Cross-border petroleum geology in the North Sea: an introduction

The North Sea has entered a phase of infrastructure-led exploration in an attempt to extend the economic lives of the main fields and arrest the overall production decline to a certain extent, while the transition to a future low-carbon use of the basin is also in progress. As the papers in this volume demonstrate, in order to find, appraise and develop the mostly smaller near-field opportunities as well as making sure to grasp the opportunities of the near-future energy transition, a regional understanding of the North Sea is still critical. Even more so, a cross-border approach is essential because 1) some of the plays currently being targeted have a clear cross-border element, 2) it allows the comparison of stratigraphic names throughout the entire basin and 3) it enables explorers to learn lessons from one part of the rift to be applied somewhere else.This volume offers an up-to-date, ‘geology-without-borders’ view of the stratigraphy, sedimentology, tectonics and oil-and-gas exploration trends of the entire North Sea basin. The challenges associated with data continuity and nomenclature differences across median lines are discussed and mitigated. Examples of under-exploited cross-border plays and discoveries are discussed.

Ten-Year Monitoring of the Grandes Jorasses Glaciers Kinematics. Limits, Potentialities, and Possible Applications of Different Monitoring Systems

In the Ferret Valley (NW Italy), anthropic activities coexist close to the Grandes Jorasses massif’s glaciological complex. In the past, break-off events have caused damage to people and infrastructure. These events concerned two specific sectors: the Montitaz Lobe (Planpincieux Glacier) and the Whymper Serac (Grandes Jorasses Glacier). Since 2010, permanent and discontinuous survey campaigns have been conducted to identify potential failure precursors, investigate the glacier instability processes, and explore different monitoring approaches. Most of the existing terrestrial apparatuses that measure the surface kinematics have been adopted in the Grandes Jorasses area. The monitoring sites in this specific area are characterized by severe weather, complex geometry, logistic difficulties, and rapid processes dynamics. Such exceptional conditions highlighted the limitations and potentialities of the adopted monitoring approaches, including robotic total station (RTS), GNSS receivers, digital image correlation applied to time-lapse imagery, and terrestrial radar interferometry (TRI). We examined the measurement uncertainty of each system and their monitoring performances. We discussed their principal limitations and possible use for warning purposes. In the Grandes Jorasses area, the use of a time-lapse camera appeared to be a versatile and cost-effective solution, which, however is not suitable for warning applications, as it does not guarantee data continuity. RTS and GNSS have warning potentialities, but the target installation and maintenance in remote environments remain challenging. TRI is the most effective monitoring system for early warning purposes in such harsh conditions, as it provides near-real-time measurements. However, radar equipment is very costly and requires extreme logistic effort. In this framework, we present data integration strategies to overcome the abovementioned limits and we demonstrate that these strategies are optimal solutions to obtain data continuity and robustness.

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Background As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. Results To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. Conclusions We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

Applications of toxicokinetic (TK) models on freshwater invertebrates in a regulation perspective

Toxicokinetic (TK) models have been developed to describe the bioaccumulation of chemicals in organisms. They are used as the first step to evaluate the toxicity of a contaminant in environmental risk assessment (ERA) and are developed to provide a theorical framework for understanding the link between exposure and accumulation by the biota, testing hypotheses, and make predictions (e.g. predictions of the chemical concentration in organisms according to environmental concentration or inversely). In France, polycyclic aromatic hydrocarbons (PAH) have generally been analyzed in sediment as part of annual monitoring. However, regulation specifies that for PAHs, the environmental quality standard (EQS) concerns biota, in this case invertebrates. However, modifying the monitoring protocol used for several years would lead to a loss of data continuity. In this context, TK models could be used to predict ( i) concentrations in the sediment equivalent to the EQS biota and ( ii) concentrations in biota, directly from data measured in sediment in situ, then compared to the EQS biota. Thus, the aim of this study was to illustrate how to use TK models to retro-predict chemical concentrations in the sediment leading to the EQS biota. To achieve this purpose, we firstly used experimental data of a TK study available in the literature (e.g. Hyallela azteca and Chironomustentans exposed to benzo(a)pyrene (BaP) spiked sediment) to estimate the distributions of the model parameters and thus to predict the concentration in the sediment that will lead to a concentration in the biota below the corresponding EQS biota (for both BaP and its metabolites). The results raised the issue of taking into account metabolites in regulation, where their concentrations in the organism could exceed the EQS biota defined for the parent compound. Secondly, we used several experimental data of TK studies which reported different amount of organic matter to account for the bioavailability of PAH in the model.

Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) Newly Released V5.2 Validation of Ozone and Water Vapor Data

<p>The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument installed on the International Space Station (ISS) has completed over three and a half years of data collection and production of science data products. The SAGE III/ISS is a solar and lunar occultation instrument that scans the light from the Sun and Moon through the limb of the Earth’s atmosphere to produce vertical profiles of aerosol, ozone, water vapor, and other trace gases. It continues the legacy of previous SAGE instruments dating back to the 1970s to provide data continuity of stratospheric constituents critical for assessing trends in the ozone layer. This presentation shows the validation results of comparing SAGE III/ISS ozone and water vapor vertical profiles from the newly released v5.2 science product with those of in situ and satellite data .</p>

N2O retrievals from IASI: a new strategy, its validation and a preliminary 13-years trend assessment

<p>N<sub>2</sub>O is the third anthropogenic greenhouse gas, after CO<sub>2</sub> and CH<sub>4</sub>. N<sub>2</sub>O is about a 1000 times less abundant than CO<sub>2</sub>, but is a much stronger greenhouse gas (265 times stronger, for the same amount of gas). N<sub>2</sub>O has an atmospheric lifetime of about 120 years, and resides mostly in the troposphere and lower stratosphere. N<sub>2</sub>O is also the principal source of nitrogen in the stratosphere, participating in the ozone destruction.</p><p>Although N<sub>2</sub>O emissions are mostly natural as a part of biogeochemical cycles, a significant part of the emissions is anthropogenic, linked to agriculture, industry and transport. The N<sub>2</sub>O concentrations are continuously increasing since the industrial era. Because its greenhouse potential is very high, identifying and regulating the anthropogenic N<sub>2</sub>O emissions is crucial for climate change mitigation.</p><p>The Infrared Atmospheric Sounding Interferometer (IASI) is a nadir viewing satellite instrument, measuring the outgoing radiation in the Infrared range. It flies on board the Metop satellite series, on a polar sun-synchronous orbit, and has been providing data since 2006 with a succession of 3 instruments. The follow-up instrument, IASI-NG (new generation), is already in preparation and will not only ensure data continuity for at least an additional decade, but it will also provide improved performances.</p><p>In this work, we present N<sub>2</sub>O profiles with a limited resolution of maximum 2 degrees of freedom, and the corresponding integrated columns, retrieved from IASI measurements using a new retrieval strategy. We assess the quality of our data through comparisons with Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) measurements. We will discuss the main “trouble makers” in this retrieval, i.e. the non-retrieved parameters that have the highest impact on the resulting N<sub>2</sub>O data quality. Finally, we will discuss a preliminary trend assessment derived from the retrieved time series covering 13-years.</p>

A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods

A dam is a complex and important water-retaining structure. Once the dam is broken, the flood will cause immeasurable damage to the lives and properties of the downstream people, so it is particularly important to have the dam risk management. Since the dam-break flood is a severe-consequence low-frequency event, the corresponding fatalities caused by it are difficult to estimate due to the lack of relevant data and poor data continuity. This paper analyzes the direct and indirect factors affecting the risk of life loss in dam failures and studies the characteristics, distribution rules, and membership functions of each factor. An adaptive differential evolution method is constructed through an optimization of the mutation factors and cross factors of the differential evolution method. This proposed evaluation method also combines with the fuzzy clustering iterative method that is capable of evaluating the similarity of life loss in dam accidents. The effectiveness of the proposed method is verified by 16 dam-break case studies.

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.