Data for: Field scale quantification of return flow

Attached are data that were used to calculate return flow for this study. We do not include geophysical data as it is too large and is still a niche field where few people are likely excited to work with those data. We will revisit publication of geophysical data if it turns out there is interest in using those data

Harmonized analysis of microplastics: Insights from practical application within the FACTS project

The pollution of the environment with plastics is of emerging concern. While first initiatives aim to reduce the input of these materials, the already existing amounts pose a problem. Due to degradation and fragmentation the initially large items form micro- and nanoplastic particles. Yet the analysis of these contaminates is hampered due to increasing number of analytical pipelines and tools in the different laboratories. In the recent years, various projects aimed to provide information about these issues at different scales. Here, we present the harmonization procedures derived and applied within the "Fluxes and Fate of Microplastics in Northern European Waters" (FACTS) project. To achieve a project internal harmonization the available sampling techniques, sample extraction methods, quality assurance measures aand protocols as well as analytical techniques were collected, evaluated and finally transferred into commonly agreed harmonized procedures. These procedures were afterwards applied in the planning of a large scale sampling campaign along the Norwegian Coast and within the Bergen Fjord. This was achieved by defining the filter mesh sizes for filtration pumps and sampling nets as well as the amount of sediment sampled. In contrast to sampling, the extraction was more difficult to harmonize yet a common agreement was derived by using Fenton's reagent oxidation for the removal of organic matter and sodium bromide for density separation following the laboratory internal protocol. Finally, the for analysis FTIR microscopy shall be applied using a common database and software tool (siMPle) allowing a following pyrolysis gas chromatography-mass spectrometry (py-GC/MS) analysis. The experience during the application of these protocols within FACTS will be discussed and further contextualized with the results of the "EUROpean Quality Controlled Harmonisation Assuring Reproducible Monitoring and assessment of plastic pollution" (EUROqCHARM) project. Also see: https://micro2022.sciencesconf.org/427364/documen

Variations in microbiome composition of sewer biofilms due to ferrous and ferric iron dosing

During transport of wastewater in force mains, sulphide and possibly methane formation take place due to prokaryotic activity. Sulphide has several detrimental effects and addition of ferrous or ferric iron for abatement by precipitation is commonly applied. Precipitation stoichiometry and efficiency of this process have been investigated in detail. However, it is largely unknown how ferrous and ferric iron influence prokaryotic populations of sewer biofilms. The microbiomes of iron-treated force main biofilms were, together with an untreated control, examined by sequencing of the 16S rDNA V3+ V4 regions. Differences in distribution and abundance of several bacterial and archaeal genera were observed, indicating that treatment with ferrous and ferric iron for sulphide abatement differentially changed sewer force main microbiomes. Furthermore, differences at the functional level (KEGG orthologs, KOs) indicate that ferrous and ferric iron treatment possibly can decrease methane formation, whereas functions related to dissimilatory sulphate reduction seemed unaffected

Particle-Vane Interaction Probability in Gas Turbine Engines

Data pertaining to Figures 11 to 14 from the paper. Figure datasets contained in separate worksheets in workbook

Variations in microbiome composition of sewer biofilms due to ferrous and ferric iron dosing

During transport of wastewater in force mains, sulphide and possibly methane formation take place due to prokaryotic activity. Sulphide has several detrimental effects and addition of ferrous or ferric iron for abatement by precipitation is commonly applied. Precipitation stoichiometry and efficiency of this process have been investigated in detail. However, it is largely unknown how ferrous and ferric iron influence prokaryotic populations of sewer biofilms. The microbiomes of iron-treated force main biofilms were, together with an untreated control, examined by sequencing of the 16S rDNA V3+ V4 regions. Differences in distribution and abundance of several bacterial and archaeal genera were observed, indicating that treatment with ferrous and ferric iron for sulphide abatement differentially changed sewer force main microbiomes. Furthermore, differences at the functional level (KEGG orthologs, KOs) indicate that ferrous and ferric iron treatment possibly can decrease methane formation, whereas functions related to dissimilatory sulphate reduction seemed unaffected

Data from: Soil hydraulic properties determined by inverse modeling of drip infiltrometer experiments extended with pedotransfer functions

