Joint Research Centre Research Articles

A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai–Tibet Plateau from Landsat Observations

Abstract The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors’ observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future. Significance Statement In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle.

Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High-Resolution Earth Observation Data.

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high-resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high-accuracy and resolution (5m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R 2, +17% Relative Contribution) and over the set of simpler predictors (+18% R 2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

EMODnet Biology: Unlocking European marine biodiversity data

EMODnet Biology (hosted and coordinated by the Flanders Marine Institute (VLIZ)) is one of the seven themes within the European Marine Observation and Data network (EMODnet). The EMODnet Biology consortium aims to facilitate the accessibility and usage of marine biodiversity data. With the principle of "collect once, use many times" at its core, EMODnet Biology fosters collaboration across various sectors, including research, policy-making, industry, and individual citizens, to enhance knowledge sharing and inform decision-making. EMODnet Biology focuses on providing free and open access to comprehensive historical and recent data on the occurrence of marine species and their traits in all European regional seas. It achieves this through partnerships and collaboration with diverse international initiatives, such as the World Register of Marine Species (WoRMS), Marine Regions and the European node of the Ocean Biodiversity Information System (EurOBIS) among others. By promoting the usage of the Darwin Core Standard (Wieczorek et al. 2012), EMODnet Biology fosters data interoperability and ensures seamless integration with wider networks such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biodiversity Information System (OBIS), serving as a significant data provider of the latter, as it is responsible for most of its data generated in Europe. Since its inception, EMODnet Biology has undertaken actions covering various areas, including providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. Furthermore, EMODnet Biology contributes to the overall EMODnet initiative, which covers multidisciplinary data and products. Thanks to the use of standard protocols and tools across disciplines, EMODnet Biology products can contribute to multidisciplinary analysis of pressures and impacts on key marine species and habitats, and, lastly, support a better management and planning of the maritime space. In conclusion, EMODnet Biology plays a pivotal role in biodiversity informatics by providing users with a wealth of accessible and reusable marine biodiversity data and products. Its collaborative approach, extensive partnerships, and adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016) as well as to the Infrastructure for Spatial Information in Europe (INSPIRE) metadata technical guidelines (European Commission Joint Research Centre 2013) and the Open Geospatial Consortium (OGC) standards make it a valuable resource for advancing knowledge, informing policies, and supporting sustainable management of marine ecosystems.

Experimental Application of the Global Technical Regulation on In-Vehicle Battery Durability

Battery aging of electrified vehicles is a key parameter to be controlled in order to ensure sufficient energy efficiency and driving range across the whole vehicle lifespan. The United Nations Economic Commission for Europe has recently adopted a new regulatory framework, the Global Technical Regulation No. 22, prescribing minimum performance requirements for in-vehicle battery durability. With the implementation of this new GTR, monitors of the battery state of certified energy and range will be available in every production vehicle, the accuracy of which will be tested statistically by applying an in-use verification procedure (Part A). Once the monitors’ correctness is checked, the battery durability performances are controlled in Part B against the defined limit values by a fleet monitoring procedure. This work presents the results of a testing campaign executed at the Joint Research Centre testing facilities on an aged pure electric vehicle to measure its capacity and range fade. The aim is to explore the applicability of GTR No. 22, assessing the in-vehicle battery performance fade of an aged electric vehicle, illustrating the several steps of the developed regulation and experimental methodology.

The Cytotoxic Effect of Thermoplastic Denture Base Resins: A Systematic Review.

