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We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

This study investigates how the data about environmental conditions can be used in order toprovide individuals with the tool to claim against industrial companies causing the pollutionand affecting people’s life. Based on extensive research in relation to the political, social,technological and ethical context of the use case, the design solution introduces an open dataplatform where individuals and NGOs can collect, store, analyse and reuse collected data. Proposed design outcome is creating a bridge between users outside of technological scopeand the emerging field of IoT devices in order to make the data collection affordable andaccessible. It introduces a workflow for implementing the off-shelf technology into the digitalinfrastructure together with supportive functionalities to use collected information forcontext-specific purposes. The concept was developed with strong emphasis on implementingdemocratic values into the design solution, such as protection of personal data, distributedgovernance and transparency.

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

This study investigates how the data about environmental conditions can be used in order toprovide individuals with the tool to claim against industrial companies causing the pollutionand affecting people’s life. Based on extensive research in relation to the political, social,technological and ethical context of the use case, the design solution introduces an open dataplatform where individuals and NGOs can collect, store, analyse and reuse collected data. Proposed design outcome is creating a bridge between users outside of technological scopeand the emerging field of IoT devices in order to make the data collection affordable andaccessible. It introduces a workflow for implementing the off-shelf technology into the digitalinfrastructure together with supportive functionalities to use collected information forcontext-specific purposes. The concept was developed with strong emphasis on implementingdemocratic values into the design solution, such as protection of personal data, distributedgovernance and transparency.

The Automatic Identification System (AIS) allows vessels to emit their position, speed and course while sailing. By international law, all larges vessels (e.g., bigger than 15m in Europe) are required to provide such data. The abundance and free availability of AIS data has created a huge interest in analyzing them (e.g., to look for patterns of how ships move, detailed knowledge about sailing routes, etc.). In this paper, we use AIS data to classify areas (i.e., spatial cells) of the South Atlantic Ocean as productive or unproductive in terms of the quantity of squid that can be caught. Next, together with daily satellite data about the area, we create a training dataset where a model is learned to predict whether an area of the Ocean is productive or not. Finally, real fishing data are used to evaluate the model. As a result, for blind movements (i.e., with no information about real catches in the previous days), our model trained on data generated from AIS obtains a precision that is 18% higher than the model trained on actual fishing data-this is due to AIS data being larger in volume than fishing data, and 36% higher than the precision of the actual decisions of the ships studied. The results show that despite their simplicity, AIS data have potential value in building training datasets in this domain., Peer Reviewed, Postprint (author's final draft)

Purpose: This thesis aims to describe how to design and implement an IoT-Based digital twin framework for environmental monitoring in the indoor environment. To fulfill the purpose of the study, the following research question is answered. How to create a digital twin solution utilizing AWS to establish interaction and convergence between the physical environment in a classroom and the virtual environment? Method: As a research method, the research has conducted design science research (DSR). DSR is a new method, and it is an effective tool for enhancing engineering education research methods. Results: The study describes in detail the steps required to create the framework. The framework enabled interaction and convergence between the physical and virtual environments in a particular location. Implications: The research contributes to broadening the knowledge on using the Internet of things (IoT), digital twin (DT), and Amazon web services (AWS). The study provides future research with reference data and a framework to build upon. Research Limitation: Due to time constraints, the study's scope and limitations are limited to the technologies that the participating company, Knowit, provides. Knowit AB is a Swedish IT consulting company that supports companies and organizations with services in digital transformation and system development. The study aims to create an AWS-based IoT framework, not improve the digital twin concept. The framework was implemented at Jönköping University. This work is also limited to temperature and light intensity as environmental parameters.

Purpose: This thesis aims to describe how to design and implement an IoT-Based digital twin framework for environmental monitoring in the indoor environment. To fulfill the purpose of the study, the following research question is answered. How to create a digital twin solution utilizing AWS to establish interaction and convergence between the physical environment in a classroom and the virtual environment? Method: As a research method, the research has conducted design science research (DSR). DSR is a new method, and it is an effective tool for enhancing engineering education research methods. Results: The study describes in detail the steps required to create the framework. The framework enabled interaction and convergence between the physical and virtual environments in a particular location. Implications: The research contributes to broadening the knowledge on using the Internet of things (IoT), digital twin (DT), and Amazon web services (AWS). The study provides future research with reference data and a framework to build upon. Research Limitation: Due to time constraints, the study's scope and limitations are limited to the technologies that the participating company, Knowit, provides. Knowit AB is a Swedish IT consulting company that supports companies and organizations with services in digital transformation and system development. The study aims to create an AWS-based IoT framework, not improve the digital twin concept. The framework was implemented at Jönköping University. This work is also limited to temperature and light intensity as environmental parameters.