A transient flow experiment using automated drip infiltrometers (ADIs) was performed on soil columns (about 6 dm3) large enough to incorporate macropore flow effects. We investigated to what extent the estimated soil hydraulic parameters obtained from inverse modeling of these experiments are reliable. A machine learning based pedotransfer function (PTF) for prediction of water content at −1, −10, and −158 m pressure head was developed. Sensitivity analysis of the van Genuchten parameters (residual and saturated water content r and s, fitting parameters , n, and , and saturated hydraulic conductivity Ks) in soils of sandy, silty, and clayey textures showed that the temporal variation of pressure heads in ADI scenarios was not sensitive to r and s. The other parameters were accurately estimated from numerically synthesized data. The uniqueness of the estimated parameters did not change when a bias, representing experimental error, was added to the data set. In actual columns, using the temporal and spatial pressure head data from the ADIs and the water contents in the drier range predicted by the developed PTF resulted in a precise estimation of the van Genuchten parameters. Not including the PTF water contents resulted in non-uniquely estimated van Genuchten parameters

Test problems for metamodeling comparison: 5 building performance metrics and 8 theoretical problems

Each dataset consists of 10.000 set of input and output values. Five dataset has been constructed using building performance simulations. Four of these relate to a generic, single-zoned office which have been modelled with the building simulation software BSim (developed by the Danish Building Research Institute). One dataset describes the variability of an educational institution during an early design stage. For this set, we have used the normative software Be15, which is based on ISO 13790. Be15 has also been developed by the Danish Building Research Institute. Eight dataset are based on theoretical problems for which 10.000 calculations have been made with Matlab

