Removal characteristics of DON in pharmaceutical wastewater and its influence on the N-nitrosodimethylamine formation potential and acute toxicity of DOM

Hu, Haidong; Jiang, Chenghua; Ma, Haijun; Ding, Lili; Geng, Jinju; Xu, Ke; Huang, Hui; Ren, Hongqiang

doi:10.1016/j.watres.2016.10.010

Cited by 67 publications

(26 citation statements)

References 47 publications

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“…This technique is highly sensitive to most toxicants in a short running time. And that, the enzyme action and the chemical condition in the test are also similar to that in many higher mammals (4,5).…”

Section: Introductionsupporting

confidence: 58%

“…There is a primary fact that most natural processes are controlled by the interactions of numerous factors. Some typical examples are the reaction effects caused by the complex relationships, such as environment pollutions (4,5), health risk assessment (6), catalytic effects (7), toxicity (8), etc. Therefore, the exploration of these complex relationships plays a decisive role in accurately grasping many natural laws in the future.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Duan¹,

Lee²,

Gao³

et al. 2019

Preprint

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<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

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Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Duan¹,

Lee²,

Gao³

et al. 2019

Preprint

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show abstract

“…The previous research has indicated that the NDMA precursors include the inorganic compounds (such as N 2 O 3 , NH 2 Cl, NHCl 2 , hydroxylamine, etc.) and a range of organic compounds [57,[84][85][86][87][88][89].…”

Section: Precursorsmentioning

confidence: 99%

“…NDMA precursors are predominantly found in DON sources [102,[107][108][109][110]. NDMA formation is correlated with the ratio of DOC to DON (DOC/DON) [89,105]. In general, the generation of NDMA is inversely proportional to the ratio.…”

Section: Organic Nitrogenmentioning

confidence: 99%

Occurrence and control of N-nitrosodimethylamine in water engineering systems

Bian

Wang

Zhu

et al. 2018

Environmental Engineering Research

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N-nitrosodimethylamine (NDMA) is a typical nitrogen disinfection by-product, which has posed a potential threat to human health during drinking water disinfection. Because of the well-known effects of mutagenesis, carcinogenesis and teratogenesis, the high detection rate in water engineering systems (such as coagulation, membrane filtration and biological systems), and difficulty to remove, it has received wide concern in the field of water engineering systems. The NDMA is a low molecular weight hydrophilic organic substance, which is difficult to remove. Also, the mechanism for NDMA formation is also recognized to be complex, and many steps still needed to be further evaluated. Therefore, the mechanistic knowledge on NDMA formation potential and their removal processes is of particularly interest. Few papers summarize the occurrence and control of NDMA in water engineering systems. It is for this reason that the content of this paper is particularly important for us to understand and control the amount of NDMA thus reducing the threat of disinfection by-products to drinking water. Four parts including the mechanisms for the NDMA formation potential, the factors affecting the NDMA formation potential, the technologies for removal of NDMA are summarized. Finally, some definite suggestions are given.

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“…Lin et al [6] researched six lakes, including Poyang Lake, and Dongting Lake in Hunan and Jiangxi Provinces, China, and reported that the DON content varied from 17.18 mg/kg to 292.31 mg/kg (mean content, 134.45 mg/kg), comprising 51.86% TDN and 7.14% total nitrogen (TN) across the six lakes; in addition, the distribution of DON was closely related to pollution levels in each lake. The sources of DON are extensive, including sediment, organic matter in the soil, microbial and biological organic debris, and their metabolites [7], besides, the effluent discharged from wastewater treatment plants is also an important source of anthropogenic DON loading to surface waters [8,9], with extremely complex composition [10] that can be bioavailable to aquatic plant species and toxic risk [11].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Dissolved Organic Nitrogen in the Sediments of Six Water Sources in Taihu Lake, China

Yang

Wei

et al. 2019

IJERPH

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KCl-extractable sediment dissolved organic nitrogen (KS-DON) extracted from sediments near drinking water intakes of six drinking water sources in Taihu Lake in China was partitioned into hydrophobic and hydrophilic fractions and high/low molecular weight fractions. The results showed that the total dissolved nitrogen (TDN) contents of the extracts ranged from 67.78 to 128.27 mg/kg. KS-DON was the main TDN species, accounting for more than 50%, with NH4+-N and NO3−-N averaging 30% and 20%, respectively. The molecular weight fractions of <1 kDa accounted for almost half of KS-DON. Hydrophilic compounds accounted for more than 75% of KS-DON. Three fluorescence peaks were identified: soluble microbial byproducts (A); protein-like substances (B); and humic acid-like substances (C). It is concluded that the KS-DON in Taihu Lake sources has higher bioavailability and higher risk of endogenous release. Ecological dredging and establishment of constructed wetlands are possible measures to reduce the release of endogenous nitrogen.

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Removal characteristics of DON in pharmaceutical wastewater and its influence on the N-nitrosodimethylamine formation potential and acute toxicity of DOM

Cited by 67 publications

References 47 publications

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Occurrence and control of N-nitrosodimethylamine in water engineering systems

Characteristics of Dissolved Organic Nitrogen in the Sediments of Six Water Sources in Taihu Lake, China

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