Current global environmental change calls for comprehensive and complementing approaches for biodiversity conservation. According to recent research, consideration of the diversity of Earth’s abiotic features (i.e. geodiversity) could provide new insights and applications into the investigation and management of biodiversity. However, methods to map and quantify geodiversity at local scale have not been developed although this scale is important for conservation planning. Here, we introduce a field methodology for observing plot-scale geodiversity, pilot the method in an Arctic–alpine tundra environment, provide empirical evidence on the plot-scale biodiversity–geodiversity relationship and give guidance for practitioners on the implementation of the method. The field method is based on observation of geofeatures, that is, elements of geology, geomorphology and hydrology, from a given area surrounding a location of species observations. As a result, the method provides novel information on the variation of abiotic nature for biodiversity research and management. The method was piloted in northern Norway and Finland by observing geofeatures from 76 sites at three scales (5, 10 and 25 m radii). To explore the relationship between measures of biodiversity and geodiversity, the occurrence of vascular plant species was recorded from 2 m × 2 m plots at the same sites. According to the results, vascular plant species richness was positively correlated with the richness of geofeatures (Rs = 0.18–0.59). The connection was strongest in habitats characterized by deciduous shrubs. The method has a high potential for observing geofeatures without extensive geological or geomorphological training or field survey experience and could be applied by conservation practitioners. Synthesis and applications. Consideration of geodiversity in understanding, analysing and conserving biodiversity could facilitate environmental management and ensure the long-term sustainability of e

This paper investigates the impact of environmental conditions on the possible output power of photovoltaic (PV) installations on top of hybrid electric vehicles (HEVs) and battery-powered electric vehicles (BEVs). First, we discuss the characteristics and behavior of PV cells in order to provide an understanding of the energy source that we aim to integrate into vehicles. Second, we elaborate on how PV cells and panels can be simulated to estimate the potential extension of the electrical driving range (ERE) of BEVs and HEVs. In particular, we concentrate on the impact of the vehicle’s curved roof surface on the possible output of the PV installation. In this research, we present considerations for vehicles in both parking and driving conditions. More precisely, we demonstrate how the frequently changing environmental conditions that occur while driving represent significant challenges to the control of the operating voltage of PV cells. As the area for deploying PV cells on top of an electric vehicle is limited, attention needs to be paid to how to optimize and maximize the degree of efficiency of PV-powered electric vehicles.

We propose a framework for the 3D exploration of heterogeneous environmental data within a geographical region of interest. Based on Unity, the system can be built for a variety of platforms and works both on regular machines as well as in virtual reality environments, where it can be considered a basis for a virtual geographic environment. Focussing on the catchment of the Müglitz River in south-eastern Germany, a large collection of observation data acquired via a wide range of measurement devices has been integrated in a geographical reference frame for the region. Results of area-wide numerical simulations for both groundwater and soil moisture have been added to the scene and allow for the exploration of the delayed consequences of transient phenomena such as heavy rainfall events and their impact on the catchment scale. This study focusses on the concurrent visualisation and synchronised animation of multiple area wide datasets from different environmental compartments. The resulting application allows to explore the region of interest during specific hydrological events for an assessment of the interrelation of processes. As such, it offers the opportunity for knowledge transfer between researchers of different domains as well as for outreach to an interested public.

Digital technologies play an increasingly important role in addressing environmental challenges, such as climate change and resource depletion. Yet, the characteristics and implications of digitalized environmental governance are still under-conceptualized. In this perspective, we distinguish three dimensions of governance: (1) seeing and knowing, (2) participation and engagement, and (3) interventions and actions. For each dimension, we provide a critical perspective on the shifts that digital technologies generate in governance. We argue against the assumption that the use of digital technologies automatically results in improved outcomes or in more democratic decision-making. Instead, attention needs to be paid to the wider political and normative context in which digital technologies are proposed, designed, and used as environmental governance tools. We conclude with key questions for academics and policymakers to broaden the debate on responsible design and use of digital technologies in environmental governance.

