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9 results for “organic micropollutants”
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
Data for: Assessing hydrology, biogeochemistry and organic micropollutants in an urban stream-aquifer system: an interdisciplinary dataset
<p>Accompanying data for data article "Assessing hydrology, biogeochemistry and organic micropollutants in an urban stream-aquifer system: a comprehensive dataset" of Popp et al., JGR:Biogeosciences.</p> <p><br>In this repository, all data described in Table 1 of the manuscript can be found, except for the data already published by Popp et al., 2020, ES&T, doi: 10.1021/acs.est.9b05393. These data can be freely accessed in ERIC (Eawag Research Data Institutional Collection): doi.org/10.25678/0001JD. </p> <p>Data are structured the following way:<br>1_logger-data: time series of logger data (water temperature, water levels, electrical conductivity and pH [the latter only for the stream]) obtained at the stream Chriesbach and piezometers P1 and P4;<br>2_tracer-data: time series of nutrients, ions, and other tracer data obtained at the stream Chriesbach, the piezometers (P1, P4, P5) and the regional groundwater well (reg-gw); <br>3_micropollutant_data: time series of organic micropolluntants obtained at the stream Chriesbach, P1, P4, P5 and the regional groundwater well (reg-gw);<br>4_R-script: R script used for statistical analysis and to create the plots shown in the manuscript.</p> <p>Units, estimated uncertainties or other measures of uncertainty such as limits of quantification are provided in the respective files. Each subfolder contains its own readme file with relevant metadata. </p> <p>Coordinates (WGS84): <br>Location Latitude Longitude<br>Stream logger monitoring 47.404613 8.6113<br>Stream sampling 47.404459 8.607777<br>Piezometer 1 (P1) 47.4044 8.6080<br>Piezometer 4 (P4) 47.4044 8.6078<br>Piezometer 5 (P5) 47.404392 8.607619<br>Regional groundwater 47.40501 8.60822</p>
Biochemical methane potential tests amended with graphene oxide and organic micropollutants: Methane production
<p>The spreadsheet comprises measurements of methane production of biochemical methane potential (BMP) assays amended with graphene oxide and organic micropollutants.</p> <p>A total of six sheets are present.</p> <p>“DOE (VS)” contains the design of the experiment with the initial set-up value for the different conditions tested.</p> <p>“Stock solution” where the concentrations of the added contaminants are calculated.</p> <p>“Inoculum-substrate” stores the characterization of the inoculum and the substrate used (i.e., microcrystalline cellulose).</p> <p>“Final_Character” contains the characterization measurements carried out at the end of the experiment.</p> <p>“Data” envelops the periodic (mostly daily) measurements used to calculate methane production via a manometric procedure.</p> <p>“Calculation” has the final calculation reporting the specific methane production (SMP) for the different conditions.</p>
Photoreactor and organic micropollutants specifications
<p>Specifications of the operating parameters of a photoreactor used.</p> <p>Specifications of organic micropollutants used.</p>
Monitoring of degradation of 5 organic micropollutant under a multitude of scenarios
<p>The spreadsheet contains HPLC integration peaks monitoring the photolytic and photocatalytic degradation of 5 organic micropollutants (ciprofloxacin, sulfamethoxazole, trimethoprim, venlafaxine and o-desmethyl-venlafaxine) under different conditions. The first sheets show the degradation by UV-A and UV-C photolysis and photocatalysis of each compound in individual solution in milliQ water (initial concentration of each, 2 mg/L). Later, all compounds are present in an initial mixture (concentration of each 2 mg/L, resulting in final mixture solution of 10 mg/L). The impact on degradation of adding 0.1 mM of peroxide in the solution for all 4 processes is studied. Additionally, the impact of: 1) of using simultaneous LED wavelengths (UV-A and UV-C combined); 2) using tap water as matrix; and 3) varying the initial pH for the 1st order kinetic rates of each compound in the mixture is investigated. Plots of the kinetic rate and calculations of EEO values (electrical energy per order consumption) are made. The last spreadsheets contain kinetic monitoring of several experiments performed by altering the composition of the matrix with the addition of nitrates, humic acids and bicarbonates. The data was used to obtain a surface-response box-behnken design of experiments.</p>
Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea - bioassays CRC
<p>Supportive material for the submitted paper: </p> <p><strong><em><span>Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea </span></em></strong></p> <p><span>from Hommel et al.</span></p> <p><span>The data includes the with R automated evaluation of the AhR-CALUX, AREc32, ERa-GeneBLAzer and SH-SY5Y assay with respective plots and excel files of the concentrations response curves.</span></p>
Reduced genetic diversity of freshwater amphipods in rivers with increased levels of anthropogenic organic micropollutants
<p><span>Anthropogenic chemicals in freshwater environments contribute majorly to ecosystem degradation and biodiversity decline. In particular</span><span>,</span><span> anthropogenic organic micropollutants (AOM), a diverse group of compounds including pesticides, pharmaceuticals, and industrial chemicals, can significantly impact freshwater organisms. AOM were found to impact </span><span>the </span><span>genetic diversity of freshwater species, however, </span><span>the</span><span> degree </span><span>to which </span><span>AOM cause changes in population genetic structure and allelic richness of freshwater macroinvertebrates remains poorly understood. Here, the </span><span>impact</span><span> of AOM </span><span>on</span> <span>the </span><span>genetic diversity of </span><span>the common</span> <span>a</span><span>mphipod</span> <span><em>Gammarus pulex</em> </span><span>(Linnaeus, 1758)</span><span> (clade E)</span> <span>was investigated </span><span>on a</span><span> regional</span> <span>scale.</span> <span>The site-specific AOM levels and their toxic potentials were determined in water and <em>G. pulex </em>tissue</span><span> sample</span><span> extracts</span><span> for 34 sites along six rivers impacted by wastewater effluents and agricultural run-off</span> <span>in central Germany. Population genetic param</span><span>e</span><span>t</span><span>e</span><span>rs were determined for <em>G. pulex</em> from the sampling sites by genotyping 16 microsatellite</span><span> loci</span><span>.</span> <span>Genetic differentiation among <em>G. pulex</em> from the </span><span>studied rivers</span><span> was</span><span> strongly</span> <span>associated </span><span>with</span> <span>geographic distance </span><span>between sites, but also </span>with <span>difference</span><span>s in</span> <span>site-specific </span><span>concentrations </span><span>of AOM. </span><span>T</span><span>h</span>us,<span> genetic diversity parameters </span><span>of</span> <em><span>G. pulex</span></em><span> were found to be </span><span>related to</span> <span>site-specific AOM levels</span><span>; </span>a<span>llelic richness was significantly </span><span>negatively correlated to levels of AOM</span><span> in <em>G. pulex</em> tissue (p < 0.003) and was reduced by up to 22% at sites with increased levels of AOM</span>. This was seen<span> despite </span><span>a </span><span>positive relationship </span>between<span> allelic richness </span><span>and</span><span> the presence of waste-water effluent. </span><span>In addition</span><span>, the inbreeding coefficient </span><span>of </span><em><span>G. pulex</span></em><span> from sites with toxic AOM levels was up to 2.5 times higher than in <em>G. pulex</em> from more pristine sites.</span><span> These results indicate that </span><span>AOM</span><span> levels commonly found in European rivers </span><span>significantly </span><span>contribute to changes in the genetic diversity of an ecologically relevant indicator species.</span></p>
Reduced genetic diversity of freshwater amphipods in rivers with increased levels of anthropogenic organic micropollutants
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