Formation of Larger Molecular Weight Disinfection Byproducts from Acetaminophen in Chlorine Disinfection

Acetaminophen is widely used to treat mild to moderate pain and to reduce fever. Under the worldwide COVID-19 pandemic, this over-the-counter pain reliever and fever reducer has been drastically consumed, which makes it even more abundant than ever in municipal wastewater and drinking water sources. Chlorine is the most widely used oxidant in drinking water disinfection, and chlorination generally causes the degradation of organic compounds, including acetaminophen. In this study, a new reaction pathway in the chlorination of acetaminophen, i.e., oxidative coupling reactions via acetaminophen radicals, was investigated both experimentally and computationally. Using an ultraperformance liquid chromatograph coupled to an electrospray ionization-triple quadrupole mass spectrometer, we detected over 20 polymeric products in chlorinated acetaminophen samples, some of which have structures similar to the legacy pollutants “polychlorinated biphenyls”. Both C–C and C–O bonding products were found, and the corresponding bonding processes and kinetics were revealed by quantum chemical calculations. Based on the product confirmation and intrinsic reaction coordinate computations, a pathway for the formation of the polymeric products in the chlorination of acetaminophen was proposed. This study suggests that chlorination may cause not only degradation but also upgradation of a phenolic compound or contaminant

Formation of Larger Molecular Weight Disinfection Byproducts from Acetaminophen in Chlorine Disinfection

Acetaminophen is widely used to treat mild to moderate pain and to reduce fever. Under the worldwide COVID-19 pandemic, this over-the-counter pain reliever and fever reducer has been drastically consumed, which makes it even more abundant than ever in municipal wastewater and drinking water sources. Chlorine is the most widely used oxidant in drinking water disinfection, and chlorination generally causes the degradation of organic compounds, including acetaminophen. In this study, a new reaction pathway in the chlorination of acetaminophen, i.e., oxidative coupling reactions via acetaminophen radicals, was investigated both experimentally and computationally. Using an ultraperformance liquid chromatograph coupled to an electrospray ionization-triple quadrupole mass spectrometer, we detected over 20 polymeric products in chlorinated acetaminophen samples, some of which have structures similar to the legacy pollutants “polychlorinated biphenyls”. Both C–C and C–O bonding products were found, and the corresponding bonding processes and kinetics were revealed by quantum chemical calculations. Based on the product confirmation and intrinsic reaction coordinate computations, a pathway for the formation of the polymeric products in the chlorination of acetaminophen was proposed. This study suggests that chlorination may cause not only degradation but also upgradation of a phenolic compound or contaminant

Formation of Larger Molecular Weight Disinfection Byproducts from Acetaminophen in Chlorine Disinfection

Acetaminophen is widely used to treat mild to moderate pain and to reduce fever. Under the worldwide COVID-19 pandemic, this over-the-counter pain reliever and fever reducer has been drastically consumed, which makes it even more abundant than ever in municipal wastewater and drinking water sources. Chlorine is the most widely used oxidant in drinking water disinfection, and chlorination generally causes the degradation of organic compounds, including acetaminophen. In this study, a new reaction pathway in the chlorination of acetaminophen, i.e., oxidative coupling reactions via acetaminophen radicals, was investigated both experimentally and computationally. Using an ultraperformance liquid chromatograph coupled to an electrospray ionization-triple quadrupole mass spectrometer, we detected over 20 polymeric products in chlorinated acetaminophen samples, some of which have structures similar to the legacy pollutants “polychlorinated biphenyls”. Both C–C and C–O bonding products were found, and the corresponding bonding processes and kinetics were revealed by quantum chemical calculations. Based on the product confirmation and intrinsic reaction coordinate computations, a pathway for the formation of the polymeric products in the chlorination of acetaminophen was proposed. This study suggests that chlorination may cause not only degradation but also upgradation of a phenolic compound or contaminant

Summary of proportion of test sequences completely classified by MT-MAG (quantified as <i>CR</i>_<i>g</i>(<i>tr</i>)) vs. classified by DeepMicrobes (quantified as <i>CR</i>_<i>r</i>), at all taxonomic ranks.