Partial or complete dentures are constructed from thermoplastic resins that are thermally processed and molded. This review examines the presently available evidence for the cytotoxicity of thermoplasticized denture base resins on human gingival epithelial cells, adipose cells, and fibroblasts; human amnion fibroblasts; and mouse fibroblasts. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar databases to identify relevant articles to be included in the review until September 2022. Clinical, in vivo, and in vitro studies in English language were searched for. The quality of the studies was assessed using the Toxicological data Reliability Assessment tool (ToxRTool) developed by the European Commission's Joint Research Centre. GRADE assessment was used to evaluate the certainty of evidence. Seven in vitro studies were included in the review. The overall risk of bias was determined to be high, with the majority of studies assessed found to be reliable with restrictions or not reliable. Only two studies were considered reliable without restrictions based on ToxRTool assessment. The effect of thermoplastic denture base resins on viability and cell adherence of human gingival or amnion fibroblasts and mouse fibroblasts (L929s) is not significant. Conditioned media from unpolished specimens of resins were significantly more toxic to cultured cells than those from polished specimens. This may be of concern in cases of poor post-processing of dentures. Based on the limited evidence available, there is low-certainty evidence that thermoplastic denture base resins appear to be biocompatible and show insignificant cytotoxicity. Further well-designed trials adhering to standard reporting guidelines and using objective measures are necessary before outlining universal guidelines for best practice. Long-term in vivo and clinical assessment is necessary to corroborate laboratory findings with clinical outcomes. Denture base resins are in constant contact with oral tissues, and cytotoxic components released by the resins may irritate or inflame the tissues or provoke an allergic response.

Migration of oligomers from Tritan™ copolyester: application of hydrolysis for overall oligomer determination

Tritan™ (a kind of glycol-modified polycyclohexylene dimethylene terephthalate) is a novel copolyester mainly in use for the production of sports bottles and food storage containers. Oligomers in three food-grade Tritan™ samples were identified after dissolution-precipitation by high performance liquid chromatography with diode array detection and mass spectrometry and quantified after alkaline hydrolysis to the monomers. The obtained overall oligomer content <1000 Da determined by hydrolysis ranged from 7.2 to 10.6 mg/g material. Three consecutive migration experiments were performed according to the Commission Regulation (EU) No 10/2011. Oligomer migration values decreased from first to third migration during all simulations. Less than 25 µg/kg (third migrate) were detected in bottle migrates when tested under room temperature storage conditions (40 °C, 24 h) with simulants 3% acetic acid, 20 and 50% ethanol and during hot-fill testing (70 °C, 2 h) with simulants 3% acetic acid and 20% ethanol, respectively, while 170 µg/kg were determined in 50% ethanol after migration at 70 °C for 2 h. Food storage containers that were labelled as microwave-suitable by the supplier were tested according to the Joint Research Centre recommendations for microwave dishware. A strong deformation of the containers as well as a loss of transparency were observed during the tests (100 °C, 2 h with 10% ethanol and 3% acetic acid in an autoclave, 121 °C, 30 min with sunflower oil), questioning the suitability of the material for microwave applications. Maximum oligomer migration was 379 µg/kg during the third migration (sunflower oil at 121 °C for 30 min). Based on the migration data and an in silico oligomer evaluation according to the threshold of toxicological concern concept, no exceedances of daily thresholds for oligomers are expected from a proper use of Tritan™ drinking bottles, even with hot drinks.

Monitoring the consumption footprint of countries to support policy‐making: An assessment of data availability in Germany

AbstractThe European Green Deal and the German Resource Efficiency Programme both aim at decoupling resource consumption and associated environmental burdens from economic growth. Monitoring the progress of such policies requires robust estimates of environmental pressures and impacts, both from a domestic and a footprint perspective. Building on the life cycle assessment‐based consumption footprint (CoF) indicator, developed by the European Commission Joint Research Centre, we assess the environmental impacts of Germany's consumption in the areas of food, mobility, housing, household goods, and appliances during the period 2010–2018. A comparison between European and national consumption statistics revealed some differences in terms of data composition, granularity, consumption intensities, and calculated environmental impacts. Using national data sources results in slightly lower environmental impacts (e.g., due to differences in the assessment scope of national statistics) and requires some data preparation to match the CoF indicator. Emerging consumption trends can be highlighted using national data. Both data sources converge on main trends: Germany transgresses the safe operating space for several impact categories, with consumption of food, household goods, and mobility being the main drivers. Domestic impacts have decreased over time at the expense of outsourcing environmental pressures and impacts to other countries. The CoF indicator could complement resource monitoring frameworks and might be further aligned to the national context using country‐level consumption statistics and life cycle inventory data.

