of physical activity and pollution) can become "embedded" in the biology of humans. We also hypothesize that the "embedding" partly occurs because of epigenetic changes, i.e., durable changes in gene expression patterns. Our concern is that once such factors have a foundation in human biology, they can affect human health (including NCDs) over a long period of time and across generations. OBJECTIVES: To analyze how worldwide changes in movements of goods, persons and lifestyles (globalization) may affect the "epigenetic landscape" of populations and through this have an impact on NCDs. We provide examples of such changes and effects by discussing the potential epigenetic impact of socio-economic status, migration, and diet, as well as the impact of environmental factors influencing trends in age at puberty. DISCUSSION: The study of durable changes in epigenetic patterns has the potential to influence policy and practice; for example, by enabling stratification of populations into those who could particularly benefit from early interventions to prevent NCDs, or by demonstrating mechanisms through which environmental factors influence disease risk, thus providing compelling evidence for policy makers, companies and the civil society at large. The current debate on the '25 × 25 strategy', a goal of 25% reduction in relative mortality from NCDs by 2025, makes the proposed approach even more timely. CONCLUSIONS: Epigenetic modifications related to globalization may crucially contribute to explain current and future patterns of NCDs, and thus deserve attention from environmental researchers, public health experts, policy makers, and concerned citizens.

The enterprise face to the environmental challenge: straddling the fence between social responsibility and penal responsibility

This contribution aims to analyse how to incur companies' criminal liability when they violate environmental protection globally. In Switzerland, companies' criminal liability has already been provided for to fight against money launder- ing (Article 102 CP). Could a similar liability be incurred, in Switzerland, for companies that infringe environmental protection? This is what our contribution is all about. Since the company is at the heart of our subject, the point is to see to what extent criminal liability could be transposed to cases of violation by companies of the environmental principles promoted by the CSR concept.

A comparison of alpha and gamma spectrometry for environmental natural radioactivity surveys.

An alpha-spectrometry, using automated borate fusion and sequential extraction and exchange chromatography, was used to determine the uranium and thorium based on environmental radioactivity of 20 soil samples. The same set of the samples was analysed using gamma-spectrometry with an HPGe detector. The two data sets were checked for coherence using Z-score and chi2 statistical tests. We show that gamma-spectrometry is a valid alternative to time-consuming alpha-spectrometry for the determination of natural uranium and thorium activity in soil (activity range: 12.5-58.2 Bq/kg). The measured activities were compared with the theoretical activities to ensure secular equilibrium in the 238U and 232Th series. For 226Ra, a special study was made on deconvolution of the 186 keV multiplet with the Levenberg-Marquardt algorithm. Finally, the combined use of Z-score and chi2-tests was found to be a powerful tool for comparing the results obtained with two different methods.

Genetic variation and environmental effects on beta-conglycinin and glycinin content in Brazilian soybean cultivars

The objective of this work was to determine genetic and environmental effects on beta-conglycinin and glycinin content in Brazilian soybean cultivars. The concentrations of these protein fractions were analyzed by scanning densitometry after electrophoresis, in 90 Brazilian soybean cultivars sown in Ponta Grossa, PR, in 2001. The effects of the sowing location were determined in the cultivar MG/BR 46 (Conquista), sown in 16 locations of Goiás and Minas Gerais states (Central Brazil), and in the cultivar IAS 5, sown in 12 locations of Paraná and São Paulo states (Southern Brazil), in 2002 soybean season. A significant variability for beta-conglycinin (7S) and glycinin (11S) protein fractions ratio was observed among the 90 Brazilian soybean cultivars. 'MS/BRS 169' (Bacuri) and 'BR-8' (Pelotas) presented the highest and the lowest 11S/7S ratios (2.76 and 1.17, respectively). Beta-conglycinin protein fractions presented more variability than glycinin protein fractions. Grouping test classified 7S proteins in seven groups, 11S proteins in four groups, and protein fraction ratios (11S/7S) in nine groups. Significant effect of sowing locations was also observed on protein fractions contents. There is a good possibility of breeding for individual protein fractions, and their subunits, without affecting protein content.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Use of niche models in invasive species risk assessments.

Risk maps summarizing landscape suitability of novel areas for invading species can be valuable tools for preventing species' invasions or controlling their spread, but methods employed for development of such maps remain variable and unstandardized. We discuss several considerations in development of such models, including types of distributional information that should be used, the nature of explanatory variables that should be incorporated, and caveats regarding model testing and evaluation. We highlight that, in the case of invasive species, such distributional predictions should aim to derive the best hypothesis of the potential distribution of the species by using (1) all distributional information available, including information from both the native range and other invaded regions; (2) predictors linked as directly as is feasible to the physiological requirements of the species; and (3) modelling procedures that carefully avoid overfitting to the training data. Finally, model testing and evaluation should focus on well-predicted presences, and less on efficient prediction of absences; a k-fold regional cross-validation test is discussed.

