BUSCADOR RECOLECTA

[UPDATE to version 2 of this upload] Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). [Description - Version 1] Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow) Table A.5. European Starch Association data on feed and potatoes for starch Table A.6. Details acute assessment across surveys (consumption days only) Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors, Peer reviewed

Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

Annex A – Benchmark dose analysis The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment The Annex is provided as a separate Excel file containing the dietary surveys per country and age group. Annex C – Occurrence data on nickel in food and drinking water The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel. Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure., Peer reviewed

Annex: Summary statistics on occurrence and consumption data and exposure assessment results Table 1 Number of analytical results excluded from the initial dataset during data cleaning, and justification for exclusion Table 2 Occurrence values of OTA (µg/kg) in food as reported in the cleaned database Table 3 Occurrence values of OTA (µg/kg) in food as used for the exposure assessment Table 4 Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Table 5 Results of chronic dietary exposure assessment on OTA (ng/kg bw per day) Table 6 Main contributing food categories to the mean LB exposure assessments to OTA across European dietary surveys and population groups Table 7 Distribution of LOQ values among the different food categories Table 8 All contributing food categories to the mean LB and UB exposure to OTA across European dietary surveys and population groups FormattedDATA_OchratoxinA.zip: Occurrence data on OTA Contains the occurrence data of OTA on 73,891 food samples presented in the opinion as described in its section 4.7 Occurrence data., Peer reviewed

Annex_III_occurrence data.xlsx This Annex is an excel file presenting tables on occurrence data on nitrate and nitrite in feed. Nitrate Nitrite_OCC_ZENODO.CSV Contains the raw (no data cleaning applied to it) occurrence dataset on nitrate and nitrite as extracted from EFSA DWH on 03 December 2019 in feed samples presented in the opinion as described in its section 3.2.2. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

The annexes A to E to the Scientific Opinion on Aflatoxins in Food included in the upload are excel files as follows: Annex A: Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Annex B: Occurrence data on aflatoxins Annex C: Proportion of left-censored data and the mean concentrations of the quantified analytical results of AFB1 for pistachios, hazelnuts, peanuts, other nuts and dried figs Annex D: AFB1 and AFM1 concentrations reported for organic farming and conventional farming in selected food categories Annex E: Mean and high chronic dietary exposure to aflatoxins per survey and the contribution of different food groups to the dietary exposure, Peer reviewed

This Annex is an excel file which presents tables on chlorinated paraffins on occurrence data in food and dietary exposure assessment for humans., Peer reviewed

Annexes to the Risk to human health related to the presence of perfluoroalkyl substances in food - available at: https://www.efsa.europa.eu/en/efsajournal/pub/6223 Annex A - Occurrence and exposure data Annex B - Distribution of analytical results Annex C - Comparison of PFOA and PFOS occurrence and exposure data with previous assessment (EFSA CONTAM Panel, 2018). Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