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R2 = 0.13). In contrast, our derived results strongly correlated with the water level series (R2 = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km2 in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources.

Insights into Toxic Prymnesium parvum Blooms as a Cause of the Ecological Disaster on the Odra River

In 2022, Poland and Germany experienced a prolonged and extensive mass fish kill in the Odra River. During the period from the end of July to the beginning of September 2022, a high level of incidental disease and mortality was observed in various fish species (dozens of different species were found dead). The fish mortality affected five Polish provinces (Silesia, Opole, Lower Silesia, Lubuskie, and Western Pomerania) and involved reservoir systems covering most of the river (the Odra River is 854 km long, of which 742 km are in Poland). Fatal cases were investigated using toxicological, anatomopathological, and histopathological tests. Water samples were collected to determine nutrient status in the water column, phytoplankton biomass, and community composition. High nutrient concentrations indicated high phytoplankton productivity, with favorable conditions for golden algal blooms. The harmful toxins (prymnesins secreted by Prymnesium parvum habitats) had not been found in Poland before, but it was only a matter of time, especially in the Odra River, whose waters are permanently saline and still used for navigation. The observed fish mortality resulted in a 50% decrease in the fish population in the river and affected mainly cold-blooded species. Histopathological examinations of fish showed acute damage to the most perfused organs (gills, spleen, kidneys). The disruption to hematopoietic processes and damage to the gills were due to the action of hemolytic toxins (prymnesins). An evaluation of the collected hydrological, meteorological, biological, and physico-chemical data on the observed spatio-temporal course of the catastrophe, as well as the detection of three compounds from the group of B-type prymnesins in the analyzed material (the presence of prymnesins was confirmed using an analysis of the fragmentation spectrum and the accurate tandem mass spectrometry (MS/MS) measurement, in combination with high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), allowed the formulation and subsequent testing of the hypothesis for a direct link between the observed fish mortality and the presence of prymnesins in the Odra River. This article systematizes what is known about the causes of the fish kill in the Odra River in 2022, based on official government reports (one Polish and one German) and the EU technical report by the Joint Research Centre. A review and critical analysis of government findings (Polish and German) on this disaster were conducted in the context of what is known to date about similar cases of mass fish kills.

Phytoplankton pigment in situ measurements uncertainty evaluation: an HPLC interlaboratory comparison with a European-scale dataset

Phytoplankton pigment data play a crucial role in ecological studies, enabling the identification of algal groups and estimation of primary production rates. Accurate measurements of chlorophyll a (TChl a) and other marine pigments are essential for the development of bio-optical algorithms and the validation of satellite data products. High-performance liquid chromatography (HPLC) is the gold standard method for quantifying multiple pigments in a single water sample. This study aims to investigate the uncertainties associated with phytoplankton pigment quantification by comparing duplicate sample analyses conducted by two laboratories, the Joint Research Centre of the European Commission (J) and the DHI Group, Denmark (D). The analyses were performed using the same HPLC method. The dataset comprised 957 natural samples collected between 2012 and 2017 from various European seas, representing different trophic conditions with TChl a concentrations ranging from 0.083 to 27.35 mg/m3. The study compared the results of the two independent analyses for TChl a and primary phytoplankton pigments, including chlorophyll b, chlorophyll c, carotens, fucoxanthin, 19′-butanoyloxyfucoxanthin, diadinoxanthin, diatoxanthin, 19′-hexanoyloxyfucoxanthin, peridin, and zeaxanthin. The percent difference between the two analyses was calculated to assess the uncertainties associated with pigment quantification. The mean percent difference observed between the two independent analyses of TChl a was 10.8%. For the primary phytoplankton pigments, the associated mean percent difference was 16.9%. These results meet the requirements of 15% and 25% uncertainties, respectively, which are applicable for the validation of satellite data products. The comparative analysis between the two laboratories demonstrates that the uncertainties associated with phytoplankton pigment quantification are within acceptable ranges for the validation of satellite data products. Moreover, the study investigates the propagation of uncertainties in diagnostic pigment values to phytoplankton indexes, which are derived using pigment-based algorithms to characterize phytoplankton populations according to functional types.

