Publications by authors named "Felix Drees"

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQS, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features.

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The evaluation of nontarget analysis (NTA) techniques for the monitoring of wastewater is important as wastewater is an anthropogenic pollution source for aquatic ecosystems and a threat to human and environmental health. This study presents the proof-of-concept NTA of industrial wastewater samples. A prototype hydrophilic-lipophilic-balanced (HLB) SPME and gas chromatography interfaced with time-of-flight high-resolution mass spectrometry (GC-TOFMS) with electron ionization (EI) and chemical ionization (CI) in parallel are employed.

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This study investigated the elimination of pharmaceuticals, corrosion inhibitors, x-ray contrast media and perfluorinated compounds from reverse osmosis concentrates during ozonation and UV/persulfate processes. Second-order rate constants for the reactions of candesartan, irbesartan, methyl-benzotriazole, and chloro‑benzotriazole with sulfate radical (SO) were determined for the first time. Experiments were conducted in buffered pure water, in buffered water added with the matrix substituents chloride, carbonate, NOM, and reverse osmosis concentrate with spiked micropollutants (MP).

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Wastewater treatment processes can eliminate many pollutants, yet remainder pollutants contain organic compounds and microorganisms released into ecosystems. These remainder pollutants have the potential to adversely impact downstream ecosystem processes, but their presence is currently not being monitored. This study was set out with the aim of investigating the effectiveness and sensitivity of non-target screening of chemical compounds, 18S V9 rRNA gene, and full-length 16S rRNA gene metabarcoding techniques for detecting treated wastewater in receiving waters.

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Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood.

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N-containing organophosphonate antiscalants such as Aminotris (methylene phosphonic acid) (NTMP/ATMP) and Diethylenetriamine penta(methylene phosphonic acid) (DTPMP) are commonly used in reverse osmosis (RO) to prevent scaling, as well as to increase permeate yields. However, the concentrate in RO still contains antiscalants which can cause adverse effects in the environment such as mobilization of heavy metals. The abatement of antiscalants from RO concentrate can promote the precipitation of oversaturated scale-forming substances and reduce the risk of adverse environmental effects.

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Reverse osmosis (RO) is an advanced technology used to produce potable water from a variety of water sources, including surface water, seawater and wastewater. The yield of the product water from the RO systems is increased by the addition of antiscalants which prevent scaling from calcium and other ions. Removal of antiscalants from RO concentrate can induce the precipitation of oversaturated scale-forming substances, enable additional water recovery from RO concentrates, and reduce the risk of eutrophication after concentrate disposal into the receiving water (e.

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Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset.

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