During flights aircraft continuously collect data regarding operations, health status and system condition. Data-driven approaches typically applied to system specific sensor data provide a way to predict failures of aircraft systems. However, it is believed that some systems deteriorate faster when subjected to particular environmental conditions, such as humidity or dust. In this study, we consider an aircraft system which is suspected to experience degradation due to humidity during ground operations. We apply a Random Forest approach to sensor data only and a combination of sensor data and environmental data from airports to estimate the system's remaining useful life. To our knowledge this is the first paper addressing the problem of integrating environmental data in prognostics for aircraft systems using raw sensor data. The method is validated on a data set provided by an airline that includes the per-second sensor data of 11 different sensors for roughly 12,300 flights, as well as 15 removals. Meteorological data for airports worldwide is obtained from the Meteorological Aerodrome Reports database. The results show that incorporating environmental data in prognostics has a potential towards more accurate prediction models., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aerospace Transport & Operations

Environmental assessment is a critical activity for ensuring buildings are performing according to specified requirements, and efficient, seamless exchange of building information is crucial for environmental assessment. Therefore, all those involved in built environment issues should be able to access and share not only building information but also data about products, especially environmental assessment results for the products used in building projects. Of the several approaches that have been proposed to achieve efficient information exchange, semantic web technologies are amongst the most promising due to their capability to share data and enhance interoperability between the most heterogeneous systems. This study proposes an approach that can be used to make environmental data available in the early phases of the building lifecycle. It relies on Semantic Web techniques, especially Linked Data principles, while building on emerging Building Information Modelling (BIM) technology to propose an approach that facilitates information exchange to enhance the sustainability assessment of buildings. The paper ends with an illustration of how lifecycle inventory databases can be integrated, linked to BIM software and used in exchanging environmental building data.

In this paper, we investigate the difference between charging electric vehicles with photovoltaic (PV) installations installed on top of buildings and PV installations embedded into vehicles. In future, photovoltaics will be integrated into hybrid electric vehicles (HEVs), battery-powered electric vehicles (BEVs) as well as into connected and autonomous vehicles (CAVs) for extending the electrical driving range (EDR) of these types of vehicles. Over the past decade, charging patterns of electric vehicles have been studied extensively and how the costs for electricity affect the time for charging electric vehicles. If PV cells are embedded for example into the roof of an electric vehicle, electricity can be produced wherever the car goes, both, under parking and driving conditions. We discuss how these circumstances will affect and change the calculation of charging patterns for electric vehicles.

Environmental assessment is a critical activity for ensuring buildings are performing according to specified requirements, and efficient, seamless exchange of building information is crucial for environmental assessment. Therefore, all those involved in built environment issues should be able to access and share not only building information but also data about products, especially environmental assessment results for the products used in building projects. Of the several approaches that have been proposed to achieve efficient information exchange, semantic web technologies are amongst the most promising due to their capability to share data and enhance interoperability between the most heterogeneous systems. This study proposes an approach that can be used to make environmental data available in the early phases of the building lifecycle. It relies on Semantic Web techniques, especially Linked Data principles, while building on emerging Building Information Modelling (BIM) technology to propose an approach that facilitates information exchange to enhance the sustainability assessment of buildings. The paper ends with an illustration of how lifecycle inventory databases can be integrated, linked to BIM software and used in exchanging environmental building data.

During flights aircraft continuously collect data regarding operations, health status and system condition. Data-driven approaches typically applied to system specific sensor data provide a way to predict failures of aircraft systems. However, it is believed that some systems deteriorate faster when subjected to particular environmental conditions, such as humidity or dust. In this study, we consider an aircraft system which is suspected to experience degradation due to humidity during ground operations. We apply a Random Forest approach to sensor data only and a combination of sensor data and environmental data from airports to estimate the system's remaining useful life. To our knowledge this is the first paper addressing the problem of integrating environmental data in prognostics for aircraft systems using raw sensor data. The method is validated on a data set provided by an airline that includes the per-second sensor data of 11 different sensors for roughly 12,300 flights, as well as 15 removals. Meteorological data for airports worldwide is obtained from the Meteorological Aerodrome Reports database. The results show that incorporating environmental data in prognostics has a potential towards more accurate prediction models., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Air Transport & Operations

This project is the first in a series of improvements for the welfare of workers in their area of work in the network of metropolitan transport workshops in Barcelona where Olivia Soler Soria carries out her business practices. The aim of this work is to study the environmental measures due to the detection of high temperatures in the garages workshops. This situation is dangerous due to the thermal stress for health and efficiency in production. In order to solve this situation, actions will be taken to alleviate the problem. First of all, a rigorous examination of the environmental conditions in a TMB workshop has been carried out following the methodology outlined by the National Institute of Safety and Hygiene in the Workplace (INSHT). Based on this study, various studies have been carried out applying UNE regulations where different concepts have been worked on such as WBGT index, PMV and PPD, thermal overload index and physiological overload. Parameters such as natural wet temperature (tnw), the balloon temperature (tg) and the air temperature (ta), average temperature (tr), partial vapour pressure (pa,), air speed (va), metabolic rate (M), etc. After calculating the working environment, the main action proposed to improve the condition of the workers is the installation of strategically placed fans to create air currents which would lower the body's thermal sensation. By proposing this solution, other aspects have been elaborated such as its electrical and physical installation in the workshop (location and fastening), which are developed throughout the project. At the end, an economic study is made and a series of possible improvements are proposed, which were discarded for economic reasons and which TMB could consider for the future.