#3356 COVID-19 PANDEMIC EFFECT ON HOSPITALIZATION OF HEMODIALYSIS PATIENTS

Abstract Background and Aims During the COVID-19 pandemic many facets of our lives were affected, including the healthcare systems worldwide. We aimed to assess the hospitalization rates of hemodialysis (HD) patients with confirmed COVID-19 infection (COVID-19+) versus other patients (i.e., patients with no documented COVID-19 infection in the European Clinical Database (EuCliD®)) in 2020 and their relation to HD patients from the pre-pandemic era in 2019. Method We included 63,216 HD patients treated in 2019-2020 in NephroCare centers from 23 countries from EuCliD® in the Europe, Middle East, and Africa (EMEA) region. Monthly hospitalization rates (hospital admission per 10,000 patients) were estimated separately for COVID-19+ and other HD patients in 2020, and HD patients treated in 2019, with COVID-19+ status as a time-varying variable. The number of COVID-19 cases and deaths were extracted from data released by the European Commission Joint Research Centre (ECJRC)[1]. The correlation between the monthly hospitalization rates and numbers of COVID-19 cases and deaths in general population (GP) were evaluated. Results Characteristics of the approximately 42,000-43,000 monthly treated HD patients were comparable between 2019 and 2020 (Table 1). The hospitalization rates were much higher for COVID-19+ HD patients throughout 2020 compared to other HD patients in 2020 and HD patients in 2019. The hospitalization rates were highest in the spring of 2020 for COVID-19+ HD patients. However, in 2020 other HD patients’ hospitalization rates were lower than the 2019 HD patients’ hospitalization rates. Interestingly the hospitalization rates of other HD patients in 2020 dipped even more below hospitalizations for HD patients in 2019 following the GP pandemic waves (Figure 1). Hospital admission for both COVID-19+ and other HD patients were inversely correlation with the pandemic conditions in GP (Figure 2; spearman correlation coefficients [ρ] with COVID-19 cases/deaths in GP, -0.77/-0.22 and -0.29/-0.64, respectively, for COVID-19+ and other HD patients). Conclusion Our study indicated the COVID-19 pandemic had an impact on hospitalization rates of both HD patients with a COVID-19 diagnosis and those patients without a documented COVID-19 infection. Potential reasons of decreased hospitalization rates in patients without a documented COVID-19 diagnosis could be healthcare resource constraints or regarding patient concerns about exposure to COVID-19 in hospital settings [2].

FTIR-ATR detection method for emerging C3-plants-derivated adulterants in honey: Beet, dates, and carob syrups

The European Union Publications Office has recently presented a report on the European Union's coordinated action with the Joint Research Centre to determine certain fraudulent practices in the honey sector, in which it has been indicated that 74% of the samples analyzed, imported from China, and 93% of the samples analyzed, imported from Turkey, the two largest honey producers worldwide, presented at least one indicator of exogenous sugar or suspicion of being adulterated. This situation has revealed the critical state of the problem of honey adulteration worldwide and the need to develop analytical techniques for its detection. Even though the adulteration of honey is carried out in a general way with sweetened syrups derived from C4 plants, recent studies have indicated the emerging use of syrups derived from C3 plants for the adulteration of honey. This kind of adulteration makes it impossible to analyze its detection using official analysis techniques. In this work, we have developed a fast, simple, and economical method based on the Fourier transform infrared spectroscopy technique, with attenuated total reflectance, for the qualitative, quantitative, and simultaneous determination of beetroot, date, and carob syrups, derived from of C3 plants; whose available bibliography is very scarce and analytically not very conclusive for its use by the authorities. The proposed method has been based on the establishment of the spectral differences between honey and the mentioned syrups at eight different points in the spectral region between 1200 and 900 cm−1 of the mid-infrared, characteristic of the vibrational modes of carbohydrates in honey, which allows the pre-discrimination of the presence or absence of the syrups studied, and their subsequent quantification, with precision levels lower than 2.0% of the relative standard deviation and relative errors lower than 2.0% (m/m).