A higher CRg(tr) (respectively CRr) is better, as it signifies that a higher proportion of genomes (resp. reads) have been completely classified (resp. classified).</p

MT-MAG pipeline for classifying two genomes, genome a and genome b, from the parent taxon Genus 1 into its two child taxa, Species 1, and Species 2 (multi-child classification).

Blue ellipses represent computation steps. Gray rectangles represent inputs to, and outputs from, computation steps. In the MT-MAG training phase (yellow box), the training set is prepared and given as the input to eMLDSP (Preprocessing).</p

Summary of MT-MAG and DeepMicrobes accuracy statistics, as well as the complete classification rates of MT-MAG and the classified rates of DeepMicrobes.

The inputs are genomes in the case of MT-MAG, and reads in the case of DeepMicrobes.</p

Methods—MT-MAG algorithm.

We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is alignment-free, with k-mer frequencies being the only feature used to distinguish a DNA sequence from another (herein k = 7). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. In addition to complete classifications, MT-MAG offers a “partial classification” option, whereby a classification at a higher taxonomic level is provided for MAGs that cannot be classified to the Species level. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. To assess the performance of MT-MAG, we define a “weighted classification accuracy,” with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 87.32% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 34.79% in weighted classification accuracy. In addition, MT-MAG is able to completely classify an average of 67.70% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that it could not classify at the Species level, resulting in the partial or complete classification of 95.13%, of the genomes in the datasets analyzed. Lastly, unlike other taxonomic assignment tools (e.g., GDTB-Tk), MT-MAG is an alignment-free and genetic marker-free tool, able to provide additional bioinformatics analysis to confirm existing or tentative taxonomic assignments.</div

Summary of total number of basepairs analyzed, number of FASTA files, number of contigs/reads, and the range of contig/read lengths for the training and test sets in Task 1 (sparse), and Task 2 (dense) for MT-MAG and DeepMicrobes.

Note that contigs come directly from the samples, while reads are simulated from the samples by the ART simulator.</p

<i>Overview of eMLDSP</i>, including the main steps that comprise eMLDSP (Preprocessing) (pink box), eMLDSP (Classify-Training) (yellow box), and eMLDSP (Classify-Classification) (lavender box).

Ellipses represent computation steps. Rectangles represent inputs to, and outputs from, computation steps. The diamond represents a condition checking. Note that the training dataset consists of (a) DNA sequences, together with (b) their taxonomic labels; and the inputs to eMLDSP (Preprocessing) and eMLDSP (Classify-Training) consists of the same training set. The output from eMLDSP (Preprocessing), consisting of predictions and classification confidences for the training set, is used for the STP algorithm in the MT-MAG Training phase (dotted arrow from the pink box), to calculate stopping thresholds. These stopping thresholds will then be used together with the output from eMLDSP (Classify-Classification) in the MT-MAG Classifying phase (dotted arrow from the lavender box), to determine final classification results.</p

Summary of MT-MAG performance metrics at all taxonomic ranks, for Task 1 (sparse) and Task 2 (dense): Constrained accuracy <i>CA</i>_<i>g</i>(<i>tr</i>), absolute accuracy <i>AA</i>_<i>g</i>(<i>tr</i>), weighted accuracy <i>WA</i>_<i>g</i>(<i>tr</i>), and complete classification rate <i>CR</i>_<i>g</i>(<i>tr</i>) (higher is better).

Summary of MT-MAG performance metrics at all taxonomic ranks, for Task 1 (sparse) and Task 2 (dense): Constrained accuracy CAg(tr), absolute accuracy AAg(tr), weighted accuracy WAg(tr), and complete classification rate CRg(tr) (higher is better).</p