Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems

A closer look at chaotic advection in the stratosphere: part II: statistical diagnostics

Statistical diagnostics of mixing and transport are computed for a numerical model of forced shallow-water flow on the sphere and a middle-atmosphere general circulation model. In particular, particle dispersion statistics, transport fluxes, Liapunov exponents (probability density functions and ensemble averages), and tracer concentration statistics are considered. It is shown that the behavior of the diagnostics is in accord with that of kinematic chaotic advection models so long as stochasticity is sufficiently weak. Comparisons with random-strain theory are made

Remote sensing for natural or man-made disasters and environmental changes

Natural and man-made disasters have become an issue of growing concern throughout the world. The frequency and magnitude of disasters threatening large populations living in diverse environments, is rapidly increasing in recent years across the world due to demographic growth, inducing to urban sprawls into hazardous areas. These disasters also have far-reaching implications on sustainable development through social, economic and environmental impact. This chapter summarises three scientific contributions from relevant experiences of the British Geological Survey and the Federico II University of Naples, where remote sensing sensors have been playing a crucial role to potentially support disaster management studies in areas affected by natural hazards. The three cases are: the landslide inventory map of St. Lucia island, tsunami-induced damage along the Sendai coast (Japan) and the landslide geotechnical characterization in Papanice (Italy). For each case study we report the main issue, datasets available and results achieved. Finally, we analyse how recent developments and improved satellite and sensor technologies can support in overcoming the current limitations of using remotely sensed data in disaster management so to fully utilize the capabilities of remote sensing in disaster management and strength cooperation and collaboration between relevant stakeholders including end users

The contribution of theory to the design, delivery, and evaluation of interprofessional curricula: BEME Guide No. 49

BACKGROUND: Interprofessional curricula have often lacked explicit reference to theory despite calls for a more theoretically informed field that illuminates curricular assumptions and justifies curricular practices. AIM: To review the contributions of theory to the design, delivery, and evaluation of interprofessional curricula. METHODS: Four databases were searched (1988-2015). Studies demonstrating explicit and a high-quality contribution of theory to the design, delivery or evaluation of interprofessional curricula were included. Data were extracted against a comprehensive framework of curricular activities and a narrative synthesis undertaken. RESULTS: Ninety-one studies met the inclusion criteria. The majority of studies (86%) originated from the UK, USA, and Canada. Theories most commonly underpinned "learning activities" (47%) and "evaluation" (54%). Theories of reflective learning, identity formation, and contact hypothesis dominated the field though there are many examples of innovative theoretical contributions. CONCLUSIONS: Theories contribute considerably to the interprofessional field, though many curricular elements remain under-theorized. The literature offers no "gold standard" theory for interprofessional curricula; rather theoretical selection is contingent upon the curricular component to which theory is to be applied. Theories contributed to interprofessional curricula by explaining, predicting, organizing or illuminating social processes embedded in interprofessional curricular assumptions. This review provides guidance how theory might be robustly and appropriately deployed in the design, delivery, and evaluation of interprofessional curricula

Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

The two-way exchange of trace gases between the ocean and the atmosphere is important for both the chemistry and physics of the atmosphere and the biogeochemistry of the oceans, including the global cycling of elements. Here we review these exchanges and their importance for a range of gases whose lifetimes are generally short compared to the main greenhouse gases and which are, in most cases, more reactive than them. Gases considered include sulphur and related compounds, organohalogens, non-methane hydrocarbons, ozone, ammonia and related compounds, hydrogen and carbon monoxide. Finally, we stress the interactivity of the system, the importance of process understanding for modeling, the need for more extensive field measurements and their better seasonal coverage, the importance of inter-calibration exercises and finally the need to show the importance of air-sea exchanges for global cycling and how the field fits into the broader context of Earth System Science

Estimación de la concentración media diaria de material particulado fino en la región del Complejo Industrial y Portuario de Pecém, Ceará, Brasil

