Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era: Event-Driven Taxonomy Links Chemical Exposures and Hazards
文献类型：期刊论文
| 作者 | Cheng, Fei4; Huang, Jiehui4; Li, Huizhen4; Escher, Beate I.3; Tong, Yujun4; K?nig, Maria4; Wang, Dali4; Wu, Fan4; Yu, Zhiqiang2; Brooks, Bryan W.1,4 |
| 刊名 | ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:11页码:1004-1010 |
| 关键词 | BIOMARKER RESPONSES CONTAMINANTS TOXICITY SEDIMENT BIOAVAILABILITY MIXTURES RIVERS |
| ISSN号 | 2328-8930 |
| DOI | 10.1021/acs.estlett.3c00250 |
| 英文摘要 | To improve the accuracy of mixture risk assessment, researchers are employing suspect analysis with expanded lists of contaminants in addition to conventional target lists. However, there are some inherent challenges for these instrument-based analyses, including subjective selection of suspect contaminants, no information for chemical bioactivity, requirements for costly verification, and limited regional coverage. As a supplementary approach, we propose a data-driven suspect screening and risk assessment method informed by mining big data from high-throughput screening bioassay platforms and the refereed literature. The Pearl River Delta (PRD) with main event drivers of arylhydrocarbon receptor (AhR) and oxidative stress (ARE) response was examined. Bioactivity concentrations were collected from the CompTox Chemicals Dashboard, which contained more than 900 000 substances. In addition, exposure metadata from 24 986 literature entries for the environmental occurrence and distribution of contaminants in the PRD over the past three decades were mined. Collectively, a regional distribution map of aquatic hazards induced by AhR- and ARE-active compounds was generated, indicating gradients of low to moderate risks. This study specifically reports a novel big data approach for addressing the increasingly common challenge of objectively selecting analytes during suspect screening, which was recently identified as an urgent research question to advance more sustainable environmental quality. ? 2023 American Chemical Society. |
| WOS研究方向 | Engineering, Environmental ; Environmental Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001012920200001 |
| 源URL | [http://ir.gig.ac.cn/handle/344008/80228]  |
| 专题 | 有机地球化学国家重点实验室 |
| 作者单位 | 1.Department of Environmental Science, Institute of Biomedical Studies, Center for Reservoir and Aquatic Systems Research, Baylor University, Waco; TX; 76798, United States 2.State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou; 510640, China 3.UFZ-Helmholtz Centre for Environmental Research-UFZ, Cell Toxicology, Leipzig; 04318, Germany 4.Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou; 511443, China |
| 推荐引用方式 GB/T 7714 | Cheng, Fei,Huang, Jiehui,Li, Huizhen,et al. Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era: Event-Driven Taxonomy Links Chemical Exposures and Hazards[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS,2023,10(11):1004-1010. |
| APA | Cheng, Fei.,Huang, Jiehui.,Li, Huizhen.,Escher, Beate I..,Tong, Yujun.,...&You, Jing.(2023).Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era: Event-Driven Taxonomy Links Chemical Exposures and Hazards.ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS,10(11),1004-1010. |
| MLA | Cheng, Fei,et al."Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era: Event-Driven Taxonomy Links Chemical Exposures and Hazards".ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 10.11(2023):1004-1010. |
入库方式： OAI收割
来源：广州地球化学研究所
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