Hur låter miljöförstöring – Självgenererande och slumpmässig sprungen ur statistiska data är ett mastersarbete som fokuserar på hur man kan omvandla data till att kontrollera musik och ljud. Om vi tillåter konstnärliga uttryck, med data som utgångspunkt, kan det få oss att uppleva och förstår original data på ett nytt sätt? Projektets resultat består av sju generativa kompositioner, där parametrar är kontrollerade av olika typer av miljödata, och försöker utforska forskningsområdet sonifikation och generativ musik genom att ställa frågan: Hur låter miljöförstöring? Med generativ musik menas att musiken skapar, utvecklar och förändrar sig själv utifrån de verktyg som bildats inom detta projekt. Texten går också djupare in på metoden av att utveckla dessa verktyg för att möjliggöra liknande kompositioner i framtiden. Med erfarenheter av att använda slumpmässiga parametrar för att manipulera bakgrundsdetaljer, utforskar det här projekt istället möjligheten att utveckla de metoderna och applicera det på hela kompositioner. De sju kompositionerna ligger också till grund för utforskandet av området sonifikation. I tidigare forskning har ämnet främst bemöts ifrån ett vetenskapligt perspektiv. Syftet med det här projektet har istället varit att bemöta det inom ramarna för musikalisk gestaltning och ett konstnärligt perspektiv. Begreppet sonifikation betyder, användandet av icke-talande ljud som uppmärksammar data och statistik, med målet att agera som ett substitut, eller ett komplement, till att visualisera data. Utifrån dessa kompositioner reflekterar sedan texten kring generativ musik i allmänhet, och sonifikation i synnerhet, där bland annat möjligheterna, framtida forskning och autenticiteten inom sonifikation tas upp., What does environmental pollution sound like? – Self generative and randomized music interprets data is a master thesis focused on transforming data and letting it control music and sounds. If we create artistic outputs out of data, will it allow us to experience and understand the original data in a new way? The core, and the result, of this project where seven compositions, created and controlled by different environmental data which tries to explore the research areas of sonification and generative music by asking the question: What does environmental pollution sound like? Generative music means that the music creates, develops and changes itself based on the established tools that this project provides. This thesis also focuses on the method of developing these tools in order to enable similar productions in the future. With previous experiences in using randomized events, to manipulate details in a production, this project delves deeper into applying the same technique to a whole composition. The seven compositions were formed in order to understand and reflect upon the research areas of sonification. Earlier research tends to approach the subject from a scientific perspective. The purpose of this project was to instead approach it from a more artistic perspective. Sonification means, the use of non-speech audio to perceptualize data which enables the possibilities as an alternative, or complement, to visualize the original data. Drawing from these seven compositions, this thesis also discusses generative music and sonification in general, as well as the opportunities, future research and authenticity of sonification., Bifogad ljudfil är ett kollage av de sju kompositionerna som arbetet resulterat i, med anledning av att i framtiden kunna publiceras i sin helhet.

This paper investigates the potential output power of photovoltaic (PV) installations on top of battery-powered electric vehicles (BEVs) and hybrid electric vehicles (HEVs). By choosing different routes within urban areas, we expect different output power levels from the vehicle’s PV installation. Hence, we calculate energy profiles to estimate the PV energy which can be gained during driving conditions to choose the most energy-efficient route for BEVs and HEVs. While driving duration, driving distance and traffic conditions are some of the criteria within the decision-making process of routing algorithms, in this paper, we focus on energy-efficiency of routing algorithms. We present experimental data to highlight the importance of energy profiles for improving and maximising of the electrical driving distance of BEVs and HEVs.

