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Spatiotemporal variation evaluation of water quality in middle and lower Han River, China.
Deng, Lele; Chen, Kebing; Liu, Zhangjun; Wu, Boyang; Chen, Zekun; He, Shaokun.
  • Deng L; Jiangxi Academy of Water Science and Engineering, Nanchang, China.
  • Chen K; State Key Laboratory of Water Resources & Hydropower Engineering, Wuhan University, Wuhan, China.
  • Liu Z; Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan, China.
  • Wu B; Jiangxi Academy of Water Science and Engineering, Nanchang, China. liuzhangjun1991@foxmail.com.
  • Chen Z; TUM Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich, Germany.
  • He S; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
Sci Rep ; 12(1): 14125, 2022 08 19.
Article en En | MEDLINE | ID: mdl-35986018
ABSTRACT
As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world's largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQImin) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQImin model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R2) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Arsénico / Contaminantes Químicos del Agua Tipo de estudio: Prognostic_studies Límite: Humans País como asunto: Asia Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Arsénico / Contaminantes Químicos del Agua Tipo de estudio: Prognostic_studies Límite: Humans País como asunto: Asia Idioma: En Año: 2022 Tipo del documento: Article