East 1 St Street Interchange and I-35 Widening, From MP 94.3 to Oralabor Road, Polk County, Iowa, Project # IM-035-4(140)92--13-77, Environmental Assessment and Section 4(f) De Minimis Impact Finding, 2015

From Proposed Action: "The proposed action consists of reconstructing the East 1st Street/I-35 interchange with a Diverging Diamond interchange, widening I-35 from four (4) lanes to six (6) lanes, and widening East 1st Street from four (4) lanes to five (5) lanes from Delaware Avenue to Frisk Drive. The project also proposes to reconstruct the intersections of East 1st Street/Creekview Drive and East 1st Street/Frisk Drive."

West 1 St Street/ IA 57 from Highland Drive to Center Street/Franklin Street, Blackhawk County, Iowa, Project # STP-57-2(28)--2C-07, Environmental Assessment and Section 4(f) De Minimis Impact Finding, 2015

From Proposed Action: "Iowa Northland Regional Council of Governments (INRCOG) and the City of Cedar Falls, in coordination with the Iowa Department of Transportation (Iowa DOT) and the Federal Highway Administration (FHWA), are proposing to upgrade and modernize an approximate 4,900-foot segment of Iowa Highway 57 (IA 57), locally known as West 1st Street, in Cedar Falls, Black Hawk County, Iowa."

Combining ability of maize grain yield under different levels of environmental stress

The objectives of this work were to caracterize the tropical maize germplasm and to compare the combining abilities of maize grain yield under different levels of environmental stress. A diallel was performed among tropical maize cultivars with wide adaptability, whose hybrid combinations were evaluated in two sowing dates, in two years. The significance of the environmental effect emphasized the environmental contrasts. Based on grain yield, the environments were classified as favorable (8,331 kg ha-1), low stress (6,637 kg ha-1), high stress (5,495 kg ha-1), and intense stress (2,443 kg ha-1). None of the genetic effects were significant in favorable and intense stress environments, indicating that there was low germplasm variability under these conditions. In low and high stresses, the specific combining ability effects (SCA) were significant, showing that the nonadditive genetic effects were the most important, and that it is possible to select parent pairs with breeding potential. SCA and grain yield showed significant correlations only between the closer environment pairs like favorable/low stress and high/intense stress. The genetic control of grain yield differed under contrasting stress environments for which maize cultivars with wide adaptability are not adequate.

Environmental and genetic variation of isoflavone content of soybean seeds grown in Brazil

The objective of this work was to evaluate isoflavone concentrations in seeds of different Brazilian soybean cultivars grown in a range of locations and environmental conditions in Brazil. Seeds of 233 cultivars grown in Ponta Grossa, PR, Brazil, during the 2001/2002 soybean season, and of 22 cultivars sown in different locations of Brazilian Northeast, Southeast on South regions were analyzed for total isoflavones, including daidzin, glycitin, genistin and acetylgenistin. The total isoflavones ranged from 12 mg 100 g-1 (cv. Embrapa 48) to 461 mg 100 g-1 (cv. CS 305) among the 233 cultivars grown in Ponta Grossa, and the differences among them are due to genetic effects since all cultivars were grown and collected at the same locatation and year. This is an indication of the possibility of breeding for isoflavone content. Differences in isoflavone content observed in the cultivars grown in different locations permit the selection of locations for optimum isoflavone content (low or high), depending on the uses of soybean. In the Northeast region (5-8°S), higher concentrations of total isoflavones were observed at São Raimundo das Mangabeiras (232 mg 100 g-1) and Tasso Fragoso (284 mg 100 g-1) municipalities, and in the South (23-30°S), isoflavones were higher in Guarapuava, Canoinhas, Vacaria and Campos Novos municipalities, ranging from 130 to 409 mg 100 g-1.

Blue-Green Algae, Iowa Department of Public Health - Division of Environmental Health, September 15, 2011

The Iowa Department of Public Health (IDPH), Division of Environmental Health, Health Assessment Program gives people information about harmful chemicals and organisms in their environment. Blue-green algae are microscopic organisms that are naturally present in lakes and streams. Some blue-green algae produce toxins that could pose a health risk to people and animals when they are exposed to them in large enough quantities. This fact sheet answers questions about blue-green algae.