Assessment of five wind farm parameterizations in WRF

This dataset presents the input files to WRF to regenerate the simulations of the North Sea region on October 14th, 2017, along with the modified WRF module files. Four modified WRF modules are attached module_first_rk_step_part1.F module_pbl_driver.F module_wind_base.F Registry.EM_COMMON which will be added to the source code as explained here. A list of namelist.input files for WRF corresponding to different simulations are attached along with the coordinates of the wind turbines in the windturbines.txt file and the performance curves of the wind turbine types used in this study in the files wind-turbine-*.tbl. Modified WRF code WRF can be downloaded from the public repository following the steps mentioned here (https://www2.mmm.ucar.edu/wrf/OnLineTutorial/compilation_tutorial.php#STEP1). Once WRF is downloaded, go to the folder containing WRF and store its directory in wrfdir by running the command wrfdir=$(pwd) Go to the folder where this document is present and run the commands: cp ./module_first_rk_step_part1.F$wrfdir/dyn_em/ cp ./module_pbl_driver.F $wrfdir/phys/ cp ./module_wind_base.F$wrfdir/phys/ cp ./Registry.EM_COMMON $wrfdir/Registry/ Now, the code is ready to compile via cd$wrfdir ./clean ./configure ./compile em_real >& compile.log If the code does not compile for any reason, a whole version of the code is attached as a compressed file "WRF-5WFP.tar.gz" which was tested and compiles perfectly. There are multiple new variables in the modified registry which are defined as follows UTEND: this variable exports the zonal momentum tendency of the turbines. VTEND: this variable exports the meridional momentum tendency of the turbines. QTEND: this variable exports the turbine-induced turbulence generation. abkar_constant: this is the correction factor $\zeta$ from (Abkar and Porté-Agel 2015). The default value is 1.0. use_ec: this integer takes the value of 0 or 1 specifying whether the energy correction should be used or not. dc_turb: this integer allows to disconnect the wind turbines from the flow by not allowing the wind farm parameterization to feedback the calculated momentum and turbulence tendencies. This can be used when we want to simulate the undisturbed wind field and to calculate the generated power in this case as well. volker_sigma_over_R: this is ratio $\sigma_o/R$ from (Volker et al. 2015). The default value is 1.7. pan_np: this specifies the number of points that are randomly generated over the surface of each wind turbine to calculate the percentage of the rotor-plane surface area that is blocked by upwind turbines. It is used with the parameterization of (Pan and Archer 2018). The default value for this entry is 1000 points. More points would increase the accuracy of the calculated blocked area but at the expense of computational cost. pan_nouter: this specifies the number of outer iterations done to calculate the blocked area of the rotor within the parameterization of (Pan and Archer 2018). The purpose of the outer iterations is to minimize the randomness in the blocked area calculations due to the randomly generated points by averaging through multiple trials. The default value is 2. pan_infZone: this entry specifies the maximum ratio between the distance between two wind turbines and the diameter of the considered wind turbine to include the upwind one in blocked area calculations. The default value is 20. Hence, all wind turbines that are more than 20-diameters away from a wind turbine are not considered for blocked area calculations. turbine_cell: is an integer specifying the method to identify the index of the grid-cell containing a wind turbine. A value of 1 corresponds to the function nint (nearest integer) which is the default in WRF. A value of 2 corresponds to the function floor which was suggested in (Volker et al. 2015; Pryor et al. 2020). The default value here is 1. The windfarm_opt in namelist.input used to have one of two values: 0: no wind farm parameterization is used. 1: Fitch's parameterization is used (Fitch et al. 2012). More options have been added to the entry windfarm_opt 0: no wind farm parameterization 1: Fitch's parameterization (Fitch et al. 2012) 2: Abkar's parameterization (Abkar and Porté-Agel 2015) 3: Volker's parameterization (Volker et al. 2015) 4: Redfern's parameterization (Redfern et al. 2019) 5: Pan's parameterization (Pan and Archer 2018) Input files Multiple namelist.input files are attached in the form of *-namelist.input and they are defined as follows A80-namelist.input: Abkar's parameterization with Abkar's correction set to 0.8. A90-namelist.input: Abkar's parameterization with Abkar's correction set to 0.9. A100-namelist.input: Abkar's parameterization with Abkar's correction set to 1.0. F25-namelist.input: Fitch's parameterization with 0.25 turbulence correction factor (Archer et al. 2020). F100-namelist.input: Fitch's parameterization with 1.0 turbulence correction factor (i.e. base Fitch's parameterization). F100-NE-namelist.input: Fitch's parameterization with 1.0 turbulence correction factor but no energy correction is used. NT-namelist.input: No wind turbines. Nonetheless, power is calculated according to Fitch's parameterization to record the ideal power generation without any wake effects. This is done by setting the entry dc_turb to one while setting windfarm_opt to one as well. PAN-namelist.input| Pan's parameterization. R25-namelist.input: Redfern's parameterization with 0.25 turbulence correction factor R100-namelist.input: Redfern's parameterization with 1.0 turbulence correction factor V80-namelist.input: Volker's parameterization with the ratio sigma/R set to 1.36 (i.e. 80% of the base value 1.7). V100-namelist.input: Volker's parameterization with the ratio sigma/R set to 1.7. V120-namelist.input: Volker's parameterization with the ratio sigma/R set to 2.04 (i.e. 120% of the base value 1.7) The performance curves of the used wind turbine types are provided in the wind-turbine-*.tbl. The coordinates of the wind turbines are stored in windturbines.txt file. To be able to differentiate between the wind turbines of different wind farms, the first and last wind turbine index per wind farm is as follows Gode Wind 1 & 2: 1 - 97 Veja Mate: 98 - 164 BARD Offshore 1: 165 - 244 Riffgat: 245 - 274 Borkum Riffgrund 1: 275 - 352 Meerwind: 353 - 432 Nordsee Ost: 433 - 480 Amrumbank West: 481 - 560 Alpha Ventus: 561 - 572 Trianel Windpark Borkum I: 573 - 612 Global Tech I: 613 - 692 Nordsee One: 693 - 746 Gemini: 747 - 896 Attached files Some files that are used in the post-processing of the conducted simulations are attached to this document. These files are: farmNames.txt: contains the names of the wind farms by the same order of numbering of WRF input file "windturbines.txt". FINO1_airtemperature_101m_20171014_20171015.dat: contains air temperature measurements by the FINO-1 mast, 101 m above sea level on 14 October 2017. FINO1_winddirection_vane_91m_315deg_20171014_20171015.dat: contains wind direction measurements by the FINO-1 mast, 91 m above sea level on 14 October 2017. FINO1_windspeed_cup_102m_20171014_20171015.dat: contains wind speed measurements by the FINO-1 mast, 102 m above sea level on 14 October 2017. MA-transect*-20171014_flight39_airborne.csv: contains processed airborne measurements over the Gode Wind farms on 14 October 2017 (Bärfuss et al. 2019). S1A_ESA_2017_10_14_17_17_*.nc: Two NetCDF files containing the *Sentinel-1* satellite imagery of the near-surface atmosphere over the North Sea on 1717 UTC 14 October 2017. The files are downloaded from https://science.globalwindatlas.info/#/map

sj-zip-1-asp-10.1177_0003702820917760 - Supplemental material for Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis

Supplemental material, sj-zip-1-asp-10.1177_0003702820917760 for Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis by Sebastian Primpke, Richard K. Cross, Svenja M. Mintenig, Marta Simon, Alvise Vianello, Gunnar Gerdts and Jes Vollertsen in Applied Spectroscop

sj-zip-1-asp-10.1177_0003702820917760 - Supplemental material for Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis

Supplemental material, sj-zip-1-asp-10.1177_0003702820917760 for Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis by Sebastian Primpke, Richard K. Cross, Svenja M. Mintenig, Marta Simon, Alvise Vianello, Gunnar Gerdts and Jes Vollertsen in Applied Spectroscop