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains

Hur låter miljöförstöring – Självgenererande och slumpmässig sprungen ur statistiska data är ett mastersarbete som fokuserar på hur man kan omvandla data till att kontrollera musik och ljud. Om vi tillåter konstnärliga uttryck, med data som utgångspunkt, kan det få oss att uppleva och förstår original data på ett nytt sätt? Projektets resultat består av sju generativa kompositioner, där parametrar är kontrollerade av olika typer av miljödata, och försöker utforska forskningsområdet sonifikation och generativ musik genom att ställa frågan: Hur låter miljöförstöring? Med generativ musik menas att musiken skapar, utvecklar och förändrar sig själv utifrån de verktyg som bildats inom detta projekt. Texten går också djupare in på metoden av att utveckla dessa verktyg för att möjliggöra liknande kompositioner i framtiden. Med erfarenheter av att använda slumpmässiga parametrar för att manipulera bakgrundsdetaljer, utforskar det här projekt istället möjligheten att utveckla de metoderna och applicera det på hela kompositioner. De sju kompositionerna ligger också till grund för utforskandet av området sonifikation. I tidigare forskning har ämnet främst bemöts ifrån ett vetenskapligt perspektiv. Syftet med det här projektet har istället varit att bemöta det inom ramarna för musikalisk gestaltning och ett konstnärligt perspektiv. Begreppet sonifikation betyder, användandet av icke-talande ljud som uppmärksammar data och statistik, med målet att agera som ett substitut, eller ett komplement, till att visualisera data. Utifrån dessa kompositioner reflekterar sedan texten kring generativ musik i allmänhet, och sonifikation i synnerhet, där bland annat möjligheterna, framtida forskning och autenticiteten inom sonifikation tas upp., What does environmental pollution sound like? – Self generative and randomized music interprets data is a master thesis focused on transforming data and letting it control music and sounds. If we create artistic outputs out of data, will it allow us to experience and understand the original data in a new way? The core, and the result, of this project where seven compositions, created and controlled by different environmental data which tries to explore the research areas of sonification and generative music by asking the question: What does environmental pollution sound like? Generative music means that the music creates, develops and changes itself based on the established tools that this project provides. This thesis also focuses on the method of developing these tools in order to enable similar productions in the future. With previous experiences in using randomized events, to manipulate details in a production, this project delves deeper into applying the same technique to a whole composition. The seven compositions were formed in order to understand and reflect upon the research areas of sonification. Earlier research tends to approach the subject from a scientific perspective. The purpose of this project was to instead approach it from a more artistic perspective. Sonification means, the use of non-speech audio to perceptualize data which enables the possibilities as an alternative, or complement, to visualize the original data. Drawing from these seven compositions, this thesis also discusses generative music and sonification in general, as well as the opportunities, future research and authenticity of sonification., Bifogad ljudfil är ett kollage av de sju kompositionerna som arbetet resulterat i, med anledning av att i framtiden kunna publiceras i sin helhet.

Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

Innovative telemetry and biologging technology has increased the amount of available movement data on aquatic species. However, real-time information on the environmental factors influencing animal movements can be logistically challenging to obtain, particularly in habitats where tides and currents vary locally. Hydrodynamic models are capable of simulating complex tidal flow, and may thus offer an alternative method of contextualizing animal movement in coastal habitats. Here we use this tool to examine the influence of tide on the movement of broadnose sevengill sharks (Notorynchus cepedianus) in the San Francisco Bay estuary. Three sharks were actively tracked using acoustic transmitters for 3 to 4 days. We then generated a hydrodynamic model of the estuary and calculated current vectors along each track. We hypothesized that the sharks would adjust their swimming speed and direction depending on current strength when passing through the channel underneath the Golden Gate Bridge. Our results indicate that sharks did tend to follow the current flow in the channel, but their overall displacement did not significantly correlate with tidal amplitude. We conclude that the sharks may respond to environmental factors other than tidal flow, altering their movement at a finer scale than initially considered. Overall, this suggests that hydrodynamic simulation models can be used to visualize and quantify environmental factors that may affect movement patterns in aquatic organisms. We recommend future studies combine these models with other biologging techniques to measure energy expenditure at a finer spatial scale.

The required estimation of performances of a building cannot be limited to its energy efficiency anymore. Environmental issues are rising concerns into national policies. However, information about construction products is still segmented into several distinct databases: the construction company gathered data for its design process into private pricing databases while environmental declarations from manufacturers are available in a public database. The interconnection of the different information about the same product is difficult because of the difference of data formatting and representation. The objective of this project was to provide first tools to facilitate this interconnection between the design process of the company and environmental data, considering incoming requirements from the new thermal regulation of 2020. This led to the creation of a SQL environmental database, based on environmental declarations, more adapted for statistical analysis than a document-based database. Specific data management functions were also developed to homogenise unit representation and to spot product performances for the purpose of multi-criteria analysis of products Finally, an estimation of the distinctiveness of products through a selection of key words was tested. The comparison of lists of words provided good performances for their classification into a limited number of lots. But it is not sufficient to identify items that can be related to the same construction product. So further works is needed into the creation of a semantic metric model of construction vocabulary.