Construction of High Spatiotemporal Continuity Surface Water Bodies Dataset in the Haihe River Basin

Surface water bodies exhibit high dynamic variability on seasonal and interannual scales, and high spatiotemporal resolution water bodies extent data are crucial for studying surface water bodies’ evolution. Existing surface water bodies datasets are mainly based on optical data acquisition, which has the advantages of long temporal coverage and convenience but is susceptible to cloud contamination, leading to low spatiotemporal continuity. Although microwave remote sensing data are not affected by clouds, early SAR acquisition and short temporal coverage limit its use. Therefore, existing surface water bodies datasets face the problem of insufficient spatiotemporal resolution or low continuity. This research integrates Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) observations to reconstruct the surface water bodies dataset with a 6-day and 10-meter spatiotemporal resolution. Then, the proposed method introduces a spatiotemporal correlation model and predicts the land cover (water or land) of Sentinel-2 cloudy pixels, which improves the spatiotemporal continuity of the reconstructed surface water bodies dataset further. Based on the proposed method, we construct the Haihe River Water Dataset (HRWD) from 2016 to 2020 with a 6-day and 10-meter spatiotemporal resolution. Compared with the European Commission’s Joint Research Centre’s (JRC’s) Global Surface Water Explorer and Global Surface Water Extent Dataset (GSWED), the HRWD shows a rational accuracy (e.g., the overall accuracy of the HRWD is more than 93%) and a better spatiotemporal continuity, which provide an improved performance in identifying and monitoring surface water bodies in the Haihe River Basin. This indicates that the proposed method can improve the spatiotemporal continuity of surface water body mapping and meet the needs of accurate and long-term quantitative observation of the distribution of large-scale and high spatiotemporal continuity surface water bodies.

A new information tool as an enabling technology: application and simulation

The research aims at identifying an information tool for design activities through enabling technologies, developed on the interactions between envelope and context. The methodology, articulated according to the phases of analysis, processing, and applicative validation, prefigures a response strategy to the phenomena of environmental and energy crisis with respect to the different scales of action. Considering the effect of climatic phenomena on the envelopes and the contemporary performance requirements they are called to meet, the contribution provides a dynamic data sheet (analysis and evaluation of energy-environmental performance) based on the model drawn up by the European Joint Research Centre, to verify the appropriateness of envelope design.

Comparison between two cancer registry quality check systems: functional features and differences in an Italian network of cancer registries dataset.

The aim of this study was to compare the functional characteristics of two computer-based systems for quality control of cancer registry data through analysis of their output differences. The study used cancer incidence data from 22 of the 49 registries of the Italian Network of Cancer Registries registered between 1986 and 2017. Two different data checking systems developed by the WHO International Agency for Research on Cancer (IARC) and the Joint Research Center (JRC) with the European Network of Cancer Registries (ENCR) and routinely used by registrars were used to check the quality of the data. The outputs generated by the two systems on the same dataset of each registry were analyzed and compared. The study included a total of 1,305,689 cancer cases. The overall quality of the dataset was high, with 86% (81.7-94.1) microscopically verified cases and only 1.3% (0.03-3.06) cases with a diagnosis by death certificate only. The two check systems identified a low percentage of errors (JRC-ENCR 0.17% and IARC 0.003%) and about the same proportion of warnings (JRC-ENCR 2.79% and IARC 2.42%) in the dataset. Forty-two cases (2% of errors) and 7067 cases (11.5% of warnings) were identified by both systems in equivalent categories. 11.7% of warnings related to TNM staging were identified by the JRC-ENCR system only. The IARC system identified mainly incorrect combination of tumor grade and morphology (72.5% of warnings). Both systems apply checks on a common set of variables, but some variables are checked by only one of the systems (for example, checks on patient follow-up and tumor stage at diagnosis are included by the JRC-ENCR system only). Most errors and warnings were categorized differently by the two systems, but usually described the same issues, with warnings related to "morphology" (JRC-ENCR) and "histology" (IARC) being the most frequent. It is important to find the right balance between the need to maintain high standards of data quality and the workability of such systems in the daily routine of the cancer registry.