A exposição ao material particulado fino (MP2,5) está associada a inúmeros desfechos à saúde. Desta forma, monitoramento da concentração ambiental do MP2,5 é importante, especialmente em áreas amplamente industrializadas, pois abrigam potenciais emissores do MP2,5 e de substâncias com potencial de aumentar a toxicidade de partículas já suspensas. O objetivo desta pesquisa é estimar a concentração diária do MP2,5 em três áreas de influência do Complexo Industrial e Portuário do Pecém (CIPP), Ceará, Brasil. Foi aplicado um modelo de regressão não linear para a estimativa do MP2,5, por meio de dados de profundidade óptica monitorados por satélite. As estimativas foram realizadas em três áreas de influência (Ai) do CIPP (São Gonçalo do Amarante – Ai I, Paracuru e Paraipaba – Ai II e Caucaia – Ai III, no período de 2006 a 2017. As médias anuais das concentrações estimadas foram inferiores ao estabelecido pela legislação nacional em todas as Ai (8µg m-3). Em todas as Ai, os meses referentes ao período de seca (setembro a fevereiro) apresentaram as maiores concentrações e uma predominância de ventos leste para oeste. Os meses que compreendem o período de chuva (março a agosto) apresentaram as menores concentrações e ventos menos definidos. As condições meteorológicas podem exercer um papel importante nos processos de remoção, dispersão ou manutenção das concentrações do material particulado na região. Mesmo com baixas concentrações estimadas, é importante avaliar a constituição das partículas finas dessa região, bem como sua possível associação a efeitos adversos à saúde da população local.Exposure to fine particulate matter (PM2.5) is associated with numerous negative health outcomes. Thus, monitoring the environmental concentration of PM2.5 is important, especially in heavily industrialized areas, since they harbor potential emitters of PM2.5 and substances with the potential to increase the toxicity of already suspended particles. This study aims to estimate daily concentrations of PM2.5 in three areas under the influence of the Industrial and Port Complex of Pecém (CIPP), Ceará State, Brazil. A nonlinear regression model was applied to estimate PM2.5, using satellitemonitored optical depth data. Estimates were performed in three areas of influence (Ai) of the CIPP (São Gonçalo do Amarante – AiI, Paracuru and Paraipaba – AiII, and Caucaia – AiIII), from 2006 to 2017. Estimated mean annual concentrations were lower than established by Brazil’s national legislation in all three Ai (8µg m-³). In all the Ai, the months of the dry season (September to February) showed the highest concentrations and a predominance of east winds, while the months of the rainy season (March to August) showed the lowest concentrations and less defined winds Weather conditions can play an important role in the removal, dispersal, or maintenance of concentrations of particulate matter in the region. Even at low estimated concentrations, it is important to assess the composition of fine participles in this region and their possible association with adverse health outcomes in the local population.La exposición al material particulado fino (MP2,5) está asociada a innumerables problemas de salud. Por ello, la supervisión de la concentración ambiental del MP2,5 es importante, especialmente en áreas ampliamente industrializadas, puesto que albergan potenciales emisores de MP2,5 y de sustancias con potencial de aumentar la toxicidad de partículas ya suspendidas. El objetivo de esta investigación es estimar la concentración diaria del MP2,5 en tres áreas de influencia del Complejo Industrial y Portuario de Pecém (CIPP), Ceará, Brasil. Se aplicó un modelo de regresión no lineal para la estimación del MP2,5, mediante datos de profundidad óptica supervisados por satélite. Las estimaciones fueron realizadas en tres áreas de influencia (Ai) del CIPP (São Gonçalo do Amarante – Ai I, Paracuru y Paraipaba – Ai II y Caucaia – Ai III en el período de 2006 a 2017. Las medias anuales de las concentraciones estimadas fueron inferiores a lo establecido por la legislación nacional en todas las Ai (8µg m-³). En todas las Ai, los meses referentes al período de sequía (de setiembre a febrero) presentaron las mayores concentraciones y una predominancia de vientos este a oeste, los meses que comprenden el período de lluvia (marzo a agosto) presentaron las menores concentraciones y vientos menos definidos. Las condiciones meteorológicas pueden ejercer un papel importante en los procesos de eliminación, dispersión o mantenimiento de las concentraciones del material particulado en la región. Incluso con bajas concentraciones estimadas es importante que se evalúe la constitución de las partículas finas de esta región, así como su posible asociación con efectos adversos para la salud de la población local