Innovative telemetry and biologging technology has increased the amount of available movement data on aquatic species. However, real-time information on the environmental factors influencing animal movements can be logistically challenging to obtain, particularly in habitats where tides and currents vary locally. Hydrodynamic models are capable of simulating complex tidal flow, and may thus offer an alternative method of contextualizing animal movement in coastal habitats. Here we use this tool to examine the influence of tide on the movement of broadnose sevengill sharks (Notorynchus cepedianus) in the San Francisco Bay estuary. Three sharks were actively tracked using acoustic transmitters for 3 to 4 days. We then generated a hydrodynamic model of the estuary and calculated current vectors along each track. We hypothesized that the sharks would adjust their swimming speed and direction depending on current strength when passing through the channel underneath the Golden Gate Bridge. Our results indicate that sharks did tend to follow the current flow in the channel, but their overall displacement did not significantly correlate with tidal amplitude. We conclude that the sharks may respond to environmental factors other than tidal flow, altering their movement at a finer scale than initially considered. Overall, this suggests that hydrodynamic simulation models can be used to visualize and quantify environmental factors that may affect movement patterns in aquatic organisms. We recommend future studies combine these models with other biologging techniques to measure energy expenditure at a finer spatial scale.

The required estimation of performances of a building cannot be limited to its energy efficiency anymore. Environmental issues are rising concerns into national policies. However, information about construction products is still segmented into several distinct databases: the construction company gathered data for its design process into private pricing databases while environmental declarations from manufacturers are available in a public database. The interconnection of the different information about the same product is difficult because of the difference of data formatting and representation. The objective of this project was to provide first tools to facilitate this interconnection between the design process of the company and environmental data, considering incoming requirements from the new thermal regulation of 2020. This led to the creation of a SQL environmental database, based on environmental declarations, more adapted for statistical analysis than a document-based database. Specific data management functions were also developed to homogenise unit representation and to spot product performances for the purpose of multi-criteria analysis of products Finally, an estimation of the distinctiveness of products through a selection of key words was tested. The comparison of lists of words provided good performances for their classification into a limited number of lots. But it is not sufficient to identify items that can be related to the same construction product. So further works is needed into the creation of a semantic metric model of construction vocabulary.

This paper investigates the potential output power of a photovoltaic (PV) installation on top of battery-powered electric vehicles (BEVs) and hybrid electric vehicles (HEVs). Firstly, we discuss the available area on the roof of BEVs and HEVs for deploying PV cells. Secondly, we verify the impact of the curved surface on the available output power of photovoltaics. Furthermore, we discuss the impact of the curved surface and the ambient conditions on the possible alignment of PV cells. Here, we present a method of calculating the effective area of PV cells, useful for PV simulation models and calculating the available output power of PV cells with different longitudinal angles. Finally, we verify our method within experiments and present the results of them, showing that the model predicts the output power of PV cells with an accuracy better than 2.5%.

This paper investigates the potential output power of a photovoltaic (PV) installation on top of battery-powered electric vehicles (BEVs) and hybrid electric vehicles (HEVs). Firstly, we discuss the available area on the roof of BEVs and HEVs for deploying PV cells. Secondly, we verify the impact of the vehicles curved roof surface on the available output power of photovoltaics. More precisely, we present a method of calculating the effective area of PV cells, useful for PV simulation models and calculating the available output power of PV cells with different longitudinal angles. We verify our method within experiments and present the results of them, showing that the model predicts the output power of PV cells with an accuracy better than 2.5%. Furthermore, we discuss the impact of the curved surface and ambient conditions on possible interconnections of PV cells. Here, we present considerations for both, stationary and moving conditions, to highlight the difficulties for interconnecting PV cells to form PV panels. From our experimental results, for frequent changes in the available solar radiation level, we can see a difference in terms of output power larger than 75% between series and parallel interconnections of PV cells.

This paper investigates the impact of the solar radiation level on the available output power of moving photovoltaic (PV) installations with the help of PV simulation models and real-world environmental data. For moving PV installations, for example, on top of hybrid electric vehicles and battery-powered electric vehicles, the sampling rate was 6000 samples per second. We analyze the changes in the amount of solar radiation that can influence on the control of the operating voltage of PVs within maximum power point tracking (MPPT) algorithms. We present recommendations for the sampling rate of environmental data, which is used for PV simulation models. Furthermore, we discuss the update frequency of vital parameters of different MPPT techniques for controlling moving PV installations. Here, we concentrate on the degree of efficiency of the perturb and observe algorithm. In addition, we show how the sampling rate of environmental data influences the test criteria for MPPT algorithms.