Life cycle assessment of different strawberry production methods in Germany with a particular focus on plastic emissions

PurposeUsing plastic for strawberry cultivation (so-called plasticulture) is widespread to extend cultivation, reduce weed stress and water and nutrient demand, and protect plants. We assess the environmental impacts of different plasticulture methods, including possible impacts caused by the (accidental) emission of plastics. The goals are to help farmers determine the most environmentally friendly cultivation technique and to test the inclusion of plastic pollution in life cycle assessment (LCA).MethodsAn LCA is conducted for a baseline scenario without plasticulture and 11 plasticulture scenarios. The scenarios are modeled using the regionalized ecoinvent 3.8 cut-off database. The functional unit is 1 kg of strawberries of marketable quality at the farm gate produced in the year 2021. The amounts of fertilizer, irrigation, and strawberry output of the scenarios are adapted depending on the plasticulture method based on an extensive literature review. The environmental impacts are assessed using the life cycle impact assessment methods of the Product Environmental Footprint (PEF) recommended by the Joint Research Centre of the European Commission, which is extended to account for plastic-related impacts. The impacts are normalized, weighted, and aggregated, resulting in one single impact score per scenario.Results and discussionThe scenarios using either a combination of conventional mulch film and row cover or biodegradable mulch film have the lowest total impacts; a macro tunnel or greenhouse causes the highest impacts. There seems to be no trade-off between maximizing productivity and lowering environmental impacts: the scenario with the highest yield also has one of the lowest overall impacts. Only direct emissions are considered regarding plastic pollution. Besides, the plastic pollution impact category is currently limited to the persistence of plastic in the environment and is extendable to but is not yet accounting for exposure and effect of plastic emission on humans and ecosystems.ConclusionPlasticulture can increase the yield and reduce environmental impacts for strawberry cultivation compared to open field cultivation. With the used normalization and weighting factors, plastic pollution makes up a considerable share of the overall environmental impact of strawberry cultivation, supporting the recent efforts of different research groups to include plastic emissions in LCA by developing suitable characterization factors.

Monitoring Surface Water Area Changes in the Aral Sea Basin Using the Google Earth Engine Cloud Platform

The surface water area and types in the Aral Sea Basin (ASB) have undergone extensive changes due to the impacts of climate change and anthropogenic activities. This study explores the changes in the surface water area in the ASB based on the Google Earth Engine cloud platform. Then, we integrate multi-source data to identify 1559 lakes and 196 reservoirs from the Joint Research Centre Global Surface Water (JRC GSW) dataset. Our results indicate that the lake area (34,999.61 km2) is about 10 times that of the reservoir area (3879.08 km2) in the ASB. The total area of surface water in the ASB decreased by 23,194.35 km2 or 34.58% from 1992 to 2020. Specifically, the areas of permanent water shrunk at a rate of 1278.6 km2/year, while the areas of seasonal water increased at a rate of 522.5 km2/year. The proportion of lakes and reservoirs in the total surface water has decreased from 79.33% (during 1992–2000) to 75.21% (during 2000–2010) to 63.94% (during 2010–2020). The water that should have flowed into the Aral Sea to maintain its permanent water may have been converted into two parts. Part of it might continue to be permanent water but show up in other regions, while part of it might convert to seasonal water (especially in the Aral Sea itself and the ASB plain area). Our study bridges the limitations of previous studies that have ignored seasonal water change and builds a water area list for 1755 lakes/reservoirs (≥0.1 km2) for the first time. The results can serve as important knowledge for water resource management and sustainable river basin development in ASB.

