The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990-2018

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990-2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011-2015, the CO2 land sources and sinks from NGHGI estimates report-90 Tg C yr-1 ± 30 Tg C yr-1 while all other BU approaches report a mean sink of-98 Tg C yr-1 (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr-1 ± 400 Tg C yr-1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of "CO2 flux"obtained from different approaches. The referenced datasets related to figures are visualized. © 2021 Ana Maria Roxana Petrescu et al

Detecting false messages in the smartphone fault reporting system

The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment

Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations

The 2018 drought was one of the worst European droughts of the twenty-first century in terms of its severity, extent and duration. The effects of the drought could be seen in a reduction in harvest yields in parts of Europe, as well as an unprecedented browning of vegetation in summer. Here, we quantify the effect of the drought on net ecosystem exchange (NEE) using five independent regional atmospheric inversion frameworks. Using a network of atmospheric CO2 mole fraction observations, we estimate NEE with at least monthly and 0.5° × 0.5° resolution for 2009–2018. We find that the annual NEE in 2018 was likely more positive (less CO2 uptake) in the temperate region of Europe by 0.09 ± 0.06 Pg C yr−1 (mean ± s.d.) compared to the mean of the last 10 years of −0.08 ± 0.17 Pg C yr−1, making the region close to carbon neutral in 2018. Similarly, we find a positive annual NEE anomaly for the northern region of Europe of 0.02 ± 0.02 Pg C yr−1 compared the 10-year mean of −0.04 ± 0.05 Pg C yr−1. In both regions, this was largely owing to a reduction in the summer CO2 uptake. The positive NEE anomalies coincided spatially and temporally with negative anomalies in soil water. These anomalies were exceptional for the 10-year period of our study. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’

High Level Models and Methodologies for Information Systems

As information technology (IT) permeates more and more aspects of human life, information systems (IS) have grown to become an essential component of organizational management