The application of agricultural pesticides in Africa can have negative effects on human health and the environment. The aim of this study was to identify African environments that are vulnerable to the accumulation of pesticides by mapping geospatial processes affecting pesticide fate. The study modelled processes associated with the environmental fate of agricultural pesticides using publicly available geospatial datasets. Key geospatial processes affecting the environmental fate of agricultural pesticides were selected after a review of pesticide fate models and maps for leaching, surface runoff, sedimentation, soil storage and filtering capacity, and volatilization were created. The potential and limitations of these maps are discussed. We then compiled a database of studies that measured pesticide residues in Africa. The database contains 10,076 observations, but only a limited number of observations remained when a standard dataset for one compound was extracted for validation. Despite the need for more in-situ data on pesticide residues and application, this study provides a first spatial overview of key processes affecting pesticide fate that can be used to identify areas potentially vulnerable to pesticide accumulation.

The Empirical Mode Decomposition (EMD) is a powerful data analysis methodthat can be used to extract embedded components within time series and otherdata. In the context of EMD, these embedded components are called IntrinsicMode Functions (IMF). Since its inception in the late 1990s, EMD has been appliedin a number of areas, including biomedicine, neuroscience, epidemiology, chemicalengineering, finance, atmospheric turbulence, seismology and oceanography. Themethod is advantageous in that it is able to analyse nonlinear and non-stationarydata. Surprisingly, the literature exploring the ability of EMD to analyse data withdifferent types of non-stationarity is relatively sparse. Also, the sensitivities associatedwith the critical initial steps of the EMD procedure are not well understood.One of the critical steps in determining each of the IMFs involves constructing upperand lower envelopes of the local maxima and minima of the time series. In theoriginal presentation of the EMD methodology, cubic-spline interpolation was usedto construct these envelopes. However, there is no a priori reason to support the useof cubic splines, and it is natural to wonder how employing alternative interpolationmethods might affect the ultimate outcome of the EMD method.This dissertation is dedicated to providing a more comprehensive understanding ofthe sensitivity of EMD to different interpolation methodologies, and to differenttypes of data non-stationarity. These sensitivities are investigated systematicallyusing synthetic time series data that cover a range of interesting features. In addition,a number of environmental data sets are used to explore how EMD sensitivitycan inffuence the inferences that might be drawn from IMFs extracted using variousforms of the EMD method. In this part of the study, temperature, sea leveland forest fire danger rating time series are chosen. There is significant interestin the way these particular variables might be changing over various time scales

This paper investigates the impact of the solar radiation level on the available output power of moving photovoltaic (PV) installation with the help of PV simulation models. For moving PV installations, for example on top of hybrid electric vehicles (HEVs) and battery-powered electric vehicles (BEVs), the sampling rate was 50 samples per second. We analyse the changes in the amount of solar radiation that can influence the control of the operating voltage of photovoltaics within maximum power point tracking (MPPT) algorithms. We present recommendations for the sampling rate of environmental data as well as for the update frequency of vital parameters of different MPPT techniques for controlling moving PV installations. In addition, we show how the sampling rate of environmental data influences the test criteria for MPPT algorithms.

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study., info:eu-repo/semantics/openAccess

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study., info:eu-repo/semantics/openAccess

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study., info:eu-repo/semantics/openAccess

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biodiversity Data Journal 5 (2017): e10989, doi:10.3897/BDJ.5.e10989., The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biodiversity Data Journal 5 (2017): e10989, doi:10.3897/BDJ.5.e10989., The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

The Ocean Biogeographic Information System (OBIS) is the world’s most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling eve

In citizen-sensing projects, more extensively and democratically gathered data are typically presented as “the reasons for measuring air pollution”, since it is through collecting data that everything from enhanced participation in environmental issues to changes in policy are hoped to be achieved. The impetus to monitor and gather data is bound up with established (and emerging) processes of understanding environments as information-based problems. Within citizen-sensing projects, data are intended to be collected in ways that complement, reroute or even circumvent and challenge the usual institutions and practices that monitor environments and manage environmental data. Data are seen to enable modes of action that are meant to offer effective ways to respond to those problems. With more data, potentially more accurate data, and more extensively distributed data, environmental problems such as air pollution are anticipated to be more readily and effectively addressed. Data are intertwined with practices, responses to perceived problems, modes of materializing and evidencing problems, and proposals for political engagement. But how are air-quality data constituted, whether through expert or citizen practices? How do differing practices of environmental monitoring inform the character and quality of data gathered, as well as the possible trajectories and effects of those data? What are the instruments, relations, and experiences of air-quality data generated through these distinctive engagements with environments and technology? And in what ways do environments become computational through the use of low-cost air-pollution monitoring technologies? I consider how citizen-sensing practices that monitor air pollution experiment with the tactics and arrangements of environmental data.