The corresponding 2.5D electrical forward and inverse problem is solved on an unstructured mesh with tetrahedral elements using BERT (Boundless Electrical Resistivity Tomography) (Rucker et al. 2006, Gunther et al. 2006) and pyGIMLi (Generalized Inversion and Modeling Library) (Rucker et al. 2017)., Surface electrical resistivity tomography (ERT) is a widely used tool to study seawater intrusion (SWI). It is noninvasive and offers a high spatial coverage at a low cost, but it is strongly affected by decreasing resolution with depth. We conjecture that the use of CHERT (cross-hole ERT) can partly overcome these resolution limitations since the electrodes are placed at depth, which implies that the model resolution does not decrease in the zone of interest. The objective of this study is to evaluate the CHERT for imaging the SWI and monitoring its dynamics at the Argentona site, a well-instrumented field site of a coastal alluvial aquifer located 40 km NE of Barcelona. To do so, we installed permanent electrodes around boreholes attached to the PVC pipes to perform time-lapse monitoring of the SWI on a transect perpendicular to the coastline. After two years of monitoring, we observe variability of SWI at different time scales: (1) natural seasonal variations and aquifer salinization that we attribute to long-term drought and (2) short-term fluctuations due to sea storms or flooding in the nearby stream during heavy rain events. The spatial imaging of bulk electrical conductivity allows us to explain non-trivial salinity profiles in open boreholes (step-wise profiles really reflect the presence of fresh water at depth). By comparing CHERT results with traditional in situ measurements such as electrical conductivity of water samples and bulk electrical conductivity from induction logs, we conclude that CHERT is a reliable and cost-effective imaging tool for monitoring SWI dynamics., This work was funded by the project CGL2016-77122-C2-1-R/2-R of the Spanish Government.We would like to thank SIMMAR (Serveis Integrals de Manteniment del Maresme) and the Consell Comarcal del Maresme in the construction of the research site. This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 480 Agreement No 722028. Author Albert Folch is a “Serra-Húnter Fellow”., Name of raw data files: chert_argentona_1.txt chert_argentona_2.txt chert_argentona_3.txt chert_argentona_4.txt chert_argentona_5.txt chert_argentona_6.txt chert_argentona_7.txt chert_argentona_8.txt chert_argentona_9.txt chert_argentona_10.txt chert_argentona_11.txt chert_argentona_12.txt chert_argentona_13.txt chert_argentona_14.txt chert_argentona_15.txt chert_argentona_16.txt chert_plus_surfaceert_argentona.txt Content of raw data files: List of electrodes X and Z positions (with topography), and list of quadripoles (a, b, m, n) with measured resistance (r), error (err), geometric factor (k), and apparent resistivity (rhoa). This files are formatted to go through the BERT program for 2.5D ERT modeling and inversion. The “chert_plus_surfaceert_argentona.txt” raw file combines cross-hole and surface ERT data from Sep 8th, 2015. Name of inverse data files: chert_argentona_sigma_model_1.vector chert_argentona_ sigma_model_2. vector chert_argentona_ sigma_model_3. vector chert_argentona_ sigma_model_4. vector chert_argentona_ sigma_model_5. vector chert_argentona_ sigma_model_6. vector chert_argentona_ sigma_model_7. vector chert_argentona_ sigma_model_8. vector chert_argentona_ sigma_model_9. vector chert_argentona_ sigma_model_10. vector chert_argentona_ sigma_model_11. vector chert_argentona_ sigma_model_12. vector chert_argentona_ sigma_model_13. vector chert_argentona_ sigma_model_14. vector chert_argentona_ sigma_model_15. vector chert_argentona_ sigma_model_16. Vector chert_plus_surfaceert_argentona.vector (Reference Model for Time-lapse Inversion), Peer reviewed

This dataset is composed of the input files of the numerical models for simulating Amposta fault stability during different phases of the project and poromechanical pressure changes induced by shear slip. The input files of each numerical simulation are included in a folder. The names of the folders and the description of the model are: - “1_Oil_production”: simulation of oil production and 7 years of post-production to quantify the changes in the Amposta fault stability - “2_Overpressure_plus_buoyancy_cushion”: simulation of the cushion gas injection considering both the effect of pore pressure increase and buoyancy to quantify the changes in the Amposta fault stability - “3_Overpressure_cushion”: simulation of the cushion gas injection considering only the effect of pore pressure increase (without gravity) to quantify the changes in the Amposta fault stability - “4_Buoyancy_cushion”: simulation of the cushion gas injection considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “5_Buoyancy_full_capacity”: simulation of the gas storage at its maximum capacity considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “6_Poromechanical_pressure_changes”: simulation of the pore pressure changes induced by shear slip and its effects in the short and long term., Each folder includes a file with the name of the folder and ended as “_gen.dat” that contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ended as “_gri.dat” that includes the information on the mesh. The “root.dat” includes the name of the model. To run simulations, just execute the Code_Bright executable “Cb_vX_Y.exe” in a folder that contains the three input files and the executable, where X and Y denote the used version of the executable., Peer reviewed