Determination of key parameters in water quality monitoring of the most sediment-laden Yellow River based on water quality index

被引:21
|
作者
Pan, Baozhu [1 ]
Han, Xu [1 ]
Chen, Yue [1 ]
Wang, Lixin [2 ]
Zheng, Xing [1 ]
机构
[1] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Shaanxi, Peoples R China
[2] Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China
基金
中国国家自然科学基金;
关键词
Sediment-laden rivers; Water quality; Heavy metal pollution; Yellow River; RISK-ASSESSMENT; CLIMATE-CHANGE; SURFACE-WATER; HEAVY-METALS; IMPACT; REACHES; LOAD; CLASSIFICATION; INDICATORS; EXPOSURE;
D O I
10.1016/j.psep.2022.05.067
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Water quality management of sediment-laden rivers is a challenging issue for global water security because sediment is a major carrier for the migration and transformation of biogenic elements and pollutants in river systems. A water quality index (WQI) based on multiple parameters is widely used when making water quality assessments. To reduce the analytical costs and time required for multi-parameter measurements, many minimum WQI (WQImin) models that rely on fewer key parameters are now being developed for river systems. However, no systematic study of WQImin has been conducted yet in sediment-laden rivers, such as China's Yellow River which has the highest sediment concentration worldwide. This study assessed the spatiotemporal variation in water quality of the Yellow River's main channel in the spring and autumn of 2019. The WQI was calculated based on 15 water parameters determined at 44 sampling points from 26 river sections and six reservoirs. A heavy metal evaluation index (HEI) was used to gauge the pollution level of eight heavy metals. Despite no distinct seasonal variation, the WQI values decreased from the Yellow River's source region to its estuary, and higher values were observed in most reservoirs than in adjacent natural river sections. Values for WQI (>50) were mainly classified as being at "good" and "moderate" water quality levels, while the HEI values (<10) indicated a low pollution level. Four weighted and four non-weighted WQImin models were developed using stepwise regression. A model consisting of six parameters-total suspended solids, ammonia-nitrogen, permanganate index, electrical conductivity, dissolved oxygen, and nitrate-nitrogen-had the best modeling performance. Both the prediction accuracy and goodness-of-fit of the WQImin model were improved after adding parameter weights. Our study provides support data for water resource management in the Yellow River Basin, and contributes to the development of a robust WQImin in sediment-laden rivers.
引用
收藏
页码:249 / 259
页数:11
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