Epidemiological studies analyze and monitor the health state of a population. They typically use different methods and techniques to capture and to integrate data of interest before they can be analyzed. As new technologies and, thus, devices are available for data capturing, such as wearables, new requirements arise for current data integration approaches. In this paper, we review current techniques and approaches as well as new trends in data capturing and the resulting requirement for its integration.

As repositories of primary source materials, archives play a central role in supporting the democratic principles of transparency and accountability. Political discourse and many official records of government have shifted from analog to web-based delivery. Web archiving programs that collect content created by elected officials and governments are vital to a robust civil society, which is central to a healthy democracy. This panel brings together information professionals and a digital historian engaged with related content. Professionals actively acquiring websites of elected officials and online government publications will discuss why and how their institutions are building web archives in these areas and what gaps, if any, exist. Panelists will offer their perspectives on the current state of researcher access and how archives can better support researcher engagement with web archives. Questions of professional and institutional responsibility as citizens and as employees of democratic institutions will be explored.

Bayesian spatial modeling of extreme values has become increasingly popular due to its ability to obtain relevant uncertainty measures for the estimates. This has implications for the problem of limited data on the study of extreme climatological events. Noticing the abundance of non-daily environmental records, 1-h and 6-h records in particular, we propose a Bayesian hierarchical model that can address multiple durations, in addition to the spatial effects, within a set of extreme records with multiple durations. The generalized Pareto distribution for threshold exceedances and the binomial distribution for exceedance frequencies are adopted for the top-most characterization of extreme data. The duration effect on spatial extremes is characterized by pooling the extremes with different durations and merging the duration into a latent spatial process structure as one of the covariates. Statistical inference is performed using Markov Chain Monte Carlo (MCMC) methods, for which an adaptive tuning algorithm is proposed. The methodology is applied to simulated datasets and real precipitation data for a region around Hong Kong. © 2015 Elsevier B.V.

Offshore wind turbines suffer a number of environmental loads which damage their support structure and decrease their lifetime. These forces are produced by the wind, waves and currents and they damage the monopile cyclically. This project aims to quantify that damage through simulating a parametric FEA (finite element analysis) and by applying Palmgren-Miner’s rule. For so, a model has been built in ANSYS Workbench and validated through a modal analysis. Then, the behaviour of a monopile in the Southern North Sea has been simulated: wind speed and wave height data were processed into applicable loads. From the results, the cumulative damage was calculated and lifetime defined. Finally, some parameters were altered to evaluate their influence on the fatigue damage. The parametric modelling and fatigue damage assessment showed, on the one hand, that cathodic protection for monopiles is indispensable and, on the other hand, that the use of larger (thicker, longer and bigger diameter) monopiles improves the lifetime of the structure as damage is reduced.

Bayesian spatial modeling of extreme values has become increasingly popular due to its ability to obtain relevant uncertainty measures for the estimates. This has implications for the problem of limited data on the study of extreme climatological events. Noticing the abundance of non-daily environmental records, 1-h and 6-h records in particular, we propose a Bayesian hierarchical model that can address multiple durations, in addition to the spatial effects, within a set of extreme records with multiple durations. The generalized Pareto distribution for threshold exceedances and the binomial distribution for exceedance frequencies are adopted for the top-most characterization of extreme data. The duration effect on spatial extremes is characterized by pooling the extremes with different durations and merging the duration into a latent spatial process structure as one of the covariates. Statistical inference is performed using Markov Chain Monte Carlo (MCMC) methods, for which an adaptive tuning algorithm is proposed. The methodology is applied to simulated datasets and real precipitation data for a region around Hong Kong. © 2015 Elsevier B.V.

The statistical literature contains many univariate and multivariate skewness measures that allow two datasets to be compared, some of which are defined in terms of quantile values. In most situations, the comparison between two random vectors focuses on univariate comparisons of conditional random variables truncated in quantiles; this kind of comparison is of particular interest in the environmental sciences. In this work, we describe a new approach to comparing skewness in terms of the univariate convex transform ordering proposed by van Zwet (Convex transformations of random variables. Mathematical Centre Tracts, Amsterdam, 1964), associated with skewness as well as concentration. The key to these comparisons is the underlying dependence structure of the random vectors. Below we describe graphical tools and use several examples to illustrate these comparisons.