Dataset Information

Experimental Design as a Tool for Optimizing and Predicting the Nanofiltration Performance by Treating Antibiotic-Containing Wastewater.

ABSTRACT: In recent years, there has been an increase in studies regarding nanofiltration-based processes for removing antibiotics and other pharmaceutical compounds from water and wastewater. In this work, a 2k factorial design with five control factors (antibiotic molecular weight and concentration, nanofiltration (NF) membrane, feed flow rate, and transmembrane pressure) was employed to optimize the NF performance on the treatment of antibiotic-containing wastewater. The resulting multiple linear regression model was used to predict the antibiotic rejections and permeate fluxes. Additional experiments, using the same membranes and the same antibiotics, but under different conditions of transmembrane pressure, feed flow rate, and antibiotic concentration regarding the 2k factorial design were carried out to validate the model developed. The model was also evaluated as a tertiary treatment of urban wastewater for removing sulfamethoxazole and norfloxacin. Considering all the conditions investigated, the tightest membrane (NF97) showed higher antibiotics rejection (>97%) and lower permeate fluxes. On the contrary, the loose NF270 membrane presented lower rejections to sulfamethoxazole, the smallest antibiotic, varying from 65% to 97%, and permeate fluxes that were about three-fold higher than the NF97 membrane. The good agreement between predicted and experimental values (R2 > 0.97) makes the model developed in the present work a tool to predict the NF performance when treating antibiotic-containing wastewater.

SUBMITTER: de Souza DI

PROVIDER: S-EPMC7408029 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Experimental Design as a Tool for Optimizing and Predicting the Nanofiltration Performance by Treating Antibiotic-Containing Wastewater.

de Souza Dalva Inês DI Giacobbo Alexandre A da Silva Fernandes Eduardo E Rodrigues Marco Antônio Siqueira MAS de Pinho Maria Norberta MN Bernardes Andréa Moura AM

Membranes 20200719 7

In recent years, there has been an increase in studies regarding nanofiltration-based processes for removing antibiotics and other pharmaceutical compounds from water and wastewater. In this work, a 2<sup>k</sup> factorial design with five control factors (antibiotic molecular weight and concentration, nanofiltration (NF) membrane, feed flow rate, and transmembrane pressure) was employed to optimize the NF performance on the treatment of antibiotic-containing wastewater. The resulting multiple l ...[more]

PMID: 32707699

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Project description:MotivationNumerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design.ResultsUsing any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library.Availability and implementationCrossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Experimental Design as a Tool for Optimizing and Predicting the Nanofiltration Performance by Treating Antibiotic-Containing Wastewater.

Publications

Experimental Design as a Tool for Optimizing and Predicting the Nanofiltration Performance by Treating Antibiotic-Containing Wastewater.

Similar Datasets

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