An absolute measurement of the neutron production rate of a spent nuclear fuel sample used for depletion code validation

A method to determine the neutron production rate of a spent nuclear fuel segment sample by means of non-destructive assay conducted under standard controlled-area conditions is described and demonstrated. A neutron well counter designed for routine nuclear safeguards applications is applied. The method relies on a transfer procedure that is adapted to the hot cell facilities at the Laboratory for High and Medium level Activity of SCK CEN in Belgium. Experiments with 252Cf(sf) sources, certified for their neutron emission rate, were carried out at the Joint Research Centre to determine the characteristics of the detection device. Measurements of a segment of a spent nuclear fuel rod were carried out at SCK CEN resulting in an absolute and non-destructive measurement of the neutron production rate avoiding any reference to a representative spent nuclear fuel sample to calibrate the device. Results of these measurements were used to study the performance of depletion codes, i.e., ALEPH2, SCALE, and Serpent2. The study includes a code-to-code and code-to-experiment comparison using different nuclear data libraries.

A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery

Lake extraction from remote sensing images is an important approach to monitoring water resources. It provides guidance for aiding in maintaining natural ecological balance through the artificial adjustment of lake water resources. Cloud shadows are common interference factors in lake extraction from optical images. In this paper, we proposed a deep learning network based on a novel Transformer architecture to extract lakes from Sentinel-2 imagery. To determine how training datasets affect misclassifications of cloud shadows, we produced five training datasets that contained different proportions of cloud shadows to train the network; the highest proportion of cloud shadows among them was 4%. We found that, when the training dataset contained 4% cloud shadows, the obtained model could effectively reduce the misclassification of cloud shadows and improve the accuracy of lake extraction, and with this model, the evaluation results over the validation dataset achieved an overall accuracy (OA) of 0.9954 and a Kappa of 0.9568. In addition, the Transformer-based network was applied in the endorheic basin of Tibetan Plateau to further evaluate the generalization ability of the network over a large area. The Global Surface Water (GSW) dataset from the European Commission Joint Research Centre was used as a reference to validate our results in this area. Our experiment and results demonstrated the underlying potential of the Transformer-based network for improving image classification of optical imagery by reducing cloud shadow inferences.

Epidemic intelligence data of Crimean-Congo haemorrhagic fever, European Region, 2012 to 2022: a new opportunity for risk mapping of neglected diseases.

BackgroundThe Epidemic Intelligence from Open Sources (EIOS) system, jointly developed by the World Health Organisation (WHO), the Joint Research Centre (JRC) of the European Commission and various partners, is a web-based platform that facilitate the monitoring of information on public health threats in near real-time from thousands of online sources.AimsTo assess the capacity of the EIOS system to strengthen data collection for neglected diseases of public health importance, and to evaluate the use of EIOS data for improving the understanding of the geographic extents of diseases and their level of risk.MethodsA Bayesian additive regression trees (BART) model was implemented to map the risk of Crimean-Congo haemorrhagic fever (CCHF) occurrence in 52 countries and territories within the European Region between January 2012 and March 2022 using data on CCHF occurrence retrieved from the EIOS system.ResultsThe model found a positive association between all temperature-related variables and the probability of CCHF occurrence, with an increased risk in warmer and drier areas. The highest risk of CCHF was found in the Mediterranean basin and in areas bordering the Black Sea. There was a general decreasing risk trend from south to north across the entire European Region.ConclusionThe study highlights that the information gathered by public health intelligence can be used to build a disease risk map. Internet-based sources could aid in the assessment of new or changing risks and planning effective actions in target areas.