Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River
文献类型：期刊论文
| 作者 | Zhao, Xiaohong1; Liu, Xiaojie2; Xing, Yue1; Wang, Lingqing2; Wang, Yong2 |
| 刊名 | ENVIRONMENTAL RESEARCH
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 211页码:11 |
| 关键词 | Water quality evaluation T-S fuzzy Neural network Source apportionment Heavy metal pollution Health risk assessment |
| ISSN号 | 0013-9351 |
| DOI | 10.1016/j.envres.2022.113058 |
| 通讯作者 | Wang, Lingqing(wanglq@igsnrr.ac.cn) |
| 英文摘要 | Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH3-N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH3-N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level IV and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57-44.58 mu g/L, 10-122.50 mu g/L and 2-28.67 mu g/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management. |
| WOS关键词 | LAND-USE ; WASTE-WATER ; IMPACT ; MANAGEMENT ; BASIN ; URBAN ; URBANIZATION ; PATTERNS ; REMOVAL ; SYSTEM |
| 资助项目 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK1003] |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:000784336200007 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 资助机构 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/174714]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Lingqing |
| 作者单位 | 1.Changan Univ, Sch Civil Engn, Xi'an 710061, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Xiaohong,Liu, Xiaojie,Xing, Yue,et al. Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River[J]. ENVIRONMENTAL RESEARCH,2022,211:11. |
| APA | Zhao, Xiaohong,Liu, Xiaojie,Xing, Yue,Wang, Lingqing,&Wang, Yong.(2022).Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River.ENVIRONMENTAL RESEARCH,211,11. |
| MLA | Zhao, Xiaohong,et al."Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River".ENVIRONMENTAL RESEARCH 211(2022):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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